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All Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) JESCEE- Journal of Emerging Supply Chain, Clean Energy, and Process Engineering
Zapata, Malvin
Unknown Affiliation
Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Technical Analysis of The Use of Shore Connection Services at Tanker Docks Zapata, Malvin; Prayogi, Soni
Journal of Emerging Supply Chain, Clean Energy, and Process Engineering Vol 3 No 1 (2024): Journal of Emerging Supply Chain, Clean Energy and Process Engineering
Publisher : Universitas Pertamina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57102/jescee.v3i1.81

Abstract

The use of shore connection services at tanker berths is an important innovation to increase energy efficiency and reduce greenhouse gas emissions in the maritime sector. This research analyzes the technical aspects of implementing shore connections, including system configuration, integration with port infrastructure, as well as operational and economic challenges. This study uses a descriptive analysis method with data collected through field observations, interviews with port operators, and a review of related literature. The research results show that the implementation of shore connections is able to reduce fuel consumption and pollutant emissions significantly. However, there are obstacles such as high initial investment costs, the need for international technical standards, and adjustments to ship operations. Recommendations include increasing cooperation between ports and shipowners, as well as developing incentive policies to encourage the adoption of this technology. This research provides valuable insights for stakeholders in efforts to achieve more sustainable shipping.
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.
Co-Authors AA Sudharmawan, AA Chandra, Annisya E Fauzi Ahmad Muda Imammah, Sofyah Marzuki, Marza I Prayogi, Soni