Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and computer engineering. General Focus and Scope of Sistemasi: Jurnal Sistem Informasi, are Information Systems, Information Technology, Data Science, Artificial Intelligence and Software Engineering. Sistemasi: Jurnal Sistem Informasi has been indexed in the Directory of Open Access Journals (DOAJ) since 2016 and the Science and Technology Index (SINTA) since 2018. Sistemasi: Jurnal Sistem Informasi is published monthly, with 12 issues per year (January through December). Sistemasi: Jurnal Sistem Informasi (p-ISSN: 2302-8149 e-ISSN: 2540–9719) published by Universitas Islam Indragiri, Tembilahan Riau Indonesia.
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The Impact of Electronic Customer Relationship Management (E-CRM) Strategy on Customer Loyalty in Mytelkomsel using a Mediation Model
This study investigates the impact of Electronic Customer Relationship Management (E-CRM) strategies, encompassing Functional Dimensions and Personal Dimensions, on Customer Loyalty in the use of the MyTelkomsel application, using Perceived Customer Relationship Quality (comprising cognitive and emotional dimensions) as the mediating construct. Data were collected from 100 MyTelkomsel users through a survey and analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. The results indicate that Functional Dimensions have a positive and significant effect on Perceived Customer Relationship Quality (path coefficient = 0.471) and also exert a positive direct effect on Customer Loyalty (path coefficient = 0.256). Meanwhile, Personal Dimensions positively influence Perceived Customer Relationship Quality (path coefficient = 0.335) but exhibit a negative direct effect on Customer Loyalty (path coefficient = −0.109). Furthermore, Perceived Customer Relationship Quality has a positive and significant effect on Customer Loyalty (path coefficient = 0.539) and serves as a mediator in the relationships between both Functional Dimensions and Personal Dimensions and Customer Loyalty. These findings provide deeper insights into the relationship between E-CRM strategies and customer loyalty in the context of the MyTelkomsel application. They also confirm the mediating role of Perceived Customer Relationship Quality and highlight the strategic value of implementing E-CRM to strengthen customer loyalty in the digital era.
Performance Evaluation of a TP-Link CPE220 Point-to-Point Wireless Backhaul
This study aims to implement and evaluate the performance of a point-to-point wireless backhaul network between two buildings using the TP-Link CPE220 based on the IEEE 802.11b/g/n standard in a high-interference environment. The implementation was carried out between two buildings approximately 100 meters apart under line-of-sight (LOS) conditions, where the Internet connection from the office building was distributed to the workshop building through the wireless backhaul and subsequently forwarded via a router and an access point. The network performance was evaluated under four testing scenarios: the wireless backhaul link, the workshop router, the access point under no-load conditions, and the access point under a multi-user environment. Measurements were conducted 15 times during the morning, afternoon, and evening using throughput, delay, jitter, and Received Signal Strength Indicator (RSSI) as the evaluation parameters. The results show that the average download throughput decreased from 95.27 Mbps at the source to 57.13 Mbps across the wireless backhaul and 55.56 Mbps at the unloaded access point. Under the multi-user scenario, where 10 devices simultaneously streamed video content, the average download throughput declined to 11.49 Mbps, while delay and jitter increased to 367.29 ms and 66.69 ms, respectively. The analysis indicates that the performance degradation was primarily caused by reduced signal quality over the backhaul link, interference within the 2.4 GHz frequency band, and increased channel contention under multi-user conditions. Nevertheless, the TP-Link CPE220 was able to provide stable inter-building connectivity for small- to medium-scale network deployments, sustaining throughput exceeding 55 Mbps under normal operating conditions.
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Performance Evaluation of OTFS over EVA, ETU, and UAV Channels with Delay Doppler Grid Variations for High-Mobility Communications
Orthogonal Time Frequency Space (OTFS) is a modulation scheme designed to improve the reliability of wireless communications in high-mobility environments, where transmission performance is significantly affected by Doppler effects. This study evaluates the performance of OTFS over three high-mobility channel models: Extended Vehicular A (EVA), Unmanned Aerial Vehicle (UAV), and Extended Typical Urban (ETU), with a particular focus on the impact of Delay-Doppler grid size on the bit error rate (BER). Simulations were conducted using Quadrature Phase Shift Keying (QPSK) modulation with a carrier frequency of 5.9 GHz, user mobility of up to 350 km/h, and a signal-to-noise ratio (SNR) ranging from 0 to 20 dB. The results show that increasing the Delay-Doppler grid size from 16 × 16 to 32 × 32 reduced the BER from the order of 10⁻³ to 10⁻⁴ across all channel models. However, further increasing the grid size beyond 32 × 32 resulted in only marginal performance improvements, indicating a saturation effect. These findings demonstrate that a 16 × 16 Delay-Doppler grid is sufficient to achieve reliable performance in high-mobility communication environments. Nevertheless, increasing the grid size to 32 × 32 provides superior performance by further reducing the BER, thereby offering an optimal balance between transmission reliability and computational complexity.
Clustering of Indonesian Provinces based on Demographic Characteristics using DBSCAN Algorithm
This research aims to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for clustering provinces in Indonesia based on demographic characteristics using a Web GIS system. The study utilizes demographic data of 34 provinces from 2020–2023, covering population size, population growth rate, poverty percentage, population density, and gender ratio. DBSCAN was applied with parameters epsilon (ε) = 0.50 and minimum samples = 2, evaluated using the Davies-Bouldin Index (DBI). The results show that DBSCAN successfully identified two consistent main clusters throughout the period: the High Cluster consisting of three provinces (West Java, Central Java, East Java), the Medium Cluster including 29–30 provinces, and DKI Jakarta as a single noise point.. Clustering quality varied with DBI values ranging from 0.3839 to 0.4123, with the best quality in 2020 and the highest internal diversification in 2023. Riau Islands fluctuated as a noise point in 2021 before returning to stability. The developed Web GIS system successfully integrated interactive map visualization using OpenStreetMap and Leaflet, providing a comprehensive spatio-temporal analysis dashboard. This study demonstrates the effectiveness of DBSCAN in detecting demographic patterns and regional anomalies, while producing a visualization tool that supports regional development planning and national demographic policies in Indonesia.
