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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Transformatika Seminar Nasional Informatika (SEMNASIF) JAIS (Journal of Applied Intelligent System) Proceeding SENDI_U Jurnal Ilmiah Dinamika Rekayasa (DINAREK) SISFOTENIKA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) JUDIMAS (Jurnal Inovasi Pengabdian Kepada Masyarakat) Jurnal Pendidikan dan Teknologi Indonesia Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Algoritma Prosiding Seminar Nasional Hasil-hasil Penelitian dan Pengabdian Pada Masyarakat Moneter : Jurnal Keuangan dan Perbankan Seminar Nasional Teknologi dan Multidisiplin Ilmu Scientific Journal of Informatics Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) INOVTEK Polbeng - Seri Informatika
L. Budi Handoko
Universitas Dian Nuswantoro
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Journal : EDUMATIC: Jurnal Pendidikan Informatika

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Analisis Performa Model Random Forest dan CatBoost dengan Teknik SMOTE dalam Prediksi Risiko Diabetes Irfannandhy, Rony; Handoko, Lekso Budi; Ariyanto, Noval
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 2 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i2.27990

Abstract

Diabetes mellitus (DM) is increasing in prevalence globally and is becoming a serious health problem. Early detection reduces long-term complications. The purpose of our research is to evaluate and compare the effectiveness of Random Forest (RF) and CatBoost models with SMOTE technique in predicting DM risk based on test data processed to produce comparative analysis performance of both models in the form of precission, recall, F1-Score and accuracy. Our research type is quantitative using methods that include EDA, transformation, dividing test and training data, implementation of RF and CatBoost methods with SMOTE and evaluation of model performance. The dataset from the platform (Kaggle) includes 768 individual health data consisting of eight independent variables of pregnancy, glucose, blood pressure, skin thickness, insulin, Body Mass Index (BMI), DM history, age as well as one target (outcome) variable of DM status. The SMOTE analysis technique was applied to balance the class distribution and improve the representation of the minority class, making the prediction model more accurate and stable. The findings of the SMOTE-RF model were 82% accuracy and SMOTE CatBoost 81% accuracy. Based on the feature importances analysis, the main variables affecting DM risk prediction of both models are glucose, BMI and age. Glucose variable is the main DM risk indicator used for prediction to be more efficient. The practical implication of improved machine learning early detection has the potential to support doctors' decision making more accurately to prevent more serious complications in diabetes mellitus.
Co-Authors ., Muslih Abdus Salam, Abdus Abdussalam Abdussalam Abu Salam Abu Salam Acun Kardianawati Ade Surya Ramadhan Adelia Syifa Anindita Aisyatul Karima Aisyatul Karima Ajib Susanto Al zami, Farrikh Alzami, Farrikh Andi Danang Krismawan Ardytha Luthfiarta Ari Saputro Ari Saputro, Ari ARIANTO, EKO Ariya Pramana Putra Ariyanto, Noval Budi Harjo Budi, Setyo Cahaya Jatmoko Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Christy Atika Sari De Rosal Ignatius Moses Setiadi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erwin Yudi Hidayat Erwin Yudi Hidayat Etika Kartikadarma Fauzi Adi Rafrastara Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Firman Wahyudi, Firman Ghulam Maulana Rizqi Guruh Fajar Shidik Hafiidh Akbar Sya'bani Hanif Setia Nusantara Hanny Haryanto Hasan Aminda Syafrudin Hendy Kurniawan Herfiani, Kheisya Talitha Irfannandhy, Rony Irwan, Rhedy Isinkaye, Folasade Olubusola Izza Khaerani Ja'far, Luthfi Junta Zeniarja Khafiizh Hastuti Khafiizh Hastuti Lucky Arif Rahman Hakim Maulana Ikhsan Megantara, Rama Aria Mira Nabila Mira Nabila Muhammad Jamhari Muslih Muslih Muslih Muslih Nurhindarto, Aris Ocky Saputra, Filmada Oki Setiono Pulung Nurtantio Andono Raihan Yusuf Rama Aria Megantara Ramadhan Rakhmat Sani Reza Pahlevi, Mohammad Rizky Rizqy, Aditya Rofiani, Rofiani Saputra, Filmada Ocky Saputri, Pungky Nabella Sarker, Md. Kamruzzaman Sendi Novianto Soeleman, M Arief Sya'bani, Hafiidh Akbar Umi Rosyidah Valentino Aldo Wellia Shinta Sari Wildanil Ghozi