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All Journal JURNAL SISTEM INFORMASI BISNIS Jurnal Buana Informatika
Tamaela, Jemaictry
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Computer Science & IT Economics, Econometrics & Finance

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Implementasi Metode Association Rule untuk Menganalisis Data Twitter tentang Badan Penyelenggara Jaminan Sosial dengan Algoritma Frequent Pattern-Growth Tamaela, Jemaictry; Sediyono, Eko; Setiawan, Adi
JSINBIS (Jurnal Sistem Informasi Bisnis) Vol 8, No 1 (2018): Volume 8 Nomor 1 Tahun 2018
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (388.588 KB) | DOI: 10.21456/vol8iss1pp25-33

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.
CLUSTER ANALYSIS MENGGUNAKAN ALGORITMA FUZZY C-MEANS DAN K-MEANS UNTUK KLASTERISASI DAN PEMETAAN LAHAN PERTANIAN DI MINAHASA TENGGARA Tamaela, Jemaictry; Sediyono, Eko; Setiawan, Adi
Jurnal Buana Informatika Vol 8, No 3 (2017): Jurnal Buana Informatika Volume 8 Nomor 3 Juli 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (815.743 KB) | DOI: 10.24002/jbi.v8i3.1317

Abstract

Abstract. The purpose of this study is to perform cluster analysis and implementation by utilizing fuzzyc-means (FCM) and k-means (KM) to process agricultural data based on the data mining results. The fuzzy c-means (FCM) and k-means (KM) are implemented to find out and form the agricultural land clusters which appropriate the commodity types based on the supporting attributes that are used. The analysis and implementation results could provide some land information such as the number of the clusters, the land areas, the region areas, the locations and the productivity levels. The results of this study could be applied as the suggestion in converting the land functions and structuring the agricultural lands. The utilization of Openstreetmap is an open source solution which is implemented in the application. It could give visual information related to the agricultural land regions based on the clusters which make it easier to comprehend. Keywords: Cluster analysis, C-means, K-means, GIS, Data mining Abstrak. Penelitian ini bertujuan untuk melakukan analisis cluster dan implementasinya dengan menggunakan algoritma fuzzy c-means (FCM) dan k-means (KM) untuk mengelola data  pertanian dari hasil data mining yang dilakukan. Fuzzy c-means (FCM) dan k-means (KM) dimplementasikan untuk menemukan dan membentuk klaster-klaster daerah lahan pertanian sesuai dengan jenis komoditi berdasarkan atribut-atribut pendukung yang digunakan. Hasil analisis dan implementasi dapat menyediakan informasi lahan seperti jumlah kluster, luas lahan, luas daerah, letak dan tingkat produktifitas. Hasil yang diperoleh dapat menjadi bahan masukan dalam proses alih fungsi dan penataan lahan pertanian. Penggunaan Openstreetmap merupakan solusi open source yang diimplementasikan pada aplikasi dapat memberikan informasi  visual daerah-daerah lahan pertanian berdasarkan klaster yang dihasilkan sehingga lebih mudah untuk dipahami.Keywords: Cluster analysis, c-means, k-means, GIS, Data mining
Co-Authors Adi Setiawan Eko Sediyono Hanung Adi Nugroho