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All Journal Bulletin of Electrical Engineering and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Journal of Geoscience, Engineering, Environment, and Technology 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) Jurnal Algoritma, Logika dan Komputasi Nusantara Hasana Journal JUSTIN (Jurnal Sistem dan Teknologi Informasi) Buletin Sistem Informasi dan Teknologi Islam Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Informatika Progres Jurnal INFOTEL Journal of Computer Science and Information Technology ARUS JURNAL SAINS DAN TEKNOLOGI Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology
Bakti, Rizki Yusliana
Universitas Muhammadiyah Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Nursing Physics Social Sciences Other

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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.
KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Jihan Izzathul Mujidah; Rizki Yusliana Bakti; Lukman; Muhammad Faisal; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Pepper fruit (Piper nigrum L.) is an agricultural commodity whose market value strongly depends on its ripeness level at harvest. Ripeness determination, which is still commonly performed through visual observation, tends to be inaccurate and subjective. This study aims to classify the ripeness level of pepper fruit based on skin color using an ensemble learning approach. The dataset consists of 1,996 pepper fruit images categorized into four ripeness levels unripe, semi ripe, ripe, and overripe. Color features were extracted from the HSV color model using color moment statistics including mean, standard deviation, and skewness. Random Forest and XGBoost models were combined using a soft voting method. The results show that the ensemble model achieved 98.25% accuracy, 98.30% precision, 98.27% recall, and 98.26% F1-score. The ensemble approach proved superior to single models by providing more accurate and stable classification of pepper fruit ripeness.
KLASIFIKASI PENYAKIT TANAMAN NILAM BERDASARKAN CITRA DAUN MENGGUNAKAN GLCM DAN SVM Sarina; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study presents a classification model for detecting diseases in patchouli (Pogostemon cablin Benth) leaves using image processing techniques. The method combines Grey Level Co-occurrence Matrix (GLCM) for texture feature extraction and Support Vector Machine (SVM) for classification, optimised using the Particle Swarm Optimisation (PSO) algorithm. A total of 2,080 leaf images were collected and categorized into four classes: healthy, leaf spot, yellowing, and mosaic. Each image was augmented and converted to grayscale to enhance the dataset and reduce computational complexity. Four GLCM features—contrast, correlation, energy, and homogeneity—were extracted to represent leaf textures. The classification model achieved an accuracy of 89.74% using SVM alone, and improved to 97.12% when optimized with PSO. The results indicate that the integration of GLCM, SVM, and PSO provides an effective and accurate solution for early detection of patchouli leaf diseases, potentially supporting farmers in decision-making and improving crop productivity and quality.
IMPLEMENTASI DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Muhammad Asygar Faeruddin; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Muhyiddin AM Hayat; Andi Makbul Syamsuri; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

This study addresses the challenge of identifying semantic relatedness between scientific journal articles by developing a classification system based on deep learning. The system applies an unsupervised learning approach using the Sentence-Transformers model and K-Means clustering to generate semantic similarity scores and categorical labels. Abstracts from journal PDFs are extracted and processed to determine similarity levels across four predefined categories. The optimal number of clusters was determined using Elbow Method, Silhouette Score, and Davies-Bouldin Index, resulting in k = 4. The system is implemented as a web-based application that allows users to upload two PDF files, compare them semantically, and receive both a similarity score and an AI-generated narrative explanation. Functional testing showed that all core features performed as expected. This system significantly reduces the time required to assess relatedness between journal articles, offering an efficient tool for academic research navigation.
IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Syahril Akbar; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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Abstract

Manual analysis of large-scale and unstructured textual feedback data is often inefficient and subjective, thereby hindering data-driven decision-making. This study aims to design and implement an integrated analytical workflow to automatically filter, cluster, and classify feedback data consisting of criticisms and suggestions. The research employs a hybrid approach that begins with TF-IDF-based data filtering, followed by dimensionality reduction using Latent Semantic Analysis (LSA), and topic clustering through K-Means clustering optimized with the Silhouette Score. The resulting cluster labels are then used as training data to build a Multinomial Naive Bayes classification model. The results show that this workflow successfully identified two main thematic clusters, namely "Criticism and Expectations" and "Suggestions and Compliments", and the classification model achieved an overall accuracy of 91%. Although class imbalance affected the recall of the minority class (47%), the model demonstrated high precision (95%) for that class. It is concluded that this hybrid approach effectively transforms raw data into structured insights, and utilizing clustering results as training data is an efficient strategy for automating feedback categorization, providing a reliable tool for institutional analysis.
IMPLEMENTASI HYBRID LEXICON-BASED DAN SVM UNTUK KLASIFIKASI ANALISIS SENTIMEN TERHADAP PELATIHAN BBPSDMP KOMINFO MAKASSAR Nur Alam; Muhammad Faisal; Rizki Yusliana Bakti; Muhammad Syafaat; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Andi Lukman Anas
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The evaluation of government training programs is often hindered by manual analysis of unstructured qualitative feedback, making the process inefficient and subjective. This study aims to implement and evaluate a sentiment classification model using a hybrid Lexicon-Based and Support Vector Machine approach to analyze participants’ perceptions of the Vocational School Graduate Academy training organized by BBPSDMP Kominfo Makassar, as well as to compare the performance of a standard SVM model with a model optimized using Particle Swarm Optimization. This quantitative research employs 2,313 unstructured review data, which undergo text preprocessing, initial lexicon-based labeling, and TF-IDF feature extraction before being classified using an SVM with an RBF kernel. The results show that the SVM model optimized with PSO consistently outperforms the standard model across all four evaluation aspects, with the most significant accuracy improvement observed in the instructor category from 84.71% to 89.02% and in the assessor category reaching 91.46%. PSO optimization has proven effective in enhancing the model’s ability to identify negative sentiments, which represent the minority class. The hybrid approach with PSO optimization is capable of producing a more accurate and balanced classification system, with practical implications as an objective automated evaluation tool.
IMPLEMENTASI SISTEM DETEKSI PRODUK BOIKOT BERBASIS WEBSITE REAL-TIME MENGGUNAKAN METODE YOLOv10 Ahmad Nur Rahman; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

