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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Aditama Azmy Musaddad
Public Administration Department Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
Computer Science & IT Control & Systems Engineering

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Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning Aditama Azmy Musaddad; Arimurti Kriswibowo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.62589

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.
Co-Authors Kriswibowo, Arimurti