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All Journal Jurnal Sain Veteriner Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter Dentika Dental Journal Jurnal Ilmiah Mahasiswa Veteriner JURNAL PENDIDIKAN TAMBUSAI Jurnal Review Pendidikan dan Pengajaran (JRPP) Ainet : Jurnal Informatika Al-Bukhari: Jurnal Ilmu Hadis Atta'dib Jurnal Pendidikan Agama Islam Nusantara Hasana Journal Indonesian Community Journal Jurnal Informatika Progres Jurnal Pengabdian kepada Masyarakat Jurnal Kedokteran Hewan The International Journal of Tropical Veterinary and Biomedical Research Jurnal Ilmiah Multidisiplin Amsir Jurnal Indonesia : Manajemen Informatika dan Komunikasi Advances in Management & Financial Reporting Jurnal Medika Veterinaria Jurnal Agripet Bata Ilyas Educational Management Review Jurnal Profesi Guru Indonesia Jurnal Pendidikan Dasar
. Darniati
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Engineering Environmental Science Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Journal : jurnal informatika progres

 1 
PENERAPAN ALGORITMA K-NEAREST NEIGHBOR DALAM ANALISIS PEMINJAMAN BARANG PADA DIVISI INVENTARIS TVRI MAKASSAR Risal; Danuputri, Chyquitha; Darniati; AM Hayat, Muhyiddin
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v17i2.474

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, data-driven inventory management strategy, enabling more efficient resource allocation.
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.
KLASIFIKASI TANAMAN OBAT TRADISIONAL BERBASIS CITRA BUAH DAN DAUN Kusumawardani, Nurul; Danuputri, Chyquitha; Darniati; Faisal, Muhammad; A.M Hayat, Muhyiddin; S. Kuba, Muhammad Syafaat; Anggreani, Desi
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.v18i1.534

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.
Co-Authors . Darmawi . Darmawi . Darmawi . Fakhrurrazi . Muthmainnah . Syafruddin ., arneta Abdil lah Imron Nasution Abdul Harris Abdul Wahid Abdullah Hamzah Agusni Yahya Agustin Indrawati Ahmad, Muh Ihsan Said AK, M Daud Akbar, Muh Akbar, Muh Ilham Alvina Felicia Watratan Andi Novita Andi Novita Ansar Ansar Arifuddin Arifuddin Arman Sayuti Ayuti, Siti Rani Azhari Azhari Azmansyah Azmansyah Baharuddin, Suardi Hi Bakti, Rizki Yusliana Balqis Alrasyid Suyoto Basri Gani Bunganan , Rey Richard Chyquitha Danuputri Cut Nila Thasmi Cut Soraya Danuputri, Chyquitha Dara Hayati Rapi Darmawi Darmawi Darnanengsih Darnanengsih Desi Anggreani Diva, Farah Dwi Rosa Selfiana Emil Agusalim Habi Talib Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Erina Ety Mukhlesiyeni Fadlah, Iga fadli amri Fahkrurrazi Fahkrurrazi Faisal Jamin Faisal Jamin Faisal Jamin Faisal Jamin Fakhrurrazi Fakhrurrazi Fakhrurrazi Fakhrurrazi Florentina Magdalena Girsang Ginta Riady Ginta Riady Hamdani Budiman Haruna, Suardi B. Hasibuan, Asnita Herrialfian, Herrialfian Iccha Elvioleta Ida Ida Mulyadi, Ida Isa, M Ismail Ismail Khoirunnisa, Fathonah Kholifah, Ida Kusumawardani, Nurul Latifa Suryandari LIA PERMATA SARI Lisa Fitriani Ishak M Daud AK M Nur Salim M. Nur Salim Mahdi Abrar Mahdi Abrar Mahdi Abrar Maryulia Dewi Maryulia Dewi Maryulia Dewi Mirna Safrani Fauzi Muhammad Amin muhammad aroza Muhammad Faisal Muhammad Faisal Muhammad Faisal Muhammad Hambal Muhsal, Muhammad Ilham Muhyiddin A.M Hayat Musdalifa Thamrin Mustari Mustari Muttaqien Bakri Muttaqien Muttaqien Nasir Usman Nazaruddin Nazaruddin nisma hayani Nugraha, Ronaldi Fajar Nur Ramadhan, Nur Nurahmad Nurfadillah Nurhayati NURLIANA NURLIANA Nurwahida Nurwahida, Nurwahida Nuzul Asmilia Olivia salsa dilla putri Pratiwi, Zahwa Amelia Prihatmono, Medy Wisnu Rahmadhini, Vivi Rahmadia Fitri Rastina Rastina Razali Daud Resti Reimena Rezi Maghfira Rezky, Annisyah Rima Rizky Amiruddin Rinidar Rinidar Risal riza maulita Robbi Ghani Roslizawaty Roslizawaty S. Kuba, Muhammad Syafa'at Safika S, Safika Saharuddin Salehha, Osey Putri Samsuria, Samsuria Sarea, Muh Syahrul Sari Dewi Sari, Wahyu Eka Satriani Satriani Silvia P.N Keliat Surachmi Setiyaningsih T. Armansyah T. Armanyah TR Tadampali, Andi Caezar To Teuku R. Ferasyi Teuku Reza Ferasyi Thamrin, Musdalifa Tiong, Piter Titin Wahyuni Tongku N Siregar Ummu Balqis wahyu eka sari Wanini, Wanini Windian Tajuk Masmah Bengi Yudha Fahrimal Zainuddin, Zainuddin Zakiah Heryawati Manaf Zakiyah Heryawati Manaf Zinatul Hayati Zuhrawati NA