Sistemasi: Jurnal Sistem Informasi is a scientific journal in the field of computer science and engineering. General Focus and Scope of Systemation, are Information Systems, Information Technology, Computer Science, Software Engineering, and Computer Engineering. Journal of Information Systems is abstracted and indexed in Directory of Open Access Journal (DOAJ) since 2018 and Science and Technology Index (SINTA) since 2018. Systemation journals are published 6 times a year, in January, March, May, July, September and November. Sistemasi: Jurnal Sistem Informasi (e-ISSN: 2540 – 9719) published by the Information Systems Study Program, Faculty of Engineering and Computer Science, Universitas Islam Indagiri, Tembilahan Riau Indonesia.
Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer
Once your article has been accepted, we will require you to complete, sign and return, a copy of the Copyright Transfer Agreement that you should send back via e-mail, fax and/or by mail. The authors will receive the link for the download of the agreement in the acceptance e-mail of the article. We will not be able to publish your paper without the Copyright Transfer Agreement.
Identification of Rice Production Clusters in Central Java Province with K-Means Technique
Central Java Province stands as a pivotal agricultural region in Indonesia, characterized by high productivity in paddy and rice cultivation. However, substantial production disparities persist across districts, attributed to varying geographical conditions, agricultural infrastructure, and farmers' cropping patterns. Consequently, the identification of production clusters is imperative for elucidating production patterns and formulating targeted policy interventions. This study aims to classify districts in Central Java based on production metrics using the K-Means Clustering technique implemented in the R statistical environment. Production data across various regions were analyzed to determine optimal clustering patterns. The clustering analysis stratified the study area into four distinct agricultural typologies: optimal performance zones (Cluster 3, n=2), land-based volume producers (Cluster 1, n=7), small-scale efficient producers (Cluster 4, n=15), and priority intervention areas (Cluster 2, n=11). These findings underscore the necessity for differentiated policy strategies addressing the disparities in efficiency and production scales.
Functional Evaluation of the Virtual Batik and Mask Museum Application using Test Scenario Based
The development of digital technology has created new opportunities to introduce and preserve local culture through interactive media, one of which is virtual museums. This study develops a 3D virtual museum application aimed at introducing and preserving batik and mask cultural heritage in response to the need for technology-based educational media. The functionality of the application was evaluated using a scenario-based testing method involving 10 main test scenarios. The testing results indicate an overall excellent performance, with a success rate of 85%. Four activities—including 3D object interaction (mask), wall collider functionality, background sound, and lighting—achieved very good performance. The main weakness was identified in the notification trigger feature, where four users failed to complete the assigned task. The total time required by 10 users was 167.8 seconds, with an average of 16.78 seconds per task. Overall, these results demonstrate that the application has high functional stability and is suitable for use as an innovative and interactive medium for cultural learning.
Copyright (c) 2026 Sistemasi: Jurnal Sistem Informasi
Prediction of Unpaid Student Fees at Muhammadiyah Ahmad Dahlan University Cirebon using the Random Forest Algorithm
This study aims to develop a predictive model for student fee payment arrears at Universitas Muhammadiyah Ahmad Dahlan Cirebon using the Random Forest algorithm. The dataset was obtained from the Academic Information System and consisted of 490 student records from four cohorts (2018–2021), which were divided into 80% training data and 20% testing data. The data processing stages included data cleaning, transformation, and feature selection using Recursive Feature Elimination (RFE). The model was optimized using GridSearchCV to obtain the best configuration. The evaluation results indicate strong performance, with an AUC of 0.980, accuracy of 88.8%, precision of 90.4%, recall of 88.8%, and an F1-score of 0.875. Feature importance analysis identified the amount of arrears variable as the most dominant factor influencing prediction outcomes. Strategic recommendations for university implementation include: (1) deploying a data-driven early warning system to identify at-risk students, (2) offering payment relief or installment programs for students with high arrears, and (3) conducting regular financial monitoring through a dashboard to support timely decision-making. Therefore, this study not only produces an effective predictive model but also provides practical solutions for improving university financial management.
Systematic Literature Review of HCI Principles in Role – Playing Game Design: Towards a Comprehensive Framework for Enhancing Programming Skills
The absence of a comprehensive Human-Computer Interaction (HCI) framework specifically tailored for Role-Playing Games (RPGs) in programming skill development represents a critical research gap. This study conducts a systematic literature review (SLR) to synthesize interdisciplinary insights from game design, HCI, and cognitive psychology, aiming to establish foundational guidelines for integrating HCI principles into RPG design. Thirty peer-reviewed studies published between 2019 and 2025 were analyzed using the PRISMA approach. Findings reveal that embedding programming tasks into narrative-driven gameplay, supported by adaptive interfaces and motivational gamification, enhances learner engagement by approximately 35-50%, usability by 30-40%, and perceived effectiveness by 25-45%, depending on design strategies. Despite these promising outcomes, existing research remains fragmented, lacking a unified conceptual model linking usability, motivation, and technological innovation within RPG-based learning environments. This review identifies five key design dimensions usability heuristics, adaptive interaction, gamified motivation, narrative immersion, and personalization as essential to improving programming skill acquisition for novice users. Accordingly, the study proposes a preliminary structured framework to guide future RPG development that balances player experience with measurable learning outcomes. The novelty of this work lies in consolidating HCI principles into a systematic model for RPG-based programming learning, bridging the current gap between interaction design and educational functionality.
