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All Journal Jurnal Informatika Kumawula: Jurnal Pengabdian Kepada Masyarakat RISK : Jurnal Riset Bisnis dan Ekonomi

Budi Susanto

Lembaga Studi Realino

Author-ID : 243758

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Social Sciences

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Penerapan Social Network Analysis dalam Penentuan Centrality Studi Kasus Social Network Twitter Budi Susanto; Herlina Lina; Antonius Rachmat Chrismanto
Jurnal Informatika Vol 8, No 1 (2012): Jurnal Informatika
Publisher : Universitas Kristen Duta Wacana

The twitter provides a kind of relation between users in specific form. When someone follow others, it doesn’t mean that she/he know well about them. We have defined a friend relationship between users in twitter as connection following and follower between two users. Based on this definition we develop a system to get friends and also friends of friends relation from a specific user. We use twitter API to get following and follower list and then construct a graph that represent a social network between those users. From this graph, we analyse the centrality using SNA (Social Network Analysis) method, i.e. closeness and betweeness. We propose to use these methods in order to find out who is the most influence user in the his/her social network to spread out the tweet or information. With this system, user can know about their social network based on their friend list on twitter. Kata Kunci : Social Network Analysis, Betweenness Centrality, Closeness Centrality

KLASIFIKASI EMAIL DPNGAN MENGGUNAKAN METODE NAIVE BAYESIAN STUDI KASUS : MAILING LIST www.tux.org Tantiny Tantiny; Budi Susanto; Widi Hapsari
Jurnal Informatika Vol 3, No 1 (2007): Jurnal Informatika
Publisher : Universitas Kristen Duta Wacana

Pada jaman modern ini, komunikasi dan penyebaran informasi merupakan hal yangsangatpenting. Salah satubentukkomunikasi yaitu surat-menyurat, tidak lagi dilakukan secaratradisional menggunakan kertas, amplop dan perangko. Surat-menyurat secara global sekarangdilakukan menggunakan teknologi email. Email saat ini menjadi salah satu alat komunikasiyang perturnbuhannya kian pesat dari hari ke hari. Hal ini dapat dicermati melalui banyaknyakomunitas mailing list yang bermunculan di Internet. Namun ada kendala yang muncul daripenggunaan entail, yakni jumlah email yang banyak dan diterima dalam waktu yangbersamaan. Hal ini berakibat infonnasi-informasi yang ada dalam email menjadi terkuburdalam tumpukan informasi yang lain. Untuk mengatasi masalah tersebut, maka telahdikembangkan beberapa aplikasi untuk mengklasifikasikan email menurut kriteria tertentu,seperti kategori, pengirim atau pun subject entail. Tugas Akhir ini bertujuan membangunsebuah sistem klasifikasi enrail dengan menggunakan Metode Naive Bayesian denganmengambil studi kasus dari mailing /rsf www.tux.org Sistem yang dibangun mampumengklasifikasikan email kedalam 3 kategori yang sudah ditentukan dengan pengetahuan iamiliki dan mengubah pengetahuan tersebut jika terjadi kesalahan klasifikasi (pembelajaranbertahap).

THE IMPLEMENTATION OF ASSOCIATION RULES IN ANALYZING THE SALES OF AMIGO GROUP Bobby Fernando; Budi Susanto
Jurnal Informatika Vol 7, No 1 (2011): Jurnal Informatika
Publisher : Universitas Kristen Duta Wacana

A retail company usually produce large sales transactions data. These data can be utilized with the application of data mining, which is also known as knowledge data discovery. Association rules is one of the most famous data mining study that can be used to generate items that frequently purchased together in sales transactions. This project is a web-based data mining project for a company called Amigo Group. The algorithm used for association rules implementation is called FP-Growth algorithm. This algorithm will form a data structure called FP-Tree and extract the rules based on its FP-Tree. The result of this application will be used to help Amigo Group’s managers understand about customers buying behavior and analyze pattern of items which are usually purchased together. Then, the manager can create marketing strategies in order to increase sales of the items. Key Words: Data Mining, FP-Growth, FP-Tree.

Co-Authors Agata Filiana Antonius Rachmat Chrismanto Bobby Fernando Gandung Satriyono, Gandung Herlina Lina Kristian Oentoro Tantiny Tantiny Tiya Saragina Pamuleh Widi Hapsari Yossie Yoellian

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