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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Insect (Informatics and Security) : Jurnal Teknik Informatika Indonesian Journal of Data and Science International Journal of Industrial Optimization (IJIO) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Nusantara Hasana Journal Indonesian Community Journal Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika Progres Journal of Computer Science and Information Technology
Darniati
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Nursing Public Health Social Sciences Other

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Journal : jurnal informatika progres

 1 
IMPLEMENTASI ALGORITMA K-MEANS DALAM PENGELOMPOKKAN PEMBERIAN ZAKAT PADA BAZ AL-MARKAZ MAKASSAR SULAWESI SELATAN Darniati
PROGRESS Vol 9 No 1 (2017): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (173.384 KB) | DOI: 10.56708/progres.v9i1.71

Abstract

Problematika utama yang sedang dihadapi oleh Badan Amil Zakat adalah masalah penyaluran dana zakat yang kurang tepat sasaran sesuai dengan syariat Islam. Tulisan ini merupakan hasil penelitian yang bertujuan untuk membantu badan amil zakat dalam menyalurkan dana zakat yang terkumpul dari para musakki (pelaku zakat) kepada para mustahik (penerima zakat) yang sesuai dengan syariat Islam. Pada penelitian ini dibuat suatu sistem yang mengimplementasikan metode K-Means. Metode K-Means ini akan mengolah atribut-atribut yang mempengaruhi proses Klasterisasi penduduk calon penerima dana zakat. Atribut-atribut tersebut terdiri dari penghasilan, pekerjaan, alamat, kepemilikan aset, pendidikan terakhir. Proses Klasterisasi terdiri dari data latih yang akan menjadi parameter pada proses klasterisasi data uji pada sistem. Sistem mengelompokkan apakah seseorang memenuhi syarat untuk menerima zakat atau tidak memenuhi syarat menerima zakat. Hasil penelitian ini akan diimplementasikan kepada Badan Amil Zakat dalam menentukan kelayakan penerima zakat.
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Chyquitha Danuputri; Darniati; Muhyiddin AM Hayat
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Inventory management in the TVRI Makassar Inventory Division is inefficient due to the lack of a predictive system, hampering proactive asset requirement planning. This study aims to apply the K-Nearest Neighbor (KNN) algorithm to analyze historical borrowing patterns, predict demand for goods three months in advance, and evaluate model accuracy. Using a quantitative approach, this study implements a systematic machine learning workflow, including data preprocessing, temporal feature engineering, class imbalance handling using the Synthetic Minority Over-sampling Technique (SMOTE), and hyperparameter optimization using GridSearchCV. The results show that the optimized KNN model achieved an overall accuracy of 80.18%, significantly outperforming the baseline model. Key findings revealed that the model's performance is contextual, with very high reliability (F1-Score > 0.95) on frequently borrowed assets, and is able to identify strong temporal demand patterns. It is concluded that KNN is effective for segmented inventory demand prediction and has the potential to serve as a basis for TVRI Makassar to adopt a proactive, datadriven inventory management strategy, enabling more efficient resource allocation.
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Nurul Kusumawardani; Chyquitha Danuputri; Darniati; Muhammad Faisal; Muhyiddin A.M Hayat; Muhammad Syafaat S.Kuba; Desi Anggreani
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Indonesia is a megabiodiversity country with extensive use of traditional medicinal plants; however, plant identification in natural environments remains largely manual and error-prone. Recent advances in deep learning, particularly Vision Transformer (ViT), provide a promising solution by effectively capturing global spatial features for image classification. This study applies a ViT-Base/16 model to automatically classify fruit and leaf images of Indonesian medicinal plants. The dataset comprises 1,000 field-collected images from Galung Village, West Sulawesi, covering 20 classes (10 medicinal and 10 non-medicinal plants). The model was fine-tuned using the AdamW optimizer with a learning rate of 2×10⁻⁵ and trained for 30 epochs with cosine annealing. The proposed approach achieved high performance, with 99.33% accuracy, 99.41% precision, 99.33% recall, and a 99.33% F1-score, while binary classification between medicinal and non-medicinal plants reached 100% accuracy. The system was deployed as a Flask-based web application, demonstrating reliable functionality and practical response times. Overall, the results confirm the effectiveness of Vision Transformer for medicinal plant classification under natural conditions and highlight its potential to support digital documentation, education, and the preservation of local ethnobotanical knowledge.
PERBANDINGAN CNN DAN YOLO PADA SISTEM PENGENALAN WAJAH BERBASIS PRESENSI Nurfadillah; Ida; Darniati; Rizki Yusliana Bakti; Titin Wahyuni; Muhammad Faisal
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.
Co-Authors Ahmad Faisal Alvina Felicia Watratan Bakti, Rizki Yusliana Chyquitha Danuputri Chyquitha Danuputri Desi Anggreani Emil Agus Salim Habi Talib Emil Agusalim H. T Fachrim Irhamna Rachman Ida Ida Mulyadi, Ida Irmawati Irmawati Khadijah Lisa Fitriani Ishak Lukman Mallu, Satriawaty Mardiah Mardiah Medy Wisnu Prihatmono Muh Ilham Akbar Muh Khayyir Muhammad Alif Syafan Muhammad Faisal Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Musdalifa Thamrin Nasir Usman Nasir Usman Nur Ramadhan Nurdiansyah Nurdiansyah Nurfadillah Nurul Kusumawardani Risal Rosnani Rosnani Rosnani Rosnani Saharuddin Saharuddin Sri Wahyuni Suardi Hi Baharuddin Suriani Suriani Swa Lee Lee Syafruddin Syarif Titin Wahyuni Zulfajri Basri Hasanuddin