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Azzahra, Fahrija
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Analisis Metode Klastering Pada Kasus Penyebab Perceraian Berdasarkan Provinsi Dengan Teknik K-Means Nurhayati, N; Azzahra, Fahrija; Ramadani, Sri; Hastuti, Sinta Dwi; Irawan, Eka
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2699

Abstract

Abstract−Divorce is no longer a strange thing in Indonesia, but divorce can be said to be a common and popular thing. It was noted that in 2017 the divorce rate reached 18.8% of the 1.9 million events. From this case, a study was conducted to obtain a grouping in the regions in Indonesia that had the most cases of divorce. There are 4 variables used, namely continual disputes and quarrels, economic problems, leaving one party, and domestic violence. This research uses the Clustering method and is tested with the help of RapidMiner software to ensure the accuracy of the method used. Clustering results show the cause of divorce from 29 data in Indonesia, the highest cluster of 3 regions and the lowest cluster of 26 regions. From this research it is expected that the results obtained can be information for the government in handling divorce cases in Indonesia so that appropriate programs can be drawn up for each province to reduce divorce rates in Indonesia.Keywords: Datamining, Clustering, K-Means, Perceraian, Wilayah
Analisis Data Mining Naive Bayes Klasifikasi Pada Kelayakan Penerima PKH Ramadani, Sri; Ayu, Nur Zannah sekar; Nurhayati, N; Azzahra, Fahrija; Windarto, Agus Perdana
KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) Vol 4, No 1 (2020): The Liberty of Thinking and Innovation
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/komik.v4i1.2726

Abstract

Abstract− Family Hope Program (PKH) is a government program that has been running since 2007 which is a poverty reduction program through the provision of cash assistance to very poor families based on the terms and conditions set. In the implementation of PKH in Limbong Village, Dolok Merawan District, this cannot be maximized because there are still many poor families that have not been channeled by the PKH program so that the funds channeled are not on target. Thus, the classification method is able to find models that distinguish data concepts or classes, with the aim of being able to estimate the class of an object whose label is unknown. Therefore, the Naive Bayes algorithm can predict future opportunities based on past experience. So this research conducts data processing using data mining to classify the eligibility of PKH recipients with the classification method using the Naïve Bayes algorithm, which is expected that the data generated from the data mining process can be an evaluation.Keywords: Data Mining, Naïve Bayes Algorithm, Classification, PKH
Co-Authors Agus Perdana Windarto Ayu, Nur Zannah sekar Eka Irawan Hastuti, Sinta Dwi N Nurhayati Ramadani, Sri