Distribution and Classification of Community Service Topics Based on the Results of Extracting Information from Proposal Documents Using Text Mining With Naïve Bayes Algorithm (Case Study: Polytechnic Caltex Riau)
Keywords:community service, data mining, text mining, naïve bayes, frequent itemset mining, php, data warehouse
AbstractCommunity service is an activity that aims to help certain communities in several activities without expecting any form of reward. Community Service it is carried out by the Research and Community Service Section (BP2M). The topics of community service are very diverse, making it difficult for BP2M to classify them and take a relatively long time if done manually. BP2M parties must first understand the contents of the documents sent. Based on the problems above, a system will be created that can help in making it easier to classify PKM topics according to the abstract in the proposal document. In addition to grouping the system to be created, it will also display the distribution of data based on Title, Year of Service, Outcomes and Location of PKM. The method used for grouping is Text Mining with Naive Bayes algorithm. Based on the results of functionality testing and UAT testing, the system can function properly and all its features are acceptable to the user and are as expected. And the results of the confusion matrix analysis show that the system is able to classify the title and synopsis of the book with the naesve Bayes algorithm with an accuracy of 70%.
Hamzah, Amir.(2012). Klasifikasi Teks Dengan Naïve Bayes Classifier (Nbc) Untuk Pengelompokan Teks Berita Dan Abstract Akademis. (2011), 269–277.
Ransi, N., & Nangi, J. (2017). Implementasi Text Mining Klasifikasi Skripsi Menggunakan Metode Naïve Bayes Classifier. 3(2), 187–194.
Nisa, D. A., Hajar, D., & Kusuma, D (2018). Implementasi Text Mining pada Judul Proyek Akhir dengan Algoritma Naive Bayes dan K-Medoids ( Studi Kasus : Politeknik Caltex Riau ). XX(X), 1–8.
Riset, K. (2018). Buku Panduan Pelaksanaan Penelitian dan Pengabdian kepada Masyarakat Edisi XII.pdf.ISBN : 978-602-73996-5-5.
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques. In Data Mining: Concepts and Techniques. https://doi.org/10.1016/C2009-0-61819-5 Kurniawan, T. A. (2007). Pengujian Struktur Program Dengan Pengujian Jalur Dasar (Basis Path Testing) : Teori Dan Aplikasi. Jurnal EECCIS, 1(1), 29–32.
Even, Yahir dan Zohar. (2002). Introduction to text mining. Automeated Learning.
Utami, D. (2011). Antinasari, P., Perdana, R. S., & Fauzi, M. A. (2017). Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku. Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, 1(12), 1733–1741.
Even, Yahir dan Rouli, M., Paulus, I.S., & Ridi, F. (2015). Usulan Metode Evaluasi User Acceptance
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