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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Digitalization of Tourism Management through Tourism Information Systems and E-Ticketing to Enhance Efficiency and Competitiveness
This research aims to design and implement an integrated Tourism Information System and E-Ticketing platform for BUMDes Tirta Arum, to evaluate the effectiveness of an integrated digital system developed for tourism destinations managed by BUMDes. This study uses a Research and Development (R&D) approach using the System Development Life Cycle (SDLC) model, combined with a before-after quantitative evaluation framework to measure changes in operational performance indicators, including transaction processing time, reporting efficiency, error rate, service capacity, and digital visibility. The results show a significant improvement in performance after the implementation of the system. The average transaction processing time decreased by 83.33%, the reporting time increased by 91.67%, and the recording error rate decreased by 95%. In addition, daily transactions recorded increased by 66.67%, reflecting an increase in service capacity. From a digital marketing perspective, the website generated 15,000 impressions within three months with an average search ranking position of 8.9, indicating strong digital visibility, although a Click-through Rate of 0.8% indicates the need for further optimization to increase user engagement. These results highlight that the digitization-enabled automation of the system increases the efficiency, transparency, and competitiveness of the system and verifies that rural tourism digitalization is a digitally oriented upgrading in strategic institutional restructuring and not only the adoption of technology.
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Comparing the Accuracy and Interpretability of CNN, Transformer, and ConvNeXt for Skin Lesion Classification
Early detection of skin cancer plays an important role in improving treatment outcomes, particularly in cases of melanoma. Numerous studies have employed deep learning models for skin lesion classification; however, most have focused on a single architectural paradigm and placed greater emphasis on improving classification accuracy than on model interpretability. This study presents a comparative analysis of three deep learning architectural paradigms: EfficientNet-B4 as a representative Convolutional Neural Network (CNN), Swin Transformer as a representative Vision Transformer (ViT), and ConvNeXt as a representative modernized CNN for the classification of seven types of skin lesions using the HAM10000 dataset. All models were trained using consistent training configurations, with analytical class weighting applied to address class imbalance. Model performance was evaluated using accuracy, precision, recall, Macro F1-score, and confusion matrices, while Explainable AI (XAI) techniques, including Grad-CAM and attention maps, were employed to assess the transparency of the models' decision-making processes. The experimental results show that ConvNeXt achieved the best overall performance, with an accuracy of 91.62% and a Macro F1-score of 0.8897. It also achieved higher recall for the melanoma class than EfficientNet-B4 and Swin Transformer. The XAI visualizations further demonstrate that ConvNeXt was able to focus its attention on lesion regions that were more clinically relevant compared with the other two models. These findings indicate that ConvNeXt provides a better balance between classification performance and model interpretability, suggesting its potential as a reliable approach for supporting artificial intelligence-based skin lesion diagnosis systems.
Hybrid Particle Swarm and Chicken Swarm Optimization for Plant Disease Diagnosis using Leaf Images
Background: Plants play a vital role in sustaining life on Earth, particularly in maintaining ecological balance. They also contribute significantly to the economies of many countries and provide other valuable benefits. These plants are susceptible to many different diseases. Traditionally, trained specialists diagnose plant diseases based on their experience. However, this approach leads to problems, as diagnoses can vary from person to person. Objective: This work aims to present a novel approach to reduce errors and eliminate guesswork using a hybrid method. Methodology: We have developed a novel hybrid method combining Particle Swarm Optimization (PSO) and Chicken Swarm Optimization (CSO) algorithms to diagnose plant diseases in a group of ten species based on images of their leaves. Therefore, images of leaves from ten different plant species were collected and processed to improve contrast and remove noise. Using a statistical method incorporating hybrid classification, features were extracted. The method was applied to leaf images of ten different plant species, such as guava, jamun, mango, grape, apple, tomato, argon, and cherry, using a MATLAB simulation program. Results: The results for the hybrid method (PSO-CSO) achieved a diagnostic accuracy of 98.9%. Conclusions: The results indicate that the proposed model is an effective tool for the automated diagnosis of plant diseases. Future efforts may include expanding the database to include new crops and integrating the model into mobile applications for immediate field use.
Detecting Gambling SEO Spam on Academic Domains using Structural and Textual Features: A Domain-Holdout Evaluation
SEO spam practices related to gambling on Indonesian academic domains pose dual risks: they undermine the integrity of institutional websites and impose an operational burden on security teams tasked with distinguishing compromised pages from legitimate content. This study develops a supervised detection approach to identify gambling-related SEO spam pages on .ac.id domains using a rigorously curated reference dataset. The final dataset contains 2,003 manually annotated pages collected from 938 unique domains, comprising 815 malicious pages and 1,188 benign pages. The proposed pipeline combines textual signals from character-level TF-IDF with structural page indicators, including hidden elements, suspicious links, iframes, and external-link patterns. To avoid overly optimistic performance estimates, evaluation was conducted using a domain-holdout protocol, in which 20% of the domains were completely excluded from model training and selection. Five models were compared: a keyword-based baseline, Naïve Bayes with word-level TF-IDF, Random Forest with structural features, and hybrid Logistic Regression and Support Vector Machine (SVM) models. Experimental results on the holdout set show that Random Forest with structural features achieved the highest F1-score of 0.8844, whereas the proposed hybrid SVM achieved the highest precision of 0.9205, with an F1-score of 0.8663. These findings indicate that structural compromise signals are more robust than textual cues in detecting stealthy SEO spam scenarios, while hybrid models remain promising when high precision and interpretability are prioritized.
