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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 171 Documents
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PERAN MEDIA SOSIAL DALAM MEMBANGUN JARINGAN PROFESIONAL UNTUK WIRAUSAHA GEN Z Burhanuddin, Muhaimin; Febriati, Farida
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.v18i1.515

Abstract

Social media has become an essential tool for Gen Z entrepreneurs in building professional networks. With more than 3.6 billion social media users worldwide in 2020, and that number expected to continue to grow, these platforms offer unlimited opportunities to interact, collaborate, and expand business networks. This study uses a literature review method to explore how social media functions as a bridge in building valuable professional connections. Through analysis of various relevant studies, it was found that social media not only facilitates communication, but also helps Gen Z entrepreneurs build reputation and credibility in their industries. By utilizing platforms such as LinkedIn, Instagram, and Twitter, these young entrepreneurs can access information, find mentors, and even discover investors. The results of this study indicate that effective use of social media can be the key to success in running a business, especially in an increasingly competitive digital era.
PENINGKATAN AKURASI DETEKSI DINI KEBAKARAN BERBASIS IOT MENGGUNAKAN ALGORITMA RANDOM FOREST Meliala, Ruth Amelia Vega S; Kiswanto, Dedy; Sianipar, Freyro Dobry; Lubis, Fauzan Azima
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.v18i1.516

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.
IMPLEMENTASI FINANCIAL TECHNOLOGY (FINTECH) PADA DEPOSITO DIGITAL BANK NEO COMMERCE (NEOBANK) Faradillah Siahaan, Yanty; Ayuningrum, Uci; Lukman; Auditya, Muhammad Raihan; Salsabila
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.v18i1.520

Abstract

Financial Technology (FinTech) transformation has driven a shift in public investment behavior toward digital banking services offering efficiency and transparency. This study aims to analyze the profitability of digital deposit services at Bank Neo Commerce (NeoBank) and map optimal investment strategies for customers. The research employs a quantitative descriptive approach with Time Value of Money (TVM) calculation simulations based on fixed returns across various time intervals. The results indicate that NeoBank's digital deposit system accumulates profits precisely from yearly to hourly intervals, providing more measurable yield certainty compared to conventional banking. Decision Tree analysis recommends this product for investors with conservative to moderate risk profiles who prioritize liquidity and LPS security guarantees. This study concludes that FinTech implementation in digital deposits offers a balance between accessibility, security, and profitability.
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 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.
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 METODE PROTOTYPE DALAM PENGEMBANGAN WEBSITE KONVERSI GAMBAR VEKTOR KE MODEL 3D Yuningsih, Nia; Rachman, Zaidan Ramadhan; Prananingrum, Lely
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.v18i1.538

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 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.

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