Optimization of Cargo Loading System in Logistics Delivery Services using Machine Learning at a Logistics Companies
A more precise data-driven approach is required to optimize lead time estimation and improve service quality. This study aims to evaluate and enhance lead time accuracy by optimizing cargo loading into containers using shipment data, including item length, width, height, weight, and volume, as well as vehicle loading capacity. The data are processed to optimize the loading process using a Genetic Algorithm, combined with a Random Forest model for determining cargo stacking and rotation. The dataset is analyzed using the CRISP-DM methodology to identify patterns, trends, and inter-variable relationships that influence the optimization of cargo placement within containers. These algorithms were selected due to their ability to capture complex relational patterns and their relevance to logistics shipment data. Model performance is evaluated using accuracy metrics and a confusion matrix to comprehensively assess predictive performance. In addition, the results of the machine learning–based models are compared to identify significant improvements in estimation accuracy. The results indicate that the Genetic Algorithm achieved a fitness value of 0.836142 in Scenario 1 without Random Forest and 3.127948 in Scenario 2 when combined with Random Forest. Furthermore, the Random Forest model achieved an accuracy of 99.23% for stacking prediction and 99.33% for rotation prediction. The developed system effectively supports optimal cargo loading optimization through accurate predictive models, enabling data-driven decision-making. With the implementation of this model, logistics companies can improve operational efficiency, minimize the risk of delays, and deliver superior customer service.
Comparative Analysis of User Experience in SeaBank and Bank Jago using the User Experience Questionnaire
The rapid development of information technology has driven a shift in banking services from conventional systems toward more efficient and flexible digital platforms. Generation Z, as digital natives, has become the primary user group of these services due to their strong preference for convenience and ease in financial transactions. According to data from the Google Play Store, the SeaBank and Bank Jago applications have each been downloaded more than 10 million times, indicating strong public interest in digital banking services. Despite having a similar number of downloads, there is a notable difference in user ratings: SeaBank has received a rating of 4.9 out of 5, while Bank Jago has obtained a rating of 4.5. However, research that directly compares user experience across popular digital banking platforms in Indonesia remains limited. Therefore, this study contributes empirical insights into the factors influencing Generation Z’s satisfaction when using digital banking services. This study aims to analyze and compare the user experience of the SeaBank and Bank Jago applications using the User Experience Questionnaire (UEQ) method. Research data were collected through questionnaires distributed using purposive sampling, targeting Generation Z respondents aged 17–28 years. The collected data were analyzed using the UEQ Data Analysis Tool. The results indicate that both applications received positive evaluations from respondents. Bank Jago outperformed SeaBank in the Attractiveness dimension, reflecting stronger visual and emotional appeal. Meanwhile, SeaBank showed superior performance across the other five dimensions: Perspicuity, Efficiency, Dependability, Stimulation, and Novelty. These findings suggest that SeaBank provides a more efficient, clear, engaging, and innovative user experience compared to Bank Jago.
Design of the 'Abdimas' Marketplace System: A Digital Platform for PKM Collaboration (Version 0.0)
Community Service (Pengabdian kepada Masyarakat or PkM) is one of the three core responsibilities (Tridharma) of higher education in Indonesia, alongside education and research. However, many lecturers especially those new to academia face difficulties in identifying suitable community partners. This study addresses that issue through the design of a digital platform called “Abdimas”, intended to function as a marketplace system for matching lecturers with PkM partners. Applying the Design Thinking methodology, focusing on the conceptual and architectural design of the “Abdimas” platform, this study implemented a user-centered design approach. Data from interviews with lecturers, partners, and university administrators were analyzed using thematic analysis to identify core requirements. Based on these findings, the system was designed to support lecturer registration, partner discovery, and need-based matching. Senior lecturers are also supported in exploring new, underserved areas, while partners can publicly express their needs to attract suitable academic collaborators. University administrators can monitor the distribution of PkM activities over time to ensure equity and effectiveness. Unlike existing administrative platforms that often function as one-way reporting tools, the “Abdimas” marketplace introduces a bidirectional matching mechanism that allows partners to actively broadcast their specific community needs, bridging the information gap for lecturers. The system design includes use case diagrams, UI/UX prototypes, an Entity Relationship Diagram (ERD), and blackbox test scenarios to validate functionality. Although still in the design phase (Version 0.0), “Abdimas” has the potential to scale beyond academic users by supporting Corporate Social Responsibility (CSR) initiatives and facilitating student-level community services. This research contributes a structured and scalable system design to improve collaboration and outreach in community service programs within higher education.
