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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Jurnal INSYPRO (Information System and Processing) JTERA (Jurnal Teknologi Rekayasa) JURNAL INSTEK (Informatika Sains dan Teknologi) Ainet : Jurnal Informatika Jurnal Digital Teknologi Informasi Madaniya Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Progres ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Muhammadiyah’s Application Technology
Bakti, Rizki Yusliana
Informatika, Universitas Muhammadiyah Makassar
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Komparasi Algoritma Svm Dan Knn Dalam Memprediksi Peminatan Akademik Mahasiswa Program Studi Man Maharani, Afifah; Fahrim Irhmna Rachman; Rizki Yusliana Bakti
Ainet : Jurnal Informatika Vol. 7 No. 2 (2025): September (2025)
Publisher : Universitas Muhammadiyah Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26618/dqm2kk92

Abstract

AbstrakPenentuan peminatan akademik mahasiswa merupakan tahapan penting dalam pendidikan tinggi karena berpengaruh terhadap keberhasilan studi dan pengembangan kompetensi. Namun, proses penentuan peminatan sering kali masih dilakukan secara subjektif dan belum sepenuhnya berbasis data akademik. Penelitian ini bertujuan untuk membandingkan performa algoritma Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) dalam memprediksi peminatan akademik mahasiswa Program Studi Manajemen Universitas Muhammadiyah Makassar. Data penelitian bersumber dari nilai mata kuliah inti mahasiswa angkatan 2018 hingga 2021 yang telah melalui tahapan prapemrosesan dan pelabelan ke dalam tiga konsentrasi, yaitu Sumber Daya Manusia, Pemasaran, dan Keuangan. Metode penelitian dilakukan dengan membangun model klasifikasi menggunakan algoritma SVM dan KNN, kemudian dievaluasi menggunakan metrik akurasi, precision, recall, dan f1-score dengan variasi parameter serta pembagian data latih dan data uji. Hasil pengujian menunjukkan bahwa algoritma SVM dengan kernel Radial Basis Function (RBF) dan test size 0,1 menghasilkan performa terbaik dengan nilai akurasi sebesar 70,55 persen. Sementara itu, algoritma KNN dengan nilai k sebesar lima, metrik jarak Euclidean, dan test size 0,1 memperoleh akurasi sebesar 57,53 persen. Temuan ini menunjukkan bahwa SVM memiliki kemampuan klasifikasi yang lebih baik dan stabil dibandingkan KNN, sehingga lebih layak diterapkan sebagai model pendukung sistem prediksi peminatan akademik mahasiswa berbasis pembelajaran mesin.Kata kunci: Support Vector Machine, K-Nearest Neighbors, Machine Learning.Abstract Determining academic specialization for university students is a crucial stage in higher education because it directly influences study success and competency development. However, the process is often conducted subjectively and is not fully based on academic data. This study aims to compare the performance of Support Vector Machine and K-Nearest Neighbors algorithms in predicting academic specialization of Management students at Universitas Muhammadiyah Makassar. The dataset consists of core course grades from cohorts 2018 to 2021 that were preprocessed and labeled into three concentrations: Human Resource Management, Marketing, and Finance. The research method involved building classification models using SVM and KNN, which were evaluated using accuracy, precision, recall, and F1-score with various parameter settings and train–test splits. The results show that SVM with a Radial Basis Function kernel and a test size of 0.1 achieved the best performance with an accuracy of 70.55 percent. Meanwhile, KNN with k equal to five, Euclidean distance, and a test size of 0.1 obtained an accuracy of 57.53 percent. These findings indicate that SVM provides more stable and accurate classification than KNN for academic specialization prediction. Therefore, SVM is considered more suitable as a machine learning based decision support model for academic specialization purposes effectively.Keyword: Support Vector Machine, K-Nearest Neighbors, Machine Learning.
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.
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, Muhyddin Adrianingsih, Rizka Ahmad Risal Akbar, Syahril AM Hayat, Muhyiddin Anas, Andi Lukman Anas, Lukman ANDI AGUNG DWI ARYA BULU Andi Yusri Anggreani, Desi Aritmawijaya, Suandi Chatarina Umbul Wahyuni Dewi MJ, Wanda Tyrana Dewi, Syamrilla Fachrim Irhamna Rachman Fachrim Irhamna Rahman Faeruddin, Muhammad Asygar Fahrim Irhmna Rachman Faturohman, Agung Hadawina Hadawina Haruna, Hanjas Hayat, Muhyddin A.M Hayat, Muhyiddin A M Indriani, Lis Iskandar, Aryansyah Ismail, La Ode Taufik Kamsurya, Rianita Kazman Riyadi La Ode Taufik Ismail La Ode Taufik Ismail Lukman LUKMAN ANAS Lukman Lukman Lukman Lukman LUKMAN, LUKMAN Maharani, Afifah Muh Nur Aqsal Aminullah Muhammad Faisal Muhyiddin A.M Hayat Mujidah, Jihan Izzathul Muslimah, Nurul Aulia Nandy Rizaldy Najib Nur Alam Nur, Makmur Jaya Prima Abdiguna, Aidhil Rahman, Fahrim Irhamna RAHMANIA Rahmania Rasyidi, Muhammad Fachri Reski Abbas Reski Awalia Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang S. Kuba, Muhammad Syafa'at Salam, Abd Sarina Sri Hastati suandi_17, suandi_aritmawijaya Sulaeman Suriani Suriani Syamsuri, Andi Makbul TANTRI INDRABULAN Thariq, Ahmad Titin Wahyuni Usman, Ansharullah Patiroi Utama, Prengki Putra Virgiawan, David Arian Wibawa. Ar, Arya Yanti, Wilda Yumi