Mapping the Number of Patients Undergoing Treatment and Care in Public Hospitals After the Covid-19 Pandemic
The COVID-19 pandemic has had profound impacts on healthcare systems globally, altering the patterns of patient care and treatment in public hospitals. This research aims to map the number of patients undergoing treatment and care in public hospitals following the pandemic, providing insights into the evolving healthcare landscape. Utilizing data from public hospitals, we analyzed trends in patient admissions, treatment types, and care requirements post-pandemic. Our findings reveal a significant shift in healthcare demands, with an increase in patients requiring long-term care and treatment for chronic conditions exacerbated by delayed medical attention during the pandemic. Additionally, mental health services have seen a notable surge in utilization, reflecting the psychological toll of the pandemic. The research highlights the need for adaptive healthcare strategies to address the changing patient demographics and ensure efficient resource allocation. These insights can guide policymakers and healthcare providers in optimizing patient care and improving healthcare delivery in the post-pandemic era.
A Novel Approach for Secure and Reliable Color Image Authentication and Recovery using Alpha Layer and Secret Sharing
In the contemporary digital environment, securing digital images against forgery has become a critical necessity, particularly in sensitive domains such as the judiciary, criminal investigations, and communications security. Any manipulation or falsification of legal documents or digital evidence can lead to serious consequences and significant challenges. This study proposes a method for protecting color scanned images and documents by utilizing an invisible component within them, specifically the alpha channel (transparency layer), to embed authentication and recovery data. The process begins by applying a Downsampling operation to the cover image to reduce its size while preserving fine visual details, taking into account the removal of aliasing to achieve the best possible representation of edges and details prior to reduction. Subsequently, 15 secret bit planes are combined to form 4 composite secret bit planes, which are then embedded into the alpha channel. During the recovery phase, these four bit planes are extracted from the alpha channel, from which the original 15 authentication bit planes are reconstructed. The reconstructed data are then matched with the verification data, and in case of any discrepancy, the image is repaired using the embedded authentication data. This approach enables the detection of any tampering and facilitates image recovery if it has been altered or manipulated. One of the key advantages of this method is that it does not affect the visual quality of the host image, as the data are embedded within the added alpha channel. Any attempt to tamper with the image can be detected, and based on the embedded data, the modified or deleted regions can be restored. The proposed method was tested on various types of color scanned images and documents under diverse attack scenarios (content modification attacks and visual quality attacks), demonstrating remarkable effectiveness in preserving image integrity. This technology is particularly important for law enforcement, information security, digital forensics, and any security teams requiring reliable image authentication. The system is also designed to be user-friendly and does not require any specialized hardware, making it practical and suitable for real-world applications.
Design and Implementation of a Digital Forensics Tool to Enhance Windows Artifact Analysis
Although the wide use of technology came with many advantages and facilities to the people daily life, it causes the cybercrime to be raised. Digital forensics is one of the most important scientific fields, aiming to investigate cybercrimes and analyze digital evidence. Among different technology’s platforms, operating systems is one of the most important sources of evidence for digital forensic analysts providing a rich information that can used to get important insights. Examples of such evidence include identifying programs that have been executed on a computer, determining files that have been accessed, and identifying storage devices that were connected via USB ports. Practically, accessing and handling this raw information using manual methods is time-consuming, in addition to the lack of accuracy in results due to human errors. In this work, a GUI-based tool is presented to handle most of the evidence provided by Windows operating system that can be used in digital forensics. The research aims to fill the gap caused by the lack of a free tool that deals with these sources, as most available tools are either commercial tools that are complex to use and require expert-level experience. In contrast, available free tools have limited-capability since they are focusing only on one type of evidence. The introduced tool was designed and developed using the C# programming language and was tested on the Windows 10 operating system, where it successfully extracted the required information efficiently and smoothly.
IoT-Based LPG Gas Leak Monitoring System with Automatic Alarms
Liquefied Petroleum Gas (LPG) leaks are one of the leading causes of fires in households and LPG distribution facilities. Limited public awareness and delays in detecting gas leaks can significantly increase the risk of property damage and endanger human safety. Therefore, this study aims to design and develop an Internet of Things (IoT)-based monitoring system for 3 kg LPG gas leaks that provides automatic, real-time early warning notifications. The proposed system employs an MQ-2 gas sensor to detect LPG concentration, a NodeMCU ESP8266 as the main controller and data communication module, and a buzzer, LED, and LCD as local warning indicators. A threshold-based method is implemented to classify safe and hazardous conditions according to predefined gas concentration limits. In addition, the system is integrated with remote notifications via Telegram, enabling users to receive alerts even when they are away from the monitored location. Experimental results demonstrate that the proposed system can accurately detect LPG gas leaks under various operating conditions and automatically send warning notifications whenever the gas concentration exceeds the predefined threshold. These findings indicate that the system is effective, responsive, and has strong potential to enhance user safety in the use of LPG.
