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All Journal MIND (Multimedia Artificial Intelligent Networking Database) Journal International Journal of Advances in Data and Information Systems Dinamika Bahari: Journal of Maritime Dynamic Journal of Applied Data Sciences Journal of Educational Technology and Learning Creativity
Onn, Choo Wou
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Mechanical Engineering Transportation

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Navigating Heart Stroke Terrain: A Cutting-Edge Feed-Forward Neural Network Expedition Praveen, S Phani; Mantena, Jeevana Sujitha; Sirisha, Uddagiri; Dewi, Deshinta Arrova; Kurniawan, Tri Basuki; Onn, Choo Wou; Yorman, Yorman
Journal of Applied Data Sciences Vol 6, No 3: September 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i3.763

Abstract

Heart stroke remains one of the leading causes of death worldwide, necessitating early and accurate prediction systems to enable timely medical intervention. While a variety of machine learning approaches have been employed to address this issue, including Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, and K-Nearest Neighbors, these models often suffer from limitations such as overfitting, insufficient generalization, poor performance on imbalanced datasets, and inability to capture complex nonlinear patterns in clinical data. Additionally, many existing works do not comprehensively integrate both clinical and demographic features or lack rigorous evaluation metrics beyond accuracy alone. This study proposes a novel Feed-Forward Neural Network (FFNN) model for heart stroke prediction, designed to overcome the shortcomings of conventional models. Unlike shallow classifiers, the FFNN architecture employed here leverages multiple hidden layers and nonlinear activation functions to learn intricate relationships within the dataset. The dataset used comprises various attributes such as age, hypertension, heart disease, BMI, and smoking status, which were preprocessed through normalization, one-hot encoding, and imputation techniques to ensure data quality and model performance. Experiments were conducted using a stratified train-test split, and the model was trained using the Adam optimizer with carefully tuned hyperparameters. Comparative evaluations against baseline models (Logistic Regression, Random Forest, and SVM) were carried out using precision, recall, F1-score, and ROC-AUC as performance metrics. The proposed FFNN achieved the highest accuracy of 96.47%, along with substantial improvements in recall and F1-score, highlighting its superior capability in identifying potential stroke cases even in imbalanced datasets. This work bridges a significant gap in heart stroke prediction by demonstrating the effectiveness of deep learning models—specifically FFNNs—in extracting complex patterns from diverse patient data. It also sets the stage for further exploration of deep learning-based clinical decision support systems.
Integrating Machine Learning and Internet of Things for Predictive Maintenance Enhancing Operational Efficiency and Maritime Digitalization Setiawan, Ariyono; Handoko, Wisnu; Onn, Choo Wou; Widyaningsih, Upik
Dinamika Bahari Vol 6 No 2 (2025): October 2025 Edition
Publisher : Politeknik Ilmu Pelayaran Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46484/db.v6i2.1031

Abstract

This study explores the implementation of Machine Learning (ML) and the Internet of Things (IoT) in predictive maintenance to enhance the operational efficiency of ships. The primary goal is to evaluate the effectiveness of these technologies in reducing maintenance costs, minimizing unexpected machinery failures, and improving fuel efficiency. The research is based on previous studies on AI-driven predictive maintenance and IoT-based real-time monitoring. It builds upon the work of Kim & Park (2021) and Li et al. (2019), who demonstrated the advantages of deep learning and IoT in improving maritime asset management. A comparative analysis was conducted using multiple ML algorithms, including Random Forest, Support Vector Machine (SVM), K-Means Clustering, and Long Short-Term Memory (LSTM). Data from IoT-enabled sensors on ship machinery were used to evaluate model accuracy, downtime reduction, and cost efficiency improvements. LSTM outperformed other models with an accuracy of 89.1%. Predictive maintenance reduced downtime by 30%, extended machinery lifespan by 20%, and decreased operational costs by 15%. Challenges include IoT infrastructure limitations, data security concerns, and the need for extensive historical data. This study highlights the necessity for shipping companies to invest in IoT infrastructure, cybersecurity measures, and workforce training to optimize predictive maintenance. The research contributes to maritime digitalization by demonstrating how ML and IoT integration can transform maintenance strategies, leading to a more efficient and cost-effective shipping industry.
Prediksi Ekspor Jasa Transportasi Indonesia Menggunakan LSTM Berbasis Data Perdagangan Global Terbuka SETIAWAN, ARIYONO; HANDOKO, WISNU; ABDUL HADI, ABDUL RAZAK BIN; ONN, CHOO WOU
MIND (Multimedia Artificial Intelligent Networking Database) Journal Vol 10, No 2 (2025): MIND Journal
Publisher : Institut Teknologi Nasional Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/mindjournal.v10i2.130-144

