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All Journal Hemera Zoa Bulletin of Electrical Engineering and Informatics Jurnal Teknik Hidro AGREGAT Applied Information System and Management Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Ilmiah Ecosystem Jurnal Abdi Insani Bandar: Journal of Civil Engineering Jurnal Teknik Informatika (JUTIF) Jurnal Algoritma, Logika dan Komputasi Buletin Sistem Informasi dan Teknologi Islam Joutica : Journal of Informatic Unisla Jurnal Informatika Progres Information Technology Education Journal Kohesi: Jurnal Sains dan Teknologi Jurnal INFOTEL ARUS JURNAL SAINS DAN TEKNOLOGI Cosmic Jurnal Teknik Journal of Green Complex Engineering Journal of Muhammadiyah’s Application Technology Journal Techno Entrepreneur Acta
Muhammad Syafaat S. Kuba
Universitas Muhammadiyah Makassar
Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Transportation Other

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Analysis of Sediment Characteristics and Sedimentation Rate of the Noling River, Luwu Regency Juliandro, Juliandro; Syafa'at S Kuba, Muhammad; Gaffar, Farida; Pawara, Ismail
BANDAR: JOURNAL OF CIVIL ENGINEERING Vol 7 No 2 (2025): Bandar: Journal of Civil Engineering
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/bjce.v7i2.5272

Abstract

The Noling River plays an important role in the lives of the surrounding communities; however, it faces sedimentation problems that lead to riverbed aggradation, infrastructure disturbances, and reduced flow capacity. This study aims to determine the sediment characteristics and the rate of sedimentation in the Noling River, Luwu Regency. Sediment samples were collected from the midstream section of the river and analyzed in the laboratory to determine grain size distribution, specific gravity, and physical characteristics of the sediment. The results show that the Noling River sediment is dominated by sand (57.4%), gravel (31.8%), and silt/clay (10.9%), with a sediment classification of sandy clay. The sedimentation rate was calculated using both direct measurement and empirical approaches (Meyer-Peter and Duboys methods). The average suspended sediment load was found to be 5,251.97 tons/month, while the bed load reached 1,102.91 tons/month. The flow velocity, which exceeds the sediment fall velocity, indicates that sediment transport occurs actively. The findings of this study are expected to serve as a reference for better river management and future sedimentation mitigation efforts.
IDENTIFIKASI PENYAKIT DAUN SELADA MENGGUNAKAN SUPPORT VECTOR MACHINE (SVM) BERBASIS EKSTRAKSI FITUR VISUAL AMRI, MUH ULIL; Danuputri, Chyquitha; Bakti, Rizki Yusliana; Kuba, Muhammad Syafaat S.; Hayat, Muhyiddin A M
Jurnal Algoritma, Logika dan Komputasi Vol 9, No 1 (2026)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v9i1.8919

Abstract

Selada (Lactuca sativa L.) merupakan komoditas hortikultura bernilai ekonomi tinggi, namun produktivitasnya sering terhambat oleh serangan penyakit. Identifikasi manual oleh petani seringkali tidak akurat karena kemiripan gejala visual antar penyakit. Penelitian ini bertujuan untuk membangun sistem identifikasi penyakit otomatis menggunakan Support Vector Machine (SVM) berbasis ekstraksi fitur visual. Penelitian berfokus pada klasifikasi empat kondisi daun selada: Sehat, Bercak Daun Cercospora, Tipburn, dan Etiolasi. Metodologi yang digunakan meliputi ekstraksi fitur warna dari ruang warna HSV dan fitur tekstur menggunakan Gray-Level Co-occurrence Matrix (GLCM). Efektivitas augmentasi data dan optimasi hyperparameter menggunakan Particle Swarm Optimization (PSO) juga dievaluasi melalui tiga skenario perbandingan. Hasil penelitian menunjukkan bahwa augmentasi data secara signifikan meningkatkan akurasi model dari baseline 69,57% menjadi 92,28%. Optimasi lebih lanjut dengan PSO berhasil meningkatkan performa hingga mencapai akurasi final sebesar 93,63%. Model terbaik menunjukkan F1-Score yang seimbang di atas 0,91 untuk semua kelas, membuktikan bahwa kombinasi metode ekstraksi fitur HSV dan GLCM, augmentasi data, dan optimasi SVM menggunakan PSO merupakan pendekatan yang andal dan efektif untuk identifikasi penyakit daun selada, serta menawarkan alat bantu yang prospektif untuk pertanian presisi
DETEKSI PENYAKIT TANAMAN MERICA MENGGUNAKAN ALGORITMA CONVOLUTIONAL NEURAL NETWORK (CNN) BERBASIS CITRA DAUN Sari, Reski Anugrah; Danuputri, Chyquitha; Lukman, Lukman; Kuba, Muhammad Syafaat S.; Hayat, Muhyiddin A M
Jurnal Algoritma, Logika dan Komputasi Vol 9, No 1 (2026)
Publisher : Universitas Bunda Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30813/j-alu.v9i1.8923

