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All Journal JISA (Jurnal Informatika dan Sains) G-Tech : Jurnal Teknologi Terapan
Kusumaningrum, Sekar Ayu Wijaya
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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Comparative Analysis of Machine Learning Methods in Predicting Diabetes Risk Based on Genetic Data Kusumaningrum, Sekar Ayu Wijaya; Soleh, Oleh; Yusup, Muhamad
JISA(Jurnal Informatika dan Sains) Vol 8, No 2 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i2.2486

Abstract

Type 2 Diabetes Mellitus (T2DM) is a global chronic disease caused by the interaction of genetic and environmental factors. The use of genetic data offers great potential for early detection and personalized intervention. However, the complex analysis of genetic data requires sophisticated approaches like machine learning. This study aims to compare the performance of three machine learning algorithms Logistic Regression, Random Forest, and K-Nearest Neighbors (KNN) in predicting T2DM risk based on genetic data. By using a Systematic Literature Review of studies published between 2019 and 2024, the accuracy data from each algorithm was compared. The analysis results show that Random Forest has the best performance with an accuracy of 99.3%. This algorithm excels due to its ability to handle high-dimensional datasets and reduce overfitting. In comparison, KNN achieved an accuracy of 87% and Logistic Regression 82%. These findings support the integration of machine learning into early detection systems and more precise and efficient clinical decision-making for T2DM management.
Design of an Expert System for Early Detection of Domestic Violence Using Keyword Matching, Sentiment Analysis and Forward Chaining Kusumaningrum, Sekar Ayu Wijaya; Soleh, Oleh; Azizah, Nur
G-Tech: Jurnal Teknologi Terapan Vol 10 No 1 (2026): G-Tech, Vol. 10 No. 1 January 2026
Publisher : Universitas Islam Raden Rahmat, Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/g-tech.v10i1.8977

Abstract

Domestic Violence (KDRT) is a critical humanitarian issue where victims often under-report due to fear, dependence, and stigma. Consequently, many victims turn to social media to express distress implicitly using vague language, rendering existing passive reporting systems and manual detection ineffective against unstructured narratives. This research aims to design a Hybrid Expert System architecture that integrates Keyword Matching and Sentiment Analysis with Forward Chaining to objectively detect indications of KDRT in Indonesian text, specifically targeting implicit venting that lacks explicit violence keywords. The study employs a systematic development method involving knowledge acquisition from psychological (cycle of abuse) and legal  domains to construct a robust knowledge base. The technical architecture combines sentiment analysis to gauge emotional intensity with Forward Chaining inference logic. This logic utilizes dynamic frequency parameters to validate findings through case tracing simulations. The results demonstrate that the proposed architecture successfully classifies various violence types, including physical, verbal, economic, and multi-type violence. The simulation confirms the system’s capability to distinguish between common household conflicts and specific abuse patterns by applying zero-tolerance thresholds for acute violence and repetition filters for chronic psychological abuse. Consequently, this system functions as a robust decision support tool, providing measurable risk assessments and appropriate intervention recommendations for early detection.
Co-Authors Muhamad Yusup Nur Azizah Oleh Soleh, Oleh