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All Journal Journal of Muhammadiyah’s Application Technology
Fachrim Irhamna Rachman
Muhammadiyah University of Makassar
Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering

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Peningkatan Akurasi Prediksi Kebutuhan Obat BPJS PRB melalui Integrasi Analisis Diferensial dan Deep Learning Chalidah Azzahrah Hermanto; Fachrim Irhamna Rachman; Muhyiddin A.M Hayat
Journal of Muhammadiyah’s Application Technology Vol. 4 No. 3 (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/k6t40472

Abstract

ABSTRAKProgram Rujuk Balik (PRB) BPJS Kesehatan bertujuan menjamin keberlanjutan pengobatan pasien penyakit kronis. Namun, fluktuasi kebutuhan obat sering menimbulkan permasalahan overstock dan stockout di apotek mitra BPJS. Penelitian ini bertujuan mengintegrasikan analisis diferensial dan algoritma deep learning Long Short-Term Memory (LSTM) untuk meningkatkan akurasi prediksi kebutuhan obat PRB. Data yang digunakan berupa transaksi penjualan obat pasien BPJS PRB di Apotek Kimia Farma Cendrawasih periode Januari 2022 hingga Juli 2024. Analisis diferensial digunakan untuk menghitung perubahan tingkat pertama (delta 1) dan tingkat kedua (delta 2) penjualan, yang selanjutnya dijadikan fitur tambahan pada model LSTM. Evaluasi model dilakukan menggunakan metrik Mean Squared Error (MSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa integrasi analisis diferensial dengan LSTM mampu meningkatkan akurasi prediksi, dengan model terbaik menghasilkan nilai MAE rata-rata di bawah 20 untuk sebagian besar produk. Temuan ini berimplikasi pada peningkatan efektivitas perencanaan dan pengadaan obat PRB berbasis data historis dan tren perubahan.Kata Kunci: Prediksi Obat, BPJS PRB, LSTM, Deep Learning, Analisis Diferensial ABSTRACTThe BPJS Kesehatan Rujuk Balik Program (PRB) aims to ensure the continuity of treatment for patients with chronic diseases. However, fluctuations in medicine demand frequently cause overstock and stockout problems at BPJS partner pharmacies. This study aims to integrate differential analysis and the Long Short-Term Memory (LSTM) deep learning algorithm to improve the accuracy of PRB medicine demand forecasting. The data used consist of transaction records of PRB patient medicine sales at Kimia Farma Cendrawasih Pharmacy from January 2022 to July 2024. Differential analysis was applied to calculate the first-order change (delta 1) and second-order change (delta 2) in sales, which were subsequently incorporated as additional features in the LSTM model. Model performance was evaluated using Mean Squared Error (MSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results indicate that integrating differential analysis with LSTM improves prediction accuracy, with the best-performing model achieving average MAE values below 20 for most products. These findings have important implications for enhancing data-driven planning and procurement of PRB medicines based on historical trends and demand dynamics.Keyworsds: Medicine Forecasting, BPJS PRB, LSTM, Deep Learning, Differential Analysis
Klasifikasi Berita Hoaks pada Media Online Menggunakan Open Source Intelligence dan Algoritma Support Vector Machine Muh. Darmawan Aryadinata; Fachrim Irhamna Rachman; Ida Mulyadi
Journal of Muhammadiyah’s Application Technology Vol. 5 No. 2 (2026)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/gv4ndf79

Abstract

ABSTRAKPerkembangan media online yang pesat memudahkan masyarakat dalam memperoleh informasi secara cepat dan luas. Namun, kemudahan tersebut juga meningkatkan penyebaran berita hoaks yang dapat menimbulkan dampak negatif bagi masyarakat. Oleh karena itu, diperlukan suatu sistem yang mampu mengklasifikasikan berita hoaks secara otomatis dan akurat. Penelitian ini bertujuan untuk membangun sistem klasifikasi berita hoaks pada media online menggunakan metode Open Source Intelligence (OSINT) dan algoritma Support Vector Machine (SVM). Dataset diperoleh dari sumber terbuka berbasis web, yaitu situs klarifikasi hoaks dan portal berita online, yang kemudian melalui tahapan Preprocessing meliputi pembersihan teks, normalisasi, dan tokenisasi. Proses ekstraksi fitur dilakukan menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF) untuk merepresentasikan teks dalam bentuk numerik. Model klasifikasi dibangun menggunakan algoritma SVM dengan kernel linear karena efektif dalam menangani data teks berdimensi tinggi.Hasil pengujian menunjukkan bahwa model yang dikembangkan mampu mengklasifikasikan berita hoaks dan non-hoaks dengan tingkat akurasi sebesar 96,20%, serta didukung oleh nilai precision, recall, dan F1-score yang tinggi. Hal ini menunjukkan bahwa kombinasi metode OSINT, TF-IDF, dan SVM efektif dalam membangun sistem klasifikasi berita hoaks berbasis web dengan performa yang baik. Kata kunci: Klasifikasi Teks, Berita Hoaks, Media Online, OSINT, TF-IDF, Support Vector Machine. ABSTRACTThe rapid development of online media makes it easier for people to obtain information quickly and widely. However, this convenience also increases the spread of hoax news, which can have negative impacts on society. Therefore, a system capable of automatically and accurately classifying hoax news is needed. This research aims to develop a hoax news classification system in online media using Open Source Intelligence (OSINT) methods and the Support Vector Machine (SVM) algorithm. The Dataset was obtained from web-based open sources, namely hoax clarification websites and online news portals. The data were then subjected to Preprocessing stages including text cleaning, normalization, and tokenization. The feature extraction process was carried out using the Term Frequency–Inverse Document Frequency (TF-IDF) method to represent text in numerical form. The classification model was built using the SVM algorithm with a linear kernel because it is effective in handling high-dimensional text data. Test results show that the developed model is capable of classifying hoax and non-hoax news with an accuracy rate of 96.20%, supported by high precision, recall, and F1-score values. This demonstrates that the combination of OSINT, TF-IDF, and SVM methods is effective in building a web-based hoax news classification system with good performance. Keywords: Text Classification, Hoax News, Online Media, OSINT, TF-IDF, Support Vector Machine.
Co-Authors Chalidah Azzahrah Hermanto Ida Mulyadi, Ida Muh. Darmawan Aryadinata Muhyiddin A.M Hayat