Implementation of the Apriori Algorithm for Clothing Store Product Recommendations based on Sales Transaction History
This study is motivated by the limitations faced by small-scale clothing stores, which generally do not have customer ratings or reviews that can be used as a basis for product recommendations. This condition necessitates an alternative method capable of utilizing available sales transaction data. The objective of this study is to generate product recommendations by identifying consumer purchasing patterns through the application of the Apriori Algorithm. The methodology involves processing sales transaction data consisting of transaction codes, lists of purchased products, and transaction timestamps. Support, confidence, and lift ratio values are calculated to generate and validate association rules among products. The analyzed data are derived from the transaction history of a clothing store and are processed using a web-based system developed with PHP and MySQL. The experimental results indicate that several product combinations achieve confidence values of 50% and lift ratios greater than or equal to 1, suggesting that these patterns can be used as a basis for product recommendations. These findings demonstrate a strong association among items that are frequently purchased together. Based on the results, this study concludes that the Apriori Algorithm is effective in identifying meaningful purchasing patterns that can support product arrangement strategies and inventory management in small-scale clothing stores.
Design of an Augmented Reality Application to Improve Understanding of Spatial Structure in Elementary Schools
Mathematics learning, particularly three-dimensional geometry in elementary schools, often requires students to visualize images presented in textbooks in order to understand the material, which can make it difficult for them to absorb the concepts being taught. This challenge is further compounded by the limited availability of interactive learning media and school policies that restrict the use of mobile devices as learning tools. These issues represent common obstacles in the implementation of effective geometry instruction at the elementary level. This study aims to design an Android-based Augmented Reality (AR) learning application to assist elementary school students in understanding abstract three-dimensional geometry concepts. The research methodology employed is the Multimedia Development Life Cycle (MDLC), which consists of six stages: concept, design, material collection, development, testing, and distribution. The outcome of this study is an Android-based AR application for learning three-dimensional shapes. Black-box testing results indicate that all features—including marker scanning, display of 3D geometric objects, transformation into net forms, rotation, zoom in and zoom out, and instructional content for each type of three-dimensional shape—functioned properly. Furthermore, user evaluation was conducted using the System Usability Scale (SUS) method. The evaluation involved 10 respondents and produced an average score of 74, placing the application in the “Good” category. These results suggest that the application can serve as an effective interactive learning medium for elementary school education.
Development of Hybrid K-Means DBSCAN Algorithm for Optimization of Landslide-Prone Area Clusters based on Web-GIS
Landslides represent one of the major geological hazards in West Java Province, posing serious impacts on social life, economic activities, and public infrastructure. A key challenge in landslide mitigation lies in the inaccuracy of spatial and temporal classification of landslide-prone areas, as well as the limitations of single-method approaches in disaster data analysis. This study aims to develop a data-driven classification model for landslide-prone areas using a hybrid clustering approach that combines the K-Means and DBSCAN algorithms. The dataset consists of landslide incident records from 2020 to 2024 and administrative spatial data at the regency/city level. The analysis stages include data integration and normalization, statistical exploration, the application of K-Means clustering as a global segmentation framework, and DBSCAN for identifying local patterns and outliers. Model validation was conducted using internal evaluation metrics, yielding a Silhouette Coefficient of 0.448 and a Davies–Bouldin Index of 0.602, indicating that the hybrid method provides superior performance in terms of cluster compactness and separation. The classification results are visualized through an interactive Web-GIS platform developed using Streamlit and Folium, enabling users to select specific years and classification methods while displaying mitigation strategies based on risk categories. This study concludes that the hybrid clustering approach enhances the accuracy of landslide-prone area classification and makes a significant contribution to the provision of more adaptive and practical spatial information to support mitigation policy decision-making in landslide-vulnerable regions.
Prototype of IoT-based Gas and Temperature Monitoring System for Genset Room with ESP8266
Generator rooms require strict environmental monitoring to ensure operational safety, as uncontrolled temperature and gas concentration conditions may pose risks of fire and workplace accidents. This study aims to develop an Internet of Things (IoT)–based generator room monitoring system capable of automatically and real-time monitoring temperature, humidity, and gas levels, with notifications delivered via a Telegram bot. The research method adopts a systematic approach through prototype development. The system utilizes DHT22 and MQ-2 sensors, a NodeMCU ESP8266 microcontroller, a buzzer, and is integrated with the Telegram Bot API. The testing results demonstrate that the device is able to responsively monitor environmental parameters, transmit real-time data to Telegram, activate the relay with a response time of 4–5 seconds, and provide automatic notifications when gas concentrations exceed safe thresholds with a delay of 1–2 seconds. Evaluation of the monitoring functionality and sensor data visualization through the Telegram application indicates that all sensor information is successfully displayed via the Telegram bot interface. Although data transmission from the sensors to the Telegram application is highly dependent on network connection stability, the experimental results show that the system is capable of delivering data with optimal response time.
