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All Journal Perfecting a Video Game with Game Metrics Jurnal Ilmiah Kursor Jurnal Teknologi dan Sistem Komputer Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Best : Journal of Applied Electrical, Science and Technology Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Endang Setyati
Sekolah Tinggi Teknik Surabaya
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Physics

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Journal : Jurnal Nasional Teknik Elektro dan Teknologi Informasi

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Self-Training Naive Bayes Berbasis Word2Vec untuk Kategorisasi Berita Bahasa Indonesia Joan Santoso; Agung Dewa Bagus Soetiono; Gunawan; Endang Setyati; Eko Mulyanto Yuniarno; Mochamad Hariadi; Mauridhi Hery Purnomo
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 7 No 2: Mei 2018
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

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Abstract

News as one kind of information that is needed in daily life has been available on the internet. News website often categorizes their articles to each topic to help users access the news more easily. Document classification has widely used to do this automatically. The current availability of labeled training data is insufficient for the machine to create a good model. The problem in data annotation is that it requires a considerable cost and time to get sufficient quantity of labeled training data. A semi-supervised algorithm is proposed to solve this problem by using labeled and unlabeled data to create classification model. This paper proposes semi-supervised learning news classification system using Self-Training Naive Bayes algorithm. The feature that is used in text classification is Word2Vec Skip-Gram Model. This model is widely used in computational linguistics or text mining research as one of the methods in word representation. Word2Vec is used as a feature because it can bring the semantic meaning of the word in this classification task. The data used in this paper consists of 29,587 news documents from Indonesian online news websites. The Self-Training Naive Bayes algorithm achieved the highest F1-Score of 94.17%.
Co-Authors Agung Dewa Bagus Soetiono Eko Mulyanto Yuniarno Elis Fitrianingsih Francisca Haryanti Chandra Gunawan Joan Santoso Luqman Zaman Mauridhi Hery Purnomo Mochamad Hariadi Muhammad Farkhan Raymond Sutjiadi Resmana Lim Surya Sumpeno Tri Septianto