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Jurnal Informatika Progres
Published by STMIK PROFESIONAL MAKASSAR
ISSN : 20868359     EISSN : 2797622X     DOI : https://doi.org/10.56708/progres.v14i1.300
Core Subject : Science,
Computer Science & IT
Jurnal Informatika Progres merupakan jurnal Blind Peer-Review yang dikelola secara profesional dan diterbitkan oleh P3M STMIK Profesional Makassar dalam upaya membantu peneliti, akademisi, dan praktisi untuk mempublikasikan hasil penelitiannya. Jurnal ini didedikasikan untuk publikasi hasil penelitian dalam bidang yang memuat artikel tentang Teknologi, Komunikasi, Informasi dan Komputer. Terbit dua kali setiap tahun, 2 nomor 1 volume, yaitu pada bulan April dan September. Semua publikasi di Jurnal Informatika Progres ini bersifat akses terbuka yang memungkinkan artikel tersedia secara online tanpa berlangganan apapun.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 207 Documents
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PENINGKATAN AKURASI DETEKSI DINI KEBAKARAN BERBASIS IOT MENGGUNAKAN ALGORITMA RANDOM FOREST Ruth Amelia Vega S. Meliala; Dedy Kiswanto; Freyro Dobry Sianipar; Fauzan Azima Lubis
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Fire is one of the most frequent disasters and poses a significant risk to human safety, environmental sustainability, and property due to delayed early detection. This study aims to design and implement an early fire warning system based on the Internet of Things (IoT) enhanced with Machine Learning to improve detection accuracy and reliability. The system utilizes an ESP32 microcontroller as an edge node integrated with a DHT11 sensor for temperature and humidity, an MQ-2 sensor for gas and smoke concentration, and a flame sensor for fire detection. Multisensor data are transmitted in real time to a Flask-based server via the HTTP protocol and processed using a Random Forest classification model to determine environmental conditions as either safe or fire-hazardous. The classification results are displayed on a web-based dashboard and accompanied by automatic notifications delivered through a Telegram bot. Experimental results show that the proposed system achieves a detection accuracy of 94%, a low false positive rate, and a notification latency of less than 3 seconds, based on experiments conducted using a dataset of 3000 samples with an 80:20 split between training and testing data.The integration of IoT and Machine Learning demonstrates superior performance compared to conventional threshold-based methods, making the system a promising preventive solution for fire risk mitigation in residential and industrial environments.
OPTIMASI PROMPT ENGINEERING SEBAGAI STRATEGI PENINGKATAN KOGNITIF: STUDI EKSPERIMEN PADA PEMBELAJARAN ALGORITMA DAN PEMROGRAMAN Abdul Majid Gaffar; Bahrin; Abdul Yunus Labolo; Zulkarnain S. Purnomo; Kristalicia Clarita Daeng Kuma; Saharuddin
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

Penetrasi ChatGPT di lingkungan akademik telah memicu kekhawatiran mengenai penurunan kemampuan berpikir kritis mahasiswa akibat ketergantungan pada jawaban instan. Penelitian ini bertujuan untuk menguji efektivitas optimasi prompt engineering (teknik pemberian instruksi terstruktur) sebagai strategi untuk meningkatkan kemampuan kognitif mahasiswa pada mata kuliah Algoritma dan Pemrograman. Dengan menggunakan metode eksperimen murni (True Experimental Design), penelitian ini melibatkan 60 mahasiswa Program Studi Teknologi Informasi yang dibagi ke dalam kelompok eksperimen dan kelompok kontrol. Kelompok eksperimen diberikan intervensi berupa panduan prompting menggunakan teknik Chain-of-Thought (CoT) dan Socratic Prompting, sementara kelompok kontrol menggunakan ChatGPT secara bebas. Hasil penelitian menunjukkan adanya perbedaan signifikan pada nilai post-test antara kedua kelompok ($p < 0,05$), dengan ratarata kelompok eksperimen (82,5) jauh melampaui kelompok kontrol (64,2). Analisis N-Gain Score menunjukkan bahwa kelompok eksperimen mengalami peningkatan kognitif kategori "Tinggi" (0,71). Temuan ini membuktikan bahwa optimasi prompting mampu mengubah peran AI dari mesin penjawab otomatis menjadi mitra dialog kognitif yang merangsang logika berpikir. Penelitian ini memberikan implikasi bagi dosen muda untuk mengintegrasikan literasi prompting dalam kurikulum sebagai strategi mitigasi terhadap risiko adiksi teknologi tanpa mengorbankan efisiensi AI.
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.
PENERAPAN ALGORITMA MOBILENETV2 UNTUK KLASIFIKASI HURUF HIJAIYAH BERBASIS GESTUR TANGAN Muh. Riswan; Titin Wahyuni; Chyquitha Danuputri; Emil Agusalim Habi Talib; Muhammad Faisal; Lukman Anas; Andi Agung
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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 METODE PROTOTYPE DALAM PENGEMBANGAN WEBSITE KONVERSI GAMBAR VEKTOR KE MODEL 3D Nia Yuningsih; Zaidan Ramadhan Rachman; Lely Prananingrum
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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Abstract

This research aims to design and build a static website capable of automatically converting SVG vector images into 3D objects. The main problem addressed is the lack of user-friendly and efficient web-based tools for converting SVG files into 3D models, which are important for 3D designers and 3D printing. The development applies the Prototype method, allowing initial prototypes to be tested and refined based on user feedback. The website is developed using JavaScript with the Three.js library for 3D object processing and visualization, and Vite as a modern development tool to ensure responsiveness. Users are able to upload SVG files, adjust object thickness, preview the conversion in real-time, and export the 3D model in STL format. The results show that this website provides a practical and interactive solution for users who require fast conversion from SVG images to 3D objects through a web platform, accessible at https://sv3dkonversi.xyz. User Acceptance Testing was conducted and achieved a score of 82.2%, indicating that users can operate the website effectively in terms of layout, interface, system, and ease of use. This demonstrates the potential of the website to support 3D designers in digital manufacturing and 3D printing workflows.
PENERAPAN MODEL ESRGAN UNTUK UPSCALING CITRA DAN VIDEO DIGITAL Syahrul Suhardi; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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 Syahrul Hasbir; Emil Agusalim Habi Talib; Fahrim Irhamna Rachman; Titin Wahyuni; Muhammad Faisal; Muhammad Syafaat S.Kuba
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

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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.

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