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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Seminar Nasional Teknologi Informasi Komunikasi dan Industri INOVTEK Polbeng - Seri Informatika Digital Zone: Jurnal Teknologi Informasi dan Komunikasi Infotekmesin Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Industri Terintegrasi (JUTIN) Jurnal Sistem Informasi dan Sains Teknologi Bhakti Nagori (Jurnal Pengabdian kepada Masyarakat) Journal of Embedded Systems, Security and Intelligent Systems Journal of Accounting Law Communication and Technology Jurnal Testing dan Implementasi Sistem Informasi Repeater: Publikasi Teknik Informatika dan Jaringan INOVTEK Polbeng - Seri Informatika
Agus Tedyyana, Agus
Politeknik Negeri Bengkalis
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering

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LightGBM for Liver Disease Detection with Hybrid Hyperparameter Optimization Fajar Ratnawati; Agus Tedyyana; Johny Custer
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7357

Abstract

In response to the growing burden of liver-related disorders, this research develops a supervised learning approach using the Light Gradient Boosting Machine (LightGBM) algorithm to support the early identification of Non-Alcoholic Fatty Liver Disease (NAFLD). The study focuses on constructing and assessing a robust classification model that differentiates individuals with NAFLD from those without the condition based on routinely collected clinical indicators and lifestyle-related characteristics. The dataset, obtained from an open-access NAFLD repository, consists of 1,700 patient records with 10 predictor variables and one binary diagnosis label. The proposed framework employs a stratified shuffle split evaluation scheme with 5-fold and 10-fold cross-validation, using out-of-fold (OOF) probabilities to compute overall performance metrics. The baseline LightGBM model already demonstrated strong performance, achieving 88.94% accuracy, 90.74% precision, 88.99% recall, 89.85% F1-score, and 92.52% AUC under 10-fold cross-validation. To further improve predictive performance, hyperparameter tuning was performed using Optuna and Bayesian Optimization. Among the evaluated approaches, Bayesian-optimized LightGBM achieved the best results, with 93.17% accuracy, 94.49% precision, 92.52% recall, 93.72% F1-score, and 93.28% AUC under 10-fold cross-validation. These findings indicate that systematic hyperparameter optimization can improve the discriminative capability of LightGBM for NAFLD detection and support its potential as a reliable decision-support tool in clinical settings.
Edge-Based Early Warning for High-Speed Boat Stability Monitoring Muhammad Asep Subandri; Jamal; Fajar Ratnawati; I Gusti Agung Putu Mahendra; Agus Tedyyana; Budhi Santoso
Journal of Embedded Systems, Security and Intelligent Systems Vol 7 No 3 (2026): September 2026
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v7i3.11866

Abstract

Purpose - This study aims to develop and evaluate an edge-based early warning prototype for monitoring the stability of high-speed boats using real-time motion data. Design/methods/approach – The study employs an engineering prototype validation design consisting of system requirement analysis, architecture design, implementation, and validation. The system integrates an IMU (MPU-6050) for motion sensing, a Raspberry Pi-based edge computing unit for real-time processing, rule-based classification (Normal/Warning/Critical), and an MQTT-based communication framework connected to the SHISTAMO dashboard. Prototype validation includes functional testing, platform integration, and operational monitoring using controlled scenarios and expert-labeled events. Findings - The results show that the prototype successfully performs end-to-end integration from sensing to visualization. The system achieved 90.4% classification accuracy, with high recall in detecting critical conditions (96.7%), ensuring reliable identification of high-risk events. The local alarm response time was 182 ms, while the dashboard update delay averaged 1.24 s, indicating near-real-time performance. Communication reliability was also high, with 98.8% data delivery success and 97.2% offline synchronization. Research implications/limitations – The findings demonstrate prototype-level feasibility; however, validation is limited to controlled scenarios and does not yet represent diverse sea conditions. The rule-based thresholds and comfort proxy require further calibration and validation through extended sea trials and reference instrumentation. Originality/value – This study contributes an integrated edge-based maritime monitoring prototype that combines motion sensing, offline-capable alarming, real-time telemetry, and fleet-level logging in a single system, specifically tailored to the operational needs of high-speed boats.
Co-Authors Abda Abda Afis Julianto Apri Siswanto Custer, Johny Danuri Danuri Danuri Danuri Danuri Danuri Danuri, Danuri Depandi Enda Deski Ari Sandi Effendy, Akmar Elgamar Syam Faizal Riza Fajar Ratnawati Fajar Ratnawati Fajri Profesio Putra Ghazali, Osman I Gusti Agung Putu Mahendra Jamal Jaroji Jaroji, - Kasmawi Lidya Wati Lumban, Andre Pardamean Muhamad Azren Muhammad Asep Subandri Muhammad Luthfi Muhammad Noor Muhammad Zuhri Febriansyah Nur Ziadah Harun Nurmi Hidayasari Onno W. Purbo Osman Ghazali Osman Ghazali Putu Mahendra, I Gusti Agung Ratnawati, Fajar Rezki Kurniati, Rezki Rima Anggraini Rima Anggraini, Rima Rosyidah Rosyidah, Rosyidah Santoso, Budhi Sobirin, M Suci Sekar Sari Suharyadi, Thoriq Nadaghoist Sunaryo Sunaryo Supria Supria Tryo Asnafi W. Purbo, Onno Yudi Yudi