Essential User Experience Principles for Programming-Oriented Role-Playing Games: An Expert-Validated Thematic Synthesis
This study aims to identify and validate the essential User Experience (UX) principles for the design of programming-oriented Role-Playing Games (RPG), a subgenre that requires players to navigate an interactive environment while simultaneously performing structured programming reasoning. Existing UX frameworks have been developed predominantly for general entertainment contexts or non-game applications and therefore do not fully address the dual cognitive load inherent in programming-oriented RPGs. To address this gap, this research employs a qualitative approach combining expert validation, thematic synthesis, and the Fuzzy Delphi Method (FDM), involving ten experts from the fields of UX design, game development, and programming. The analysis yielded six empirically validated UX principles, namely Usability and Learnability, Accessibility and Readability, Responsiveness and Interaction Smoothness, Consistency and Predictability, Meaningful Feedback and Guidance, and Engagement Support. The defuzzified consensus values of these principles ranged from 0,59 to 0,72, with all six principles surpassing the acceptance threshold of 0,50, indicating sufficient expert agreement. Further analysis revealed that the six principles do not operate in isolation but form a network of interdependencies that can be represented as a three-layer hierarchical framework consisting of Foundation, Communication and Responsiveness, and Engagement. This study contributes by providing a specific, coherent, and empirically validated UX framework that can serve as a design and evaluation reference for programming-oriented Role-Playing Games.
Analysis and Clustering of Plantation Crop Production in Indonesia using K-Means Clustering and Principal Component Analysis Methods
Indonesia has substantial potential in the plantation sector, with production levels varying considerably across provinces. These differences are influenced by geographical conditions, land availability, and the leading commodities of each region. This study aims to identify production patterns of plantation crops in Indonesia using K-Means Clustering and to visualize the resulting clusters using Principal Component Analysis (PCA). Official 2024 BPS data covering the production of oil palm, coconut, rubber, coffee, cocoa, tea, and sugarcane across 38 provinces were analyzed. The preprocessing procedure included outlier treatment using Interquartile Range (IQR)-based capping, logarithmic transformation, normalization using StandardScaler, dimensionality reduction using PCA, and clustering using the K-Means algorithm. The optimal number of clusters was determined using the Elbow Method, Silhouette Score, and Davies-Bouldin Index (DBI). The results indicate that K = 5 was the optimal clustering solution, achieving a Silhouette Score of 0.5883 and a DBI of 0.4744. The clustering results classified Indonesian provinces into five production categories: very high, high, moderate, low, and very low. Provinces in Sumatra and Kalimantan dominated the very-high and high production categories, while several provinces fell into the moderate category. In contrast, most provinces in eastern Indonesia were classified into the low and very-low production categories. Furthermore, the combination of K-Means Clustering and PCA proved effective for categorizing provincial production data and identifying spatial patterns in plantation crop production across Indonesia. The findings can support government agencies and other stakeholders in formulating plantation-sector development policies and establishing regional priorities based on the production characteristics of individual provinces.
Design of Information Technology Governance for Employee Training Data Management using the COBIT 2019 Design Toolkit
The rapid advancement of technology in the digital era requires companies to continuously innovate to remain competitive. The implementation of information technology has become a crucial factor in sustaining business operations, particularly in data management and data quality. PT XYZ faces challenges in managing employee training data due to the absence of an integrated system, resulting in data inaccuracies and inconsistencies. The COBIT 2019 framework enables the company to identify gaps between its current governance practices and the governance processes required to support organizational objectives. The recommendations derived from the audit based on the COBIT 2019 Design Factors indicate that the company needs to develop an integrated system as a fundamental foundation for improving data quality and harmonization in alignment with business objectives and decision-making requirements.
Charitas Fibriani (SCOPUS ID=57192643331)
LGI: Label-Graph Inference Multi-Benchmark Emotion Detection Transformer Framework for Intelligent Chatbots
The fundamental ability of intelligent chatbots is to detecting emotions evident in intelligent recommendation systems, learning agent systems and mental health systems. Emotions are a key element in facilitating interaction and communication between humans. Researchers face challenges in studying the transmission of emotions through text. Despite significant advances in language transformation models, emotion recognition remains a major challenge due to the high semantic overlap between emotion categories and unbalanced distributions in datasets. This paper suggests an improved framework for enhanced transformer model based on a Label-Graph Interface (LGI) for ratings to robustly detect emotions across texts using various criteria specific to social media and conversations between humans and human-machine. It combines a pretrained transformer encoder with lightweight label relation inference layer that uses a row normalized graph prior and sample adaptive gate to propagate evidence among related emotion labels. In single label case, cross entropy loss is used to optimize the model, but in multi label detection a binary cross entropy is used with logits, class aware positive weighting and threshold selection based on validation sets. Six datasets were used in order to assess the framework: DAIR Emotion, achieved an macro-F1 to 89.10%. TweetEval Emotion is the second dataset reached an macro-F1 is 81.56%. DailyDialog, the macro-F1 arrived to 58.62%. GoEmotions, got micro-F1 about 60.03%. Empathetic Context dataset reached to 85.80% Top-3 accuracy and macro-F1 is 85.69% on the 32-class task. Lastly EmoWOZ is the most important dataset because it contains real conversation between human-machine has an accuracy about 92.86%, macro-F1 to 59.95% and AUC to 94.97%. The results demonstrate that the significant value and usefulness of relational inference at the classification level for emotion-based chatbot systems.
