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All Journal Bulletin of Electrical Engineering and Informatics 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  
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning S. Kuba, Muhammad Syafaat; Faisal, Muhammad; Nurnawaty, Nurnawaty; Abdul Rahman, Titik Khawa; Syamsuri, Andi Makbul; Hayat, Muhyiddin AM; Bakti, Rizki Yusliana
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11168

Abstract

Sustainable planning of green open spaces (GOS) requires decision-making models that combine expert evaluation with public input. This study proposes a novel hybrid framework that integrates multi-criteria group decision making (MCGDM) with public sentiment analysis to support community-based and data-driven urban planning. The workflow consists of evaluating 25 community-proposed GOS locations using stepwise weight assessment ratio analysis (SWARA) for criteria weighting and MABAC-BORDA for multi-criteria ranking, resulting in 11 feasible alternatives. To incorporate community perspectives, a term frequency-inverse document frequency-support vector machine (TF-IDF–SVM) classifier was applied to 1500 public comments, where SVM achieved the highest accuracy (0.80–0.96). The integrated approach improves ranking stability, reduces decision ambiguity, and strengthens alignment between expert judgment and community sentiment. This study contributes a transparent, participatory decision-support model that unifies MCGDM and sentiment analysis to enhance the effectiveness of sustainable GOS planning.
IMPLEMENTASI SISTEM DETEKSI PRODUK BOIKOT BERBASIS WEBSITE REAL-TIME MENGGUNAKAN METODE YOLOv10 Nur Rahman, Ahmad; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Bakti, Rizki Yusliana; Faisal, Muhammad; S. Kuba, Muhammad Syafaat
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.525

Abstract

Manual identification ofboycott products remains a challenge for the public due to limited access to information and the complexity of brand affiliations. This study aims to develop a real-time, website-based boycott product detection system using the You Only Look Once version 10 (YOLOv10) algorithm. The dataset consists of images of food and beverage product packaging collected from various online sources, annotated using the bounding box method, and classified into five categories. The model was trained and tested using separate test data, while performance evaluation was conducted using a confusion matrix with precision, recall, and f1-score metrics. In addition, functional testing of the system was performed using the Black Box Testing method. The result indicate that the YOLOv10 model is capable of detecting boycott product with good performance and can be effectively integrated into a real-time web-based system. The proposed system is expected to assist users in identifying boycott products more quickly and accurately.
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI Nurfadillah; Ida; Darniati; Yusliana Bakti, Rizki; Wahyuni, Titin; Faisal, Muhammad
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.532

Abstract

Face recognition based on image data has been widely applied in automated attendance systems; however, it still faces challenges related to accuracy and efficiency under varying lighting conditions and facial pose variations. This study aims to compare the performance of Convolutional Neural Network (CNN) and You Only Look Once (YOLO) methods for face detection and recognition in a deep learning–based attendance system. The dataset consists of facial images collected from students in a limited campus environment with several variations in viewpoint and illumination. The research stages include image preprocessing, training of CNN and YOLO models, and performance evaluation using accuracy, precision, recall, and computation time metrics. The experimental results indicate that YOLO outperforms CNN in terms of detection speed and performance stability, while CNN demonstrates competitive classification performance on limited datasets. This study provides empirical insights into the characteristics of both methods in attendance system scenarios and can serve as a reference for selecting appropriate models for real-world implementation. The main limitations of this study are the dataset size and the restricted data acquisition scope.
Analisis Kemiripan Source Code Project Menggunakan Metode CodeBERT dan Winnowing Algorithm Fauzan Azhari Rahman; Rizki Yusliana Bakti; Muhyiddin A M Hayat
Arus Jurnal Sains dan Teknologi Vol 4 No 1: April (2026)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v4i1.2623

