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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Jurnal Informasi dan Komputer EDUMATIC: Jurnal Pendidikan Informatika Antivirus : Jurnal Ilmiah Teknik Informatika Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Sistem Informasi Triguna Dharma (JURSI TGD) Industrial Research Workshop and National Seminar Jurnal Teknik Informatika dan Teknologi Informasi Journal of Informatics and Computing Jurnal Pengabdian kepada Masyarakat (Nadimas) Prosiding SeNTIK STI&K Julia Jurnal Jurnal Teknik Informatika dan Teknologi Informasi
Pramadhana, Dian
Politeknik Negeri Indramayu
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Klasifikasi Penyakit Diabetes Menggunakan Metode CFS Dan ROS dengan Algoritma J48 Berbasis Adaboost Dian Pramadhana
Jurnal Pendidikan Informatika (EDUMATIC) Vol 5, No 1 (2021): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v5i1.3336

Abstract

Diabetes was a disease that occurs due to high blood-glucose levels. Researchers tried to prevent complications from developing by using data mining techniques. One of the techniques used in data mining was classification. The purpose of this study improves the accuracy of the classification of diabetes for a better and optimal result. The method in this study is Correlation Feature Selection (CFS) as attribute selection, Random Over Sampling to handle unbalanced data and AdaBoost to improve the performance of the J48 algorithm so the result obtained best. Based on the result of this study, showed that Correlation Feature Selection for attribute selection and Random Over Sampling to handle imbalance's class with the Adaboost-based J48 algorithm proved can increase the results of the diabetes classification with an accuracy of 92.3%. For the further research recommended to apply other methods so that the accuracy results obtained are more optimal for comparison.
Co-Authors Ahmad Lubis Ghozali Amalia, Dita Rizki Darsih Farismana, Riyan Febrianti, Kurnia Astrin Ghozali, A. Lubis Karina Lena, Sonty Moh Ali Fikri Moh. Ali Fikri Muhammad Rivai Rendi Robi Robiyanto Sholihah, Debi Nabilah