Image Forensics Analysis of the Authenticity of Digital Payment Evidence using the K-Nearest Neighbor Algorithm
The rapid growth of digital transactions has also increased the risk of digital payment evidence forgery, such as screenshot manipulation or digital image editing. This study aims to develop an automated authenticity validation system for digital payment evidence by integrating Image Processing, Image Forensics, and Optical Character Recognition (OCR) technologies. The processing pipeline begins with image preprocessing, followed by forensic feature extraction and OCR-based text analysis, which are then classified using the K-Nearest Neighbor (KNN) algorithm. This study evaluates 15 experimental scenarios based on combinations of training and testing data ratios (90:10, 80:20, 70:30, 60:40, and 50:50) and random state values (42, 32, and 22). Model performance is assessed using accuracy, precision, recall, and F1-score metrics across a range of k values from 1 to 15. The results indicate that the optimal performance is achieved at k = 7, with an accuracy of 97.1%. The proposed system is able to efficiently distinguish between authentic and manipulated digital payment evidence. The system is implemented as an Android application that allows users to upload payment evidence via the device camera or gallery, after which the system automatically analyzes its authenticity. The findings demonstrate that the integration of image forensic techniques and the K-Nearest Neighbor (KNN) algorithm effectively detects indications of manipulation in digital payment evidence and enhances the efficiency of the verification process within the digital financial services ecosystem.
Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning
Mental health among university students has become a major concern in higher education, particularly in the post-pandemic era, which has left students facing various academic, social, and psychological pressures. Unfortunately, efforts for early detection of mental health issues on campus remain limited, especially in the context of Muslim students who live within an Islamic cultural framework. This study offers an innovative approach by integrating advanced machine learning technology with the depth of Islamic educational values to develop an early detection system that is not only accurate but also humanistic and contextually relevant. The dataset for this study was obtained through a survey of 127 students at Universitas Muhammadiyah Kudus, including variables related to psychological conditions and the intensity of religious practices, used to detect whether students experience mental health problems or maintain good mental health. The research methodology includes data collection, preprocessing, feature analysis, model development using classification algorithms such as Random Forest, SVM, KNN, and Decision Tree, model performance optimization using GridSearchCV, and evaluation. Evaluation of the four models indicated that prior to optimization, SVM and KNN achieved the best performance, both with an accuracy of 88.46%. After optimization with GridSearchCV, SVM became the top-performing model, achieving an accuracy improvement of more than 5%, reaching 94.05%. Feature analysis revealed that levels of anxiety, fatigue, and religious practices such as prayer and dhikr were the primary determinants in mapping students’ mental health conditions. These findings suggest that Islamic values such as tawakkul (trust in God), sabr (patience), and syukur (gratitude) are not merely theological concepts but can also serve as scientific instruments, converted into predictive features in data-driven technologies. This study demonstrates that an SVM model optimized with GridSearchCV is effective in detecting university students’ mental health and has the potential to serve as an early warning system in Islamic campus settings.
Evaluation of IT Governance for the Inti Accounting System in Retail SMEs using COBIT 2019
The utilization of information technology (IT) in retail micro, small, and medium enterprises (SMEs) is essential for improving operational efficiency and service quality. However, limited resources and dependence on external vendors pose significant operational risks. Swalayan Pasar Pagi in Tegal utilizes a locally hosted Inti Accounting System supported by external technical services, making it vulnerable to system downtime and weak internal controls. This study aims to evaluate the governance of the system using the COBIT 2019 framework through an analysis of design factors and the governance system design workflow. The research adopts a qualitative descriptive approach, with data collected through semi-structured interviews, observations, and documentation. The results indicate that the supermarket’s strategy focuses on cost efficiency and improving customer service, with primary objectives including enhancing the quality of management information, optimizing business processes, and increasing operational efficiency. Critical risks identified include vendor dependency, reliance on local server infrastructure, and the absence of internal audits. COBIT 2019 mapping identifies BAI10, EDM05, APO14, and APO12 as priority governance and management objectives. The recommended improvements include the establishment of Service Level Agreements (SLAs), regular data backups, periodic system audits, and training for non-IT staff. This study provides practical contributions to strengthening IT governance in retail SMEs and extends the literature on the application of COBIT 2019 in small enterprises, which remain underexplored. The findings also demonstrate that adapting the COBIT 2019 framework is effective in enhancing operational efficiency and reducing IT-related risks in retail SMEs.