Vegetable Sales Transaction Segmentation using K-Means Clustering for Sales Strategy Optimization
Managing fresh vegetable inventory requires accurate decision-making because of its limited shelf life and variations in unit prices and sales volumes. This study aims to segment vegetable sales transactions at D2 Vegetables, a vegetable supplier, using K-Means Clustering based on unit price and sales volume; evaluate cluster quality; and develop operational recommendations based on the characteristics of each segment. The dataset consists of 1,016 vegetable sales transactions recorded from June 25, 2024, to June 23, 2025. The analytical procedures included data preprocessing, standardization, determination of the optimal number of clusters using the Elbow Method, evaluation using the Silhouette Coefficient and Davies-Bouldin Index, K-Means clustering, and comparison with Agglomerative Hierarchical Clustering. Internal evaluation indicated that K = 2 achieved the highest Silhouette Coefficient (0.6114) and the lowest Davies-Bouldin Index (0.5156). However, K = 3 was selected by considering the Elbow pattern, cluster structure quality, and interpretability of the resulting three transaction segments. At K = 3, K-Means achieved a Silhouette Coefficient of 0.5719 and a Davies-Bouldin Index of 0.6029, outperforming Agglomerative Clustering, which achieved scores of 0.5031 and 0.6595, respectively. The resulting segmentation comprised 682 Standard transactions (67.13%), 235 Bulk transactions (23.13%), and 99 Premium transactions (9.74%). The Bulk segment had the highest total sales volume, the Standard segment had the largest number of transactions and the greatest diversity of vegetable types, while the Premium segment had the highest average unit price and relatively low sales volume. The segmentation results can serve as a decision-support basis for procurement planning, inventory management, storage allocation, and segment-specific promotional strategies, enabling operational decisions to be aligned with the characteristics of each transaction segment.
Design of an Information System for Participants of the National Values Enhancement Program at Lemhannas RI using the Design Thinking Method
The National Values Enhancement Program (Program Pemantapan Nilai-Nilai Kebangsaan—PPNK) of the National Resilience Institute of the Republic of Indonesia (Lemhannas RI) is a strategic program aimed at strengthening national insight, character, and national values among members of society. However, the participant registration process for PPNK is still conducted manually, resulting in several challenges, including slow information processing, a high risk of data-entry errors, and suboptimal dissemination of information to prospective participants. This study aims to develop a design prototype for the PPNK Participant Information System at Lemhannas RI by applying the Design Thinking methodology to facilitate and streamline the participant registration process. This approach was selected because it emphasizes identifying and exploring user needs, defining problems, and developing innovative, user-centered solutions. The study resulted in a PPNK Participant Information System prototype designed based on identified user requirements and incorporating training management and participant data management features. To evaluate the system's usability, the prototype was assessed using the System Usability Scale (SUS). The evaluation involved 10 respondents and yielded an average SUS score of 81.25. This score falls within the Excellent category, indicating that the proposed system is easy to use, meets user needs, and provides a positive user experience. The proposed prototype is expected to support more effective, efficient, and structured registration and participant management processes for the PPNK program at Lemhannas RI, while also serving as a foundation for developing an information system ready for implementation within the Lemhannas RI environment.
Data-Centric AI based Indonesian ALPR System using Yolov11 And TrOCR
The implementation of Automatic License Plate Recognition (ALPR) in Indonesia faces challenges arising from visual degradation and anomalies in real-world datasets, including annotation errors, data leakage, and imbalanced distributions of regional license plate codes. This study aims to develop a precise and adaptive end-to-end ALPR system for Indonesian vehicle license plates by optimizing both license plate detection and character recognition architectures. A Data-Centric AI approach was employed to improve dataset quality through bootstrap relabeling and the targeted synthesis of 2,773 images to address regional distribution imbalances. The proposed system adopts a two-stage processing pipeline, consisting of license plate localization using YOLOv11m and text recognition through a comparative evaluation of PaddleOCR, Tesseract, EasyOCR, and TrOCR-base. Specifically, TrOCR was optimized using a two-stage fine-tuning strategy (K4 Two-Stage) that combines synthetic data during pretraining with real-world data during subsequent fine-tuning. The results demonstrate that the proposed framework effectively mitigates dataset anomalies. The YOLOv11m model achieved a license plate detection mAP@50 of 97.05%. Among the recognition models, TrOCR K4 achieved the best performance, with an Exact Match (EM) accuracy of 86.21% and a Character Error Rate (CER) of 3.03%. End-to-end inference evaluation achieved an overall exact-match accuracy of 87.07%, with a low inference speed of 2.84 FPS (approximately 206 ms per license plate) on an NVIDIA L4 GPU. Overall, the integration of Data-Centric AI, YOLOv11, and TrOCR produced a highly accurate and efficient Indonesian license plate recognition system, demonstrating strong potential for real-time vehicle monitoring applications.