Web-based Information System for Ornamental Fish Business in Surabaya
Digital transformation has emerged as a strategic imperative for small and medium enterprises (SMEs) in emerging economies, yet the ornamental fish retail sector in Indonesia remains predominantly offline, constrained by limited digital infrastructure and high customer knowledge barriers. No prior identified study has implemented artificial intelligence (AI)-assisted consultation within a domain-specific ornamental fish e-commerce platform, and comprehensive security implementation combined with multi-layer testing has been largely absent in comparable SME web systems. This study presents the design, implementation, and evaluation of a web-based information system for Toko Oasis, an ornamental fish and aquascape SME in Surabaya, Indonesia, developed within a Design Science Research (DSR) paradigm following a structured Software Development Life Cycle (SDLC). The system integrates a configurable large language model (LLM) consultation module—supporting OpenAI GPT-4 and Google Gemini—that delivers domain-specific advisory on ornamental fish species selection, aquarium parameters, and aquascape design through natural language interaction. System development produced twelve Unified Modeling Language (UML) artifacts and was evaluated through a tri-layer testing protocol operationalized against the ISO/IEC 25010:2011 software quality model. Functional testing achieved a 100% pass rate across 24 use cases. Performance testing recorded a mean response time of 4.2 seconds under 25 concurrent users, within the defined threshold of 5 seconds. Usability evaluation yielded a mean System Usability Scale (SUS) score of 80.0, classified as Good. Security validation confirmed full compliance across HTTPS/SSL-TLS transport and AES-256 at-rest encryption domains. Comparative analysis against prior literature and analogous commercial platforms confirms that this system constitutes the first identified deployment of AI-assisted consultation in ornamental fish retail, contributing a replicable digitalization architecture for niche-market SMEs in developing economies.
Comparison of Linear Regression and Holt-Winters Methods for Gold Price Prediction
Gold is one of the most stable investment instruments and is widely favored by both individual investors and businesses. However, gold prices are influenced by various economic factors and often fluctuate significantly, making pricing decisions more challenging. Toko Mas Jasa Sejahtera, a gold trading business, faces difficulties in determining appropriate selling prices due to this uncertainty. Therefore, an accurate prediction method is required to minimize pricing errors and support more informed decision-making. This study aims to design and implement a gold price prediction system using the Linear Regression and Holt-Winters methods while comparing the predictive accuracy of both approaches. Model performance was evaluated using Mean Absolute Error (MAE) as the primary indicator of prediction accuracy. The results show that both methods are capable of forecasting gold prices, although with different levels of accuracy. The Linear Regression method achieved an MAE of IDR 44,097, whereas the Holt-Winters method produced an MAE of IDR 305,984. The substantially lower MAE obtained by Linear Regression indicates that it provides more accurate predictions than the Holt-Winters method. Therefore, Linear Regression is recommended as the preferred approach for the gold price prediction system at Toko Mas Jasa Sejahtera.
Mobile-Based Big Five Personality Score Prediction from Handwriting Using VGG19
Handwriting is a non-verbal approach that can be used to identify an individual’s personality traits. However, manual scoring methods still involve subjectivity and require specialized skills; therefore, a more objective, efficient, and consistent automated approach is needed. This study aims to develop a system for predicting personality scores based on handwriting images. The personality dimensions used refer to the Big Five Personality traits, namely Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism. The research methodology applies an adaptation of the CRISP-DM framework, which includes the stages of business understanding, data analysis, data preparation, model development, evaluation, and system implementation. This study employs the VGG19 architecture by comparing three optimizers: Adam, SGD, and RMSProp. Evaluation results indicate that the RMSProp optimizer achieved the best performance on the validation data, with an MSE of 0.0128, MAE of 0.0930, RMSE of 0.1133, PCC of 0.4648, and accuracy of 90.70%. The VGG19 model with RMSProp as the best optimizer was subsequently implemented into a mobile application capable of receiving handwriting image inputs and generating personality score predictions. This study demonstrates that the integration of Deep Learning and mobile applications has the potential to serve as a practical solution for predicting Big Five Personality scores based on handwriting.
Implementation of a Face Recognition API for an Automated Web-based Attendance System using Extreme Programming
Manual attendance recording is still vulnerable to data manipulation, entry delays, and high reliance on manual input. This research focuses on the implementation and integration of the Facial Recognition REST API into a web-based attendance information system to automate attendance recording. The novelty of this research lies in the integration of REST API-based facial recognition services with a three-layer architecture consisting of a camera device, a Raspberry Pi 5-based edge server, and a Laravel-based application server, developed using the Extreme Programming (XP) method, resulting in a modular, responsive, and easily scalable attendance system compared to previous approaches. The system was developed in four sprints over one month with an average speed of 15.5 story points per sprint, based on twelve user stories defined in the planning stage. In the facial recognition module, the identification process utilizes a combination of Haar Cascade for facial position detection, FaceNet for facial embedding formation, and Cosine Similarity for similarity measurement against a reference database consisting of 18 students with 6 reference photos per student. System testing was conducted using three class videos with a resolution of 1920×1080 pixels with an average duration of 5 minutes and 28 seconds. Evaluation of the accuracy of the facial recognition model was part of a collaborative study reviewed separately; this paper focuses on the system's integration performance and functionality. Functional testing using black box methods across 20 scenarios demonstrated that all modules—authentication, master data management, facial registration, facial recognition-based attendance, reporting, and error handling—performed according to specifications, with a consistent system response time of 2–4 seconds. The results demonstrate that the system reduces reliance on manual input, improves record-keeping efficiency, and provides real-time attendance data, and is ready for further development for broader adoption in educational settings.