Abstract

Pemeriksaan keaslian kode pada tugas Capstone Project umumnya masih dilakukan secara manual, sehingga tidak efisien dan berisiko melewatkan kasus plagiarisme yang disamarkan melalui refactoring atau penggantian nama variabel. Penelitian ini bertujuan menerapkan kombinasi metode CodeBERT dan Winnowing Algorithm untuk mendeteksi kemiripan kode sumber secara semantik dan tekstual, serta mengintegrasikan fitur tersebut ke dalam sistem pengumpulan tugas Capstone Project mahasiswa Program Studi Informatika Fakultas Teknik Universitas Muhammadiyah Makassar. CodeBERT digunakan untuk menganalisis kemiripan semantik, sedangkan Winnowing Algorithm digunakan untuk mendeteksi kemiripan tekstual berbasis fingerprint k-gram. Hasil dari kedua metode digabungkan untuk menghasilkan penilaian kemiripan yang lebih komprehensif. Pengujian dilakukan terhadap 17 proyek valid yang menghasilkan 136 pasangan unik. Hasil analisis menunjukkan 16 pasangan termasuk kategori Plagiarisme Kuat, 30 pasangan Mirip Semantik, 30 pasangan Mirip Tekstual, dan 60 pasangan Normal. Selain itu, seluruh 11 skenario black box testing berhasil dijalankan dengan tingkat keberhasilan 100%. Hasil ini menunjukkan bahwa kombinasi CodeBERT dan Winnowing Algorithm efektif diterapkan untuk mendukung analisis kemiripan kode pada lingkungan akademik
Analisis Persentase Area Kerusakan Daun Bawang Menggunakan Model Hybrid Transformer Berbasis SegFormer-TransUNet Muliana; Rizki Yusliana Bakti; Muhyiddin AM Hayat
Arus Jurnal Sains dan Teknologi Vol 4 No 1: April (2026)
Publisher : Arden Jaya Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57250/ajst.v4i1.2673

Abstract

Penelitian ini bertujuan mengembangkan sistem segmentasi citra untuk menghitung persentase area kerusakan daun bawang menggunakan model hybrid transformer berbasis SegFormer-TransUNet. Permasalahan utama yang diangkat adalah keterbatasan penilaian visual manual yang cenderung subjektif, lambat, dan sulit diterapkan secara konsisten pada skala lahan yang luas. Data penelitian berupa citra daun bawang dan mask anotasi yang memisahkan area daun sehat, daun rusak, dan background. Tahapan penelitian meliputi akuisisi citra, preprocessing, pembentukan label mask, pelatihan tiga skenario model, evaluasi kuantitatif, serta perhitungan persentase kerusakan berbasis rasio piksel. Model hybrid dirancang dengan memanfaatkan SegFormer sebagai encoder untuk menangkap konteks global dan TransUNet sebagai decoder untuk merekonstruksi detail spasial. Hasil evaluasi menunjukkan bahwa model hybrid memperoleh accuracy 0,9590, mIoU 0,8333, dan Dice coefficient 0,8686. Nilai tersebut lebih tinggi dibandingkan SegFormer (accuracy 0,9543; mIoU 0,7671; Dice 0,8057) dan TransUNet (accuracy 0,9583; mIoU 0,8145; Dice 0,8509) pada metrik utama segmentasi. Temuan ini menunjukkan bahwa integrasi fitur global dan detail lokal mampu meningkatkan kualitas segmentasi serta menghasilkan dasar kuantitatif untuk estimasi tingkat kerusakan daun bawang.
Co-Authors . Darniati A M Hayat, Muhyddin Abdul Rahman, Titik Khawa Adrianingsih, Rizka Ahmad Risal Akbar, Syahril ANDI AGUNG DWI ARYA BULU Andi Yusri Aritmawijaya, Suandi Chatarina Umbul Wahyuni Desi Anggreani Dewi MJ, Wanda Tyrana Dewi, Syamrilla Emil Agusalim Habi Talib Fachrim Irhamna Rachman Faeruddin, Muhammad Asygar Fahrim Irhmna Rachman Faturohman, Agung Fauzan Azhari Rahman Hadawina Hadawina Haruna, Hanjas Hayat, Muhyddin A.M Ida 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 Muhyiddin A.M Hayat Muhyiddin AM Hayat Mujidah, Jihan Izzathul Muliana Muslimah, Nurul Aulia Nandy Rizaldy Najib Nur Alam Nur Rahman, Ahmad Nur, Makmur Jaya Nurfadillah Nurnawaty 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