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All Journal International Journal of Artificial Intelligence Research Jurnal Borneo Informatika dan Teknik Komputer (JBIT)
Albertus Dwiyoga Widiantoro
Universitas Katolik Soegijapranata
Computer Science & IT Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Other

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Design of BPJS Participant Patient Referral Information System Tri Cicik Wijayanti; Brian Sebastian Salim; Nur Sucahyo; Albertus Dwiyoga Widiantoro; Edi Wahyu Wibowo
International Journal of Artificial Intelligence Research Vol 6, No 1.1 (2022)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v6i1.1.594

Abstract

BPJS, Social Security Administrator, is a state-owned health insurance company previously serving as PT. ASKES (Health Insurance), whose services are implemented in several health agencies in Indonesia. One example is hospitals. In doing the community to provide complete individual health services, the Hospital is one of the health agencies supporting the BPJS (Social Security Administrator) program. One of the hospitals that participated in realizing and launching the BPJS program was the XYZ Hospital in AA City. Currently, in managing one of the BPJS social security programs, XYZ Hospital is still manually processing data in the patient referral process, namely by still using patient referral letters. That is what is being demanded by researchers to realize this study's objectives, namely design, a system where later patient referral data can be computerized and efficiently. Based on observations in the field regarding the data collection process, which is still manual, the researchers will be able to design a patient referral information system with software development tools using database design with MySQL, while programs can use PHP. The researchers in this study used a data collection method, such as interviewing several related parties. This information system design will make it easier for hospital managers in the next stage to develop an information system so that it can be used to process patient referral system data and present computerized information as a whole and efficiently.
ANALISIS KOMPARATIF ALGORITMA MACHINE LEARNING UNTUK KLASIFIKASI SENTIMEN MULTI-KELAS BERITA ONLINE MULTI-DOMAIN MENGGUNAKAN TF-IDF Daniel Christian Gunawan; Albertus Dwiyoga Widiantoro
Jurnal Borneo Informatika dan Teknik Komputer Vol 6, No 1 (2026): Edisi April - September
Publisher : Jurusan Teknik Komputer, Fakultas Teknik, Universitas Borneo Tarakan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35334/jbit.v6i1.7587

Abstract

Pesatnya pertumbuhan platform berita online memerlukan analisis sentimen otomatis di berbagai domain. Penelitian ini mengevaluasi dan membandingkan empat algoritma supervised machine learning Logistic Regression, Naïve Bayes, Random Forest, dan Support Vector Machine (SVM) untuk klasifikasi sentimen multi-kelas pada 3.500 artikel berita dalam tujuh kategori. Data teks mentah melalui tahap preprocessing (case folding, pembersihan URL/HTML, penghapusan tanda baca/angka, filtering stopword, dan stemming), dilanjutkan dengan vektorisasi TF-IDF serta seleksi fitur berbasis varians. Dataset dibagi menggunakan stratified split 80:20, dan model dievaluasi menggunakan Akurasi, Presisi, Recall, dan F1-score rata-rata makro untuk menangani ketidakseimbangan kelas. Di antara algoritma yang diuji, Random Forest meraih kinerja klasifikasi tertinggi dengan akurasi keseluruhan sebesar 75,10% dan macro F1-score sebesar 63,39%. Hasil ini memberikan wawasan praktis dalam pemilihan algoritma machine learning yang efektif untuk klasifikasi sentimen berita online multi-domain secara otomatis.
Co-Authors Brian Sebastian Salim Daniel Christian Gunawan Edi Wahyu Wibowo Sucahyo, Nur Tri Cicik Wijayanti