Expert System for Mental Health Disordes in Women and Children based on Android using the Certainty Factor Method
This study focuses on the limited access to mental health services in Gorontalo and the social stigma surrounding mental health disorders, which discourages women and children from seeking help. The main problem addressed in this research is the management of mental health disorders, particularly the complexity of the initial diagnostic process. The study aims to develop an expert system using the Certainty Factor method to assist in the early diagnosis of mental health disorders in women and children, specifically Depression, Anxiety, and Stress disorders. The research employs a Research and Development (R&D) approach with qualitative methods, including interviews with experts such as psychologists to obtain a knowledge base comprising symptoms, Measure of Belief, and Measure of Disbelief values. The expert system is implemented on the Android platform, facilitating user access to early diagnostic services. Results from the Certainty Factor calculations on user data indicate that the early diagnosis confidence levels are 97.04% for Depression, 63.11% for Anxiety, and 59.72% for Stress. The highest value is observed for Depression, suggesting that the symptoms selected by users most strongly indicate this disorder, with the highest confidence level across all Depression symptoms. Both manual calculations by experts within the system and Black Box testing confirm that the Certainty Factor method can effectively support early diagnosis of mental health disorders. The study concludes that the expert system using the Certainty Factor method is effective and can be implemented as an early mental health detection tool. The strength of this research lies in the integration of qualitative expert knowledge with mobile technology implementation, providing a practical and easily accessible solution for the community.
Sentiment and Topic Analysis of Digital Community Application Gamer Reviews using SVM-LDA and CRISP-DM
Impatient behavior among gamers is often reflected in sharp and emotionally charged digital reviews, particularly in the use of community applications such as Discord. This study explores expressions of impatience through sentiment and topic analysis. By adopting the CRISP-DM framework, a total of 10,000 Indonesian-language reviews collected from the Google Play Store were analyzed. The analytical process begins with sentiment labeling using IndoBERT, followed by polarity classification using the Support Vector Machine (SVM) algorithm, and topic exploration through the Latent Dirichlet Allocation (LDA) method. The results indicate that 57.4% of the reviews express positive sentiment, primarily related to voice communication quality and community interaction features. In contrast, 42.6% of the negative reviews commonly convey frustration regarding login issues and verification processes. The SVM model optimized using Bayesian Optimization achieved an accuracy of 90.46%. This study highlights that Discord serves not only as a communication platform but also as a reflection of users’ high expectations for system speed and stability. The main contribution of this research lies in the integration of SVM–LDA methods within the CRISP-DM framework to better understand the digital behavior of Indonesian gamers. The practical implications of these findings provide strategic insights for developers to improve authentication reliability and community features in alignment with user characteristics.
The Influence of Experience-Centric IT Governance on Digital Ethics Perception in Social Commerce
Social commerce is a rapidly growing new form of e-commerce that integrates social functions into digital buying and selling activities. However, studies explicitly examining the influence of Experience-Centric IT Governance (ECITG) approach on digital ethics perceptions are limited. This study aims to evaluate ECITG's influence on users perceptions of digital ethics in social commerce. A quantitative approach was used to measure ECITG through five variables: Governance Responsiveness, Transparency and Trust, Experience Personalization, User Participation, and User Empathy. Digital ethics perceptions encompass five variables: Platform Accountability, System Fairness, Data Privacy, Consumer Protection, and Algorithm Transparency. Data were gathered from 100 respondents and processed using partial least squares–structural equation modeling (PLS-SEM) through measurement and structural. The results of the analysis indicate that several ECITG dimensions significantly shape digital ethics perception, particularly governance responsiveness, transparency, and user participation, while personalization and empathy demonstrate weaker influence. Overall, these results confirm that the ECITG approach has a statistically significant impact on digital ethics perceptions in the context of social commerce, though its strength varies across dimensions. The study contributes to the development of experience-based IT governance models and offers practical insights for platform managers to improve ethical transparency and user trust.
AI-Driven Fraud Detection in Digital Banking: A Hybrid Approach using Deep Learning and Anomaly Detection
The rapid digital transformation in the banking sector has introduced new opportunities for efficiency and customer convenience but has also amplified the risks of financial fraud. Traditional fraud detection mechanisms, often reliant on static rule-based systems, struggle to keep pace with the dynamic, evolving nature of fraudulent activities. This paper proposes a novel hybrid framework that integrates deep learning models with anomaly detection techniques to enhance the accuracy, robustness, and adaptability of fraud detection in digital banking. The proposed approach leverages a deep neural network (DNN) architecture trained under supervised learning to capture complex transactional patterns and combines it with autoencoder-based unsupervised anomaly detection to uncover previously unseen fraud strategies. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications and its potential for multi-institutional deployment, enabling secure inter-bank fraud intelligence sharing without compromising data privacy. Extensive experiments on benchmark financial datasets demonstrate that the hybrid system significantly outperforms state-of-the-art methods in terms of precision, recall, and false-positive reduction. Furthermore, the study highlights the scalability of the approach for real-time banking applications. This work contributes to the growing field of AI-driven financial security by addressing both detection performance and adaptability to emerging fraud behaviors.
