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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Robotics and Automation (IJRA) International Journal of Applied Power Engineering (IJAPE) Majalah Ilmiah Teknologi Elektro International Journal of Renewable Energy Development Jurnal Teknologi Reaktor Nuklir Tri Dasa Mega Techno.Com: Jurnal Teknologi Informasi Jurnal Neutrino : jurnal fisika dan aplikasinya JSI: Jurnal Sistem Informasi (E-Journal) JFA (Jurnal Fisika dan Aplikasinya) JURNAL ILMIAH PENDIDIKAN FISIKA AL BIRUNI Jurnal Teknologi Elektro Journal of Teaching and Learning Physics JPSE (Journal of Physical Science and Engineering) Jurnal Rekayasa Bahan Alam dan Energi Berkelanjutan Journal of Physics: Theories and Applications BERDIKARI : Jurnal Inovasi dan Penerapan Ipteks JRST (Jurnal Riset Sains dan Teknologi) Indonesian Journal of Chemistry Natural: Jurnal Ilmiah Pendidikan IPA Jurnal Sisfokom (Sistem Informasi dan Komputer) Jambura Journal of Electrical and Electronics Engineering Indonesian Journal of Electrical Engineering and Computer Science G-Tech : Jurnal Teknologi Terapan Indonesian Community Journal Jurnal Pijar MIPA Jurnal Algoritma PREVENIRE: Journal of Multidisciplinary Science Cakrawala Journal of Artificial Intelligence and Digital Business JESCEE- Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Jurnal Pendidikan Sains Indonesia (Indonesian Journal of Science Education) International Journal of Renewable Energy Development Majalah Ilmiah Teknologi Elektro Jurnal Teknik AMATA Jurnal Pendidikan MIPA
Prayogi, Soni
Department Of Electrical Engineering, Faculty Of Industrial Engineering, Pertamina University, Jakarta 12220 Indonesia
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Journal : Jurnal Sisfokom (Sistem Informasi dan Komputer)

 1 
Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) Fauzi, Ahmad; Chandra, Annisya E; Imammah, Sofyah; Zapata, Malvin; Marzuki, Marza I; Prayogi, Soni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2261

Abstract

Potato production in Indonesia has grown very rapidly, making Indonesia the largest potato producer in Southeast Asia. However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. The project "Potato Plant Leaf Disease Detector (MR-PoLod)" aims to design an android application that can classify leaves on potato plants into 3 classifications, namely healthy, early, and late blight disease. This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. The total data used for training data, data validation and data testing is 3165 images. With each division of the data process on the training data of 70%, validation of 15% & testing of 15% to test the effectiveness of the model that has been created. The performance of MR-PoLod for each class, obtained a precision value, recall, and f1-score of 0.99. Likewise, the accuracy value achieved by the model is 0.99 or 99%. Thus, the expected application can facilitate farmers in classifying diseases on potato plant leaves.
Machine Learning-Potato Leaf Disease Detection App (MR-PoLoD) Fauzi, Ahmad; Chandra, Annisya E; Imammah, Sofyah; Zapata, Malvin; Marzuki, Marza I; Prayogi, Soni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 3 (2024): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i3.2261

Abstract

Potato production in Indonesia has grown very rapidly, making Indonesia the largest potato producer in Southeast Asia. However, there are challenges for farmers in growing potatoes. Such as treating potatoes for various diseases. 2 diseases will occur in potato plants if not treated quickly, namely early blight disease caused by the fungus Alternaria solani and late blight disease caused by the microorganism Phytophthora infestans. The project "Potato Plant Leaf Disease Detector (MR-PoLod)" aims to design an android application that can classify leaves on potato plants into 3 classifications, namely healthy, early, and late blight disease. This application uses the CNN (Convolutional Neural Network) Machine Learning Algorithm because currently, CNN is recognized as the most efficient and effective model in pattern and image recognition tasks. This application uses the Python programming language which is rich in library and framework availability so that it can meet the needs of machine learning and image classification tasks. The total data used for training data, data validation and data testing is 3165 images. With each division of the data process on the training data of 70%, validation of 15% & testing of 15% to test the effectiveness of the model that has been created. The performance of MR-PoLod for each class, obtained a precision value, recall, and f1-score of 0.99. Likewise, the accuracy value achieved by the model is 0.99 or 99%. Thus, the expected application can facilitate farmers in classifying diseases on potato plant leaves.
Implementation of CNN-Based Computer Vision for Personal Protective Equipment Detection in the Oil and Gas Industry prayogi, soni
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 4 (2025): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i4.2459

