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All Journal Journal of Applied Computer Science and Technology (JACOST) Jurnal Andalas: Rekayasa dan Penerapan Teknologi
Baik Budi
Universitas Andalas
Computer Science & IT Education Electrical & Electronics Engineering Health Professions Industrial & Manufacturing Engineering Other

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Heart Disease Prediction Using KNN, Decision Tree, and Naïve Bayes Baik Budi
Jurnal Andalas: Rekayasa dan Penerapan Teknologi Vol. 6 No. 1 (2026): Juni 2026
Publisher : Electrical Engineering Department Faculty of Engineering Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jarpet.v6i1.142

Abstract

Cardiovascular Disease (CVD) remains a leading global cause of mortality, making early and accurate diagnosis critical for effective medical intervention. Machine Learning (ML) algorithms offer promising solutions for automating clinical decision support systems. This study compares three supervised learning algorithms—K-Nearest Neighbors (KNN), Decision Tree (DT), and Naive Bayes (NB)—to evaluate their diagnostic efficacy in predicting heart disease. The models were trained and tested using a clinical dataset of 205 instances (100 normal and 105 heart disease cases) with an 80:20 data split. Performance was evaluated based on Accuracy, Precision, Recall, and F1-Score derived from confusion matrices. The experimental results demonstrate that the Decision Tree algorithm achieved the highest aggregate accuracy of 98.54%, exhibiting exceptional clinical reliability with a perfect precision score (zero false positives) and high sensitivity (only three false negatives). The KNN model performed comparably well, achieving 98.05% accuracy and zero false positives. In contrast, the Naive Bayes algorithm underperformed, with 82.93% accuracy and high rates of both Type I and Type II errors. In conclusion, the Decision Tree model emerges as the most robust, precise, and safe algorithmic architecture for clinical implementation in heart disease screening, effectively minimizing both false alarms and missed diagnoses.
Design and Implementation of a Solar-Powered IoT Smart Fish Feeder for Sustainable Freshwater Aquaculture Micko Tomas; Baik Budi; Khadlel Muhammad Romiz
Journal of Applied Computer Science and Technology Vol. 7 No. 1 (2026): Juni 2026 (In progress)
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/njwes527

Abstract

The utilization of solar energy in aquaculture automation still encounters challenges related to energy efficiency, stability, and adaptive control within IoT-based systems. This research presents the design and implementation of a solar-powered IoT Smart Fish Feeder, developed to enable adaptive feeding schedules with optimized power management. The system is composed of a 100 Wp solar panel, a 25 A MPPT charge controller, a 14.8 V lithium battery, a 2P DC MCB (440 V/25–16 A), and an APZEM-017 ModBus DC wattmeter, integrated with a DC–DC Boost Converter to regulate power delivery for the feeder prototype. Experimental tests were conducted to evaluate solar energy performance under real environmental conditions, focusing on parameters such as voltage, current, power output, and energy conversion efficiency. Results demonstrated that the solar panel achieved an average conversion efficiency of 87.2%, the MPPT controller maintained an efficiency of 95%, the battery system reached a charge–discharge efficiency of 90.4%, and the DC–DC converter operated at 92% efficiency, resulting in an overall system efficiency of 68.8%. The system maintained voltage stability within ±2% and was capable of autonomous 24-hour operation without external power. Compared to previous studies that lacked solar–IoT integration and adaptive control, this prototype provides a novel and energy-efficient solution for sustainable aquaculture. The findings confirm that the proposed design enhances renewable energy utilization, operational reliability, and environmental sustainability in innovative aquaculture applications.
Co-Authors Khadlel Muhammad Romiz MICKO TOMAS