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All Journal Journal of Software Engineering JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Pilar Nusa Mandiri Information System for Educators and Professionals : Journal of Information System JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Komputasi dan Teknologi Informasi JURIKOM (Jurnal Riset Komputer) JISICOM (Journal of Information System, Infomatics and Computing) Journal of Information System, Applied, Management, Accounting and Research Jurnal Mantik INTI Nusa Mandiri Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro Journal Automation Computer Information System (JACIS) Malcom: Indonesian Journal of Machine Learning and Computer Science
Harsih Rianto
Universitas Bina Sarana Informatika
Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Neuroscience Transportation Other

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Journal : journal of informatics and telecommunication engineering

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Improving Liver Disease Diagnosis Accuracy Using Synthetic Minority Oversampling Technique and Particle Swarm Optimization Harsih Rianto; Amrin amrin
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 10 No. 1 (2026): Issues July 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v10i1.17943

Abstract

This study aims to improve liver disease diagnosis accuracy using the Synthetic Minority Oversampling Technique (SMOTE) and Particle Swarm Optimization (PSO). The dataset used in this study is the Indian Liver Patient Dataset (ILPD), which consists of 583 patient records with 10 input attributes and one class attribute. The main problem in this dataset is the imbalance of class distribution between liver and non-liver patients, which may affect the performance of classification models. The research stages include data preprocessing, missing value imputation, stratified 70:30 train-test splitting, applying SMOTE to the training data, feature selection using PSO, and evaluating several classification models, namely Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and Logistic Regression (LR). The experimental results show that SMOTE-RF achieved the best overall performance with an AUC of 0.8610, accuracy of 0.7784, F1-score of 0.7783, and MCC of 0.5570. Meanwhile, SMOTE-PSO-DT improved the Tree model by selecting five important attributes, namely Direct Bilirubin, SGOT, Total Proteins, Albumin, and A/G Ratio. These results indicate that SMOTE effectively improves classification performance, while PSO helps simplify the model through feature selection.
Co-Authors Adika May Sari Afiandri, Dhea Aulia AHMAD FAHRIZAL Alfian Alfian Amrin Amrin Amrin Amrin Astriana Mulyani Bimo Tri Cahyo Budi Supriyadi Budiman, Ade Surya Cahyani Budihartanti Citra Radita Utami Desmulyati Dwiza Riana Eka Wulansari Fridayanthie, Eka Wulansari Firstianty Wahyuhening Fibriany Hasan Basri Juarni Siregar khairul rizal Linda Marlinda Mohammad Syamsul Azis Noer Hikmah Nur Fadillah, Nur Omar Pahlevi Rafli Naufal Alief Rahmat Tirta Kusuma Rana, Dipo Yudhis Romi Satria Wahono Roris, Renaldi Putra Sahamony, Nur Fitriyani Saputra, Andhika Sari Dewi Siti Chaerul Fadilah Susi Susilowati, Susi Syamsiar, Syamsiar Syarifah, Asyifa Raudha Terttiaavini, Terttiaavini Tri Hartati Tyas Setiyorini Tyas Setiyorini Yamin Nuryamin Yesni Malau Yumi Novita Dewi