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All Journal bit-Tech Journal of Applied Computer Science and Technology (JACOST)
Samuel Ryon Elkana
Universitas Buddhi Dharma
Computer Science & IT

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Prediksi Penggunaan Obat Peserta Jaminan Kesehatan Nasional Menggunakan Algoritma Naïve Bayes Classifier Tugiman; Lily Damayanti; Alexius Hendra Gunawan; Samuel Ryon Elkana
Journal of Applied Computer Science and Technology Vol 3 No 1 (2022): Juni 2022
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v3i1.295

Abstract

Currently, most of the patients seeking treatment at the hospital use the National Health Insurance (JKN) organized by the Healthcare and Social Security Agency (BPJS Kesehatan). In some hospitals, the figure is above 80%. Considering the very high number of BPJS Kesehatan participant seeking treatment at the hospital, a good data management method is needed, especially regarding the management of drug. Drug supply needs to be analyzed from time to time so that it can help predict future needs. An adequate supply of drugs and as needed is one of the things that affect service to patients. The availability of sufficient stock is expected to accelerate service to patients so that they do not have to wait long. Patients who are served quickly are expected to be satisfied. The impact of this patient satisfaction will increase the number of patient visits to the hospital. To support this, it is necessary to create a system that can estimate drug needs. The system can predict drug demand by using drug sales data to JKN participant patients for five years. Drug data used as research samples and then processed using an algorithm is the Naive Bayes Classifier. The Naive Bayes Classifier method is a method used to predict future opportunities using the basis of previous experience. A distinctive feature of this method is that it uses a very strong assumption of the independence of each event. While software testing uses the User Acceptance Test (UAT) model. Based on testing using this method, the system can be well received by users with a score of 78.64% (good).
Analysis and Design of Disease Diagnosis Systems and Patient Medicine Recommendations with Forward Chaining Method Samuel Ryon Elkana; Verri Kuswanto
bit-Tech Vol. 6 No. 2 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i2.937

Abstract

Technological developments have had a significant impact on many aspects of life, one of which is patient care in hospitals. Apart from using technology in patient care, technology also provides access to effective and efficient information storage and management to record patient data for treatment purposes. In outpatient services at hospitals, there are often complaints from employees regarding the health recording system which is less than optimal. Therefore, a system that manages disease diagnoses and patient treatment recommendations is something that needs to be developed, with the hope of speeding up the performance of medical personnel, so that they can help more patients who need help. The application system design aims to help manage information related to disease diagnosis and patient drug recommendations, where this system uses Forward Chaining to assist users in identifying diseases and prescribing drugs according to the diagnosis the patient is complaining about. By using the Forward Chaining methodology, medical personnel are able to obtain patient diagnosis results more quickly. The result is an application that can help medical personnel in serving outpatients from registration, examination, to exchanging medicines. This application has been tested using black-box testing involving several respondents, where respondents can feel that this application works well and helps hospital staff in examining patients.
Co-Authors Alexius Hendra Gunawan Lily Damayanti Tugiman Verri Kuswanto