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Nurochman Nurochman

Informatics Department, Faculty Of Science And Technology, State Islamic University Sunan Kalijaga Yogyakarta

Author-ID : 500835

Religion Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics

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The Development of Decision Support System For The Selection of Outstanding Teachers Kholis Hermawan; Hendra Dea Arifin; Elvanisa Ayu Muhsina; Nurochman Nurochman
IJID (International Journal on Informatics for Development) Vol. 3 No. 2 (2014): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

The Selection of Outstanding Teachers is an agenda of Department of Education Youth and Sports (Disdikpora) intended to encourage motivation, dedication, loyalty and professionalism of teachers, the expected positive impact on performance improvement. In general elections outstanding teachers have been running smoothly according to specified criteria. However, its implementation is still not optimal felt so necessary to improve, especially in the aspects of assessment. In the assessment of the current selection are inconsistent because one of the criteria assessment (portfolio) that have different valuation range. This makes the need for a decision support system that is able to provide consistency with the application of the normalization judgment and able to show the rank results of the selection.Decision support system used in this research is Simple Additive Weight (SAW) and the Analytic Hierarchy Process (AHP). Both methods support the assessment criteria and weighting. The criteria used for the assessment refer to the Outstanding Teacher Selection Handbook 2012 and based on the results of data analysis assessment Outstanding Teacher Selection of Junior High School, Senior High School and Vocational High School in 2012. The method for developing the system that used in this research is waterfall method.The system is capable of displaying two alternative rank outstanding selection of teachers based on the SAW and AHP methods. These results sorted from the highest alternative to the lowest alternative outcomes. These results are also presented in the form of trend graphs to compare the results of each method. This system is useful to provide a reference for the field of PPTK Disdikpora particularly relevant in decision-making to determine the selection of outstanding teachers. Based on the results of testing the system's functionality, 96.7% stated functionality of the system running well. While the results of the testing interface, 38.96% for Strongly Agree, and 48.05% Agree.

Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network Ramadhan, Ade Umar; Siregar, Maria Ulfah; Nafisah, Syifaun; Anshari, Muhammad; Ndungi, Rebeccah; Mulyawan, Rizki; Nurochman, Nurochman; Gunawan, Eko Hadi
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5129

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry.

Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network Ramadhan, Ade Umar; Siregar, Maria Ulfah; Nafisah, Syifaun; Anshari, Muhammad; Ndungi, Rebeccah; Mulyawan, Rizki; Nurochman, Nurochman; Gunawan, Eko Hadi
IJID (International Journal on Informatics for Development) Vol. 12 No. 1 (2023): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2023.5129

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry.

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