Optimalizing Big Data in Reducing Miss-Targeting Family Hope Program (PKH) in Sidoarjo Disctrict with Approach Machine Learning

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.

Keywords

• family hope program
• miss-targeting
• big data
• machine learning