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All Journal Techno.Com: Jurnal Teknologi Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Indonesian Journal on Software Engineering (IJSE) Perspektif : Jurnal Ekonomi dan Manajemen Universitas Bina Sarana Informatika Jurnal Teknik Komputer AMIK BSI JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURIKOM (Jurnal Riset Komputer) Jurnal Riset Informatika Systemic: Information System and Informatics Journal TEKNOKOM : Jurnal Teknologi dan Rekayasa Sistem Komputer Indonesian Journal of Business Intelligence (IJUBI) Jurnal Infortech Jurnal Scientia Akrab Juara : Jurnal Ilmu-ilmu Sosial Jurnal Sistem Informasi RESOLUSI : REKAYASA TEKNIK INFORMATIKA DAN INFORMASI Bulletin of Computer Science Research Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro JURNAL ELEKTRO DAN INFORMATIKA SWADHARMA (JEIS) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Hexagon Jurnal Ticom: Technology of Information and Communication
Omar Pahlevi
Universitas Bina Sarana Informatika
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences

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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
Data Mining Optimization Based on Particle Swarm Optimization For Diagnosis of Inflammatory Liver Disease Amrin Amrin; Omar Pahlevi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 1 (2021): EDISI JULY 2021
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i1.5312

Abstract

Inflammation of the liver is a contagious disease that is a public health problem that affects morbidity, mortality, public health status, life expectancy, and other socio-economic impacts. Early diagnosis of this disease is very important so that it can be quickly treated and treated. In this study the researchers will apply and compare several data mining and optimization classification methods with particle swarm optimization (pso), including the C4.5 algorithm, k-Nearest Neighbor, C4.5 with PSO, and k-Nearest Neighbor with PSO to diagnose inflammatory diseases. carefully, then compare which of the several of these methods is the most accurate. Based on the results of measuring the performance of the three models using the Cross Validation, Confusion Matrix and ROC Curve methods. Based on the research results, it is known that the C4.5 method with PSO is the best method with an accuracy of 79.51% and an under the curva (AUC) value of 0.950, then the k-Nearest Neighbor method with PSO has an accuracy of 75.59% and an AUC value of 0.909, then the C4.5 method with an accuracy rate of 70.99% and an AUC value of 0.950, then the k-Nearest Neighbor method with an accuracy rate of 67.19%, and an AUC value of 0.873. This proves that particle swarm optimization can improve the performance of the classification method used.
Implementation of Logistic Regression Classification Algorithm and Support Vector Machine for Credit Eligibility Prediction Amrin - Amrin; Omar - Pahlevi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 5, No 2 (2022): Issues January 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v5i2.6220

Abstract

Credit is a provision of money or bills that can be equated with it, the provision of loans or credit. A good credit analysis is very necessary, because it is one of the most important processes in the form of an investigation regarding the smooth or substandard credit repayments. The stages of identifying and predicting customers properly and correctly can be done before the loan process. This is done by examining the historical data of the customer's loan. At this time this activity is an effort made by the banking industry in dealing with credit risk problems. In this research, researchers will apply several data mining classification methods, including Logistic Regression algorithms and Support Vector Machines to predict creditworthiness. The dataset used 481 record motorized vehicle loan data, both problematic and non-problematic. The input variables in this study consisted of thirteen variables, including marital status, number of dependents, age, residence status, home ownership, occupation, employment status, company status, income, down payment, education, length of stay, and housing conditions. From the results of research and testing, the performance of the Logistic Regression model for predicting creditworthiness provided an accuracy rate of 94.81% with an area under the curve (AUC) value of 0.987. While the performance of the Support Vector Machine model provides an accuracy of 94.19% with an area under the curve (AUC) value of 0.978. Based on the T-Test test, the Logistic Regression method has the same performance compared to the Support Vector Machine.
Co-Authors Agni Isador Harsapranata Amrin Amrin Amrin Amrin Astriana Mulyani Atmojo, Muhamad Kusumo Bachtiar, Dimas Agung Cucu Ika Agustyaningrum Danny Ong Dewi Ayu Nur Wulandari Dony Ardiyansah Enok Tuti Alawiah Fahrizal Fahrizal Faiz Octa Reynaldi Fryda Fatmayati Harmen Harmen Harriansyah Harriansyah Harsih Rianto Henny Leidiyana Himawan Wijaya Imelda Sari Indah Suryani Ita Dewi Sintawati Ita Dewi Sintawati Luci Kanti Rahayu Miftahul Khoir Muhammad Syafrizal Rahmadian, Jefri Renal Verdiyan Erlangga Reni Widyastuti Riyanto, Umbar Rizka Dahlia Rony, Zahara Tussoleha Satriadi, Irawan Sismadi Sismadi Sriyadi Sriyadi, Sriyadi Sugandi, Anton Sugiharja, Danur Sunarti Syamsiar, Syamsiar Tri Santoso Tri Santoso Yopi Handrianto, Yopi