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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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Journal : Computer Science (CO-SCIENCE)

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Optimasi Algoritma Naïve Bayes Berbasis Particle Swarm Optimization Untuk Klasifikasi Status Stunting Pahlevi, Omar; Amrin, Amrin; Handrianto, Yopi
Computer Science (CO-SCIENCE) Vol. 4 No. 1 (2024): Januari 2024
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v4i1.2963

Abstract

Every parent wants their children to grow up healthy. Eating a healthy diet can minimize stunting. Long-term nutritional deficiencies can lead to stunting, a chronic nutritional problem that impairs physical growth and development, including low body weight and height. Preventive action against stunting is a fundamental activity that must be done immediately in the form of counseling and taking further medical action.  In data mining there are several methods for extracting information including classification. There are various methods for extracting information using data mining, such as classification. In this research, researchers will apply Naïve Bayes with Particle Swarm Optimization (PSO) for the classification of stunting status in order to determine whether a child has a case of stunting or not based on gender, age, birth weight, body weight, body length, and breastfeeding. In the final results of the research, it is known that the accuracy of the truth obtained through the performance of the Naïve Bayes algorithm model is 80.69% and a score of 0.801 resulting from Area Under the Curva (AUC). Then based on the calculation results with the Naïve Bayes algorithm model with Particle Swarm Optimization, it can be obtained a truth accuracy rate of 83.06% with an Area Under the Curve (AUC) value of 0.801. Based on the final value obtained, the pattern of applying Particle Swarm Optimization to the Naïve Bayes algorithm can improve the performance of the classification method used in this research activity.
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