Abstract

ABSTRAKPenelitian ini memprediksi nilai ekspor jasa transportasi Indonesia menggunakan model Long Short-Term Memory (LSTM) berbasis data terbuka perdagangan global dalam mengatasi pola nonlinier dan ketergantungan temporal. Peneliti melatih model LSTM tiga lapis dengan aktivasi ReLU dan optimasi Adam menggunakan data ekspor tahunan (2005–2023) dari World Bank dan UNCTAD, dengan pembagian data latih-uji 80:20. Model mencapai MAPE 0,89% dan koefisien korelasi r = 0,999 (p < 0,0001), menunjukkan presisi tinggi. Model secara akurat menangkap gangguan akibat pandemi dan tren pemulihan, menawarkan alat prediksi berbasis AI untuk perencanaan ekspor dan kebijakan perdagangan. Ini merupakan studi pertama yang menerapkan LSTM pada ekspor jasa transportasi Indonesia dengan data terbuka, memberikan kontribusi metodologis dan praktis untuk negara berkembang.Kata kunci: kecerdasan buatan, peramalan ekspor, Indonesia, LSTM, layanan transportasiABSTRACTThis study forecasts Indonesia’s transport service export values using a Long Short-Term Memory (LSTM) model based on open global trade data in capturing nonlinear patterns and temporal dependencies. A three-layer LSTM model is trained using ReLU activation and Adam optimization on annual export data from 2005 to 2023 sourced from the World Bank and UNCTAD. The dataset is split into 80% training and 20% testing portions. The model achieves a MAPE of 0.89% and a correlation coefficient of r = 0.999 (p < 0.0001), indicating high precision.The model accurately reflects pandemic-induced shocks and subsequent recovery trends, provides an AI-driven forecasting tool for export planning and trade policy. This is the first study to apply LSTM to Indonesia’s transport service exports using open data, contributing methodological advancement and practical value for developing economies.Keywords: artificial intelligence, export forecasting, Indonesia, LSTM, transport services
Digital Transformation in Teacher Performance Assessment: Development and Implementation of E-PKG System for Enhancing Vocational Education Quality and Industry Alignment Waluyo, Surryanto Djoko; Rustanto, Agung Edi; Winanti, Yunea Kusuma; Onn, Choo Wou; Sufian, Muhammad; Mesina, James Ronald
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2490

Abstract

Purpose of the study: This study aims to develop and evaluate the Electronic Teacher Performance Assessment System (E-PKG) as a digital platform to transform teacher evaluation in vocational education from administrative compliance into strategic professional development to better align vocational graduate competencies with industry requirements. Methodology: This study employs a Research and Development (R&D) design through four stages: needs analysis via stakeholder consultation, prototype development using user-centered design, iterative field testing in vocational schools and industry partnerships, and refinement from empirical feedback. Data were collected from 34 respondents using questionnaires, structured observations, and interviews, and analyzed with descriptive statistics and thematic analysis. Main Findings: The E-PKG system reduced assessment completion time by 67%, eliminated data entry errors, and improved inter-rater reliability from 0.62 to 0.89. Real-time analytics supported evidence-based professional development planning. User satisfaction ranged from 3.88 to 4.26 (5-point scale), with highest scores for system stability and navigation ease, though challenges remained in interface clarity for non-technical users and indicator comprehension. Novelty/Originality of this study: This study introduces a comprehensive digital teacher assessment system specifically tailored to vocational education, directly linking evaluation results to industry competency frameworks. It advances knowledge by demonstrating how technology-mediated assessment can strategically connect quality improvement and workforce development in economically disadvantaged settings, while emphasizing the importance of pedagogical alignment, stakeholder inclusivity, and contextual adaptation.
IoT-VR Integrated Framework for Precision Prebiotic Dosing in Intensive Aquaculture: A Technology-Based Approach to Sustainable Fish Production Junfithrana, Anggy Pradiftha; Suryana, Anang; Saputri, Utamy Sukmayu; Hasbullah, Dasep; Langlangbuana, Muhammad Labib; Mahamad, Abd Kadir; Utomo, Wahyu Mulyo; Onn, Choo Wou; Korkmaz, Hatice Betul
Journal of Educational Technology and Learning Creativity Vol. 3 No. 2 (2025): December
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jetlc.v3i2.2497