Abstract

Deteksi penyakit pada tanaman merica (Piper nigrum) sangat penting untuk menjaga produktivitas dan kualitas panen, namun metode identifikasi manual oleh petani seringkali tidak efisien dan subjektif. Keterbatasan ini mendorong pemanfaatan teknologi kecerdasan buatan, khususnya Convolutional Neural Network (CNN), untuk menciptakan sistem deteksi yang cepat dan akurat. Penelitian ini bertujuan untuk menerapkan arsitektur CNN MobileNetV2 untuk mengklasifikasi empat kondisi daun merica (sehat, busuk pangkal batang, penyakit kuning, dan bercak daun) serta mengevaluasi secara kuantitatif pengaruh teknik augmentasi data terhadap performa model. Metode penelitian yang digunakan adalah pendekatan eksperimental dengan membandingkan dua skenario: Model A yang dilatih menggunakan 420 citra daun asli dan Model B yang dilatih dengan dataset yang sama namun diperbanyak melalui teknik augmentasi meliputi rotasi, flipping, dan penyesuaian kecerahan. Kedua model diuji menggunakan 60 citra data uji yang terpisah dan dievaluasi kinerjanya berdasarkan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan perbedaan kinerja yang signifikan, di mana Model B (dengan augmentasi) mencapai akurasi keseluruhan sebesar 93%, meningkat dari 88% yang dicapai oleh Model A (tanpa augmentasi). Analisis kurva pelatihan juga membuktikan bahwa augmentasi data efektif menekan overfitting dan meningkatkan kemampuan generalisasi model. Kesimpulannya, arsitektur MobileNetV2 terbukti efektif untuk deteksi penyakit daun merica, dan penerapan teknik augmentasi data merupakan strategi krusial yang secara substansial meningkatkan akurasi dan keandalan model. Implikasi dari temuan ini adalah terbukanya potensi pengembangan aplikasi deteksi penyakit berbasis smartphone yang praktis dan dapat diandalkan untuk membantu petani dalam pengambilan keputusan perawatan tanaman yang lebih efektif.
Analisis Debit Limpasan Pada Perencanaan Sistem Underground Drainage Jalan Lingkar Tengah Makassar Muh. Syafaat S Kuba; Andi Makbul Syamsuri; M Agusalim
Journal Techno Entrepreneur Acta Vol. 4 No. 2 (2019)
Publisher : Universitas Fajar

