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All Journal Scientific Journal of Informatics Indonesian Journal of Information System Jurnal INFOTEL
H. Aly, Moustafa
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Computer Science & IT Electrical & Electronics Engineering

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Application Of Expert System In Determining Diseases In Potato Plants Ikhwan, Ali; Bi Rahmani , Nur Ahmadi; H. Aly, Moustafa; Aslami, Nuri; Dedi Irawan, Muhammad; Ahmad, Imam
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10213

Abstract

This research aims to develop an expert system in diagnosing diseases in potato plants using the Case Based Reasoning (CBR) method approach combined with the K-Nearest Neighbor (K-NN) algorithm. The system is designed to help farmers identify the type of disease based on the symptoms that appear, as well as provide relevant solutions to increase crop productivity. In previous research, the CBR method showed a limited accuracy rate of 74% because it only relied on one algorithm. Through the application of two methods in data analysis, namely CBR and K-NN, this study succeeded in increasing the diagnosis accuracy to be higher than the previous approach of 80%. The system is implemented in the form of a web-based application that is easily accessible by farmers. The results show that the integration of these two methods provides more optimal, effective, and accurate results in detecting potato plant diseases based on symptom data. The findings are expected to contribute significantly to the development of agricultural technology, especially in improving the harvest success of potato farmers in Indonesia.
Implementation of MOORA and MOORSA Methods in Supporting Computer Lecturer Selection Decisions Sitorus, Zulham; Karim, Abdul; Nasyuha, Asyahri Hadi; H. Aly, Moustafa
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1184

Abstract

The selection of computer science lecturers is an important process for educational institutions, requiring a balanced assessment of various criteria to find the most suitable candidates. This paper examines the implementation of Multi-Objective Optimization based on Ratio Analysis (MOORA) and its variant, namely Multi-Objective Optimization based on Ratio Analysis with a Subjective Attitude (MOORSA), as a tool to support decision making. in this case. This selection process is often complex, requiring consideration of various criteria, such as academic qualifications, teaching experience, research capabilities, and others. This research was conducted to support the decision-making process. by developing a Decision Support System (DSS) using the Multi-Objective Optimization on The Basic of Ratio Analysis (MOORA) and MOORSA methods. Many methods are used, such as SAW, AHP, Topsis and others. based on the calculation of the MOORA method, the highest result has been achieved by A1 worth 0.651819 and similarly, in the MOOSRA method the highest alternative result is A1 worth 0.592177.
Application Of Expert System In Determining Diseases In Potato Plants Ikhwan, Ali; Bi Rahmani , Nur Ahmadi; H. Aly, Moustafa; Aslami, Nuri; Dedi Irawan, Muhammad; Ahmad, Imam
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10213

Abstract

This research aims to develop an expert system in diagnosing diseases in potato plants using the Case Based Reasoning (CBR) method approach combined with the K-Nearest Neighbor (K-NN) algorithm. The system is designed to help farmers identify the type of disease based on the symptoms that appear, as well as provide relevant solutions to increase crop productivity. In previous research, the CBR method showed a limited accuracy rate of 74% because it only relied on one algorithm. Through the application of two methods in data analysis, namely CBR and K-NN, this study succeeded in increasing the diagnosis accuracy to be higher than the previous approach of 80%. The system is implemented in the form of a web-based application that is easily accessible by farmers. The results show that the integration of these two methods provides more optimal, effective, and accurate results in detecting potato plant diseases based on symptom data. The findings are expected to contribute significantly to the development of agricultural technology, especially in improving the harvest success of potato farmers in Indonesia.
Clothing Sales Prediction Information System Using Web-Based Double Exponential Smoothing Method Sitorus, Apriyanti Anggraini; Ikhwan, Ali; H. Aly, Moustafa
Scientific Journal of Informatics Vol 10, No 3 (2023): August 2023
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v10i3.44919

Abstract

Purpose: The purpose of this research is to determine the smallest error value so that the resulting prediction data is more accurate. This prediction data is used to help Raja Fashion Medan in processing goods data and help predict the amount of goods that must be provided to meet customer needs in the next period.Methods: This research uses the Double Exponential Smoothing method because it is used on data that is more stable and has a trend pattern. To test the accuracy of the prediction results with the Double Exponential Smoothing method, the Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) data testing methods are used by finding the smallest error value.Result: This test is carried out by determining the smallest error value on 118 data types of goods with error results, namely the average Root Mean Square Error (RMSE) of 26.5, Mean Absolute Deviation (MAD) 1.2, Mean Squared Error (MSE) 37.8 and Mean Absolute Percent Error (MAPE) of 10%, it can be concluded that the accuracy of theprediction is very good.Novelty: Testing on prediction results uses 4 methods to determine more accurate results, namely with Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percent Error (MAPE) which are used to find values smallest error.
Co-Authors Abdul Karim Ali Ikhwan Asyahri Hadi Nasyuha Bi Rahmani , Nur Ahmadi Imam Ahmad Muhammad Dedi Irawan Nuri Aslami Sitorus, Apriyanti Anggraini Sitorus, Zulham