Usability Evaluation of the Indodax Website User Interface using Heuristic Evaluation and Severity Rating Methods
The growth of crypto-asset investment in Indonesia has increased the demand for trading platforms with good usability. This study aims to evaluate the usability of the Indodax website using Heuristic Evaluation based on Nielsen's ten usability heuristics and to determine improvement priorities using Severity Rating. A descriptive quantitative approach was employed, involving 13 active Indodax users as respondents. Data were collected through a Likert-scale questionnaire that was tested for validity, reliability, and normality. The results showed that all questionnaire items were valid, with the calculated r-values exceeding the critical r-value of 0.553. The instrument achieved a Cronbach's Alpha of 0.985, indicating very high reliability, while the Shapiro–Wilk normality test yielded a significance value of 0.377 (>0.05). The evaluation identified ten usability problems, consisting of two critical, six major, and two minor issues. Improvement priorities were focused on Error Prevention, User Control and Freedom, and Consistency and Standards to enhance the quality of the user interface, user experience, and transaction security of the Indodax website. The findings provide practical insights for prioritizing interface improvements and enhancing the overall usability and reliability of crypto-asset trading platforms.
Adaptive Mix-Gated Multi-Attention Fusion in YOLOv11s for Dense Person Detection: A Systematic Evaluation of Dual and Triple Attention Configurations
Person detection in a crowded environment is very challenging due to excessive occlusion, scale variation, and heavy overlap between individuals. This paper proposes a lightweight Mix-Gated attention framework to enhance the feature representation capacity of YOLOv11s for dense person detection. The proposed framework incorporates two complementary channel-wise and spatial attention mechanisms under a flexible channel-wise gating strategy, while preserving the original YOLOv11s architecture and real-time inference ability. Unlike previous studies, which primarily employed either a single attention mechanism or fixed combinations of multiple attention modules, the proposed framework introduces an adaptive Mix-Gated feature selection mechanism that dynamically learns the contribution of complementary attention modules through trainable channel-wise gating. Furthermore, this study systematically evaluates ten dual and triple attention configurations under identical training and evaluation conditions, providing a comprehensive and fair comparison of their effectiveness. this study evaluated the performance on four popular benchmark datasets - CrowdHuman, WiderPerson, MOT17Det and MOT20Det - using Precision, Recall, F1-score, mAP@50 and mAP@50-95 as evaluation metrics. The experimental results show that all proposed Mix-Gated configurations outperform the baseline YOLOv11s model. Mix-Gated-CBAM+SE+SA has the best detection accuracy and efficient inference performance among Mix-Gated configurations. These results indicate that adaptive fusion of complementary attention mechanisms can improve the feature representation and person detection performance in crowded scenes while not sacrificing computational efficiency.
Comparative Analysis of Instance Segmentation Models for House-Level Visual Socioeconomic Classification using Satellite Imagery
House-level visual socioeconomic information can provide a more detailed spatial understanding of residential areas and can support applied urban and commercial analysis. However, official socioeconomic data are often available only at broader administrative levels and may not capture variation among small residential clusters or individual houses. This study compares five instance segmentation models for detecting, segmenting, and classifying individual houses into lower, middle, and upper visual socioeconomic classes using high-resolution satellite imagery. The evaluated models are Mask R-CNN, Cascade Mask R-CNN, YOLO11n-seg, SOLOv2, and Mask2Former. The dataset consists of 1,209 training images with 7,052 annotations and 213 validation images with 1,353 annotations from residential areas in Banten and DKI Jakarta. Annotation reliability was assessed using 100 annotation pairs, producing a quadratic-weighted Cohen’s kappa of 0.8077. Model performance was evaluated using COCO metrics for both segmentation masks and bounding boxes. The results show that Cascade Mask R-CNN achieved the highest observed overall validation performance among the tested configurations. Under the current experimental setting, it produced the strongest combination of object-localization and mask-segmentation metrics. These findings show that comparing multiple instance segmentation models can help identify a more suitable method for house-level visual socioeconomic classification. Unlike previous studies that generally perform area-level socioeconomic estimation or building extraction alone, this study compares multiple instance-segmentation approaches for expert-defined socioeconomic classification at the individual-house level. The resulting output can serve as a supplementary visual socioeconomic layer that complements demographic, accessibility, and commercial data in applied spatial and market-development analyses.
Comparison of Sequential and Logarithmic Data Access Methods based on Archiving Scenarios Validity
The growing volume of customer documents in financing companies presents challenges in archive retrieval when the search method is not well suited to increasing data volumes. This study aims to compare Linear Search and Binary Search methods within a web-based customer document archiving information system at PT Multindo Auto Finance. An experimental comparative analysis was conducted by empirically evaluating both algorithms under identical conditions and using the same dataset. The dataset consisted of 4,996 customer documents and was evaluated under two testing scenarios: searching for data that exists in the system and searching for data that does not exist. The primary performance metric was execution time, measured in milliseconds (ms). The results show that Binary Search outperformed Linear Search in both scenarios, achieving an execution time of 0.01 ms, while Linear Search required 0.09 ms when the target data was found and 0.23 ms when the target data was not found. However, Binary Search requires the data to be sorted, which may limit its suitability for systems containing frequently updated or dynamically changing data. The study concludes that Binary Search is recommended for large-volume systems with relatively static datasets, whereas Linear Search is more suitable for systems with dynamic datasets that are continuously updated.