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

Abstract

Pembangunan Jalan Lingkar Tengah (Middle Ring Road) Makassar rencananya akan dilaksanakan secara bertahap, dimana tahap pertama direncanakan sepanjang 3,050 km mulai dari Jl. Perintis Kemerdekaan melintasi Jl. Dr. Laimena dan berakhir di Jl. Borong Raya dengan lebar total 42 m termasuk prasarana saluran drainase. Dalam pembangunan jalan harus dibarengi dengan sistem saluran drainase yang baik dimana sistem drainase tersebut mampu melayani air limpasan hujan yang terjadi di permukaan jalan dan kawasan – kawasan sekitar jalan yang akan membebani saluran – saluran drainase. Tujuan dari penelitian ini adalah untuk mengetahui debit limpasan yang terjadi di kawasan Pembangunan Jl. Lingkar Tengah (Middle Ring Road) Makassar dan sekitarnya yang akan membebani saluran drainase jalan dan untuk merencanakan dimensi saluran drainase tertutup (underground drainage) yang mampu mengalirkan debit aliran air maksimum. Hasil yang diharapkan adalah perencanaan sistem underground drainage sebagai alternatif pemecahan masalah banjir dan genangan pada ruas Jl. Lingkar Tengah (Middle Ring Road) Makassar.
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.
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 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.
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.
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 Abdul Rakhim Nanda Ade Irfan Aedah Abd Rahman Agus, Fauziah Agusalim, Agusalim Ahmad Nur Rahman Ahmad Syafi'i Zulmi Akrar Syah Al Imran, Hamzah Ali, Muhammad Yunus Alizha Nur Arspandy Alvian Syah Burhani Amal, Citra Amalia Amrullah Anas, Andi Bunga Tongeng Andi Agung Andi Bunga Tongeng Anas Andi Makbul Syamsuri ANDI MAWADDA TAIBA MAWADDA TAIBA Andi Rahmat Anis Dandi Juandani Antaria, Sukmasari Arina Manasikana Arman, Muayyanah Arsyad, Zulfikar Ashabul Kahfi Asnita Virlayani, Asnita Bakti, Rizki Yusliana Berni Satria Gemilang Chyquitha Danuputri Danuputri, Chyquitha Danuputri, Chyquitha Darniati Dayang Aisyah Desi Anggreani Dince Danga Dupa Djunur, Lutfi Hair E. Sulistiyowati . Emil Agus Salim Habi Talib Emil Agusalim Habi Talib Erdi Katanga Yani Ersita Hambajawa Eunike Mita Ga Fachrim Irhamna Rachman Fahrim I. Rahman Fahrim Irhamna Rachman Fahrim Irhamna Rachman Farida Gaffar Fausiah Latief Fauzan Hamdi Firrofiqil A'la Kadram Fithriyah Arief Wangsa Gaffar, Farida Gemilang, Berni Satria Hamzah Al Imran Hasanuddin, Novianingsih Hasbir, Syahrul Hasmi . Ida Mulyadi, Ida Indriyanti Indriyanti Azis Irma Suryana Irwan Irwan, Muhammad Ahlil Khairi Ismail Pawara Jesynta Landu Natar Jihan Izzathul Mujidah Juandani, Anis Dandi Juliandro Juliandro Juliandro, Juliandro Karim, Nenny Kasmawati Kato, Muh Alvin Achmad Kusumawardani, Nurul Lantara, Andi Bintang Latief, Fausiah Leonard Rihi Lisnawati Lisnawati Lukman Lukman Lukman Anas LUKMAN ANAS Lukman, Lukman Lutfi Hair Djunur M Agusalim M. Fikri Haikal Ayatullah Ma'rupah, Ma'rupah Mahmud, Rajib Mahmuddin Mahmuddin Mahmuddin Mohamad Munawir Muh Alvin Achmad Kato Muh Faisal Muh Ilham Akbar Muhammad Aminuddin, Muhammad Muhammad Asygar Faeruddin Muhammad Faisal Muhammad Hasraddin Hasnan Muhammad Khadafi Muhsin, Muh Arief Muhyiddin A.M Hayat Muhyiddin A.M Hayat Munawir, Mohamad Nenny Nenny Nenny Nenny, Nenny Nini Apriani Rumata Nur Alam Nur Annisa Syarifuddin Nur Rahman, Ahmad Nurdiansah, Nurdiansah Nurdiansyah Nurdiansyah Nurnawaty Nurul Kusumawardani Panguriseng, Darwis Pawara, Ismail Putri, Adriani Rahmasari, St. Rajib Mahmud Risman, Andi Muh. Riswal Karamma Runal Rezkiawan S.R. Setyaningsih . Sahril Sandi, Andi Muhammad Sari, Reski Anugrah Saribanong . Sarina Siba, Ikhsan St. Narwiyani . St.S. Yasin . Suhardi, Syahrul Suriani Suriani Suriati . Swa Lee Lee Syadiah Nor Wan Shamsuddin Syah, Akrar Syahril Akbar Syahrul Hasbir Syahrul Suhardi Syahrul, Syahrulrahman Syamsuri, Andi Maqbul Syatriana, Eny T Karim, Nenny Taufiq, Muh Teto Putri Asgetri Titik Khawa Abd Rahman Titik Khawa Abdul Rahman Titin Wahyuni Toha Andi Lala Try Gustaf Said Ulil Amri Umbu Leha Kulandima Usman, Sucipto W. Suharni . Wa Nanda Sulystrian Wangsa, Fithriyah Arief Wiwin Fuad Sanjaya Yesinta Pangga Limu Yosua Pebryanto Blegur Yundari, Yundari Zulfikar Arsyad Zulhaidir DJ, Muhammad Zulmi, Ahmad Syafi'i