Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth

*Jemaictry Tamaela  -  Universitas Kristen Satya Wacana, Indonesia
Eko Sediyono  -  Universitas Kristen Satya Wacana Salatiga, Indonesia
Adi Setiawan  -  Universitas Kristen Satya Wacana Salatiga, Indonesia
Received: 25 Jan 2018; Published: 30 Apr 2018.
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Abstract

BPJS services cannot be separated from criticism and complaints of the people in Indonesia. Twitter is one of the social media choose to share experiences related to things about BPJS. The information that is shared can be processed to gain new knowledge (knowledge discovery), which is related to public opinion about BPJS. Tweets collected from the national BJPS twitter are divided into words, then, specified words can be used as items to form the itemset. The association rule technique with the FP-Growth algorithm that is implemented in the application can process text data from Twitter to form the item set. Each item set contains a collection of tweets that are responses and the opinion of the community about an event or phenomenon related to BPJS services. The tree structure of FP-Growth simplifies the process of the validation because it can track and display the frequency of occurrence of each word and itemset, before and after branch pruning which is not included in the support value. The OSM API integration with the application in this study provides visual information about where the tweet comes from, so it can be used to generate itemset from a collection of tweets from a particular region.

Keywords
Association rule; Data Mining; Knowledge Discover; FP-Growth; Twitter; BPJS.

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