Abstract

Purpose of the study: This study aimed to develop and validate an integrated precision aquaculture framework that combines IoT-controlled multi-prebiotic dosing, centralized environmental monitoring, and virtual reality-based operator training to improve water quality, fish growth performance, and operational competency in intensive multi-pond fish farming systems. Methodology: A closed-loop precision aquaculture system was implemented for 30 days in 40 homogeneous circular ponds. The system used centralized sensors (dissolved oxygen, pH, temperature), IoT-actuated solenoid valves with inline flow sensors for four prebiotic formulations, Water Quality Index computation, VR-based operator training, and statistical analysis using one-way ANOVA, multiple linear regression, and paired t-tests. Main Findings: IoT-based management significantly improved Water Quality Index, survival rate, and specific growth rate compared with manual management. Automated prebiotic dosing was volumetrically accurate and consistently on time. Higher water quality strongly correlated with better growth and survival. VR training substantially reduced operator task completion time and operational errors, enhancing overall system efficiency and reliability. Novelty/Originality of this study: This study presents a fully integrated multidisciplinary precision aquaculture framework that uniquely combines IoT-driven multi-prebiotic automated dosing, centralized environmental monitoring for homogeneous pond networks, and VR-based immersive training as an active human–system interaction layer. It advances current knowledge by demonstrating a scalable, technology-mediated model that unites automation, water quality management, and skill development in intensive aquaculture.
Uneven Transitions in Container Ship Capacity Across Indo-Pacific Economies (2010–2022): Integrating PCA, ANOVA, and Clustering Evidence Setiawan, Ariyono; Otok, Bambang Widjanarko; Handoko, Wisnu; Hadi, Abdul Razak Abdul; Onn, Choo Wou; Arli, Denni
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1442

Abstract

We examine uneven transitions in container ship capacity (TEU per ship) across five Indo-Pacific economies  China, Singapore, Australia, Vietnam, and Indonesia  during 20102022 using an integrated statistical framework that combines ANOVA, Welch ANOVA, GamesHowell post-hoc tests, Principal Component Analysis (PCA), and clustering. Results reveal persistent divergence: China and Singapore maintain high-capacity fleets (>10,000 TEU/ship), Australia stabilizes in the mid-tier range (~7,000 TEU/ship), while Indonesia and Vietnam experience rapid but low-level growth (<6,000 TEU/ship). ANOVA confirms significant cross-country differences (F=28.33; p<0.001; 0.65), with Welch ANOVA yielding consistent results under unequal variances (p<0.01). PCA indicates one dominant component (PC199.5%) explaining most variance, forming three readiness clusters: high, medium, and low capacity economies. These patterns suggest that policy inertia, infrastructure bottlenecks, and green transition constraints drive the uneven capacity development. The study contributes by introducing TEU per ship as a cross-national indicator for maritime readiness, linking statistical divergence to SDG targets 8, 9, 10, 13, and 14, and offering empirical guidance for low-carbon fleet transition and port modernization in emerging economies..
Interpretable Temporal Risk Modeling for Contributor Inactivity Prediction: A Comparative Study of Tree-Based Ensembles Paramita, Adi Suryaputra; Maryati, Indra; Christian, Christian; Witanto, Elizabeth Nathania; Tileubaevna, Auezova Raya; Onn, Choo Wou
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1311

Abstract

This study aims to develop an interpretable temporal risk modeling framework for predicting contributor inactivity in collaborative development environments, thereby supporting sustained participation and improving productivity. The research focuses on contributor activity data collected from a collaborative software development platform, in which participation histories are represented by temporal engagement features that capture activity recency, participation intensity, and contribution patterns over time. To model inactivity risk, several tree-based ensemble learning algorithms, including Random Forest, XGBoost, LightGBM, and a stacking ensemble, are employed and evaluated under imbalanced classification conditions. Experimental results demonstrate strong predictive performance across models, with Random Forest achieving the highest AUC of 0.9401, while XGBoost obtains the best Matthews Correlation Coefficient (0.7353). The novelty of this study lies in prioritizing structured temporal behavioral representation through normalized temporal engagement features rather than increasing model complexity, enabling more interpretable inactivity risk modeling. The findings provide practical implications for collaborative platform managers by enabling early identification of contributor disengagement, supporting sustained participation, improving productivity, and facilitating continuous product innovation.
Co-Authors ABDUL HADI, ABDUL RAZAK BIN Adi Suryaputra Paramita Agung Edi Rustanto Anang Suryana Anggy Pradiftha Junfithrana Arli, Denni Bambang Widjanarko Otok christian christian Dewi, Deshinta Arrova Elizabeth Nathania Witanto Hadi, Abdul Razak Abdul Handoko, Wisnu Hasbullah, Dasep Indra Maryati Korkmaz, Hatice Betul Kurniawan, Tri Basuki Langlangbuana, Muhammad Labib Mahamad, Abd Kadir Mantena, Jeevana Sujitha Mesina, James Ronald Praveen, S Phani Saputri, Utamy Sukmayu Setiawan, Ariyono Sirisha, Uddagiri Sufian, Muhammad Surryanto Djoko Waluyo Tileubaevna, Auezova Raya Wahyu Mulyo Utomo Widyaningsih, Upik Winanti, Yunea Kusuma Yorman