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All Journal Semantik Seminar Nasional Informatika (SEMNASIF) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputer Terapan Jurnal Elementer (Elektro dan Mesin Terapan) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Communications in Science and Technology Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA ComTech: Computer, Mathematics and Engineering Applications Jurnal Pengabdian Masyarakat Multidisiplin INSERT: Information System and Emerging Technology Journal Jurnal Akuntansi Keuangan dan Bisnis Proceeding Applied Business and Engineering Conference Proceeding International Applied Business and Engineering Conference Fleksibel: Jurnal Pengabdian Kepada Masyarakat JITER-PM (Jurnal Inovasi Terapan - Pengabdian Masyarakat) Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics
Kartina Diah K. W.
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Journal : join jurnal online informatika

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Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Kartina Diah Kusuma Wardhani; Memen Akbar
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
Diabetes Risk Prediction Using Extreme Gradient Boosting (XGBoost) Wardhani, Kartina Diah Kusuma; Akbar, Memen
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.970

Abstract

One of the uses of medical data from diabetes patients is to produce models that can be used by medical personnel to predict and identify diabetes in patients. Various techniques are used to be able to provide a diabetes model as early as possible based on the symptoms experienced by diabetic patients, including using machine learning. The machine learning technique used to predict diabetes in this study is extreme gradient boosting (XGBoost). XGBoost is an advanced implementation of gradient boosting along with multiple regularization factors to accurately predict target variables by combining simpler and weaker model set estimations. Errors made by the previous model are tried to be corrected by the next model by adding some weight to the model. The diabetes prediction model using XGBoost is shown in the form of a tree, with the accuracy of the model produced in this study of 98.71%
Co-Authors - Sugianto Akbar, Memen Akbar, Memen Alfin H Anam, M Khairul Ari Azhar Cakra Ramadhana chintia romauli rambe Rambe Dewi, Meillany Dhimas Sena Rahmantara Efrizoni, Lusiana Heri Subagiyo Ike Yunia Pasa Isnaini, Mei Nanda Jan Alif K Jan Alif Kreshna Juni Nurma Sari Kamila, Aida Khairul Umam S Maksum Ro’is Adin Saf Maksum Ro’is Adin Saf Mardhiah Fadhli, Mardhiah Memen Akbar Muhammad Bambang Firdaus Muhammad mahrus zain Nurmalasari, Dini Puja Hanifah Sari, Juni Sri Devi Anjasmara Sugeng Purwantoro E.S.G.S Suhatman, Rahmat Surya, Ibnu Syaparudin BS Tisa Magrisa Tobi Arfan Tobi Arfan, Tobi Tri Wahyuni, Retno Wawan Yunanto Wenda Novayani Weslie Weslie Yohana Dewi Lulu Widyasari Yuli Fitrisia