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All Journal Nusantara Hasana Journal Indonesian Community Journal Jurnal Informatika Progres ROUTERS: Jurnal Sistem dan Teknologi Informasi
Emil Agus Salim Habi Talib
Universitas Muhammadiyah Makassar, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Environmental Science Library & Information Science Nursing Public Health Social Sciences Other

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COMPARATIVE ANALYSIS OF THE APPLICATION OF FEATURE SELECTION IN RANDOM FOREST REGRESSION FOR STOCK PRICE PREDICTION Habi Talib, Emil Agusalim; Alvina Felicia Watratan; Saharuddin, Saharuddin
Nusantara Hasana Journal Vol. 5 No. 3 (2025): Nusantara Hasana Journal, August 2025
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i3.1641

Abstract

The rapid development of information technology and data mining has encouraged the use of machine learning algorithms in various fields, including the financial sector and capital markets. One of the main challenges in stock price prediction is the large number of available variables, not all relevant to the target variable, potentially reducing accuracy and causing overfitting. This study aims to analyze the benefits of applying feature selection in improving the performance of the Random Forest Regression algorithm for stock price prediction. The dataset used in this research consists of ten years of historical stock price data from PT Aneka Tambang Tbk (ANTM). The research was conducted using an experimental approach by developing two models: (1) Random Forest Regression without feature selection and (2) Random Forest Regression with feature selection using the Spearman Correlation method. Model performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Coefficient of Determination (R²), and Mean Absolute Percentage Error (MAPE). The experimental results show that the model with feature selection achieved better performance, with improvements in all evaluation metrics, such as reduced error values (MAE: 26.22; RMSE: 51.82; MAPE: 1.32%) and increased R² (0.9895). These findings confirm that integrating feature selection with Random Forest Regression can improve prediction accuracy, reduce model complexity, and minimize overfitting risk. Therefore, feature selection plays a significant role in enhancing the effectiveness of machine learning models in stock price prediction.
IMPLEMENTASI METODE HYBRID FUZZY JARO WINKLER DAN COSINE SIMILARITY PADA SISTEM PENCARIAN AYAT AL-QURAN BERBASIS TRANSLITERASI LATIN Tahir, Gempar Perkasa; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna
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.v17i2.482

Abstract

This research addresses the challenge of retrieving Qur’anic verses in Latin transliteration, which is hindered by the absence of a standardized orthography, leading to diverse spelling variations. The study aims to design and implement a hybrid information retrieval system that integrates Fuzzy Jaro-Winkler for lexical similarity and Cosine Similarity on fine-tuned DistilBERT embeddings for semantic relevance. The system workflow begins with preprocessing and normalization of the dataset, followed by initial candidate selection using Jaro-Winkler, and final reranking through semantic similarity scoring. Evaluation was conducted using black-box testing across scenarios including ideal queries, spelling variations, incomplete queries, and varying query lengths. Results show high accuracy for ideal (96%) and varied spelling queries (92%), with performance improving as query length increases, reaching 96% for four-word queries. The hybrid approach effectively bridges lexical and semantic gaps, outperforming single-method baselines, and demonstrates robustness in handling non-standard transliteration in Qur’anic text retrieval.
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.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Riswan, Muh.; Wahyuni, Titin; Danuputri, Chyquitha; Habi Talib, Emil Agusalim; Faisal, Muhammad; Anas, Lukman; Agung, Andi
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.v18i1.535

Abstract

The digitalization of religious education offers significant opportunities to enhance Hijaiyah letter learning, particularly for the hearing-impaired community through visual gesture recognition. This study aims to develop and evaluate a real-time web-based classification system for 28 Hijaiyah hand gestures using the MobileNetV2 architecture. The research methodology involves a quantitative approach utilizing transfer learning with a balanced dataset of augmented images. The model was trained using fine-tuning techniques and deployed on a web platform using TensorFlow.js and MediaPipe for efficient on-device inference. Experimental results demonstrate that the model achieved an overall accuracy of 84% on the independent test set, with specific classes reaching near-perfect detection in real-time scenarios, although misclassification persisted among visually similar gestures. The system effectively balances computational efficiency with classification performance, minimizing latency during user interaction. In conclusion, the implementation of MobileNetV2 facilitates a responsive and accessible educational tool, proving the viability of computer vision in creating inclusive religious learning environments without requiring complex server-side infrastructure.
PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL Suhardi, Syahrul; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Wahyuni, Titin; 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.v18i1.539