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; Rizki Yusliana Bakti; Titin Wahyuni; Muhammad Faisal
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.
OPTIMASI KLASIFIKASI JENIS INDUSTRI KECIL MENENGAH (IKM) MENGGUNAKAN DEEP NEURAL NETWORK BERBASI SHAP ANALYSIS M. Fikri Haikal Ayatullah; Desi Anggreani; Rizki Yusliana Bakti; Muhammad Faisal; Muhammad Syafaat; M. Agusalim
PROGRESS Vol 18 No 2 (2026): September
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i2.463

Abstract

Small and Medium Industries (SMEs) in Makassar City face a significant gap between high labor absorption (66.25%) and low GDP contribution (20%), often due to conventional and experience-based determination of industry types. This study implements a Deep Neural Network (DNN) model to classify four categories of SMEs, Bread and Cake, Processed Food, Vehicle Repair, and Textiles and Clothing to facilitate data-driven decisions. Using a supervised learning approach on 31,824 data samples for the 2022-2024 period, this model was developed through feedforward and backpropagation mechanisms. The results showed superior performance with overall accuracy of (93.99%) and balanced accuracy (96.70%), which signified an increase of (15.88%) compared to the Naïve Bayes baseline model. All F1-scores above (90%) indicate strong performance stability in each class. Furthermore, SHAP's analysis revealed that textual features (87.1%) were the dominant factors, followed by the type of business entity and investment value. This study confirms that DNN is effective in modeling complex non-linear interactions, providing objective tools for classification and strategic economic planning in Makassar City.
Co-Authors . Darniati A M Hayat, Muhyddin Abdul Rakhim Nanda Adrianingsih, Rizka Afil Anugrah Ahmad Nur Rahman Ahmad Risal ANDI AGUNG DWI ARYA BULU Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Yusri Ari Ahmad Dahril Azzah Aulia Syarif Burhanuddin, Fathurrahman Chatarina Umbul Wahyuni Danuputri, Chyquitha Darniati Desi Anggreani Dewi MJ, Wanda Tyrana Dewi, Syamrilla Emil Agus Salim Habi Talib Erika Yanti Fachrim Irhamna Rachman Fahrim I. Rahman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Fahrim Irhmna Rachman Faturohman, Agung Fauzan Azhari Rahman Firdaus Fidaus Galbi Nadifah Hadawina Hadawina Haruna, Hanjas Hayat, Muhyddin A.M Ibnul Imamul Muttaqin Ida Ida Indriani, Lis Iskandar, Aryansyah Ismail, La Ode Taufik Jihan Izzathul Mujidah Kamsurya, Rianita Kazman Riyadi La Ode Taufik Ismail La Ode Taufik Ismail Lukman Lukman Lukman Anas LUKMAN ANAS Lukman Lukman Lukman Lukman Lukman Lukman LUKMAN, LUKMAN M Agusalim M. Fikri Haikal Ayatullah Maharani, Afifah Makmur Jaya Nur Muh Nur Aqsal Aminullah Muh.Dirham Rahim Muhammad Aditya Yudhistira Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Faturrachman Iswan Muhammad Hasraddin Hasnan Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Muliana Muslimah, Nurul Aulia Muthalib, Ade Nirwani Abdurahman Mutiara yusuf Nandy Rizaldy Najib Nini Apriani Rumata Nur Alam Nur Rahman, Ahmad Nurfadillah Nurfadillah Nurnawaty Prima Abdiguna, Aidhil Rahmania RAHMANIA Rahmania Rasyidi, Muhammad Fachri Reski Abbas Reski Awalia Rezki Asriani Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Sa'ban Sa'ban Salam, Abd Sarina Sri Hastati Suandi Aritmawijaya Sulaeman Suriani Suriani Syahril Akbar Syahrul Ramadhan Syamsul Alam TANTRI INDRABULAN Thariq, Ahmad Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Ulil Amri Usman, Ansharullah Patiroi Utama, Prengki Putra Virgiawan, David Arian Wa Nanda Sulystrian Wibawa. Ar, Arya Yanti, Wilda Yumi