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Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning
https://doi.org/10.22146/ijccs.62589 Aditama Azmy Musaddad(1), Arimurti Kriswibowo(2*)
(1) Public Administration Department Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
(2) Public Administration Department Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
(*) Corresponding Author
Abstract
Machine learning approaches have been used to solve various problems. PKH experienced miss-targeting. This study aims to compare the result of big data by SIKS-NG and machine learning based on the same data and measurement indicators. Obtained algorithms Averaged Neural Network with optimal output compared to others. As for data testing obtained on SIKS-NG and machine learning that uses elevated matrix evaluations with the following 3 indicators: 1) Accuracy obtained by SIKS-NG 72.40% increased to 81.18% for Machine Learning; 2) Precision at the center is getting a high percentage of 91,01%, but it is capable of increasing once the data is given Machine Learning to 95,37%; 3) Recall with the cycle was obtained at 75.49%, while Machine Learning obtained a higher yield of 82.19%. Thus, machine learning has been proven to reduce miss-targeting and can be used as an alternative recommendation in automatic decision making and innovative management practices in government circles.
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DOI: https://doi.org/10.22146/ijccs.62589
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