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All Journal Energy: Jurnal Ilmiah Ilmu-ilmu Teknik
Syaad Patmanthara
Department of Electrical Engineering and Informatics, State University of Malang, 65114, Indonesia
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

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The Application of Machine Learning in Liver Disease Diagnosis: Analysis of Algorithm Performance and Axiological Implications Sri Farida Utami; Syaad Patmanthara
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.253

Abstract

Liver disease remains a significant global health challenge, requiring accurate and timely diagnosis to improve patient outcomes and reduce healthcare costs. This study investigates the application of four machine learning classification algorithms—Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbors (KNN)—to predict the presence of liver disease using a dataset sourced from Kaggle. These algorithms were evaluated based on performance metrics such as accuracy, precision, recall, and F1 score. Both Decision Tree and Random Forest achieved the highest accuracy rate of 72.41%, demonstrating their robustness in classifying liver disease cases. However, these models showed some limitations in identifying patients without liver disease. Naïve Bayes, with an accuracy of 60.34%, exhibited an impressive recall rate of 96.97%, indicating its potential in detecting liver disease cases, though at the cost of lower precision. KNN, with an accuracy of 70.69%, proved to be a competitive option in the classification task. Beyond technical performance, the study also explores the ethical and axiological implications of using machine learning in healthcare, emphasizing the importance of fairness, transparency, and human oversight. The research highlights the need for responsible deployment of machine learning technologies, ensuring they are aligned with ethical standards to avoid biases and enhance healthcare outcomes. This study demonstrates that machine learning can significantly support liver disease diagnosis, though it must be integrated with a comprehensive ethical framework to ensure equitable and transparent decision-making in clinical practice.
Solar Powered Street Lighting in Rural Areas: A Value-Use Analysis of Green Technology Axiology Didik Riyanto; Syaad Patmanthara; Arif Nur Afandi
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK (Special Issue on Engineering Paradigm 2025 Edition)
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.si2025.254

Abstract

This study aims to analyze the utility value and axiological implications of the application of green technology, namely Solar Powered Street Lighting (PSL), in Duri Village, Slahung District, Ponorogo Regency. The main problem in the village is the lack of a public street lighting system due to the limited PLN electricity network on the connecting roads between villages. Through an axiological review, this solar power plant technology is analyzed not only from a technical aspect, but also from its beneficial value for community life. The research method includes field studies, planning, implementation of independent Public Street Lighting technology equipped with automatic sensors, implementation testing, and mentoring. The results of the implementation of one Public Street Lighting unit using solar electricity using Smart Bright Solar cell technology with 4000 lm lighting show that this technology provides an independent lighting solution for the general public, improves security, and supports environmental sustainability. The application of solar power plant on Public Street Lighting in rural areas realizes the axiological value of science as a means to improve the quality of life and create energy independence in remote areas.
Co-Authors Arif Nur Afandi Didik Riyanto Sri Farida Utami