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All Journal e-CliniC Diponegoro Journal of Accounting Legal Opinion BAHAS Engineering, Mathematics and Computer Science Journal (EMACS) Bulletin of Data Science Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Al-Wasith : Jurnal Studi Hukum Islam Jurnal Sadewa: Publikasi Ilmu Pendidikan, Pembelajaran dan Ilmu Sosial JMMR (Jurnal Medicoeticolegal dan Manajemen Rumah Sakit) Venus: Jurnal Publikasi Rumpun Ilmu Teknik
Irwan Syah
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Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Optimizing Deep Learning using Feature Selection for Accurate Health Insurance Claim Classification Wardhani, Anindya Khrisna; Irwan Syah; Sudra, Rano Indradi; Lakhmudien, Lakhmudien; Bireedan, Abdulmunem Abdullah Mohemmed
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 8 No. 2 (2026): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v8i2.15796

Abstract

Accurate prediction of health insurance claims is essential for improving hospital service quality, financial management, and operational decision-making. Health insurance claim data contains multiple attributes that may affect the performance and efficiency of classification models. Data mining techniques, particularly deep learning, are widely applied because of their ability to identify complex patterns in large datasets. However, the use of numerous attributes can increase model complexity and computational requirements. Therefore, feature selection can be applied to reduce data dimensionality while retaining relevant information for the classification process. This study aims to optimize a deep learning model using the Weight by User Specification method as a feature selection technique for classifying BPJS Health insurance claims. The dataset consists of 2,388 BPJS Health insurance claim records from Kumala Siwi Hospital, with variables including total rate, hospital rate, profit, diagnosis, and INACBG. The research process follows data preprocessing, data transformation, deep learning classification, feature selection, and model evaluation using a confusion matrix. Two classification models were evaluated: deep learning without feature selection and deep learning combined with Weight by User Specification. The results show that the deep learning model without feature selection achieved an accuracy of 98.87%, while the optimized model achieved 99.20%, representing an improvement of 0.33%. The classification error also decreased from 1.13% to 0.80%. These findings indicate that integrating feature selection with deep learning can improve classification accuracy and reduce classification error in BPJS Health insurance claim prediction. Therefore, weight by user specification combined with deep learning provides an effective approach for health insurance claim classification.
Co-Authors Alamsyah Alamsyah Alif Kautsar Apriliano Chandra Diva Bireedan, Abdulmunem Abdullah Mohemmed Didik Aribowo Dilinar Adlin Adlin Freddt Wagey Ilham Akbar Darmawan Jak, Yanuar Lakhmudien, Lakhmudien Maria Loho Muhammad Reza Nugraha Mustofa Abi Hamid Rahmi, Dian Rano Indradi Sudra, Rano Indradi SATRIYAS ILYAS Silfia Hanani Siti Annisa Nindia Larasati Sulistyowati, Yeny Syafira Putri, Alsa Wardhani, Anindya Khrisna Yusnizar Heniwaty