Digital Infrastructure Determinants of SPBE Implementation Effectiveness: Evidence from the North Kalimantan Provincial Government
The implementation of the Electronic-Based Government System (SPBE) has become a key strategy for digital government transformation in Indonesia. However, its effectiveness remains influenced by various aspects of digital infrastructure that are not yet fully optimized, particularly in geographically challenging regions such as North Kalimantan Province. The implementation of SPBE in North Kalimantan Province has demonstrated a substantial improvement in its SPBE index over the past five years. Despite this continuous increase, limited empirical research has examined the contribution of individual digital infrastructure dimensions to the effectiveness of SPBE implementation. This study aims to analyze the effects of network infrastructure, IT resources, information security, and government system integration on the effectiveness of SPBE implementation in North Kalimantan Province. A quantitative approach was employed using 20 research indicators and 20 questionnaire items, with data collected from 75 respondents through a census sampling technique. The data were analyzed using multiple linear regression. The descriptive statistical results indicate that information security had the highest mean score, whereas IT resources had the lowest mean score among the variables examined. The multiple regression analysis shows that network infrastructure, IT resources, information security, and government system integration significantly affect SPBE effectiveness, both individually and simultaneously. Network infrastructure was identified as the most dominant determinant (β = 0.361; p < 0.001), while the regression model explained 97.0% of the variance in SPBE effectiveness (Adjusted R² = 0.970). Based on these findings, the study recommends accelerating the equitable development of digital infrastructure and providing continuous training for IT personnel to improve the effectiveness of SPBE implementation. The findings provide practical guidance for the North Kalimantan Provincial Government to strengthen SPBE implementation by accelerating digital infrastructure development, particularly in remote areas with limited network coverage, while enhancing the capacity and availability of qualified IT resources.
Implementation of SHA-256 and Reed-Solomon Algorithms in Quick Response (QR) Codes at the Class IIB Correctional Facility System in Pati Regency
Correctional facilities play an important role in the rehabilitation and supervision of inmates. In practice, the process of recording the number of inmates in each room is still largely performed manually, requiring considerable time and increasing the risk of recording errors. This approach also reduces the efficiency of correctional officers in conducting routine checks. Therefore, a system capable of providing fast, accurate, and secure information is needed. This study aims to implement a QR Code-based information system at the Class IIB Correctional Facility in Pati Regency to support inmate-room data management. A QR Code is placed at the entrance of each room and contains information regarding the room number, room capacity, and number of inmates occupying the room. To protect data integrity, the SHA-256 algorithm is employed to generate hash values for verifying data integrity and preventing unauthorized modification of information. In addition, the Reed–Solomon algorithm is implemented as an error-correction mechanism to ensure that QR Codes remain scannable even when partially damaged. The system was developed as a web-based application and can be accessed by correctional officers using mobile devices to scan the QR Codes. The results demonstrate that integrating QR Codes with SHA-256 and Reed–Solomon algorithms improves the efficiency of inmate-room data verification, accelerates information access, enhances data integrity and security, and reduces errors associated with manual data recording.
An Extended Approach for Color Image Authentication and Recovery Against Composite Attacks
This paper proposes an extended approach for color image authentication and recovery utilizing the alpha layer and a secret sharing scheme to counter sequential composite attacks. Building upon our previous work which proved effective against traditional single attacks such as cropping, noise, and superimposition this study addresses the complex challenges posed by successive and combined manipulations. The proposed approach embeds authentication and recovery data within the alpha channel using a spatial correlation phase, ensuring that authentication and repair data are embedded in non-contiguous blocks to guarantee high accuracy in tamper detection and image restoration. Experimental results on diverse color images demonstrate remarkable efficiency in withstanding sequential composite attacks, recording outstanding values of (PSNR=32.85 dB) and (SSIM=0.95) even under the most severe composite sequential attacks. This demonstrates exceptional robustness against tampering while preserving the high visual quality of both the stego and recovered images. Ultimately, the findings confirm that this framework significantly enhances the reliability and security of image transmission and storage compared to conventional methodologies.
Analysis of Wireless Network Performance at UGM Academic Hospital based On QoS Parameter
RSA UGM integrates healthcare, education, and research services, making it highly dependent on reliable wireless network infrastructure. However, the performance of the RSA UGM wireless network often deteriorates when user demand approaches the maximum capacity of access points, particularly in public areas such as auditoriums and patient waiting rooms. This condition may result in channel interference, reduced throughput, and increased response times. This study analyzes wireless network performance based on Quality of Service (QoS) parameters while integrating a digital forensics approach to distinguish legitimate user traffic surges (flash crowds) from Distributed Denial-of-Service (DDoS) attacks. The study employs an Action Research methodology consisting of diagnosis, planning, action implementation, and evaluation. QoS measurements were conducted using iPerf3 and Wireshark/tcpdump over three days across three time sessions, accompanied by a SYN Flood attack simulation in a laboratory environment replicating the hospital network topology. QoS parameters were analyzed based on the TIPHON standard, including throughput, packet loss, delay, and jitter, while the integrity of digital evidence was verified using SHA-256 hashing and a chain-of-custody procedure. The results show that under normal conditions (11 samples), throughput ranged from 9,877.75 to 23,452.12 Kbps, with a TIPHON Index of 4.00, indicating very good performance. Under flash crowd conditions, throughput increased sharply to 104,602.77 Kbps, while the other QoS parameters remained stable, resulting in a TIPHON Index of 4.00. In contrast, during the DDoS attack condition (7 samples), packet loss increased substantially to 42.64%, accompanied by significant increases in delay and jitter, causing the TIPHON Index to decline to 3.25–3.75. These findings indicate that throughput alone is insufficient to distinguish between flash crowds and DDoS attacks, whereas packet loss represents the most significant distinguishing parameter. All 19 digital evidence items (C-01–C-19) matched during SHA-256 hash verification, demonstrating that data integrity was preserved throughout the investigation. The study concludes that integrating TIPHON-based QoS analysis with digital forensics can support early detection and accurate investigation of network security incidents, particularly in hospital environments where high network reliability is essential.
