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All Journal CogITo Smart Journal
Kevin Kristi Toar
Universitas Katolik De La Salle Manado
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering

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Implementation of the C4.5 Algorithm in Predicting the Number of Outpatient Visits Using JKN-KIS at Noongan Hospital Liza Wikarsa; Vivie Deyby Kumenap; Kevin Kristi Toar
CogITo Smart Journal Vol. 8 No. 1 (2022): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v8i1.351.13-24

Abstract

 Since 2013, the government has issued the National Health Insurance (JKN) program through the Social Security and Health Administration (BPJS Kesehatan) to provide social and health insurance services. JKN participants will get a Healthy Indonesia Card (KIS) to ease the burden of medical expenses at the hospital. During the pandemic of Covid-19, Noongan Hospital was included as one of the referral hospitals for COVID-19 patients for nearby hospitals and health centers with a coverage of the Southeast Minahasa district, North Sulawesi. Noticeably, 70% of its patients use the JKN-KIS card to get health treatments and more than half the number of patients are outpatients. To anticipate the number of outpatients visits using JKN-KIS, a web-based application was built to generate a predictive model using the C4.5 algorithm. The performance of this predictive model has a classification accuracy of 91,7% and both precision and recall of 95%. The number of outpatient visits using JKN-NIS has increased by 83,33% since the pandemic of Covid-19. Examination flow, medical check-up, queue length, doctor’s expertise, and health treatment objectives are the most influencing factors for outpatient visits. This predictive model provides future insights for the hospital management to rationally allocate healthcare resources and improve the efficiency of outpatient services. Keywords—3 Health Treatments, C4.5 Algorithm, Prediction, Covid-19
Co-Authors Hendri Hermawan Adinugraha Vivie Deyby Kumenap