Garuda - Garba Rujukan Digital

Marzuki, Marza I

Unknown Affiliation

Author-ID : 8045390

Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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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

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.

Pengembangan Sistem Penerangan Jalan Pintar Berbasis IoT dengan Arduino dan NodeMCU Hidayat, Robi T; Nugroho, Irvan A; Saputra , Duta M W; Marzuki, Marza I; Prayogi, Soni
Jurnal Algoritma Vol 21 No 2 (2024): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.21-2.1850

The development of this system is to improve energy efficiency and security in urban areas by optimizing the use of street lights based on real-time environmental conditions. The method used involves the design and implementation of hardware and software, where light sensors, motion, and wireless communication modules are integrated with Arduino and NodeMCU. Data from the sensors is used to automatically control the intensity of street lights, adjusting the lighting based on the natural brightness level and the presence of vehicles or pedestrians. Testing was carried out in simulated and field environments to evaluate the performance of the system under various conditions. Key findings from this study indicate that the developed system is able to reduce energy consumption by up to 40% compared to conventional street lighting systems, without sacrificing security. In addition, this system also provides flexibility in managing street lighting through a web-based interface that allows remote monitoring and control. Overall, the development of an IoT-based smart street lighting system with Arduino and NodeMCU has proven effective in improving energy efficiency and security in urban environments. This system offers a practical and widely implementable solution to support sustainable smart city initiatives.

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

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.

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