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All Journal CommIT (Communication & Information Technology) Embrio : Jurnal Kebidanan Conference on Innovation and Application of Science and Technology (CIASTECH) Jurnal Kesehatan Jurnal Medikes (Media Informasi Kesehatan) Oksitosin : Jurnal Ilmiah Kebidanan Jurnal Ilmiah PANNMED (Pharmacist, Analyst, Nurse, Nutrition, Midwivery, Environment, Dentist) Jurnal Ners Jurnal Farmasi Tinctura International Journal of Health Science (IJHS) Ahsana: Jurnal Penelitian dan Pengabdian kepada Masyarakat Sabangka Abdimas Jurnal Pengabdian Masyarakat Sabangka
Lia Fitria
Universitas Ibrahimy
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Journal : CommIT (Communication

 1 
Comparison of the Performance Results of C4.5 and Random Forest Algorithm in Data Mining to Predict Childbirth Process Muhasshanah Muhasshanah; Mohammad Tohir; Dewi Andariya Ningsih; Neny Yuli Susanti; Astik Umiyah; Lia Fitria
CommIT (Communication and Information Technology) Journal Vol. 17 No. 1 (2023): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v17i1.8236

Abstract

Technology advancements in the world of information have made it easier for many people to process data. Data mining is a process of mining more valuable information from large data sets. The research aims to determine the difference between the C.45 and random forest algorithms in data mining to predict the childbirth process of pregnant women. It compares the accuracy of the performance results of the C4.5 and random forest algorithms to predict the delivery process for pregnant women. Then, experimental research is conducted to classify the childbirth process in Situbondo, Indonesia, by applying the C.45 and the random forest algorithm in the data mining. The decision tree J48 algorithm is used for the C4.5 algorithm in the research. Both algorithms are compared for their error classification and accuracy level. The research uses 1,000 data for training and 200 data for testing. The results show the accuracy of implementing the C4.5 and random forest algorithms with data mining using 10-fold cross-validation, generating 96% and 95% as correctly classified data. Then, the Relative Absolute Error for both algorithms has the same result. It is 15%. The C4.5 algorithm has a better result than the random forest algorithm by comparing the performance results. Further research can add more data to improve the accuracy of the analysis results by using another algorithm.
Co-Authors Anisa F Arianggara, Andi Wilda Arifah A Arifah Arifin Arifah Arifin Arifatul H Astik Umiyah Azizatul Hamidiyah Azizatul Hamidiyah Dewi Andariya Ningsih Dianatul F Ernawati Ernawati Ernawati Ernawati Febriana Sari Hayatul Rahimah Herri Sastramihardja Indah Sri Wahyuni M. Nurhalim Shahib Mohammad Tohir Mona Rahayu Putri Muhasshanah Muhasshanah Muldaniyah Muldaniyah Naning Suryani Neny Yuli Susanti Nuri Khusnul Khotimah Riswanti Riswanti Saraswati Haylian Chiani Sherly Agustina Siti Nurrosyidah Titi Purwitasari Handayani Umi Kulsum Venny Diah Venny Diah Ningsih Wawan Juandi