A Multimodal Deep Learning Framework for Amyotrophic Lateral Sclerosis Diagnosis using Clinical and Audio Morphology Features
Amyotrophic Lateral Sclerosis (ALS) is a highly progressive neurodegenerative disease that impairs motor and speech function. Conventional diagnostic methods, both invasive and non-invasive, are often time-consuming and produce limited sensitivity. This leads to delays in treatment and worsening disease progression. This study proposes a multimodal deep learning framework that utilizes and integrates invasive medical records with non-invasive morphological features of patient speech audio extracted into Mel-Spectrograms. Unlike previous studies that focused solely on speech or clinical features, this study introduces an integrated multimodal diagnostic framework that effectively combines both data sources to achieve reliable diagnostic accuracy. The study included two experimental scenarios. In the first scenario, the audio-trained model used a Convolutional Neural Network (CNN) and was systematically optimized by testing variations in network depth, feature fusion techniques, and layer dropout probabilities to improve model generalization and stability. From the experimental results of the first scenario, the CNN achieved the best performance, achieving 80.33% accuracy in classification using audio data alone from all the tested model variations. In the second experimental scenario, when the best model was trained by incorporating clinical data, the model demonstrated improved diagnostic performance, achieving 100% accuracy. This finding highlights the importance of combining data modalities or sources from various domains, both invasive and non-invasive, to achieve optimal model performance for early ALS detection.
Development of an Intelligent Platform for Drug and Food Interaction Analysis using a Combination of Fuzzy Logic and Certainty Factor
Interactions between drugs and food are a critical public health issue, as they can cause unwanted side effects or reduce the effectiveness of treatments. Unfortunately, awareness of these potential interactions among the Indonesian population remains low, while existing platforms generally focus only on drug–drug interactions. This study aims to develop an intelligent platform for analyzing drug–food interactions by combining fuzzy logic and certainty factor (CF) methods. Fuzzy logic is employed to handle uncertainty in interaction data, while the certainty factor enhances confidence levels based on clinical literature and expert knowledge. Drug–food interaction data were collected from validated sources and modeled using fuzzy membership functions, IF–THEN rule-based reasoning, defuzzification processes, and integration with CF. The web-based system was evaluated through accuracy testing and usability assessment using the System Usability Scale (SUS). Accuracy tests conducted on 50 interaction scenarios demonstrated a 100% match with clinical references, while the SUS evaluation involving 100 respondents yielded an average score of 77.44, falling into the “Acceptable” category and approaching “Good Usability.” These results indicate that the platform has the potential to serve both as an educational tool and as a practical aid for the public to enhance self-management of health, while also supporting government health programs.
Development of Android GIS Applications for Mapping Clean Water Sources in Natural Resource Management in Disaster-Affected Areas
This research was motivated by the post-disaster challenges faced by the people of Sarampad Village, Cugenang District, Cianjur Regency, after the 2022 earthquake, which severely damaged vital infrastructure and disrupted access to clean water. The lack of a systematic mapping system for clean water sources highlights the urgent need for technology-based solutions to support effective and sustainable water resource management. The study employed a software development method using an Agile Programming approach, allowing iterative development and adaptation based on user feedback. Data were collected through field surveys, interviews with local communities and village officials, and direct observations of clean water source conditions. The system was designed using the Unified Modelling Language (UML), and the Android application was developed with Flutter Dart via the Visual Studio Code platform. Application functionality was tested using the black-box testing method to ensure performance reliability. The developed Android-based GIS application successfully maps and visualizes clean water sources, providing users with accurate and accessible spatial information. The system enables communities to identify the nearest clean water sources efficiently, particularly in post-disaster conditions. The findings demonstrate that integrating GIS with mobile technology can significantly improve public access to clean water information while promoting community involvement in environmental resource management. This innovation serves as a practical step toward sustainable and participatory water resource management in disaster-affected areas.
Implementation of User-Centered Design (UCD) in Developing a Mobile Attendance Application to Improve User Experience
The desktop- or web-based attendance system currently used by Sevenpion is considered to have suboptimal performance, particularly in terms of speed and user convenience. This limitation is primarily due to the system’s reliance on web browsers, internet connection stability, and a user interface that is not fully responsive on mobile devices. This study aims to design a more comfortable, efficient, and adaptive mobile attendance application by improving its user interface (UI) and user experience (UX). The design process follows a User-Centered Design (UCD) approach, which emphasizes user needs through stages including requirements identification, context-of-use analysis, prototyping from low- to high-fidelity, and system evaluation. Usability testing was conducted using the System Usability Scale (SUS) method, involving five participants, yielding an average score of 77, categorized as “Good.” The results indicate that the prototype application enhances comfort, security, and efficiency in the attendance process compared to the existing web-based system. The study highlights the importance of user-centered approaches in mobile application development to ensure optimal usability.
