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All Journal JUITA : Jurnal Informatika CESS (Journal of Computer Engineering, System and Science) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Abdimas BSI: Jurnal Pengabdian Kepada Masyarakat Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Pilar Nusa Mandiri ILKOM Jurnal Ilmiah MULTINETICS Simpatik: Jurnal sistem Informasi dan Informatika J-Intech (Journal of Information and Technology) J-ABDIPAMAS (Jurnal Pengabdian Kepada Masyarakat) Ladang Artikel Ilmu Komputer
Satia Suhada, Satia
Program Studi Sistem Informasi, Universitas Bina Sarana Informatika, Sukabumi, Indonesia
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : MULTINETICS

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Optimasi Linear Sampling dan Information Gain pada Algoritma Decision Tree Untuk Diagnosis Penyakit Diabetes Gunawan, Gunawan; Rahmawati, Ami; Suhada, Satia; Hidayatulloh, Taufik; Wintana, Dede
MULTINETICS Vol. 7 No. 2 (2021): MULTINETICS Nopember (2021)
Publisher : POLITEKNIK NEGERI JAKARTA

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Abstract

Diabetes which is assigned to be in the top 10 list of diseases that cause death in the last 10 years has increased. What was observed was that this increase occurred in developing countries with middle to lower social status. In Indonesia, diabetes is included in the top 10 diseases with a large number of sufferers. And more than that, diabetes becomes comorbid that causes complications in Covid 19 patients. Then to detect diabetes more quickly and accurately, it is necessary to make research that can produce a better level of accuracy to detect diabetes. By using a public dataset taken from the UCI repository consisting of 520 records, obtained from Diabetes Sylhet Hospital, Bangladesh. In this research, classification will be carried out using the Decision Tree algorithm with optimization of Linear Sampling and Information Gain. After calculating using these methods and calculating the accuracy, the results obtained are 99.04% accuracy with a comparison with previous research which only used a Random Forest of 97.04%.
Co-Authors A.Gunawan Abidin Abidin Acep Suherman Ami Rahmawati Andi Riyanto Ardi Winardi Arif Purnama Daniel Ratag Dasya Arif Firmansyah Dede Wintana Dede Wintana Desi Susilawati Dicky Prayudi Dwiza Riana Eva Marsusanti Eva Marsusanti Eva Nursyifa Gunawan Gunawan Gunawan Gunawan Gunawan Hamdun Sulaiman Jamal Maulana Hudin, Jamal Maulana Jasisca Marleftan Lestari Yusuf Muhamad Reza Vahlefi Purwaningtias, Deasy Rahmawati, Ami Ramdhan Saepul Rohman Rina Riniawati Risdiansyah, Deni Rusda Wajhillah Rusli Nugraha Rusli Nugraha Saeful Bahri Saeful Bahri Selvia Septi Palupi Setyo Bagus Nugraha Siskawati Siskawati Siti Fatimah Susilawati Susilawati Taufik Hidayatulloh Taufik Hidayatulloh Wahyu Nugraha Wintana, Dede Yahya Iskandar Yulhan Wahyudin