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All Journal TEKNIK INFORMATIKA JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Mantik Penusa MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Varian Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Abdi Masyarakat Indonesia Indo-MathEdu Intellectuals Journal Abdi Widya: Jurnal Pengabdian Masyarakat Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Jurnal Ilmiah Pengabdian dan Inovasi INTAN CENDEKIA: JURNAL PENGABDIAN MASYARAKAT EMPOWERMENT: Journal of Community Practice
Anthony Anggrawan
Ilmu Komputer, Fakultas Teknik, Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemistry 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

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Journal : JOIV : International Journal on Informatics Visualization

 1 
Improvement Performance of the Random Forest Method on Unbalanced Diabetes Data Classification Using Smote-Tomek Link Hairani Hairani; Anthony Anggrawan; Dadang Priyanto
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1069

Abstract

Most of the health data contained unbalanced data that affected the performance of the classification method. Unbalanced data causes the classification method to classify the majority data more and ignore the minority class. One of the health data that has unbalanced data is Pima Indian Diabetes. Diabetes is a deadly disease caused by the body's inability to produce enough insulin. Complications of diabetes can cause heart attacks and strokes. Early diagnosis of diabetes is needed to minimize the occurrence of more severe complications. In the diabetes dataset used, there is an imbalanced data between positive and negative diabetes classes. Diabetes negative class data (500 data) is more than diabetes positive class (268), so it can affect the performance of the classification method. Therefore, this study aims to apply the Smote-Tomeklink and Random Forest methods in the classification of diabetes. The research methodology used is the collection of diabetes data obtained from Kaggle, as many as 768 data with eight input attributes and 1 output attribute as a class, pre-processing data is used to balance the dataset with Smote-Tomeklink, classification using the random forest method, and performance evaluation based on accuracy, sensitivity, precision, and F1-score. Based on the tests conducted by dividing data using 10-fold cross-validation, the Random Forest algorithm with Smote-TomekLink gets the highest accuracy, sensitivity, precision, and F1-score compared to Random Forest with Smote. The Random Forest algorithm with Smote-Tomeklink has 86.4% accuracy, 88.2% sensitivity, 82.3% precision, and 85.1% F1-score. Thus, using Smote-Tomeklink can improve the performance of the random forest method based on accuracy, sensitivity, precision, and F1-score.
Co-Authors Abdul Rahim Ahmat Adil Alfilail, Nur Anggriani, Rini Aprilia Dwi Dayani Ariq, Tomy Ayu Dasriani, Ni Gusti Azhar, Raisul Azhari Azhari Bidari Andaru Widhi Cahyadi, Irwan Canggih Wahyu Rinaldi Cecep Kusmana christofer satria Christofer Satria Dadang Priyanto Dadang Pyanto Dafa Awanta Dayani, Aprilia Dwi Dedi Aprianto Dewa Ayu Oki Astarini Diah Supatmiwati Dian Syafitri Chani Saputri Dias Nabila Huda Didiharyono, D. Donny Kurniawan Dwi Kurnianingsih Dyah Susilowati Dyah Susilowati Efrizoni, Lusiana Elyakim Nova Supriyedi Patty, Elyakim Nova Supriyedi Erwin Suhendra Fadiel Rahmad Hidayat Hairani Hairani Haryono Haryono Hasbullah Hasbullah Hasbullah Helna Wardhana Hengki Tamando Sihotang Herawati, Baiq Candra Hilda Hastuti Huda, Dias Nabila Husain Husain I Nyoman Subudiartha I Nyoman Yoga Sumadewa I Nyoman Yoga Sumadewa Ikang Murapi Irwan Cahyadi Jean Suciasti Gunawan Junendri Ardian Kamil, Wahyu Katarina Katarina Khairan marzuki Khasnur Hidjah Kurniadin Abd Latif Lalau Ganda Rady Putra Lalu Ganda Rady Putra Lanang Sakti Lutfie, Muhammad Hilal Mumtaz M Najmul Fadli M. Ade Candra M. Thontowi Jauhari Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Miswaty, Titik Ceriyani Mokhammad Nurkholis Abdillah Muhammad Innuddin Muhammad Ridho Akbar Muhammad Rosikhu MUHAMMAD TAJUDDIN Muhammad Zaki Pahrul Hadi Muhammad Zulfikri Muhsin, Lalu Busyairi Nurhidayati, Maulida Nurul Azmi Nurul Hidayah Peter Wijaya Sugijanto Primajati, Gilang Purnama, Baiq Kartika Putu Tisna Putra R. Ayu Ida Aryani Raden Bagus Faizal Irani Sidharta Rahmat Maulana Rahmawati, Lela Rahmiati, Baiq Fitria Rini Anggriani Rini Anggriani Riosatria Riosatria Riosatria, Riosatria Santoso, Heroe Sarjon Defit Satuang Satuang Sirojul Hadi Siti Soraya Sri Astuti Iriyani Sugijanto, Peter Wijaya Sunardy Kasim Supriantono, Herman Sutarman Syahrir, Moch. Syamsurrijal Syamsurrijal Tomi Tri Sujaka Triwijoyo, Bambang Krismono v, Sovian Veithzal Rivai Zainal Wayan Canny Naktiany Wenny Wijaya Wiya Suktiningsih Zulkipli Zulkipli