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INDONESIA
Sistemasi: Jurnal Sistem Informasi
Published by Universitas Islam Indragiri
ISSN : 23028149     EISSN : 25409719     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Sistemasi adalah nama terbitan jurnal ilmiah dalam bidang ilmu sains komputer program studi Sistem Informasi Universitas Islam Indragiri, Tembilahan Riau. Jurnal Sistemasi Terbit 3x setahun yaitu bulan Januari, Mei dan September,Focus dan Scope Umum dari Sistemasi yaitu Bidang Sistem Informasi, Teknologi Informasi,Computer Science,Rekayasa Perangkat Lunak,Teknik Informatika
Arjuna Subject : -
Articles 950 Documents
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Detecting Muslim Students Mental Health with an Islamic Educational Approach using Machine Learning Pratama, Taftazani Ghazi; Rafsanjani, Toni Ardi; Rahmawati, Riana Putri; Imaduddin, Helmi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5732

Abstract

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.
Systematic Literature Review of HCI Principles in Role – Playing Game Design: Towards a Comprehensive Framework for Enhancing Programming Skills Setiawan, Panji Rachmat; Yatim, Maizatul Hayati Mohamad
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5493

Abstract

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.
Implementation of Image Processing in Scanning KTP Data using Optical Character Recognition (OCR) Hanafi, Bachtiar; Stiaji, Pratomo; Triyanto, Wiwit Agus
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5856

Abstract

The Indonesian National Identity Card (Kartu Tanda Penduduk or KTP) serves as the primary identification document for Indonesian citizens in various administrative processes across both the public and private sectors. However, manual data entry of KTP information is still commonly practiced, leading to potential input errors, delays, and inefficiencies. This study aims to develop an Android-based application capable of automatically scanning and extracting KTP data using Optical Character Recognition (OCR) enhanced with a Convolutional Neural Network (CNN). The CNN is applied during the image preprocessing stage to improve text area segmentation and detection accuracy prior to the OCR process. The application is developed using Python, Dart, and PHP, and is designed with a user-friendly interface. Extracted data—including name, national identification number (NIK), place and date of birth, and address—are stored in a MySQL database through web API integration. The research adopts a software engineering approach comprising requirement analysis, system design, implementation, and testing. Experimental results indicate that the integration of CNN into the OCR system improves character recognition accuracy up to 86.7%, particularly for low-quality or noisy images. Therefore, the proposed application is expected to provide an effective solution for faster, more accurate, and more efficient population data digitization.
AI-Driven Fraud Detection in Digital Banking: A Hybrid Approach using Deep Learning and Anomaly Detection Mohammed, Harman Salih; Sallow, Zina Bibo; Zangana, Hewa Majeed
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5757

Abstract

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.
Design of an Augmented Reality Application to Improve Understanding of Spatial Structure in Elementary Schools Romadhon, Syahrul; Astuti, Ika Asti; Umri, Buyut Khoirul
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5652

Abstract

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.
Building Transparent and Efficient Community Administration: Agile Development of a Neighborhood Information System at Kertamukti Sakti Residence Irawan, Reza Riyaldi; Pramudito, Dendy K; Danny, Muhtajuddin
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5784

Abstract

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.
Evaluation of IT Governance for the Inti Accounting System in Retail SMEs using COBIT 2019 Elizabeth, Jesslyn Felicia; Maria, Evi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5738

Abstract

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.
Optimization of Cargo Loading System in Logistics Delivery Services using Machine Learning at a Logistics Companies Dioza, Arman; Budiyanto, Utomo
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5531

Abstract

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.
Adaptive Traffic Signal System Utilizing YOLOv11 and Fuzzy Logic for Congestion Mitigation Permadi, Dio Damas; Yudono, Muchtar Ali Setyo; Kuspranoto, Abdul Haris; Rozandi, Ardin; Artiyasa, Marina; Mubarok, Alvin; Septiani, Dwi
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5865

Abstract

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.
A Multimodal Deep Learning Framework for Amyotrophic Lateral Sclerosis Diagnosis using Clinical and Audio Morphology Features Switrayana, I Nyoman; Sujaka, Tomi Tri; Silpiana Putri, Imelda
Sistemasi: Jurnal Sistem Informasi Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5763

Abstract

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.

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