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All Journal Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) EXPERT: Jurnal Manajemen Sistem Informasi dan Teknologi Scientific Journal of Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Script Journal: Journal of Linguistic and English Teaching Syntax Literate: Jurnal Ilmiah Indonesia JURIKOM (Jurnal Riset Komputer) Budimas : Jurnal Pengabdian Masyarakat Abdimasku : Jurnal Pengabdian Masyarakat DEDIKASI PKM Walisongo Journal of Information Technology Indonesian Journal of EFL and Linguistics Jurnal Rekayasa elektrika SmartComp Advance Sustainable Science, Engineering and Technology (ASSET)
Muljono, -
Department Of Computer Science, Universitas Dian Nuswantoro, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences Other

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Klasifikasi Berita Televisi Menggunakan Metode K-NN, Naïve Bayes dan SVM Tri Wuryantoro; Muljono Muljono; Pujiono Pujiono
JURIKOM (Jurnal Riset Komputer) Vol 11, No 6 (2024): Desember 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v11i6.8420

Abstract

News through television media is still one of the media that is widely used by the public in obtaining the latest information. The Central Java TVRI Public Broadcasting Institution has a news program called Berita Jawa Tengah which airs every day and  doesn’t have a classification system. This research was carried out in several stages, in the initial stage preprocessing was carried out which included: data collection, cleaning, case folding, tokenizing, normalization, stopword removal, stemming, then continued with word weighting (TF-IDF) and finally applying the K-Nearest Neighbor classification method (K-NN), Naïve Bayes and Support Vector Machine (SVM). The results of the classification carried out show that the K-NN classification method has higher results compared to other methods, namely an Accuracy value of 0.94, Precision 0.92, Recall 0.94 and f1-score 0.93, so it can be concluded that Television news classification using the K-NN method is the method that provides the most accurate results.
Co-Authors Abimanyu Cahya Pramudhita Aditya Wahyu Ramadhan Agus Sri Giyanti Ahmad Fanani Akbar Dwi Syahputra Al Ghiffari Bintang Saputra Alvino Dwi Rachman Prabowo Angga Adetiya Anisa Larassati Aripin Azhara Devi Sandi Bayu Aryanto Budi Harjo Cinantya Paramita Dewa Ayu Putri Tesalonika Edi Noersasongko Erlin Dolphina Erna Zuni Astuti Etika Kartikadarma Faezal Hartono Fandi Yulian Pamuji Farikh Al Zami Filmada Ocky Saputra Hanny Haryanto Heni Indrayani Junta Zeniarja Jutono Gondohanindijo Karis W. Magus Sarasnomo Mangatur Rudolf Nababan Moch Arief Soeleman Muhamad Hasan Basori Muhamad Ni'am Syukri Roni Asmi Muhammad Misdram Muslih Muslih Nina Setyaningsih Nur Rokhman Nurhindarto, Aris Pujiono Pujiono Pujiono Pujiono Purwanto Purwanto R Arief Nugroho Raden Arief Nugroho Rahma Hayuning Astuti Restu Poetra Alqurni Ricardus Anggi Pramunendar Robiatul Adawiyah Saraswati, Galuh Wilujeng Satriya, Candra Yudha Septian Enggar Sukmana Sunardi Sunardi Sunardi Sutriawan Sutriawan Syaiful A. Septemuryantoro Tri Wuryantoro Utari Fatma Dewi Valentina Widya Suryaningtyas, Valentina Widya Wildani Eko Nugroho, Wildani Eko Wulanda Yuniar Permatasari