Usability Analysis of the SIPBSI Website using the System Usability Scale as a Basis for Interface Improvement Priorities
The Information System of the Indonesian Badminton Association (SIPBSI) is a web-based system designed to manage athlete, club, and ranking data in an integrated manner. The successful implementation of such a system is determined not only by its functionality but also by its level of usability, which influences user satisfaction and acceptance. However, the usability of SIPBSI from the users’ perspective has not yet been clearly established. This study aims to evaluate the usability level of the SIPBSI website using the System Usability Scale (SUS) and to develop prioritized recommendations for interface improvement. A quantitative approach was employed, with data collected through a SUS questionnaire administered to 30 respondents who were active club coaches in Salatiga City. The research procedures included calculating the SUS score, analyzing the mean scores of individual items, and mapping the results onto usability aspects, including learnability, consistency, complexity, need for assistance, and interface clarity. The results show that the SIPBSI website achieved an average SUS score of 62. Based on SUS interpretation, this score corresponds to Grade D, with an adjective rating of “Poor” and an acceptability rating of “Marginal.” Further analysis revealed that the main usability issues were related to need for assistance (2.20), learnability (2.22), consistency (2.30), complexity (2.33), and interface clarity (2.46). These findings indicate that the usability of the system still needs improvement, as usability issues may hinder the effectiveness of data management and access in supporting PBSI operations at both regional and national levels. The redesign recommendations focus on simplifying navigation and user flows, improving interface consistency, and providing clear instructions or user assistance features. This study is expected to serve as an evaluation reference and provide practical recommendations for developers to improve the quality and usability of the SIPBSI website.
Performance Evaluation of Plane Detection in Markerless Augmented Reality under Variations in Surface Characteristics and Light Intensity
Plane detection is a key component of markerless augmented reality (AR) systems, as it determines the system’s ability to recognize surfaces for virtual object placement. However, plane detection performance is influenced by surface characteristics and lighting intensity, which can affect the quality and stability of feature points during the tracking process. This study aims to evaluate the performance of plane detection in a Unity-based markerless AR system using AR Foundation under varying surface characteristics and lighting intensities. The experiments were conducted using three surface types: highly textured, low-textured, and reflective surfaces, under three lighting conditions: bright, dim, and dark. Each scenario was tested five times using four evaluation parameters: tracking time, detection success rate, drift distance, and coverage area. The results show that highly textured surfaces provided the best overall performance across all evaluation parameters, with the fastest tracking time ranging from 2.82 to 6.66 seconds, a detection success rate of 100%, the lowest drift distance ranging from 0.04 to 1.55 cm, and a wider coverage area of 81.04% compared with the other conditions. In contrast, low-textured surfaces presented the most challenging condition, particularly under dark lighting, which resulted in detection failure across all trials. Meanwhile, reflective surfaces were still able to detect planes in most trials but exhibited reduced performance due to the instability of feature points caused by light reflections. Overall, the findings demonstrate that plane detection performance varies across different combinations of surface characteristics and lighting intensities.
Improving Marketing and Operational Efficiency in PT Rumekar Agribusiness through an Integrated E-Commerce based on a Design Thinking Approach
The advancement of information technology has driven the need for digital transformation across various sectors, including the agribusiness industry. PT Rumekar currently faces several operational challenges due to the continued use of conventional methods, including manual stock recording, fragmented financial data reporting, slow information flows, and limited marketing reach. This study aims to design an integrated web-based agribusiness e-commerce information system for PT Rumekar to address these challenges. The novelty of this study lies in integrating a sales module (digital storefront) with real-time inventory management and financial reporting within a single platform specifically tailored to agribusiness workflows. The system was designed using the Design Thinking methodology, which adopts a user-centered approach through five iterative stages: Empathize, Define, Ideate, Prototype, and Test. The system was developed using the Laravel framework and provides interfaces and functional features tailored to customers (users) and system administrators (admins). System testing was conducted using two approaches: Black Box Testing to evaluate system functionality and the System Usability Scale (SUS), involving five respondents consisting of three users and two system administrators to assess usability. The Black Box Testing results indicate that all system features functioned as designed and met the specified requirements. Meanwhile, the SUS evaluation yielded scores of 78.0 from users and 78.5 from administrators. Both scores correspond to Grade B and fall within the “Acceptable” acceptability range. These findings indicate that the Rumekar e-commerce website is feasible as a solution for improving sales efficiency, report management, and information dissemination. The system also has potential for adoption by other agribusiness companies facing similar operational challenges.