Unveiling the Determinants of Digital Library Success: A HOT-Fit Analysis of E-Perpus Kota Depok
E-Perpus Kota Depok is a digital library service provided by the Depok City Library and Archives Service to reach a wider community which can be accessed via smartphone by downloading this application via Play Store. Digital libraries are public services that support the achievement of Sustainable Development Goals (SDGs), particularly SDG 4 on Quality Education and SDG 9 on Industry, Innovation, and Infrastructure. However, information system success is influenced not only by technological aspects but also by human and organizational factors. This study analyzes the determinants of the success of the E-Perpus Kota Depok digital library system using the Human-Organization-Technology Fit (HOT-Fit) model. A quantitative approach was employed using a survey involving 200 users of the E-Perpus Kota Depok application. Data were collected through an online questionnaire and analyzed using Partial Least Squares-Structural Equation Modeling (PLS-SEM) with SmartPLS. The results showed that nine out of twelve hypotheses were significant. System Quality positively influenced System Use and User Satisfaction, while Information Quality significantly influenced User Satisfaction. Service Quality did not significantly affect System Use or User Satisfaction. In addition, organizational and human factors contributed significantly to Net Benefit. The model demonstrated strong predictive power, with System Use obtaining the highest R-Square value of 71.6%. The study provides practical insights for improving digital public library services, although it is limited to a single regional context.
Hanin Salwa Salsabila Hidayati Nurrohman
Usability and User Experience Analysis of the HumicPrint Mobile Application Using the USE Questionnaire and UEQ
This study aims to evaluate the usability and user experience of the HumicPrint mobile application as a digital certificate management system. A descriptive quantitative approach was employed, involving 26 respondents. Usability was evaluated using the USE Questionnaire, which measures four dimensions: usefulness, ease of use, ease of learning, and satisfaction. User experience was assessed using the User Experience Questionnaire (UEQ), with the results analyzed using the UEQ Data Analysis Tool and compared against the established UEQ benchmark. The usability evaluation yielded an overall mean score of 86.23%, indicating an excellent level of usability. The scores for the individual dimensions were 86.26% for usefulness, 86.12% for ease of use, 86.30% for ease of learning, and 86.12% for satisfaction. The user experience evaluation showed that the Attractiveness and Efficiency dimensions were rated Above Average, while Stimulation achieved a Good rating. However, the Perspicuity, Dependability, and Novelty dimensions remained in the Below Average category. Overall, the findings indicate that the HumicPrint mobile application provides an excellent level of usability and a positive user experience. Nevertheless, improvements to the Perspicuity, Dependability, and Novelty dimensions are recommended to further enhance the overall quality of the user experience.
Evaluation of the STTNF 360° Virtual Tour System Success based on the Delone and McLean Model using the PLS-SEM Approach
This study aims to evaluate the success of the STTNF 360° Virtual Tour System as an information and promotional medium for Sekolah Tinggi Teknologi Terpadu Nurul Fikri (STTNF). The system enables prospective users to explore the campus environment virtually; however, its success from the users' perspective has not yet been systematically evaluated. This study adopts a quantitative approach based on the DeLone and McLean Information Systems Success Model to examine the effects of System Quality, Information Quality, and Service Quality on Use, User Satisfaction, and Net Benefits. Data were collected through a questionnaire administered to 97 respondents and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The results indicate that System Quality has a significant positive effect on User Satisfaction (t = 2.454, p = 0.014). Furthermore, User Satisfaction has significant positive effects on both Use (t = 4.011, p < 0.001) and Net Benefits (t = 10.129, p < 0.001), whereas the remaining quality constructs do not exhibit statistically significant effects. The coefficient of determination (R²) indicates moderate explanatory power, with values of 0.527 for User Satisfaction, 0.663 for Use, and 0.511 for Net Benefits. Overall, the findings demonstrate that the STTNF 360° Virtual Tour System provides meaningful benefits by enabling users to better understand the campus environment through an immersive virtual experience. These results offer practical insights for STTNF to further improve system quality and optimize the virtual tour as an effective platform for campus information dissemination and promotion.
Analysis of Higher Education Alumni Careers using LinkedIn Web Scraping and K-Means Clustering
The use of alumni data to support curriculum evaluation continues to face challenges due to the limitations of conventional data collection methods, such as manual surveys, which often result in low response rates. Meanwhile, LinkedIn provides relatively comprehensive and up-to-date alumni career information; however, its potential for supporting tracer studies remains underutilized. This study aims to analyze the career patterns of higher education alumni using LinkedIn data collected through web scraping and analyzed with the K-Means clustering algorithm within the Knowledge Discovery in Databases (KDD) framework. The proposed approach applies the KDD process to generate a data-driven mapping of alumni career patterns as a complement to conventional tracer studies. The dataset consisted of 133 alumni profiles, which were processed through the stages of data selection, preprocessing, transformation, clustering, and evaluation. The results indicate that the majority of alumni are employed in the technology sector and occupy mid-level or specialist positions. The K-Means algorithm identified three distinct career clusters, representing career tendencies in business process and operations, systems and technology development, and data utilization and software quality assurance. These findings reveal the distribution of alumni competencies across business, data, and technology domains. However, the clustering quality was relatively low, as indicated by a Silhouette Score of 0.0321 and a Davies-Bouldin Index of 3.0487, suggesting limited separation among the identified clusters. Therefore, the clustering results should be interpreted as an initial mapping of alumni career patterns rather than definitive classifications. Overall, this study demonstrates the potential of professional social media data as a valuable resource for supporting data-driven alumni career analysis and complementing traditional tracer study practices.