Building Transparent and Efficient Community Administration: Agile Development of a Neighborhood Information System at Kertamukti Sakti Residence
Community management in residential areas often relies on manual paper-based administration, leading to inefficiency, unclear financial records, data loss, and limited transparency, which undermine good governance and residents’ trust. This study aims to develop a web-based neighborhood (RT/RW) management information system to improve administrative effectiveness, financial transparency, and service quality. The system was built using CodeIgniter, PHP, MySQL, Bootstrap, and jQuery, applying the Agile development method to ensure flexibility and iterative improvement through continuous feedback between the developers and the community. The development process consisted of planning, design, coding, testing, and release stages, with flowcharts and wireframes supporting interface design and black box testing used for functional validation. The system was evaluated using a user-centered usability assessment (System Usability Scale – SUS), obtaining an average score of 82.5, which falls under the Excellent category. In addition, the financial reporting process time was reduced from three days to one hour, and data entry errors decreased by 90%, proving that the system significantly improves operational efficiency and transparency compared to manual methods. In conclusion, the combination of Agile methodology and lightweight frameworks such as CodeIgniter successfully delivers a responsive, transparent, and user-oriented information system that enhances trust and collaboration within the community. Future development will focus on integrating QRIS, e-wallets, and bank transfers to further streamline financial transactions and support sustainable digital transformation in community management.
Novel Genre Classification based on Synopsis using the Random Forest Algorithm
Novel genre classification based on synopses presents a significant challenge in text processing, as each genre exhibits distinct lexical characteristics. This study evaluates the performance of the Random Forest algorithm in classifying novel genres under conditions of imbalanced data distribution. The research stages include text preprocessing—comprising case folding, tokenization, stopword removal, and stemming—feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and model training with Random Forest. In addition, manual data balancing was applied by increasing samples in minority classes through simple oversampling. The model was evaluated using accuracy metrics and confusion matrix analysis. The results indicate that Random Forest is able to identify most genres with moderate accuracy, particularly for classes with larger data volumes. The initial model achieved an accuracy of 42.11%, which increased to 46.67% after the application of data balancing. Misclassification primarily occurred in genres with limited samples that share similar vocabulary with dominant genres. These findings demonstrate that Random Forest can still be applied to synopsis-based novel genre classification without fully relying on balancing techniques. However, performance remains uneven across classes, highlighting the need for per-genre analysis to obtain a more comprehensive evaluation.
Charitas Fibriani (SCOPUS ID=57192643331)
Systematic Review of the Use of the MIT-BIH Polysomnography Database for the Detection and Classification of Sleep Disorders
The MIT-BIH Polysomnography Database (SLPDB) is a widely adopted benchmark for the development of automated methods for sleep disorder detection and sleep stage classification. This study presents a Systematic Literature Review of 35 articles that utilize the SLPDB, examining research focus areas, types of physiological signals employed, and the computational approaches applied. Five major methodological categories were identified: Sleep Apnea Detection, Sleep Staging, Signal Processing Enhancement, Multichannel Fusion Methods, and Interpretable Artificial Intelligence, with the first two categories being the most dominant. Four groups of physiological signals—EEG, ECG, respiratory signals, and multichannel data—form the basis for model development, where EEG is predominantly used for sleep staging and ECG for sleep apnea detection. Deep learning approaches, particularly CNNs, LSTMs, and hybrid models, are the most frequently employed techniques. Reported model accuracies range from 78% to over 99%, depending on the signal modality and modeling strategy. Future research should prioritize the development of more interpretable hybrid models and broader clinical validation to enhance reproducibility and implementation readiness.
Implementation of a Hybrid Filtering Approach in a Website-based Football News Recommendation System
The rapid growth of football news on digital portals has made it increasingly difficult for users to find information that matches their interests. This study develops a web-based news recommendation system by combining Content-Based Filtering and Collaborative Filtering through a feature-level Hybrid Filtering approach. The proposed hybrid approach constitutes the main novelty of this research, as it does not rely on score aggregation methods commonly used in previous studies, making it lighter, simpler, and more suitable for small datasets and limited user interactions. The system employs Term Overlap Matching to measure the similarity between news titles and Cosine Similarity to assess user preference similarity based on bookmark data. The evaluation results show that Content-Based Filtering achieves the best performance, with a Precision of 0.60, Recall of 0.75, and an F1-score of 0.67, while Collaborative Filtering performs poorly due to data sparsity in user interactions, resulting in a Precision of 0, Recall of 0, and an F1-score of 0. Overall, the feature-based hybrid approach is able to provide relevant recommendations from both content and preference perspectives, although system accuracy is still predominantly driven by Content-Based Filtering. These findings indicate that the proposed simple hybrid model can serve as an effective solution for small-scale sports news platforms and has the potential to be further improved through increased data availability, enhanced user interaction, and the adoption of more advanced NLP techniques.