Cyber Attack Classification using a Multilayer Perceptron
The continued evolution of Distributed Denial-of-Service (DDoS) attacks poses a serious threat to network security. Numerous studies have employed machine learning and deep learning techniques for DDoS detection; however, most have focused on improving overall accuracy without adequately addressing the class imbalance characteristic of the CICDDoS2019 dataset. Moreover, the exploration of the Focal Loss function within a Multilayer Perceptron (MLP) architecture for multiclass classification remains limited. This study aims to develop an MLP-based cyberattack classification model using Focal Loss to address class imbalance. The research process includes data preprocessing, feature normalization, class weighting, and model training using two hidden layers with ReLU activation and a Softmax output layer. The model was evaluated using accuracy, precision, recall, F1-score, and confusion matrix analysis. The experimental results show that the MLP model achieved an accuracy of 98.80%, with a weighted F1-score of 0.99 and a macro F1-score of 0.87. Although Random Forest achieved a higher overall accuracy of 99.18%, the MLP model demonstrated competitive and stable performance in handling imbalanced data. This study provides preliminary evidence of the potential of an MLP model with Focal Loss for the development of a deep learning-based Intrusion Detection System (IDS), although further isolated testing is required.
Comparative Performance Analysis of EfficientNet-B0, ConvNeXt-Tiny, and MobileNetV2 for Oil Palm Fruit Ripeness Classification
The classification of Fresh Fruit Bunch (FFB) ripeness levels in oil palm is a crucial aspect of agricultural automation for maintaining production quality. This study evaluates the performance of three deep learning architectures through a comparative analysis of EfficientNet-B0, ConvNeXt-Tiny, and MobileNetV2 for distinguishing different fruit ripeness classes. The experimental results show that MobileNetV2 achieved the highest accuracy of 97%, followed by ConvNeXt-Tiny at 94%, whereas EfficientNet-B0 achieved only 32%, primarily due to systematic prediction bias on the complex dataset. The confusion matrix analysis indicates that although MobileNetV2 and ConvNeXt-Tiny performed effectively, visual ambiguity during transitional ripeness stages remains a challenge for purely computer vision-based approaches. These findings highlight the importance of selecting an appropriate backbone architecture and the potential need for hybrid approaches to improve system reliability. As a direction for future research, the application of Neuro-Symbolic AI is proposed to combine deep learning-based feature extraction with knowledge-based reasoning to resolve ambiguous predictions, together with model optimization to achieve greater computational efficiency on edge devices. This study provides a strategic contribution to the development of transparent and accurate automated sorting systems for the oil palm plantation sector.
Labuan Bajo Culinary Tourism Recommendation System: A Comparison of Content-Based Filtering Similarity Methods
Labuan Bajo, as a national super-priority tourism destination, has experienced a significant increase in tourist visits, dominated by foreign tourists, which has driven the expansion of the culinary sector with a continuously increasing number of restaurants. This makes it difficult for tourists to choose dining options that match their preferences among the many available choices. Previous culinary tourism recommendation system research has only used a single similarity measurement technique without evaluating its effectiveness compared to other techniques. This study aims to build a culinary tourism recommendation system in Labuan Bajo using the Content-Based Filtering method, as well as to compare three similarity measurement techniques Cosine Similarity, Euclidean Distance, and Jaccard Similarity to determine which technique produces the most relevant recommendations. Data was obtained through scraping techniques on 150 restaurants in Labuan Bajo, covering category and rating attributes, which were then processed into a feature matrix for inter-item similarity calculation. Evaluation was carried out using Precision@5 with relevance criteria based on category similarity and rating proximity, followed by a Wilcoxon Signed-Rank Test to examine statistical significance between methods. The test results show that Cosine Similarity and Euclidean Distance produce an equal precision of 0.6947, higher than Jaccard Similarity at 0.6320, with the difference proven statistically significant (p < 0.001). The system was then implemented as a web application using the CodeIgniter framework, allowing users to select a similarity method and view restaurant recommendations interactively. This study demonstrates that considering the rating attribute, not just category, produces more relevant recommendations than a category-only approach.
Enhancing DES Encryption Efficiency through a Metrics-Centric Pipeline and AI-Driven Analytical Framework
This paper introduces an improved Data Encryption Standard (DES) framework that incorporates a metrics-driven data processing pipeline, AI-powered analytical decision support and a quantum-inspired entropy-based key generation process to enhance both encryption efficiency and key security. It validates data, preprocesses it, encodes it, optimizes it, encrypts it, monitors it in real-time, analyzes entropy and also analyzes the execution time, encryption speed, throughput, CPU utilization, memory consumption and key randomness. Experimental results indicate that the encryption throughput was achieved on average at 5.11 MB/s, with a maximum value of 12.15 MB/s, while the time required for encryption and decryption were on average 42.67 ms and 44.21 ms, respectively. The proposed quantum-inspired key generator has an average key entropy of 0.82 (compared with 0.68 for standard random keys) and a higher uniformity of 0.88 (compared with 0.72 for standard random keys), which corresponds to an approximately 20.6% improvement. Furthermore, the quantum-inspired approach achieved a maximum entropy of 0.95 compared with 0.89 for random generation, and its strongest keys had a collision rate of no more than 0.01%. The overall average security improvement for the AI analysis was 13.30% and the overall completion rate for the full processing pipeline was 98.50%. The findings highlight practical solutions for optimizing the performance, randomness, and security evaluation of DES-based encryption by incorporating AI-driven analysis, entropy-driven key generation, and regular monitoring.