Classification of Indonesian Batik Motifs using CNN VGG16 with Transfer Learning and Fine-Tuning
Indonesian batik is a cultural heritage passed down through generations and officially recognized by UNESCO. However, the identification of batik motifs still relies heavily on manual assessment by experts. This study aims to develop an automated batik motif classification system using a Convolutional Neural Network (CNN) based on the VGG16 architecture with a transfer learning approach. The dataset was obtained from the Indonesia Batik Motifs repository on Kaggle and consists of three batik motif classes: Batik Bali, Batik Kawung, and Batik Megamendung. To address class imbalance, data augmentation was applied to produce a balanced dataset of 1,500 images, with 500 images per class. The dataset was then divided into training, validation, and testing sets using an 80:10:10 ratio. The preprocessing stage included grayscale image conversion to reduce computational complexity. The proposed model, fine-tuned on the last eight layers of VGG16, achieved a test accuracy of 98.00% with an F1-score of 0.98. Among the three classes, Batik Megamendung achieved the highest F1-score (0.99), followed by Batik Kawung (0.98) and Batik Bali (0.97). Comparative experiments showed that the proposed VGG16 transfer learning model outperformed both MobileNetV2 with transfer learning (97.33% accuracy) and VGG16 trained from scratch (33.33% accuracy). This study contributes to the development of an accurate batik motif classification system that can be deployed in real time through a Streamlit-based web application.
Classification of Fish Species using Digital Images and Convolutional Neural Networks
Accurate fish species identification is essential for fisheries management and the seafood industry; however, manual identification remains time-consuming, challenging, and prone to human error. This study develops an automated fish species classification system using a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture with supervised learning. The dataset was manually collected from the Roboflow platform by gathering and integrating images from multiple sources into a single collection. Three fish species were selected as the target classes: Red Snapper, Barramundi (Asian Sea Bass), and Scad. The preprocessing pipeline included data augmentation, image normalization, and image resizing to 224 × 224 pixels. The final dataset consisted of 1,500 images, with 500 images per class, and was divided into training, validation, and testing sets using a 70:15:15 ratio. To enhance the classification performance of MobileNetV2, the proposed model incorporated a classification head consisting of Batch Normalization, a Dense layer (128 units, ReLU activation), Dropout (0.6), and a Dense output layer (3 units, Softmax activation). During training, the model was optimized using the Adam optimizer with the categorical cross-entropy loss function. Experimental results demonstrate that the proposed model achieved a test accuracy of 98.67% and a macro-averaged F1-score of 0.99. These findings indicate that MobileNetV2 with supervised learning is highly effective for fish species classification from digital images and provides a strong foundation for the development of automated fish identification systems in fisheries applications.
Benchmarking the Performance of AES and ECC Encryption for Secure Digital Communication in Cloud Systems
This study compares the performance of the Advanced Encryption Standard (AES) and Elliptic Curve Cryptography (ECC) for securing digital communications in cloud computing environments. A quantitative experimental-comparative approach was employed using a counseling conversation dataset comprising 9,036 valid records. The dataset was divided into five quintiles based on text length (number of characters), from which 1,805 samples were selected through stratified random sampling. The experiments were conducted in Google Colab using Python 3.10, and the algorithms were evaluated in terms of encryption and decryption time, throughput, CPU utilization, memory consumption, and ciphertext entropy. The results indicate that AES outperformed ECC in computational efficiency, achieving an average encryption time of 0.00013 seconds while requiring only 0.83–0.86 kB of memory. In contrast, ECC demonstrated superior suitability for secure key exchange despite its higher computational overhead. These findings suggest that AES is the preferred choice for efficient data encryption in cloud-based digital communication systems, whereas ECC is more appropriate for cryptographic key exchange in applications requiring a higher level of security.
Internal Compliance Audit of the Information Security Management System in a Cybersecurity Company based on ISO/IEC 27001
Information Security Management Systems (ISMSs) require periodic evaluation to assess their level of compliance with established standards and frameworks. This study evaluates the implementation of an Information Security Management System in an Indonesian cybersecurity services company, with a particular focus on the Compliance Division and the Security Operations Center (SOC). The case study was selected because the organization's ISMS had not been implemented consistently, particularly regarding the regular updating of security policies and operational procedures. As a cybersecurity service provider, the company is expected to conduct periodic compliance assessments to ensure alignment with recognized information security management standards, such as ISO/IEC 27001:2022. The study employed a Gap Analysis approach that combined qualitative and quantitative data. Data were collected through direct observation and a review of internal documentation after obtaining the company's authorization. The results indicate an overall compliance level of 77.5%. Three control areas achieved full compliance: the timely execution of internal audits, incident documentation, and log management. Based on the identified gaps, a set of improvement recommendations was developed to assist the organization in achieving greater compliance with the requirements of ISO/IEC 27001:2022. This study provides practical contributions by demonstrating the application of the ISO/IEC 27001:2022 framework for conducting internal compliance audits of Information Security Management Systems and offering actionable recommendations for strengthening organizational information security governance.
An Enhanced Type II Fuzzy Set Algorithm for Satellite Images Contrast Improvement
Satellite images contain detailed information that is of great importance in the field of remote sensing. However, these images often suffer from low contrast due to numerous atmospheric obstructions. However, many methods have been developed to enhance these images but most of them have not achieved satisfactory results. Therefore, satellite imagery processing remains an active area of research. Hence, this research has proposed a modified type of II fuzzy set algorithm to improve the contrast of grayscale and color satellite images appropriately so as to maintain the overall brightness of the image and also give natural colors. The proposed algorithm employs a modified Hamacher t-conorm with a new lower and upper ranges. The resulting output is further processed based on sigmoid function and contrast stretching techniques to produce the final improved image. The proposed algorithm’s performance was assessed with natural degraded satellite images and compared with six other methods as well as the evaluation of the comparison’s outcomes was done using two metrics in addition to the processing time. It scored the optimum in both metrics, which obtain (20.907) in BRISQUE and (3.467) in NSS. The experimental results of the proposed algorithm demonstrated outstanding performance compared to the other methods as it produced images with clear details and natural colors without increasing image brightness.