Abstract

Low-resolution images and videos remain a common problem in various digital applications due to limited visual quality. Conventional interpolation-based upscaling methods often produce blurry results and lead to the loss of important texture details. This study aims to apply the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) to improve the resolution of digital images and videos. The dataset used consists of low-resolution images and videos that are processed through preprocessing, model training, and testing stages using the Google Colab environment. The ESRGAN model is trained to generate high-resolution images while preserving visual details and structural information. Model performance is evaluated using the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and visual comparison between images before and after the upscaling process. The results show that ESRGAN significantly improves the quality of images and videos compared to conventional interpolation methods, both quantitatively and qualitatively. Therefore, the application of ESRGAN is considered effective for enhancing the resolution of digital images and videos and can be utilized in applications that require high visual quality.
MONITORING DAN NOTIFIKASI REAL-TIME PERUBAHAN FILE PADA WEB SERVER MENGGUNAKAN WATCHDOG DAN TELEGRAM BOT SEBAGAI SISTEM PERINGATAN DINI Hasbir, Syahrul; Habi Talib, Emil Agusalim; Rachman, Fahrim Irhamna; Wahyuni, Titin; 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.v18i1.540

Abstract

Web servers are critical infrastructures for delivering digital services and are highly vulnerable to unauthorized file changes that may threaten system security and service availability. However, many conventional monitoring systems still rely on periodic checking mechanisms, which often fail to provide timely detection of security incidents. This study aims to design and implement a real-time file change monitoring system on a web server using the Watchdog library and a Telegram Bot as an early warning mechanism. The research adopts an applied research method with an experimental approach. The system is developed using the Python programming language and evaluated in a local XAMPP-based web server environment, with the uploads directory selected as the monitoring target. Experimental results demonstrate that the proposed system is capable of detecting various file change events, including file creation, deletion, content modification, and file renaming, in real time without event loss. Notifications delivered via the Telegram Bot provide clear, timely, and actionable information to administrators. These findings indicate that the proposed event-driven monitoring system is effective and efficient in enhancing web server security and improving incident response capabilities.
Education on Hybrid Multi-Criteria Decision Making and Machine Learning through the Morning Class Program: Integration of Engineering and Technology Nasir Usman; Muhammad Faisal; Sri Wahyuni; Saharuddin Saharuddin; Lisa Fitriani Ishak; Darniati Darniati; Musdalifa Thamrin; Emil Agusalim Habi Talib; Alvina Felicia Watratan
I-Com: Indonesian Community Journal Vol 6 No 2 (2026): I-Com: Indonesian Community Journal (Juni 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i2.9453

Abstract

The Society 5.0 era requires mastery of transparent and intelligent decision systems, yet practical understanding of integrating Machine Learning (ML) and Multi-Criteria Decision Making (MCDM) through Hybrid Intelligence frameworks remains limited in academic environments. This community service aims to enhance the scientific capacity of academics through the "Morning Class" international program. The methodology employed an online joint lecture approach involving collaboration between Universitas Muhammadiyah Makassar and Multimedia University Malaysia. The activity involved 48 participants and was divided into three phases: initial evaluation, delivery of theoretical-practical integration modules, and final evaluation. Results indicate a significant increase in understanding, with the average score rising from 65.8 in the pre-test to 87.5 in the post-test. The highest improvement (36%) was recorded in the hybrid framework implementation indicator. These findings confirm that the synergy between human expert ethical values and machine data processing speed is a crucial solution for modern decision-making. The program recommends further technical workshops to support deeper research implementation for partner institutions.
Strengthening AI and DSS Synergy for Sustainable Research: A Community Engagement for Lecturers and Researchers in Palopo Muhammad Faisal; Nasir Usman; Emil Agus Salim Habi Talib; Medy Wisnu Prihatmono; Lisa Fitriani Ishak; Musdalifa Thamrin; Darniati Darniati; Alvina Felicia Watratan; Saharuddin Saharuddin; Muh Ilham Akbar
I-Com: Indonesian Community Journal Vol 5 No 4 (2025): I-Com: Indonesian Community Journal (Desember 2025)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v5i4.8547