Copyright (c) 2025 Sistemasi: Jurnal Sistem Informasi
Implementation of Image Processing in Scanning KTP Data using Optical Character Recognition (OCR)
The Indonesian national identity card (Kartu Tanda Penduduk or KTP) serves as the primary identification tool for citizens across various administrative processes, both in the public and private sectors. However, manual data entry from KTPs remains common, leading to potential errors, inefficiencies, and delays. This study aims to develop an Android-based application capable of automatically scanning and extracting data from KTPs using Optical Character Recognition (OCR) enhanced by Convolutional Neural Network (CNN) methods. CNN is applied during the image preprocessing stage to improve text area detection and character segmentation accuracy before the OCR process. The application is developed using the Python, Dart, PHP programming language and designed with a user-friendly interface. Extracted data such as full name, NIK, place and date of birth, and address are stored in a MySQL database via integrated web APIs. The research approach follows a software engineering methodology consisting of requirements analysis, system design, implementation, and system testing. Experimental results show that integrating CNN with OCR increases character recognition accuracy up to 86,7%, especially on low-quality or noisy images. Therefore, this application is expected to serve as an effective solution for accelerating and improving the accuracy of population data digitization in various institutions.
Adaptive Traffic Signal System Utilizing YOLOv11 and Fuzzy Logic for Congestion Mitigation
The increasing number of vehicles in urban and suburban areas has led to traffic congestion, resulting in longer travel times, higher exhaust emissions, and an increased risk of accidents. Conventional fixed-time traffic signal systems often fail to respond dynamically to changing traffic conditions, leading to inefficient vehicle queues. This study proposes the development of an adaptive traffic signal system that utilizes YOLOv11 and fuzzy logic to detect vehicle volume and adjust green light durations in real time. YOLOv11 is employed to detect vehicles in each lane, while fuzzy logic is used to regulate green signal durations based on the detected vehicle counts. Experimental results demonstrate a detection accuracy of 0.92 and a recall of 0.93. The green light duration varies from 80 seconds for low traffic volumes to 100 seconds for high traffic volumes. The traffic signal cycle is dynamically adjusted according to vehicle density, with a maximum total cycle time of 100 seconds. Overall, the proposed system is proven effective in reducing congestion and improving traffic management efficiency at intersections with high vehicle volumes.
Predict Airline Customer Satisfaction using a Machine Learning Model
Customer satisfaction is a strategic factor for the sustainability of airline businesses amid increasingly intense competition in the aviation industry. This study aims to predict airline customer satisfaction using an Artificial Neural Network (ANN) approach by leveraging a publicly available Kaggle dataset containing 22 airline service features. Two ANN architectures were developed, differing primarily in the number of hidden layers, the number of neurons, and the application of Batch Normalization and LeakyReLU in the second model. The experimental results show that the first ANN model achieves an accuracy of 92.31%, while the second model attains significantly higher performance, with an accuracy of 95.75% on the test dataset. The second model also demonstrates a strong balance between precision and recall (0.94–0.97), with an average F1-score of 0.95–0.96 and a minimal number of misclassifications. These results confirm that employing a more complex ANN architecture can deliver highly accurate predictions of customer satisfaction. The implementation of ANN-based predictive models not only enhances passenger experience quality but also strengthens customer loyalty and helps airlines maintain long-term competitiveness.
Optimization of the Linear Regression Algorithm using GridSearchCV for Rice Crop Production Prediction
Rice production in Central Java Province fluctuates annually, affecting food security and agricultural output distribution. Therefore, accurate prediction methods are essential to assist stakeholders in agricultural planning and strategic decision-making. This study applies the Linear Regression algorithm to predict rice production based on historical data from 2014 to 2023 obtained from the official website of the Central Java Provincial Agriculture and Plantation Office. The model is developed using multiple linear regression with variables including planted area, harvested area, and productivity. The novelty of this study lies in the structured application of hyperparameter tuning using GridSearchCV to optimize Linear Regression performance, as well as the integration of a preprocessing pipeline based on data distribution stabilization to improve accuracy and model generalization. The research process includes data collection, preprocessing, modeling, optimization, model evaluation, and deployment as a web-based application using Streamlit Cloud. GridSearchCV optimization results indicate a cross-validation accuracy of 98.26%, confirming the model’s strong predictive capability. Model evaluation shows an R² value of 0.9754, with MAE of 0.0957, MSE of 0.0307, and RMSE of 0.1753, indicating low prediction errors and stable model performance. The optimized model is implemented as a web application via Streamlit Cloud, enabling direct use by end-users. For future research, it is recommended to incorporate additional variables such as rainfall, temperature, and rice variety, or to compare performance with other algorithms such as Random Forest, Support Vector Regression, or Long Short-Term Memory (LSTM) to further enhance prediction accuracy.
Norhikmah M.Kom (SCOPUS ID: 57216417658)