QoS Optimization and Bufferbloat Mitigation in 5G Standalone Networks using 5QI Mapping
Bufferbloat in the Best Effort traffic profile (5QI = 9) of open-source 5G Standalone (5G SA) networks leads to excessive packet queuing at the User Plane Function (UPF) and gNodeB, resulting in significant degradation of 4K video streaming performance under network congestion. This study investigates the effectiveness of 5QI = 4 mapping for mitigating bufferbloat and optimizing the Quality of Service (QoS) of 4K video streaming in 5G SA networks using secondary data compiled from previous studies. A quantitative comparative analysis was conducted using QoS metrics, including throughput, delay, jitter, and packet loss, systematically extracted from 30 peer-reviewed publications published between 2021 and 2026 following the PRISMA protocol. The results demonstrate that mapping traffic to the 5QI = 4 priority class consistently maintains 4K video throughput above the critical threshold of 25 Mbps across different experimental platforms, reduces end-to-end latency to below 150 ms in accordance with the ITU-T Y.1541 Class 4 recommendation, and limits jitter to less than 15 ms. The proposed mapping strategy prioritizes packet scheduling at the UPF through the N4 interface under the control of the Session Management Function (SMF), while intentionally deprioritizing and, when necessary, dropping lower-priority background traffic to preserve service quality for delay-sensitive applications. Furthermore, the findings demonstrate that modifying the MongoDB policy database within the Policy Control Function (PCF) of Open5GS is an effective approach for mitigating network congestion and eliminating visual stuttering during Ultra High Definition (UHD) video streaming.
User Experience Evaluation of the Internal Quality Assurance Information System (E-AMI) using the User Experience Questionnaire Plus (UEQ+)
This study evaluates the user experience (UX) of the E-AMI system at Primakara University, an academic quality assurance system managed by the Quality Assurance Institution (LPM). A sequential explanatory mixed-method design was employed, combining quantitative measurement using the User Experience Questionnaire Plus (UEQ+) across 15 scales with qualitative data collected through observations and semi-structured interviews. A total of 41 respondents participated, consisting of administrators, auditors, and auditees actively involved in the Internal Quality Audit (AMI) process. UEQ+ scores range from -3 (very negative) to +3 (very positive), with scores above 1.0 generally classified as good. The quantitative results yielded an average KPI score of 2.10, indicating good user experience quality, with the highest scores recorded in usefulness (M = 2.51), efficiency (M = 2.26), and perspicuity (M = 2.38). These findings were corroborated by qualitative data confirming the system's effectiveness in supporting core AMI processes. However, both quantitative and qualitative findings converged on several persistent UX weaknesses: Importance-Performance Analysis (IPA) identified visual aesthetics (M = 1.41), novelty (M = 1.90), attractiveness (M = 1.91), stimulation (M = 1.95), and value (M = 1.98) as priority improvement dimensions, consistent with qualitative findings of unintuitive navigation, insufficient system feedback, interface inconsistencies, and incomplete feature support. It is concluded that while the E-AMI system performs well in functional dimensions, further development in visual, experiential, and feature dimensions is required to achieve optimal UX quality, with future improvements recommended to adopt a user-centered design approach.
Designing an Integrated Digital Marketing Governance Model for Products Made by Inmates in Correctional Institutions
This study aims to develop an integrated digital marketing governance model for products made by inmates in correctional institutions. The research addresses the lack of integration in the marketing activities of inmate-produced products, where product management, digital promotion, sales transactions, and activity reporting are largely performed through fragmented and manual processes. The study adopts the Design Science Research (DSR) methodology, encompassing the stages of system requirements identification, model design, system architecture development, business process visualization, and conceptual evaluation through expert review and focused group discussions. The proposed model integrates product management, digital promotion, sales transactions, activity documentation, digital monitoring, and institutional oversight into a unified digital marketing governance framework. The conceptual evaluation involved four experts, comprising two correctional rehabilitation officers and two vocational program managers, who confirmed that the proposed model adequately addresses the digital marketing management needs of correctional institutions. The findings further demonstrate that the use of social media, online marketplaces, and digital communication platforms can effectively support the marketing of inmate-produced products while maintaining appropriate institutional oversight and security controls. This study contributes by proposing an institutionally governed digital marketing model that can be implemented through a low-cost digital architecture, making it particularly suitable for organizations operating in environments with strict digital access restrictions.
Feature Selection and Explainable AI for Heart Disease Detection using Machine Learning
Early detection of heart disease is essential for supporting timely clinical intervention, improving treatment outcomes, and enhancing the quality of patient care. This study compares the performance of three machine learning algorithms—Random Forest, XGBoost, and Support Vector Machine (SVM)—combined with two feature selection methods, Chi-Square and Recursive Feature Elimination (RFE), using the UCI Heart Disease dataset. Six modeling scenarios were evaluated based on accuracy, precision, recall, and F1-score. The experimental results demonstrate that the Random Forest model achieved the best overall performance, with an accuracy of 85.2% and a recall of 97.0%, indicating a strong capability to identify patients with potential heart disease. To enhance model transparency and interpretability, SHAP (SHapley Additive exPlanations) was employed as an Explainable AI (XAI) technique and integrated into a web-based decision support system to provide intuitive explanations of prediction outcomes. The proposed system is intended to serve as an initial clinical decision-support tool and is not designed to replace diagnosis or clinical judgment by healthcare professionals.
