Jurnal SINTA 2
Algoritma Naive Bayes untuk Klasifikasi Sumber Belajar Berbasis Teks pada Mata Pelajaran Produktif di SMK Rumpun Teknologi Informasi dan Komunikasi
Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) · Vol. 6 No. 4 (2019) · pp. 430–435
- ISSN
- 2528-6579, 2355-7699
- Published
- 2019-07-15
- Publisher
- Fakultas Ilmu Komputer, Universitas Brawijaya
- Language
- id
Abstract
Salah satu komponen esensial dalam kegiatan pembelajaran di Sekolah Menengah Kejuruan Rumpun Teknologi Informasi dan Komunikasi (SMK TIK) adalah ketersediaan sumber belajar mata pelajaran produktif. Media internet atau online adalah sumber belajar yang berbentuk media elektronik yang dapat dimanfaatkan oleh siswa dan guru melalui jaringan internet. Salah satu bentuk media online adalah halaman web berformat.html (Hypertext Markup Language) yang berupa dokumen teks sangatlah banyak. Sehingga sumber belajar tersebut perlu di kelompokkan berdasarkan kriteria atau ciri esensial setiap mata pelajaran produktif di SMK TIK. Proses pengelompokkan menggunakan algoritma Naive Bayes karena algoritma tersebut dapat digunakan untuk dokumen teks dan menggunakan teorema Bayes dengan menganggap semua atribut saling tidak berhubungan. Tujuan penelitian ini adalah untuk mendeskripsikan hasil klasifikasi dan evaluasi kualitas klasifikasi sumber belajar berbasis teks dengan menggunakan Algoritma Naïve Bayes. Tahapan penelitian yang dilakukan adalah pengoleksian data set, pemrosesan awal dengan text mining, pembobotan Tf-Idf, pengklasifikasian Naïve Bayes, dan evaluasi akurasi. Pengklasifikasian teks menghasilkan sembilan kelompok mata pelajaran produktif dan pengujian menghasilkan nilai akurasi tertinggi 81,48%, sedangkan nilai akurasi terendah sebesar 79,63%. AbstractThe availability of learning resources for productive subjects is one of the essential components in learning activities for Vocational High Schools, especially for Information and Communication Technology competence field. Internet or online media is a learning resource in the form of electronic media that can be used by students and teachers through the internet. One form of online media is a web page formatted in.html (Hypertext Markup Language) in the form of very many text documents. So that learning resources need to be grouped based on the essential criteria or characteristics of each productive subject in Vocational High Schools. The grouping process uses the Naive Bayes algorithm because the algorithm can be used to text documents and use the Bayes theorem by assuming all attributes are mutually unrelated. The purpose of the study was to describe the results of the classification and classification quality evaluation of text-based learning sources using the Naïve Bayes Algorithm. The stages of the research carried out are collecting data sets, pre-processing with text mining, Tf-Idf weighting, Naïve Bayes classifying, and accuracy evaluation. Text classification results shows that there are nine productive subject groups and based on uji results shows that the highest accuracy value was 81,48%, while the lowest accuracy value was 79,63%.
Collaboration network of the authors
40 authors · 91 co-authorship links · 17 across different organisations · showing the 40 most connected
Hover or tap a name to highlight its co-authors. Double-click to open the author's page.
- University of Brawijaya
- Institut Teknologi Sepuluh Nopember
- Universitas Muhammadiyah Malang
- National Research and Innovation Agency
- Politeknik Negeri Malang
- Different organisations
- Same organisation
- Affiliation unknown
- Thicker line = more joint articles · bigger circle = more articles
Show as table
| Co-author | Organisation | Joint articles |
|---|---|---|
| Aditya Rachmadi | University of Brawijaya | 5 |
| Satrio Agung Wicaksono | University of Brawijaya | 5 |
| Faizatul Amalia | University of Brawijaya | 4 |
| Yusi Tyroni Mursityo | University of Brawijaya | 3 |
| Fahrur Rozi | Institut Teknologi Sepuluh Nopember | 3 |
| Septiyan Andika Isanta | Universitas Muhammadiyah Malang | 3 |
| Mochamad Chandra Saputra | University of Brawijaya | 3 |
| Cindy Felita Nur Alimah | University of Brawijaya | 2 |
| Meilita Dwi Aryati | University of Brawijaya | 2 |
| Diana Purwitasari | Institut Teknologi Sepuluh Nopember | 1 |
| Yufis Azhar | Universitas Muhammadiyah Malang | 1 |
| Wahyu Nur Hidayat | Politeknik Negeri Malang | 1 |
| Raden Arief Setyawan | University of Brawijaya | 1 |
| Issa Arwani | University of Brawijaya | 1 |
| Kartika Utami | University of Brawijaya | 1 |
| Rahmana Ilmi Hakim | University of Brawijaya | 1 |
| Nurur Rohmah | University of Brawijaya | 1 |
| Ridho Fadhlurrahman | University of Brawijaya | 1 |
| Savira Fahrunisa | University of Brawijaya | 1 |
| Azri Putri Rahmatika | University of Brawijaya | 1 |
| Dinar Indah Dwi Utami | University of Brawijaya | 1 |
| Safira Widya Hapsari | University of Brawijaya | 1 |
| Muhammad Rosyid Mubarok | University of Brawijaya | 1 |
| Anisah Ikhsawiyanthi | University of Brawijaya | 1 |
| Pratama Aprilianto | University of Brawijaya | 1 |
| Hanifah Muslimah Az-Zahra | University of Brawijaya | 1 |
| Alfansya Achmad Mulyadi | University of Brawijaya | 1 |
| Azka Azkiyah | University of Brawijaya | 1 |
| Allisa Majid | University of Brawijaya | 1 |
| Ahmad Afif Supianto | University of Brawijaya | 1 |
| Abdul Harris Wicaksono | University of Brawijaya | 1 |
| Didik Krisnandi | National Research and Innovation Agency | 1 |
| Ana Heryana | National Research and Innovation Agency | 1 |
| Yafie Arbian Cipto | University of Brawijaya | 1 |
| Fatmawati Fatmawati | University of Brawijaya | 1 |
| Tri Afirianto | University of Brawijaya | 1 |
| M. Ro'if | University of Brawijaya | 1 |
| Dian Eka Ratnawati | University of Brawijaya | 1 |
Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.