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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control
Khusnul Khotimah, Bain
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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A Hybrid Tabu Search and Genetic Algorithm Imputation Approach for Incomplete Data Khusnul Khotimah, Bain; Kustiyahningsih, Yeni; Miswanto, Miswanto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1340

Abstract

The common problem for data collection is happening missing value during the data collection and processing process that the quality of the data testing is decreased. A computational based technique for dealing with missing values, namely Genetic Algorithm Imputation (GAI). The usage was used to estimate the dataset's missing values. GAI generates the optimal set of missing values with the acquisition of information as a function of fitness to measure individual solutions' performance. GAI conducts continuous searching until the missing criteria value is found according to best fitness. So, it is trapped in optimal conditions temporarily. The improvement of GAI with tabu search is known as TS-GAI, that strength is two metaheuristic techniques modified at the mutase stage to distract the local optima's search. In applying missing values, this technique works better when many possible values are used instead of the mixed attribute having missing values. Because the new generation chromosome values generate many opportunities to make up for the missing values. The experimental results show that the TS-GAI shows better performance on 30% MV with a fitness value of 0.212. It converges at 159 iterations. Generally, TS-GAI is a faster iteration than simple GAI and it has a lower RMSE level than other imputation techniques.
Performance Comparison between Double Exponential Smoothing and Double Moving Average Methods in Seasonal Beef Demand Khusnul Khotimah, Bain; Setiani; Wulandari, Ana Yuniasti Retno; Anamisa, Devie Rosa
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 9, No. 4, November 2024
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v9i4.1934

Abstract

Beef demand relies on seasonal patterns because it depends on feed supplies, especially in the rural areas, that still rely on natural feeds. Beef supply is regulated by the government as it is one of the highly demanded commodities. It is a livestock product containing nutritional value to meet the protein needs of the community. The supply is influenced by several factors such as beef production, beef consumption, and the people's income level. In order to anticipate the increasing demand for beef, it is necessary to conduct a forecast to estimate the demand for meat in the future. In forecasting, various methods were examined to choose the method with the lowest error rate. This research compared the Mean Absolute Percentage Error (MAPE) resulted from Double Exponential Smoothing (DES) and Double Moving Average (DMA) methods. Based on the test results and analysis on beef supplies in Madura, it can be concluded that the method with the lowest MAPE value is Double Exponential Smoothing, i.e. 9.50% with an alpha parameter of 0.5. Meanwhile, the test using the Double Moving Average method to determine the best MAPE value, resulted the best time order of 2 with a MAPE value of 29.8408%. After finding the parameter with the lowest MAPE value, that parameter was used for the data testing. In the measurement, the data used for the testing were the data of 1-year, 2-year, 3-year, and 4-year period. Each method has a level of error value that increases the same; the number of data entered can affect the MAPE value. Therefore, the more data entered, the lower the error value.
Co-Authors Devie Rosa Anamisa Miswanto Setiani Wulandari, Ana Yuniasti Retno Yeni Kustiyahningsih