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All Journal Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Jurnal INSYPRO (Information System and Processing) Ainet : Jurnal Informatika Madaniya Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika Progres ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Fachrim Irhamna Rachman
Universitas Muhammadiyah Makassar
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Social Sciences Other

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Journal : Journal of Muhammadiyah’s Application Technology

 1 
Pendeteksi Penyakit Daun Padi Menggunakan Algoritma YOLOv8 di Desa Jangan-Jangan Kecamatan Pujananting Kabupaten Barru Aritmawijaya, Suandi; Rachman, Fahrim Irhamna; Bakti, Rizki Yusliana; suandi_17, suandi_aritmawijaya
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/kn1zxt55

Abstract

ABSTRAKProduksi padi di Indonesia memiliki peran penting dalam menjaga ketahanan pangan nasional, namun produktivitasnya sering mengalami penurunan akibat serangan penyakit pada daun padi. Penyakit seperti blast, bercak coklat, dan hawar daun bakteri merupakan penyakit utama yang dapat menimbulkan kerugian signifikan jika tidak terdeteksi sejak dini. Identifikasi penyakit daun padi secara konvensional umumnya masih dilakukan secara manual dan bergantung pada pengalaman petani, sehingga berpotensi menimbulkan kesalahan diagnosis. Oleh karena itu, penelitian ini bertujuan mengembangkan sistem pendeteksi otomatis penyakit daun padi berbasis deep learning menggunakan algoritma YOLOv8. Dataset diperoleh dari pengambilan citra langsung di lahan pertanian Desa Jangan-Jangan, Kabupaten Barru, yang merepresentasikan kondisi lapangan nyata dan mencakup tiga jenis penyakit utama. Tahapan penelitian meliputi anotasi data menggunakan Roboflow, pelatihan model dengan Google Collab, serta evaluasi performa menggunakan confusion matrix, precision, recall, F1-score, dan mean Average Precision. Hasil pengujian menunjukkan bahwa model YOLOv8 mampu mendeteksi penyakit daun padi dengan akurasi tinggi dan waktu inferensi cepat, sehingga berpotensi diterapkan sebagai solusi deteksi dini penyakit padi secara real-time. Kata Kunci: YOLOv8, Deteksi Penyakit Padi, Deep learning, Citra Digital, Pertanian Presisi, Roboflow,CNN.   ABSTRACTRice production in Indonesia plays a crucial role in maintaining national food security, but productivity often declines due to leaf disease attacks. Diseases such as blast, brown spot, and bacterial leaf blight are major diseases that can cause significant losses if not detected early. Conventional rice leaf disease identification is generally still done manually and relies on farmer experience, potentially leading to misdiagnosis. Therefore, this study aims to develop an automatic rice leaf disease detection system based on deep learning using the YOLOv8 algorithm. The dataset was obtained from direct imagery captured in agricultural fields in Jangan-Jangan Village, Barru Regency, which represents real-world conditions and includes three main types of diseases. The research stages include data annotation using Roboflow, model training with Google Colab, and performance evaluation using a confusion matrix, precision, recall, F1-score, and mean Average precision. The test results show that the YOLOv8 model is capable of detecting rice leaf diseases with high accuracy and fast inference time, thus potentially being implemented as a real-time early detection solution for rice diseases. Keyworsds: YOLOv8, Rice Disease Detection, Deep learning, Digital Imagery, Precision Farming, Roboflow,CNN.
Peningkatan Akurasi Prediksi Kebutuhan Obat BPJS PRB melalui Integrasi Analisis Diferensial dan Deep Learning Hermanto, Chalidah Azzahrah; Rachman, Fachrim Irhamna; A.M Hayat, Muhyiddin; H, chalidah_azzahra00
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
Konversi Tulisan Tangan Huruf Kapital Menjadi Teks Menggunakan Metode Deep Learning Berbasis YOLOv8 dan CTC Bakti, Rizki Yusliana; Rachman, Fahrim Irhamna; nur, makmur jaya
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: 10.26618/9wdk0e43

Abstract

ABSTRAKPenelitian ini mengkaji pengembangan sistem konversi tulisan tangan ke teks digital menggunakan metode deep learning dengan mengombinasikan arsitektur Convolutional Neural Network (CNN), YOLOv8, dan Connectionist Temporal Classification (CTC). Dataset yang digunakan terdiri dari 700 citra tulisan tangan huruf kapital (A–Z) yang diperoleh dari dokumen resmi Dinas Kependudukan dan Pencatatan Sipil Kabupaten Barru. Tahapan penelitian meliputi prapemrosesan citra berupa grayscale, normalisasi, perataan teks, serta augmentasi data, dilanjutkan dengan anotasi bounding box menggunakan Roboflow. Dataset kemudian dibagi menjadi data pelatihan, validasi, dan pengujian. Model YOLOv8 dilatih untuk mendeteksi karakter dan hasilnya diproses menggunakan CTC untuk menghasilkan teks akhir. Evaluasi menunjukkan performa yang baik dengan precision 98,38%, recall 87,25%, F1-score 92,44%, serta mAP@0.5 sebesar 87,19%. Hasil ini menunjukkan metode yang diusulkan efektif untuk mendukung digitalisasi dokumen administrasi publik.Kata Kunci: YOLOv8, Konversi Tulisan Tangan, Deep Learning, Citra Digital, Administrasi Publik, Roboflow, CNN, CTC ABSTRACTThis study investigates the development of a handwritten text-to-digital text conversion system using deep learning by combining Convolutional Neural Network (CNN), YOLOv8, and Connectionist Temporal Classification (CTC) architectures. The dataset consists of 700 images of uppercase handwritten letters (A–Z) obtained from official documents of the Department of Population and Civil Registration of Barru Regency. The research stages include image preprocessing such as grayscale conversion, normalization, text alignment, and data augmentation, followed by bounding box annotation using Roboflow. The dataset is then divided into training, validation, and testing sets. The YOLOv8 model is trained to detect characters, and the outputs are processed using CTC to generate the final text. Evaluation results demonstrate strong performance, achieving a precision of 98.38%, recall of 87.25%, an F1-score of 92.44%, and an mAP@0.5 of 87.19%. These findings indicate that the proposed method is effective in supporting the digitalization of public administrative documents.Keyworsds: YOLOv8, Handwriting Conversion, Deep Learning, Digital Image, Public Administration, Roboflow, CNN, CTC  
Co-Authors A.M Hayat, Muhyiddin Adrianingsih, Rizka Ahmad Faisal Anas, Lukman Aritmawijaya, Suandi Bakti, Rizki Yusliana H, chalidah_azzahra00 Habi Talib, Emil Agusalim Hermanto, Chalidah Azzahrah Indriani, Lis Iskandar, Aryansyah Kamal, Safutri La Ode Taufik Ismail La Ode Taufik Ismail Lukman Lukman Mujadilah, Siti Nur Alam Nur, Makmur Jaya Reski Awalia Salam, Abd Setiawan, Tommy Reynaldy Sri Hastati suandi_17, suandi_aritmawijaya Tahir, Gempar Perkasa Titin Wahyuni Wahyuni , Titin Wibawa. Ar, Arya Yanti, Wilda