Multi-Class Skin Disease Classification using Transfer Learning and Explainable AI
This study investigates the application of transfer learning and Explainable Artificial Intelligence (XAI) for multi-class skin disease classification. The dataset was obtained from the Kaggle Skin Diseases Image Dataset and consists of 29,153 original images spanning 10 skin disease classes. To reduce the bias introduced by class imbalance, the dataset was balanced through directed undersampling, resulting in 12,000 images, with 1,200 images per class. Three pretrained convolutional neural network (CNN) architectures—EfficientNetB0, ResNet50, and DenseNet201—were implemented and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The experimental results demonstrate that DenseNet201 achieved the highest classification performance, with an accuracy of 0.8779, precision of 0.8751, recall of 0.8748, and F1-score of 0.8745, outperforming ResNet50 (accuracy: 0.8629) and EfficientNetB0 (accuracy: 0.8269). Model interpretability was investigated using Grad-CAM, SHAP, and LIME. Grad-CAM highlighted that the models primarily focused on the central and peripheral regions of skin lesions during prediction. SHAP identified the dominant contribution of lesion regions and pigmentation patterns to the classification process, while LIME emphasized the importance of local superpixels associated with lesion boundaries, color, and texture in supporting the model's predictions. The findings indicate that combining transfer learning with Explainable AI provides a promising foundation for developing clinical decision support systems for dermatological image classification. Future research should incorporate external dataset validation, more robust class balancing strategies, and clinical interpretation by dermatology experts to facilitate the deployment of such systems in real-world healthcare settings.
Usability Analysis of an Electronic Immunization Monitoring and Logistics System using PIECES and SUS
The Electronic Immunization Monitoring and Logistics System (SMILE) is an information system designed to support the integrated management of immunization records and healthcare logistics. The successful implementation of such a system depends not only on its functionality but also on its usability, which significantly influences user satisfaction and acceptance. This study aims to evaluate the usability of the SMILE application using the PIECES framework—covering Performance, Information, Economy, Control, Efficiency, and Service—and the System Usability Scale (SUS). A descriptive quantitative approach was employed, with data collected through observations, a literature review, and questionnaire surveys administered to 56 users of the SMILE application at the Gorontalo Provincial Health Office. The results of the PIECES analysis indicate that all evaluation dimensions achieved the "Satisfied" category, with mean scores of 3.68 for Performance, 4.28 for Information, 4.36 for Economy, 4.26 for Control, 4.25 for Efficiency, and 4.25 for Service. The usability assessment using the SUS yielded an average score of 53, which falls within the "OK" adjective rating (score range: 51–67) and the "Marginal" acceptability category. According to the SUS grading scale, this score corresponds to Grade D, indicating that the application's overall usability requires improvement. Although users expressed satisfaction with the system's performance and service quality, the findings suggest that enhancements are still needed, particularly in terms of ease of use, navigation, and overall user experience. The findings of this study provide valuable insights and practical recommendations for developers to improve the quality and usability of the SMILE application, thereby supporting more effective adoption and use in healthcare services.
Web-based Supply Chain Information System using SCOR at PT Ebier Suth Cokran
A web-based supply chain information system is a strategic solution for addressing operational data transparency limitations in local commodity processing industries. PT Ebier Suth Cokran in Manokwari, West Papua, faces manual coordination constraints that create inefficiencies in production planning and product distribution processes for Mansel Chocolate. This study aims to design, implement, and deploy a web-based supply chain information system developed based on performance evaluation using the Supply Chain Operations Reference (SCOR) model. The research method includes field observation, KPI mapping across the five core SCOR processes, and system development using PHP, MySQL, HTML5, and responsive CSS3. The SCOR evaluation results show that the Plan and Deliver processes are at a marginal level with scores of 85% and 88%, respectively, while the Source process achieved maximum performance of 100%. Based on this gap analysis, an information system was successfully built with Production, Inventory, Logistics, and Historical Records modules, as well as a centralized archive consisting of 13 structured sections that categorize operational records from raw material procurement, production batch monitoring, quality control, inventory management, outbound delivery scheduling, distribution status tracking, return records, workforce data, plantation data, supplier records, SCOR performance history, notifications, and system settings — enabling comprehensive supply chain traceability. The live-deployed system has been proven to improve real-time data visibility, reduce dependence on manual coordination, and strengthen operational decision-making across the company’s supply chain.
Benefit Analysis of Machine Learning Implementation for Data Engineering Automation in MSMEs
Micro, Small, and Medium-Sized Enterprises (MSMEs) are facing increasing pressure to undergo digital transformation. However, many MSMEs continue to rely on manual data management processes, making them vulnerable to human error and operational inefficiencies. This study aims to examine the benefits of implementing machine learning to automate data engineering processes in MSMEs, while identifying the key barriers to adoption and highlighting unresolved research gaps in the existing literature. A Systematic Literature Review (SLR) was conducted using the Population, Intervention, Comparison, Outcome, and Context (PICOC) framework. A total of 25 peer-reviewed studies published between 2021 and 2026 were selected from six academic databases: Google Scholar, ResearchGate, DOAJ, Garuda, Scopus, and IEEE Xplore. The synthesis identified four major benefits of machine learning implementation: reduced dependence on manual data processing, improved business prediction accuracy, operational cost savings of up to 42% through more efficient data pipelines, and more responsive inventory and customer management. Despite these advantages, adoption remains constrained by limited digital capabilities of human resources, inadequate infrastructure and financial resources, and insufficient historical data for model development. Furthermore, the literature analysis revealed that approximately 80% of existing studies focus primarily on the operational use of machine learning outputs, whereas the design of automated upstream data engineering foundations tailored to the resource constraints of MSMEs has received very limited attention. This research gap represents the primary focus and contribution of the present study.
