PREDIKSI KEHADIRAN MENGGUNAKAN METODE KLASIFIKASI NAÏVE BAYES, ONE-R, DECISION TREE
Bilah Samping Artikel
Read Counter : 1656
Download : 3949
Isi Artikel Utama
Abstrak
Abstrak
Proses mencari pola atau informasi yang berguna pada suatu kumpulan data dengan menggunakan metode tertentu, saat ini telah menjadi topik yang menarik. Salah satu manfaatnya yaitu dapat menunjang pengambilan keputusan dalam suatu organisasi baik itu organisasi profit maupun non profit. Pada makalah ini akan dilakukan pengujian terhadap sekumpulan data yang diambil dari kejadian nyata untuk diolah, guna mendapatkan informasi atau pola yang dapat berguna untuk penentuan pengambilan sebuah keputusan. Pengujian pada makalah ini merupakan prediksi terhadap pengguna jasa sebuah operator seluler akan kehadirannya pada suatu acara berdasarkanbeberapa indicator, cuaca, jarak relative terhadap lokasi acara, serta apakah pengguna jasa tersebut merupakan termasuk pelanggan pasca bayar atau tidak. Pengujian dilakukan dengan menggunakan tiga metode klasifikasi, yakni naïve bayes, decision tree, dan oneR. Hasil dari percobaan ini bisa menunjukkan prediksi dari setiap percobaan dengan tingkat akurasi prediksi yang berbeda-beda disetiap metode yang digunakan.
Kata kunci : Penambangan data, klasifikasi, naïve bayes, oneR, pohon keputusan.
Abstract
The searching process for a pattern or useful information in a data set using a particular method, has now become a interesting topics. One of benefits is to support decision making in an organization event profit organizations or non-profit organizations. In this paper will be tested to a dataset taken from real events to be processed, in order to get information or a pattern that can be useful for the decision-making. Testing in this paper is the prediction users mobile service of his presence at an event based on several indicators, the weather, the distance relative to the location of the event, as well as whether the user services are included postpaid subscribers or not. Testing conducted by using three classification methods; Naive Bayes, Decision Tree, and Oner. The results of the experiments are able to show prediction of each trial with different accuracy on every method used.
Keywords : Data mining, classification, naïve bayes, oneR, decision tree.
Rincian Artikel
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Referensi
Jiawei Han, Micheline Kamber, Jian Pei. Data Mining: Concepts And Techniques 3rd Edition. Urbana-Champaign: Morgan Kaufmann, 2011.
Median Yuli Hartanto, Daniel Oranova S, Sarwosri. Prediksi Umur Bug Dalam Poyek Pengembangan Perangkat Lunak. Surabaya, 7 27, 2011.
Munawar. Pemodelan Visual dengan UML,. Yogyakarta: GrahaIlmu, 2005.
Natalius, Samuel. Metoda Naïve Bayes Classifier dan Penggunaannya pada Klasifikasi Dokumen. Makalah, Bandung: Makalah II2092 Probabilitas dan Statistik, 2010.
Oded Z. Maimon, Lior Rokach. Decomposotion methodology for knowledge discovery and data mining : theory and applications. World Scientific, 2005.
Selvia Lorena Br Ginti, Reggy Pasya Trinanda. "Teknik Data Mining Menggunakan Metode Bayes Classifier Untuk Optimalisasi Pencarian Pada Aplikasi Perpustakaan (Studi Kasus : Perpustakaan Universitas Pasundan – Bandung." Jurnal Teknologi dan Informasi UNIKOM, Volume 1 No 6, 2014: 8-23.
Silipo, Rosario dan Phil Winters. "big data, Smart Energy, and Predictive Analytics." Time Series Prediction of Smart Energy Data, 2013: 1-37.
Silipo, Rosario. KNIME and big data. 2013. http://www.dataminingreporting.com/blog/knime¬-and-¬big-¬data (accessed September 28, 2014).
Weka 3. Data Mining with Open Source Machine Learning Software in Java. n.d. http://www.cs.waikato.ac.nz/ml/weka/index.html (accessed April 06, 2011).
Widodo, Pudji. "Rule-Based Classifier Untuk Mendeteksi Penyakit Liver." Bianglala Informatika Vol . II No 1, 2014: 71-80.