Abstract

Due to the inherently hazardous nature of operations in the oil and gas industry, strict compliance with safety protocols, including the obligatory use of Personal Protective Equipment (PPE), is essential for all workers. Nonetheless, monitoring PPE compliance through manual observation remains inefficient and prone to error, especially in expansive, intricate work settings. To address this challenge, there is a growing demand for an intelligent system capable of accurately and instantaneously detecting PPE use. This research introduces a Computer Vision approach employing Convolutional Neural Networks (CNNs) to identify PPE usage among workers within oil and gas environments. The system leverages a comprehensive dataset of images of workers wearing various types of PPE, including helmets, safety vests, and face masks. These images are used to train a CNN model designed to distinguish and classify the safety equipment. Experimental results demonstrate that the proposed CNN model achieves an impressive 94.2% detection accuracy on the validation data and maintains reliable performance across varying lighting conditions and camera angles. Moreover, the system can identify PPE violations in under 1 second per frame, making it suitable for real-time surveillance applications. As a result, this solution offers a promising enhancement to workplace safety oversight, with the potential to markedly reduce accident rates in the industry. The findings also pave the way for future integration with IoT-based monitoring platforms and further refinement of model adaptability across diverse industrial scenarios. The primary innovation of this study lies in the optimized deployment of CNNs tailored to the challenging conditions of oil and gas sites, delivering high detection precision and rapid response times. This area has seen limited exploration in existing literature.
Co-Authors . Saminan A, Muhammad AA Sudharmawan, AA Alfredo, Andromeda J Amsal Aritonang Anandra, Muhammad F Apta Prana Mas Erlangga Arif Murti Rozamuri Aufa, Nurul Ayunis Ayunis Aziiz, Ahmad Mushawwir Beny Ragadita Cahyono, Yoyok Chandra, Annisya E Dadan Hamdani Dadan Hamdani, Dadan Darminto . Darminto Darminto Darminto Darminto Darminto Darminto Darminto Darminto Elmi Mahzum Fauzi Ahmad Muda Fitria Silviana Fitria Silviana Fitria Silviana Fitria Silviana Fitria Silviana Frisca Elfrisa Nainggolan Gatut Yudoyono Gatut Yudoyono Giganta Rose Kakke Hartanxia Hartanxia Herminarto Nugroho Hidayat, Robi T I Nur Fajar Imammah, Sofyah Irgi Faturrahman Ketut Saha Kesta Dinatha Mahzum, Elmi Marza Ihsan Marzuki Marza Ikhsan Marzuki Marza Ikhsan Marzuki Marzuki, Marza I Marzuki, Marza Ikhsan Maulana, Rifqi Muhamad FE Nugroho Muhammad Muhammad Abdillah Muhammad Abdillah Muhammad Muhammad Muhammad Muhammad Muhammad Syukri Nanda Adiva Yusman Nita I Pertiwi Nita Indriani Pertiwi Nugroho, Herminarto Nugroho, Irvan A Nugroho, Teguh A Nugroho, Teguh Aryo Nur Fajar, Iqbal Nurul Aufa Pertiwi, Nita Indriani Pramudito, Wahyu Agung Pratama, Aldi Putra, Yehezkiel P. Rizky Agung Ramadhan Roffi, Teuku Muhammad Roong, Inggrid IO Safira Luthfia Saminan Saminan Saminan Saminan Saminan, Saminan Saputra , Duta M W Silviana, Fitria Sirait, Juanto Syahrul Ghufron Tan, Dennis Tarmizi Hamid, Tarmizi Teguh Aryo Nugroho Teuku Muhammad Roffi Wahyu Agung Pramudito Wahyu Agung Pramudito Wahyu Kunto Wibowo Wibowo, Wahyu K. Wibowo, Wahyu Kunto Yoyok Cahyono Zainuddin Zainuddin Zainuddin Zainuddin Zainuddin Zainuddin Zainuddin Zainuddin* Zapata, Malvin