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All Journal PROtek : Jurnal Ilmiah Teknik Elektro Ainet : Jurnal Informatika Jurnal Algoritma, Logika dan Komputasi Jurnal Informatika Progres
Muhyiddin A M Hayat
Informatika, Universitas Muhammadiyah Makassar
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media

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Journal : Jurnal Informatika Progres

 1 
IMPLEMENTASI K-MEANS DAN ANALISIS SENTIMEN KRITIK SARAN BERBASIS NLP PADA DATA MONEV BBPSDMP KOMINFO MAKASSAR Akbar, Syahril; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Lukman
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.v17i2.465

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 DEEP LEARNING MENGGUNAKAN HYBRID SENTENCE-TRANSFORMERS DAN K-MEANS UNTUK PERBANDINGAN JURNAL Faeruddin, Muhammad Asygar; Faisal, Muhammad; Bakti, Rizki Yusliana; Syafaat, Muhammad; AM Hayat, Muhyiddin; Syamsuri, Andi Makbul; Anas, Andi Lukman
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.v17i2.466

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.
KLASIFIKASI TINGKAT KEMATANGAN LADA MENGGUNAKAN ENSEMBLE LEARNING BERDASARKAN CITRA WARNA KULIT Mujidah, Jihan Izzathul; Bakti, Rizki Yusliana; Lukman; Muhammad Faisal; Muhammad Syafaat; AM Hayat, Muhyiddin; Syamsuri, Andi Makbul
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.v17i2.467

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; Bakti, Rizki Yusliana; Muhammad Faisal; Muhammad Syafaat; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
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.v17i2.469

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.
PREDIKSI PEMAKAIAN AIR BULANAN DI PDAM KECAMATAN TAMALATE MENGGUNAKAN METODE AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) Syarifuddin, Nur Annisa; Wahyuni, Titin; Faisal, Muhammad; Syafaat, Muhammad; Syamsuri, Andi Makbul; AM Hayat, Muhyiddin; Anas, Andi Lukman
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.v17i2.471

Abstract

Water consumption forecasting is a crucial aspect of efficient water resource management, particularly in urban areas with increasing demand. This study aims to predict the monthly water usage volume at the PDAM of Tamalate District using the Autoregressive Integrated Moving Average (ARIMA) method. The dataset consists of historical water usage data from January 2022 to December 2024, totaling 36 monthly observations. The analysis process includes stationarity testing using the Augmented Dickey-Fuller (ADF) test, model parameter identification through ACF and PACF plots, and performance evaluation using MAE, RMSE, and MAPE metrics. The results show that the best-performing model is ARIMA, which demonstrates high prediction accuracy, with a MAE of 26,049.80 m³, RMSE of 37,459.00 m³, and MAPE of 4.12%. This model is capable of generating predictions close to actual values and can be relied upon as a basis for PDAM’s water distribution planning. It is expected that this research will contribute to data-driven decision-making and support digital transformation in the public service sector.
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.
IMPLEMENTASI HYBRID CNN, FACIAL LANDMARK DAN LIVENESS DETECTION PADA SISTEM ABSENSI WAJAH Akbar DB, Andi Muhammad; Faisal, Muhammad; 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.v17i2.483

Abstract

This paper presents the implementation of a hybrid approach for face recognition attendance systems, combining Convolutional Neural Network (CNN), facial landmark detection, and liveness detection. The CNN model extracts facial features for identity recognition, while facial landmark detection captures dynamic movements such as eye blinking and mouth motion. Liveness detection ensures system robustness against spoofing attempts including photo and video replay. The system was developed using Python with OpenCV, MediaPipe, and TensorFlow, and tested under multiple spoofing scenarios. Results show a detection accuracy of 95.5%, with real-time performance and resilience against common spoofing threats.
Co-Authors . Darniati Akbar DB, Andi Muhammad Akbar, Syahril Anas, Andi Lukman Anas, Lukman Anggreani, Desi Bakti, Rizki Yusliana Danuputri, Chyquitha Danuputri, Chyquitha Faeruddin, Muhammad Asygar Hadawina Hadawina Lukman Lukman Lukman Muh Nur Aqsal Aminullah Muhammad Faisal Mujidah, Jihan Izzathul RAHMANIA Rahmania Ridwang Ridwang Risal RIZKI YUSLIANA BAKTI S. Kuba, Muhammad Syafa'at Sarina Setiawan, Dedi Syamsuri, Andi Makbul Syarifuddin, Nur Annisa TITIN WAHYUNI Wahyuni, Titin