Abstract

The rapid development of digital technology demands a more innovative and data-driven research paradigm, yet the utilization of Artificial Intelligence (AI) and Decision Support Systems (DSS) in academic environments remains hindered by digital literacy gaps and the dominance of subjective manual methods. This community engagement program aims to introduce and strengthen participants’ understanding of the synergy between AI and DSS in supporting sustainable research in the era of digital transformation. The program employed a participatory approach through the Quadruple Helix model involving 359 participants consisting of lecturers, researchers, and practitioners. Methods included interactive lectures, technical mentoring on hybrid intelligence (integration of Machine Learning and Multi-Criteria Decision Making), and collaborative discussions via the Zoom platform. The results indicate a 35.6% improvement in participants' digital literacy, with the mean score increasing from 62.5 to 84.8. Furthermore, the technical readiness survey yielded a high score of 4.35 on a Likert scale, with participants successfully identifying practical AI–DSS applications in smart agriculture and MSME development. This program has successfully established an initial foundation for an adaptive and inclusive research ecosystem.
Analisis Komparatif Decision Tree, Random Forest, dan Naive Bayes untuk Deteksi Website Phishing Menggunakan K-Fold Cross Validation dan Evaluasi Multi-Metrik Nasir Usman; Muhammad Faisal; Alvina Felicia Watratan; Emil Agusalim Habi Talib; Nurahmad
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 4 No. 2, Juli 2026 (In Progress)
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v4i2.5082

Abstract

Phishing websites remain difficult to detect because attackers can create new domains faster than blacklist systems can update. This study compares Decision Tree, Random Forest, and Naive Bayes for phishing website classification using 11,055 records with 30 technical features. The models were evaluated with Stratified 10-Fold Cross-Validation and five metrics: accuracy, precision, recall, F1-score, and ROC-AUC. Random Forest produced the best and most stable performance, with 97.23% accuracy, 97.70% precision, 96.02% recall, 96.85% F1-score, and 99.58% ROC-AUC. Decision Tree also performed strongly, while Naive Bayes showed very high recall but many false positives. Feature importance analysis identified SSLfinal_State and URL_of_Anchor as the most influential predictors, supporting lightweight technical-feature screening for phishing detection.
IMPLEMENTASI METODE HYBRID FUZZY JARO WINKLER DAN COSINE SIMILARITY PADA SISTEM PENCARIAN AYAT AL-QURAN BERBASIS TRANSLITERASI LATIN Gempar Perkasa Tahir; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman
PROGRESS Vol 17 No 2 (2025): September
Publisher : P3M STMIK Profesional Makassar

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

Abstract

This research addresses the challenge of retrieving Qur’anic verses in Latin transliteration, which is hindered by the absence of a standardized orthography, leading to diverse spelling variations. The study aims to design and implement a hybrid information retrieval system that integrates Fuzzy Jaro-Winkler for lexical similarity and Cosine Similarity on finetuned DistilBERT embeddings for semantic relevance. The system workflow begins with preprocessing and normalization of the dataset, followed by initial candidate selection using Jaro-Winkler, and final reranking through semantic similarity scoring. Evaluation was conducted using black-box testing across scenarios including ideal queries, spelling variations, incomplete queries, and varying query lengths. Results show high accuracy for ideal (96%) and varied spelling queries (92%), with performance improving as query length increases, reaching 96% for four-word queries. The hybrid approach effectively bridges lexical and semantic gaps, outperforming single-method baselines, and demonstrates robustness in handling non-standard transliteration in Qur’anic text retrieval.
Co-Authors Agung, Andi Ahmad Nur Rahman Alvina Felicia Watratan Alvina Felicia Watratan Alvina Felicia Watratan Andi Agung Bakti, Rizki Yusliana Chyquitha Danuputri Danuputri, Chyquitha Darniati Fachrim Irhamna Rachman Gempar Perkasa Tahir Hasbir, Syahrul Lisa Fitriani Ishak LUKMAN ANAS Medy Wisnu Prihatmono Muh Ilham Akbar Muh. Riswan Muhammad Faisal Muhammad Faisal Muhammad Syafaat S. Kuba Musdalifa Thamrin Nasir Usman Nasir Usman Nur Rahman, Ahmad Nurahmad Riswan, Muh. Saharuddin Saharuddin Saharuddin Sri Wahyuni Suhardi, Syahrul Syahrul Hasbir Syahrul Suhardi Tahir, Gempar Perkasa Titin Wahyuni