Information Systems Analysis as a Preliminary Study for Smart Campus Development
The trend of smart campus development has increased over the past 10 years, marked by digital transformation and the implementation of IoT and big data. This trend is not limited to state and private universities; civil service universities are also participating in the development of smart campuses. Unlike state and private universities, civil service universities face limitations in internal institutional management that pose significant challenges for planning and building large projects, such as smart campuses. The smart campus development plan at the Institut Pemerintahan Dalam Negeri (IPDN) is still hampered by various issues related to the management of scattered information and low use of information systems within the campus environment. Various stakeholders hold different opinions on these issues, so the nature of the problems faced remains unclear. These issues require special attention in follow-up and solution-finding, given that the implementation and use of information technology and information systems are the core of smart campus projects. This study analyzes the problem, organizes it more clearly, and identifies the root cause of the problem from scattered issues using Soft Systems Methodology (SSM) integrated with several stages of Root Cause Analysis (RCA). The identified root causes were then mapped into the smart campus dimensions: smart economy, smart society, smart environment, and smart governance. The analysis identified 27 root causes, divided into three categories: human resources, management, and policy. Of these 27 root causes, nine fall within the smart campus dimension, namely the smart economy, smart society, and smart governance subdimensions.
Validation and Error Detection in Relational Data using a Hybrid Rule-based System
Relational databases form the backbone of modern information systems. However, data quality issues, such as duplicate records, invalid formats, missing values, and cross-table inconsistencies, can significantly reduce the accuracy of data-driven decision-making. Conventional rule-based validation is effective for detecting structured errors but has limited capability in identifying ambiguous errors, such as typographical variations in entity names. This study proposes a hybrid rule-based system that combines SQL triggers for structured error detection with fuzzy matching using the RapidFuzz Python library to identify semantically similar records across multiple relational database tables. The proposed system was implemented using six primary database tables, six corresponding quarantine tables, and a centralized error_log table. The evaluation was conducted using a synthetic dataset containing 2,775 records distributed across the six tables. The dataset was systematically generated using the generate_dataset.py script, with various intentionally injected data quality issues to enable accurate verification of the detection results. The experimental results show that the proposed system detected 472 data quality issues, with 297 records automatically moved to the quarantine tables. The rule-based component identified 311 errors (65.9%), including format violations, negative values, and referential integrity violations. Meanwhile, the fuzzy matching component detected 127 semantic errors that could not be identified using SQL rules alone, including 112 duplicate customer names, 7 similar product names, and 5 inconsistent product categories. On the experimental dataset, the proposed hybrid approach detected 34.1% more data quality issues than a rule-based validation approach alone. These findings demonstrate that integrating rule-based validation with fuzzy matching substantially improves error detection capability in relational databases, particularly for semantic inconsistencies that are difficult to capture using conventional validation rules.
Accuracy Evaluation of the Naïve Bayes Classifier for Sentiment Classification of Diploma Authenticity Issues using Orange
The rapid growth of digital text data has increased the demand for effective methods to extract meaningful information, particularly for understanding public opinion. Sentiment analysis is widely used to classify opinions into positive, negative, and neutral categories. However, challenges such as linguistic ambiguity, subjectivity, and class imbalance often degrade classification performance. This study aims to evaluate the performance of the Naïve Bayes algorithm for sentiment classification on the issue of diploma authenticity using a publicly available dataset, while examining the impact of data distribution on model performance. A quantitative experimental approach was employed using an original dataset of 1,014 instances and an oversampled dataset of 1,767 instances. The data were processed through preprocessing, Bag-of-Words feature extraction, and sentiment classification using Orange Data Mining with 10-fold cross-validation. Model performance was evaluated using accuracy and the Area Under the Receiver Operating Characteristic Curve (AUC). The results indicate that the Naïve Bayes model achieved an accuracy of 37.2% and an AUC of 0.704 on the original imbalanced dataset, reflecting relatively poor classification performance. After applying oversampling to balance the class distribution, the model's accuracy increased substantially to 82.1%, while the AUC improved to 0.970. These findings demonstrate that class distribution has a significant impact on the performance of the Naïve Bayes algorithm in sentiment classification and highlight the importance of addressing class imbalance to achieve more reliable classification results.
Implementation of Linear Regression for Predicting School Attribute Orders
Fluctuating demand for school accessories, such as caps and neckties, presents a significant challenge for the garment manufacturing industry in determining optimal production quantities. Inaccurate production planning can lead to overstocking or stock shortages, resulting in increased operational costs and reduced customer service quality. This study aims to implement a Linear Regression model to predict school accessory orders using historical sales data from 2021 to 2025. A quantitative research approach was adopted, consisting of data preprocessing, feature engineering, dataset partitioning into 80% training and 20% testing sets, and the development of a prediction model using Linear Regression. The input variables included year, month, education level, product type, time index, semester, and new academic year period. The experimental results demonstrate that the proposed model achieved a Mean Absolute Percentage Error (MAPE) of 12.91% and a Mean Absolute Error (MAE) of 149.61 units, indicating good predictive performance for forecasting school accessory orders. Furthermore, semester, product type, and the new academic year period were identified as key factors influencing demand fluctuations. The proposed model can serve as a decision-support tool for production planning and inventory management in the garment manufacturing industry, enabling more efficient resource allocation and demand-driven production planning.