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All Journal SMARTICS Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Pengabdian Masyarakat IPTEKS JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) Generation Journal Kontribusia : Research Dissemination for Community Development Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat BERNAS: Jurnal Pengabdian Kepada Masyarakat Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Sistem Informasi dan Sistem Komputer Jurnal Computer Science and Information Technology (CoSciTech) JIWAKERTA: Jurnal Ilmiah Wawasan Kuliah Kerja Nyata BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer MIMBAR INTEGRITAS Jurnal Publikasi Teknik Informatika (JUPTI) JUSTIFY : Jurnal Sistem Informasi Ibrahimy J-ENSITEC (Journal of Engineering and Sustainable Technology) Journal Of Humanities Community Empowerment Indonesian Journal of Innovation Science and Knowledge
Rahman, Miftahur
Universitas Muhammadiyah Jember
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Journal : J-ENSITEC (Journal of Engineering and Sustainable Technology)

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STUDI KLASIFIKASI TOPIK BERITA DENGAN ALGORITMA MACHINE LEARNING Guruh Wijaya; Dudi Irawan; Zainul Arifin; Hardian Oktavianto; Miftahur Rahman; Ginanjar Abdurrahman
J-ENSITEC Vol. 11 No. 01 (2024): December 2024
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/jensitec.v11i01.12037

Abstract

As a result of the use and access of social media, it also has an impact on increasing the amount of data and information, especially text data. Text has become one of the most natural forms of data that is stored, so that the field of text mining is believed to be an advanced field of data mining. Facts that emerge from research studies that have been conducted show that 80% of company information is presented in text documents. Text mining is a multidisciplinary field, involving information retrieval, text analysis, information extraction, and clustering. The text mining classification method is one technique that can be used to carry out classification. Text classification specifically works to group text documents based on categories, and within the scope of news datasets, categories are generally divided into politics, economics, military, sports and others. Statistical methods are one of the most frequently applied methods in text emotion classification. As a method in statistics, Naive Bayes is a classification algorithm that is easy to understand in text classification. Apart from that, Naïve Bayes has good classification effects and performance for processing large-scale data. The conclusion of this research is, Naïve Bayes gets an accuracy value of 77.78%. Random Forest gets an accuracy of 70.1%. KNN gets an accuracy of 24.88% and SVM gets an accuracy value of 80.60%. Meanwhile, the respective running times are Naïve Bayes 0.046 seconds, Random Forest 150 seconds, KNN 15 seconds, and SVM 0.43 seconds.
Co-Authors Adhif Aditama Afandi, Abidah Wafiyah Ramadhaniah Ahadini Izzatus Zahro An’ars, M. Ghufroni Ashrifa, Diba Azise, Nur Daryanto Dasuki, Moh. Faishol, Dimas Erisma Fitriyah, Nina Sa'idah Ginanjar Abdurrahman Heni Pujiastuti Intan Ayu Islami Irawan, Dudi Istikomah Istikomah Kahar Haerah Lusiana, Dewi Maryam Zakiyyah, Amalina Moh. Dasuki Moh. Dasuki Muhammad Ali Ridla Muhammad Fauzen Adiman Muhammad Khoirur Roziqin Muhammad Rivansyah Muharom, Lutfi Ali Nirwana Hendrastuty Nur Azise Nurhalimah Nurhalimah Oktavianto, Hardian Pater, Dewi Lusiana Prayitno, Moh. Asy Syam Iriansyah pribadi, kukuh Qurrota A’yun Rahma Wahyuningsih Rahmadianti, Fitrah Amalia Ravi Budi Handwika Rega Sukmawati Resti Sutianingtyas Rohman, Rizal Abdur Saifudin, Ilham Setiawansyah Setiawansyah Suharso, Wiwik Sulistyo, Henny Wahyu Syamsul Arifin Syarif Hidayatullah Tiara Puspita Arin Triawan Adi Cahyanto Umilasari, Reni Very Hendra Saputra Wardiyanto, Dedep Vicky Wijaya, Guruh ZAINUL ARIFIN Zakiyyah, Amalina Maryam Zakiyyah, Amalina Maryam