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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Jurnal Ilmiah Sinus JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) METIK JURNAL Jurnal Informatika Kaputama (JIK) Jurnal Mantik Rambideun : Jurnal Pengabdian Kepada Masyarakat Jurnal Tika Mejuajua TECHSI - Jurnal Teknik Informatika Lentera Jurnal Malikussaleh Mengabdi
Angga Pratama
Universitas Malikussaleh
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Journal : JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING

 1 
APPLICATION OF MACHINE LEARNING WITH THE BINARY DECISION TREE MODEL IN DETERMINING THE CLASSIFICATION OF DENTAL DISEASE Mutammimul Ula; Fajar Tri Tri Anjani; Ananda Faridhatul Ulva; Ilham Sahputra; Angga Pratama
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol 6, No 1 (2022): Issues July 2022
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i1.7341

Abstract

The dangers of health problems in dental disease are common for children and adults. Many dental problems get priority treatment based on data from Riskesdas, about 67.6% of the Indonesian population suffers from dental and oral problems. This affects other parts of the organ that are interrelated. Therefore, this study formulates how to solve the determination of dental disease, by applying the UDB model in machine learning. The purpose of this study was to determine the application of machine learning Binary Decision Tree (BDT) in the classification of classified dental diseases identified by decision trees in determining the results of dental disease predictions including groups and how to solve them. The research methodology in the first stage of data collection was carried out directly with the dental clinic at Cut Meutia Lhokseumawe Hospital. Then input the dental disease data along with the dental disease symptom data. The final stage is dividing the attribute values in viewing the value at a predetermined branch which is then in the form of a decision tree as a reference for the final prediction. The results of the assessment have each value indicating a high level of accuracy, with an accuracy of 92 percent and an inaccuracy of 8 percent of the 40 data points tested. Furthermore, the conclusion of this study can produce an appropriate classification of dental disease and is able to produce accurate results seen from a small error rate
Co-Authors Abror Abror Afta Adiyatma Wicaksana Afta Adiyatma Wicaksana Ananda Faridhatul Ulva Ar Razi Burhanuddin Burhanuddin Cut Ita Erliana Desuriyan Ramadhan Desvina Yulisda Eka Susanti Emi Maulani Eri Saputra Eza Pradanta Sitepu Fajar Tri Tri Anjani Fakrurrazi Hafizh Al Kautsar Aidilof Himmatur Rija ilham - sahputra Ilham Saputra Ilham Saputra Intan Aquiera Janner Simarmata M Wahyu Suranta Maulida Hasbi Mochamad Ari Saptari Muhammad Habib Mutammimul Ula Muthmainnah Muthmainnah Mutiara Fajar Rafika Rafika Ria Zulhusna Richki Hardi Rizki Mela Kurnia Rizki Putra Fhonna Rizki Setiawan Rizky Putra Phonna Salamah Salamah Sapna Biby Sayed Fachrurrazi Suheri Sujacka Retno Syukriah Syukriah Tsania Asha Fadilah Daulay Veri Ilhadi Wiwik Handayani yuli asbar Yulisda, Desvina Yunia Silfitri Zahratul Fitri Zurhijjah Zurhijjah