Severity Classification of Diabetes Mellitus Patients at the Puskesmas Pembantu of Binjai City using the K-Nearest Neighbor (KNN) Algorithm
Diabetes mellitus (DM) is a chronic disease with an increasing prevalence, including in Binjai City. Manually determining the severity level of DM patients requires a high degree of accuracy and careful assessment because it involves multiple medical indicators. Therefore, a classification method is needed to assist healthcare professionals in making faster and more accurate assessments. This study aims to develop a classification system for determining the severity of Type 1 and Type 2 diabetes mellitus in patients at Puskesmas Pembantu in Binjai City using the K-Nearest Neighbors (KNN) algorithm and to evaluate its classification performance. The dataset consisted of 594 patient medical records collected from 2024–2025, comprising 13 attributes, including age, height, weight, body mass index, waist circumference, systolic blood pressure, diastolic blood pressure, blood glucose level, and encoded attributes. The data preprocessing stage involved transforming categorical attributes and applying Z-score normalization before calculating similarity using the Manhattan distance metric. The model was evaluated using values of k ranging from 1 to 15 to determine the optimal k. The results show that k = 1 achieved the highest accuracy of 98.86%, with a precision of 97.71%, recall of 100%, and an F1-score of 0.99. The KNN algorithm was subsequently implemented in a web-based system that enables healthcare professionals to enter patient data and obtain classification results directly. These findings demonstrate that the KNN algorithm using the Manhattan distance metric is effective for classifying DM severity levels and can serve as a decision-support tool for healthcare professionals at Puskesmas Pembantu in Binjai City.
Analysis of Factors Influencing User Acceptance and Satisfaction with Google Classroom in Hybrid Learning using an Integrated EUCS and TAM Approach
The use of Google Classroom in hybrid learning needs to be evaluated to determine the extent to which users perceive the system as useful and satisfactory for their learning activities. This study examines user acceptance and satisfaction with Google Classroom in hybrid learning by integrating the Technology Acceptance Model (TAM) and the End-User Computing Satisfaction (EUCS) model. A quantitative approach was employed involving 155 respondents. Data were collected through questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the support of SmartPLS software. The study investigates how various EUCS dimensions, including Format, Timeliness, Content, and Accuracy, are associated with TAM constructs, namely Perceived Ease of Use, Perceived Usefulness, User Satisfaction, and Behavioral Intention. The results show that eight of the ten hypotheses were supported, while the remaining two were not supported. Format has a significant effect on Perceived Ease of Use. In addition, Timeliness and Content significantly affect Perceived Usefulness. Perceived Ease of Use and Perceived Usefulness significantly affect User Satisfaction, while Perceived Usefulness and User Satisfaction significantly affect Behavioral Intention. In contrast, Accuracy does not significantly affect Perceived Usefulness, and Perceived Ease of Use does not have a significant effect on Behavioral Intention. These findings indicate that Perceived Usefulness and User Satisfaction are important factors in driving users’ Behavioral Intention to use Google Classroom in hybrid learning.
Intelligent Technique for Optimizing Requirements Elicitation in Software Engineering Projects
Requirements elicitation is a critical activity in software engineering, as incomplete, ambiguous, or inconsistent requirements can adversely affect software quality and project outcomes. Although Large Language Models (LLMs) have demonstrated considerable potential for supporting Requirements Engineering, conventional prompting approaches typically rely on direct or predefined interactions and lack systematic mechanisms for identifying and resolving information gaps during elicitation. This study proposes Adaptive Context-Driven Prompting (ACDP), an iterative framework designed to improve LLM-supported requirements elicitation through context preservation and gap-driven refinement. ACDP progressively constructs an Elicitation Context (EC) that maintains project objectives, stakeholders, constraints, assumptions, and previously elicited requirements. In parallel, a Requirement Gap Matrix (RGM) systematically identifies missing, partial, ambiguous, and conflicting information and transforms these gaps into targeted re-elicitation prompts for subsequent LLM interactions. The framework was evaluated using public software requirements documents from the PURE dataset and compared with Zero-Shot Prompting and Fixed Multi-Step Prompting under controlled LLM settings. Performance was assessed using reference-based Precision, Recall, and F1-score, together with requirement-quality dimensions including completeness, correctness, consistency, clarity, relevance, ambiguity, and redundancy. Experimental results demonstrate that ACDP achieved a Precision of 0.93, Recall of 0.92, and F1-score of 0.92, outperforming both baseline prompting approaches while also improving requirement completeness, correctness, and overall quality. These findings demonstrate that combining persistent elicitation context with explicit gap diagnosis provides a systematic and effective mechanism for improving LLM-assisted requirements elicitation and offers practical support for requirements analysts in complex software projects.
Network Emergent Coverage Patterns from Stochastic Dynamic Models
This work investigates the influence of the movement patterns (mobility) of network nodes on the overall performance of the network in terms of the area that can be covered by nodes' communications. This is important because, in urban areas, there are still areas that cannot be covered due to a lack of communication range of network nodes (sensors). Usually covering all areas may lead to a heavy cost due to the number of static sensors that might be implanted in particular areas. Therefore, this research comes to overcome this issue by testing a variety of mobility models and seeing which of them lead to a better performance. This work is based on simulations performed on a wide range of settings and variables such as mobility models, number of nodes within the network, network distribution of nodes, communication range of nodes, and other settings. The evaluation is based on the amount of area that is covered under particular settings. The findings show that the Exponential and Levy Flight models reflect the highest exploration efficiency (MNVL = 96.39 and 95.59 respectively). The Rayleigh Flight model show the largest average coverage area with alomost 36.3%. However, it shows high variability of CV=5.04%. The Levy with Exponential Cutoff model reflects high stability rate with CV=0.16% and moderate coverage of 31.7%.