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All Journal PROFESI || JURNAL KESEHATAN PROFESIONAL ISLAMI Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Kesehatan Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Indonesian Journal of Health Information Management (IJHIM) PELS (Procedia of Engineering and Life Science)
Sri Sugiarsi
APIKES Mitra Husada Karanganyar
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Health Professions Medicine & Pharmacology Nursing Public Health

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DATA MINING PENYAKIT TERBANYAK BERDASARKAN DECISION TREE ALGORITMA C4.5 DI RSUD PANDAN ARANG BOYOLALI Al Mubarok, Hafiddhudin; Sutrisno, Trismianto Asmo; Sugiarsi, Sri
Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Vol. 10 No. 1 (2025): Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda Edisi Februari
Publisher : Akademi Perekam dan Informasi Kesehatan Imelda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52943/jipiki.v10i1.1512

Abstract

Top 10 disease data is data on diseases that often appear and occur most often. Data collection of the top 10 diseases is used to determine disease patterns in the community. Decision tree classification method is a model that maps observations of an item so that a conclusion is obtained about the target value of an item described in the form of a tree model. The C4.5 algorithm is method for creating a decision tree based on the training data that has been provided. The purpose of this research is to result in the classification of the most diseases based on the C4.5 decision tree algorithm of Pandan Arang Boyolali Hospital using excel and RapidMiner. This research design is descriptive observational. The data collection technique in this study was observation, with a total net data of 3449 data. The results of this research, there are no differences in results between excel and RapidMiner which results in the classification of the most diseases, which are blocks A00-B99, Z00-Z99, I00-I99, O00-O99, J00-J99, and E00-E90, and 18 rules are obtained. Evaluation of the performance of the C4.5 algorithm decision tree classification model using confusion matrix produces an accuracy value of 25.14%.
Co-Authors ADITA KUSUMAWATI, ERNA Aditia Kusumawati, Erna Al Mubarok, Hafiddhudin Ali Djamhuri Asmo Sutrisno, Trismianto DEWI ARIANI Ediyono, Suryo Hartini Jayanti, Asykar Katarina Yuni Saptariningsih Kelvin Ferniawan MOCHAMAD FAHMI, MOCHAMAD FAHMI Ningtyas, Nandani Kusuma Nurcahyani, Intan Ari Rohmadi Rohmadi Rohmadi Sawal, Samsul sri erawati, sri Sri Mulyono sri wariyanti, astri Susanti, Ade Novalia Trismianto Asmo Sutrisno Wariyanti, Astri Sri Waskito, Dito Yogo Wulandari, Dewi Septiana Yuniati, Putri Utami