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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Informatika Jurnal Teknik Komputer AMIK BSI Jurnal Khatulistiwa Informatika Paradigma JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Techno Nusa Mandiri : Journal of Computing and Information Technology Jurnal Mantik Penusa JURIKOM (Jurnal Riset Komputer) Jurnal Informasi dan Teknologi Jurnal Infortech RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro IMTechno: Journal of Industrial Management and Technology
Amrin Amrin
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science

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Data Mining with Logistic Regression and Support Vector Machine for Hepatitis Disease Diagnosis Amrin, Amrin; Rudianto, Rudianto; Sismadi , Sismadi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 2 (2025): Issues January 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i2.13218

Abstract

Hepatitis is a chronic and dangerous disease that can lead to death. Making early predictions to detect hepatitis is very important because many people still underestimate the disease. These predictions can be made by collecting patient data or health examination results, so that preventive measures can be taken faster and better. Early diagnosis of the disease is important for prompt management and treatment. The right stage of diagnosis activities and accurate disease prediction in time can save many patients. The magnitude of this disease problem in Indonesia can be known from various studies, studies, and disease observation activities. In this study, researchers will apply and compare data mining classification methods, namely the Logistic Regression method and Support Vector Machine to diagnose hepatitis disease. Based on the research, it is known that the Logistic Regression method has an accuracy rate of 84.62% and an under the curve (AUC) value of 0.841, then the Support Vector Machine method has an accuracy rate of 87% and an AUC value of 0.865. From the t-test results, it can be seen that there is no significant difference between the Logistic Regression and Support Vector Machine methods, because the value = 0.520>0.05. This shows that the Logistic Regression method has almost the same performance as the Support Vector Machine method. Hopefully the results of this research can help doctors determine a diagnosis more quickly and reduce the possibility of misdiagnosis so that early detection of hepatitis can be carried out more widely, especially in remote areas with limited health facilities
Analisa Komparasi Model Data Mining Algoritma C4.5, CHAID, dan Random Forest Untuk Penilaian Kelayakan Kredit Amrin, Amrin; Pahlevi, Omar; Rianto, Harsih
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.6208

Abstract

Credit has now become a trend in society. The problem with credit is the improper history of credit card usage. The resulting impact can lead to bad credit. If customers fail to pay off debts that have been agreed upon with the bank, they can increase their credit risk. This study aims to conduct a comparative analysis of three data mining classification methods: the C4.5 algorithm, Chi-Squared Automatic Interaction Detection (CHAID), and Random Forest. The goal is to classify creditworthiness status. The researcher used 481 vehicle credit records with "bad" and "good" reviews. In this study, the independent variables used are dependent status, age, marital status, occupation, income, employment status, company status, last education, length of stay, house condition, and down payment. For creditworthiness assessment, the C4.5 model shows a truth accuracy rate of 91.90% with an area under the curve (AUC) value of 0.915. The CHAID model shows a truth accuracy rate of 63.83% with an AUC value of 0.661, and the Random Forest model shows a truth accuracy rate of 78.60% with an AUC value of 0.907. The evaluation results show that both the Random Forest and C4.5 algorithms have high accuracy rates and AUC values.
Co-Authors Andi Nana Mariana Budiarto, Balla Wahyu David Ridwan Savero Donny Dharmawan Eni Irfiani Frans Sudirjo Hafdiarsya Saiyar Harsih Rianto Irawan Satriadi Irawan Satriadi Luh Komang Candra Dewi Mita Diah Larasati Muhammad Alawi Muhammad Reza Aldiansyah Oki Rosanto Omar Pahlevi Priyono, Agil Hendro Rudianto Rudianto Satriadi, Irawan Sismadi , Sismadi Yopi Handrianto, Yopi