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All Journal Jurnal Keperawatan PROtek : Jurnal Ilmiah Teknik Elektro Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Promotif: Jurnal Kesehatan Masyarakat Jurnal INSYPRO (Information System and Processing) Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informasi Kesehatan Indonesia (JIKI) Journal of Health Sciences Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Ainet : Jurnal Informatika Jurnal Ilmiah Perekam dan Informasi Kesehatan Imelda (JIPIKI) Babali Nursing Research Madaniya J-PEN Borneo : Jurnal Ilmu Pertanian Tangible Journal Jurnal Pendidikan, Sains, Geologi, dan Geofisika (GeoScienceEd Journal) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Joutica : Journal of Informatic Unisla Al-Khidmah Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer PELS (Procedia of Engineering and Life Science) Economic Reviews Journal Jurnal Informatika Progres Media Bina Ilmiah International Journal of Health and Information System (IJHIS) Jurnal Abdimas Jatibara Pangulu Abdi: Jurnal Pengabdian Kepada Masyarakat VISA: Journal of Vision and Ideas Jurnal Intelek Dan Cendikiawan Nusantara ARUS JURNAL SAINS DAN TEKNOLOGI Jurnal Intelek Insan Cendikia International Journal of Research in Counseling Journal of Muhammadiyah’s Application Technology Kajian Ilmiah Mahasiswa Administrasi Publik (KIMAP) Jurnal Medis Umum
Titin Wahyuni
Informatika, Universitas Muhammadiyah Makassar
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Journal : jurnal informatika progres

 1 
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.
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.
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.
Co-Authors . Darniati A.MUHAMMAD SYAFAR Achmad Yanu Aliffianto Adi Malik Muhammad Mutsuhito Aditya, Dwi Martha Nur Adrianingsih, Rizka Agung, Andi Agustiawal Agustiawal Agustin Dwi Syalfina Ahmad Faisal Ahmad Risal Aiman , Ailul Alfina Aisatus Saadah Alfina Aisatus Saadah Amelia, Azarine Nahdah Amir Ali Anang Sulistyo ANDI AGUNG DWI ARYA BULU Andi Yusri andi Yusri Anita Dahliana ardi24, ardiansyah_01 Arfandi, Viki Fahril Arianti, Kencana Indah Arini, Gusti Ayu Arshy Prodyanatasari Arvianda Asep Indra Syahyadi Aswad, Muh. Akhwan Adam Baba, Haedir Bakti, Riski Yusliana Bakti, Rizki Yusliana Bambang Nudji Bisono, Eva Firdayanti Budi Setiawan Cantika Aprilia Santi Chatarina Umbul Wahyuni Cholifah . Cholifah, Cholifah Christine Christine Danuputri, Chyquitha Dewi, Syamrilla Djalil, Sony Achmad Dzakki Adam, Ahmad Wildan Erwin Astha Triyono Fachrim Irhamma Rahman Fachrim Irhamna Rachman Fadhillatul Lailia, Salsabilla Fahmi Ramadhan S Fahrim Irhamna Rahman Fahrim Irhmna Rachman Ferdiansyah Firdaus , Abidatu Zahrotul Firman Firman Fitrianti, Dwi Framz Hardiansyah Habi Talib, Emil Agusalim Haidul, Haidul Halisah Duli, St Nur Haruna, Hanjas Hasbir, Syahrul Hidayanti, Sukria Hidayat, Ali Akbar Hidayat, Andra Dwitama Ida Ilmiyah Rosyiari, Ahniyatul Indriani, Lis Jaelan Usman, Jaelan Kamal, Safutri Kazman Riyadi Khafi, Moh. Zainul khairat, arikal Kotte, Erick Yusuf Krisnita Dwi Jayanti Krisnita Dwi Jayanti, Krisnita Dwi La Ode Taufik Ismail Listiawan, Nadhila Lukman LUKMAN ANAS Lukman Lukman Maharani, Eva Ratih Masyfufah, Lilis Masyfufah, Lilis  Maulia, Rizky Maylina Surya Wirawati Pribadi Mone, Ansyari Muh. Akhwan Adam Aswad Muhadi, Muhadi Muhammad Faisal Muhyiddin A.M Hayat Mujadilah, Siti Muslimah, Nurul Aulia Mustakim Mustakim Nadhila Listiawan Naila, Faiqotun Nandy Rizaldy Najib Natsir, Fitra M. Nisha, Khairun Nova Mellania Novianti, Siti Nur Alam Nurfadilla, Destiani Irma Nurfadillah Octavia, Winda Dwi Pandin, Maria Yovita R. Pribadi, Maylina Surya Wirawati Puspadewi, Intan Putra, Yunior Bimasekti Rahman , Fahrim Irhamna Rahman, Fahrim Irhamna RAHMANIA Rahmania Rahmawati, Ayu Isnaini Ramadhan S, Fahmi Reski Awalia Resky Samudra, Anugrah Retnowati Prihandini Ridwang Ridwang Ridwang Ridwang Ridwang, Ridwang Rinaldy, Muh Riswan, Muh. Rosyiari, Ahniyatul Ilmiyah S. Kuba, Muhammad Syafa'at Salsabila, Damai Arsila Sari, Selvi Permata Sa’adah, Alfina Asiatus Setiawan, Mohammad Yusuf Setiawan, Muhammad Yusuf Setiawan, Tommy Reynaldy Shafira Trisnanda Fatimatus Zahra Siti Fatimatuz Zahroh Siti Mujanah Slamet Riyadi Sri Hastati Suhardi, Syahrul Sukmantoro, Agung Anjar SULASTRI Suryadinata, Rivan Virlando Sutha, Diah Wijayanti Syamsuri, Andi Makbul Syarifuddin, Nur Annisa TANTRI INDRABULAN Uddin , Ardiansyah Umi Khoirun Nisak Wibawa. Ar, Arya Wilda Faida, Eka xss, aa xx Yulianita, Novi Eka Zainuddin, Mohammad Ramadhan Zul fikar