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Kab. indragiri hilir,
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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 1,146 Documents
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Classification of Online Game Player Engagement Levels using the Random Forest Algorithm Febrian Joseph Laia; Charitas Fibriani (SCOPUS ID=57192643331)
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6514

Abstract

The rapid growth in the number of online game players has generated large volumes of behavioral data that can be leveraged to analyze player engagement levels. However, the primary challenges include class imbalance in engagement data and the limited interpretability of predictive models regarding the factors influencing their decisions. This study aims to develop a classification model for online game player engagement levels using the Random Forest algorithm, while addressing class imbalance through the Synthetic Minority Over-sampling Technique (SMOTE) and identifying the most influential features using Feature Importance analysis. The study utilized the Online Gaming Behavior Dataset from Kaggle, comprising 40,034 records. The Random Forest model was optimized using RandomizedSearchCV with five-fold cross-validation to determine the optimal hyperparameter configuration. The experimental results demonstrate that the proposed model achieved an overall accuracy of 92%, with recall values of 90%, 94%, and 89% for the Low, Medium, and High engagement classes, respectively. Feature Importance analysis using both impurity-based and permutation approaches consistently identified SessionsPerWeek (0.4375 and 0.4309) and AvgSessionDurationMinutes (0.3371 and 0.3540) as the two most influential features, jointly accounting for more than 77% of the model's predictive decisions, whereas demographic features contributed only marginally. The novelty of this study lies in the integration of Random Forest, SMOTE, and Feature Importance to develop a classification model that is not only highly accurate but also interpretable. These findings provide valuable insights for game developers in designing evidence-based player retention strategies, such as implementing daily login rewards to increase session frequency and time-limited events to encourage longer gameplay sessions.
Comparative Performance Analysis of Convolutional Neural Network and EfficientNet-B0 Transfer Learning for Pneumonia Detection in Chest X-Ray Images Aditya Pratama Werdana; Amali Amali; M Syaibani Anwar
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6639

Abstract

Pneumonia is an acute lung infection and remains one of the leading causes of mortality among children under five in Indonesia. Pneumonia is commonly diagnosed through the analysis of chest X-ray (CXR) images by radiologists; however, this process is still subject to human variability and the limited availability of medical specialists. This study aims to develop a Convolutional Neural Network (CNN)-based pneumonia detection system by comparing four model variants: CNN with Color Jitter (Model 1), CNN with Color Jitter and Layer Augmentation (Model 2), CNN with Batch Normalization and Color Jitter (Model 3), and EfficientNet-B0 transfer learning (Model 4). The experiments were conducted using the Chest X-Ray Images (Pneumonia) dataset from Kaggle, comprising 5,856 images, which were divided into 70% for training, 15% for validation, and 15% for testing. The experimental results demonstrate that the EfficientNet-B0 transfer learning model achieved the best performance, with an accuracy of 96.92%, precision of 97.67%, recall of 98.13%, and an F1-score of 97.90%. This model improved classification accuracy by 1.93% compared with the conventional CNN baseline (Model 1), which achieved an accuracy of 94.99%. Furthermore, the incorporation of Layer Augmentation in Model 2 effectively reduced the number of false negatives from 21 to 10 cases. These findings demonstrate the effectiveness of EfficientNet-B0 transfer learning for medical image classification and highlight its potential as a clinical decision support tool for pneumonia diagnosis in healthcare settings, rather than as a replacement for clinical diagnosis performed by qualified medical professionals.
Design of an Artificial Intelligence-based Personalized Learning Governance Framework Ucu Nugraha; Sri Titi Handayani; Hernalom Sitorus; Agus Nursikuwagus; Yeffry Handoko Putra; Rio Yunanto
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6545

Abstract

The rapid advancement of Artificial Intelligence (AI) in education has created significant opportunities for personalized learning while simultaneously introducing governance challenges for learners with disabilities. Existing studies have examined AI, adaptive learning, accessibility, and inclusive education; however, these areas remain fragmented and lack an integrated governance-oriented framework. This study aims to develop a Personalized Learning Governance Framework (PLGF) to support inclusive digital literacy through a systematic literature review and bibliometric analysis. The research methodology consisted of Scopus database retrieval, PRISMA-based screening, Biblioshiny-assisted bibliometric analysis, literature synthesis, gap identification, and conceptual framework development. From 196 Scopus-indexed records published between 2023 and 2026, a total of 97 studies were selected for analysis. The findings reveal strong conceptual relationships among AI, personalized learning, inclusive education, accessibility, disability, and ethical technology; however, their systematic integration remains limited. The proposed Personalized Learning Governance Framework (PLGF) comprises five interconnected layers: Learner Disability Profile Input, Machine Learning Personalization Engine, Inclusive Accessibility Adaptation, Governance and Ethical Control Center, and Digital Literacy Outcome Evaluation. The framework provides a comprehensive governance model for supporting accountable, inclusive, and AI-driven personalized learning systems while promoting equitable digital literacy for learners with disabilities.
Spatial Variability Analysis of Earthquake Magnitude and Depth in Indonesia using Random Forest based on Historical Data Holmes Heriyanto Silalahi; Anief Fauzan Rozi
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6282

Abstract

Indonesia lies at the convergence of three major tectonic plates, resulting in high seismic activity and complex spatial earthquake patterns. This study analyzes the spatial variability of earthquake magnitude and depth in Indonesia using a Random Forest Regressor based on historical earthquake data from the United States Geological Survey (USGS). A total of 24,981 earthquake records (1976–2026) were collected, and 21,886 valid observations were obtained after data cleaning. To reduce data heterogeneity caused by different magnitude measurement methods, the dataset was grouped into six magnitude types (Mb, Ms, Mw, Mwb, Mwc, and Mww), and separate Random Forest models were developed for each group using longitude and latitude as input features. The novelty of this study lies in the magnitude-type-specific modeling approach, which allows spatial patterns to be analyzed more consistently across different earthquake measurement scales. The models were evaluated using 10-fold cross-validation and standard regression metrics. Results show that spatial coordinates have limited ability to explain earthquake magnitude, as indicated by low R² values across all models. In contrast, depth prediction shows better performance, with the best result achieved by Mw (R² = 0.6801), indicating stronger spatial structure in earthquake depth distribution. Overall, the findings demonstrate that geographic location is more informative for modeling earthquake depth than magnitude. The proposed approach effectively captures spatial depth variability and provides a more structured analysis of earthquake characteristics across Indonesia.
Analysis of User Satisfaction Factors for the FOTOYU Application using EUCS Method Revi Amelia Dwifa Futri; Apriansyah Putra; Ari Wedhasmara; Mira Afrina
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6704

Abstract

The increasing adoption of digital technologies, particularly those powered by Artificial Intelligence (AI), has accelerated the development of applications aimed at improving service quality and user experience, one of which is the FotoYu application. This study aims to evaluate user satisfaction with the FotoYu application using the End-User Computing Satisfaction (EUCS) model, which comprises five dimensions: Content, Accuracy, Format, Ease of Use, and Timeliness. A quantitative research approach was employed using a survey method, with questionnaires distributed to 145 FotoYu users selected through purposive sampling. The collected data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS) with SmartPLS software. The results indicate that all measurement indicators satisfied the required validity and reliability criteria, confirming that the research instrument was appropriate for assessing user satisfaction. The structural model evaluation produced an adjusted R² value of 0.784, indicating that the five EUCS dimensions collectively explained 78.4% of the variance in user satisfaction, while the remaining 21.6% was attributable to factors outside the proposed research model. Furthermore, the effect size (f²) analysis revealed that Content had the strongest influence on user satisfaction, followed by Ease of Use, Format, Timeliness, and Accuracy. These findings demonstrate that the EUCS model is an effective approach for evaluating user satisfaction with the FotoYu application while providing valuable insights to support service quality improvement and enhance the overall user experience.
UI/UX Design of a Web-based Automated Essay Scoring System using the Double Diamond Method Akhmam Fahmi; Talitha Nurshafiyyah Supardi; Misna Asqia
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6607

Abstract

Manual essay assessment is often time-consuming and increases educators' workload, creating the need for an automated essay scoring system that can improve the efficiency of the assessment process. To address this need, the Nurul Fikri Integrated Institute of Technology (STT-NF) has developed the Essay Analytic Online (ESAO) platform. However, discussions with the platform developers and administrators revealed several UI/UX issues, including an outdated interface design, unclear feature presentation, login-related usability problems, and inconsistencies in interface elements that negatively affect system usability. This study aims to design the UI/UX of the ESAO web platform using the Double Diamond Design Framework to produce a more intuitive, consistent, user-friendly interface that better meets user needs. The design process followed the four phases of the Double Diamond Framework—Discover, Define, Develop, and Deliver—resulting in user flows, a design system, and high-fidelity prototypes developed in Figma. The proposed design was evaluated using the Single Ease Question (SEQ) method involving 20 participants, comprising students and lecturers from STT-NF, across five task scenarios. The evaluation yielded an overall mean SEQ score of 6.46 on a 7-point scale, with average scores of 6.48 for students and 6.30 for lecturers. All task scenarios achieved scores above the usability acceptance threshold of 5.0, indicating a high level of perceived ease of use. These findings demonstrate that the proposed UI/UX design effectively enhances the usability of the ESAO platform. Nevertheless, the evaluation was limited to ESAO users within STT-NF. Future studies are therefore recommended to involve a more diverse group of participants to obtain more comprehensive and generalizable usability evaluation results.

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