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All Journal TESLA: Jurnal Teknik Elektro Logic : Jurnal Rancang Bangun dan Teknologi Jurnal Teknologi Informasi dan Terapan (J-TIT) Jurnal Teknik Elektro dan Komputasi (ELKOM) Discovery : Jurnal Ilmu Pengetahuan Among : Jurnal Pengabdian Masyarakat BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Abdimas Awang Long: Jurnal Pengabdian dan Pemberdayaan Masyarakat J-ENSITEC (Journal of Engineering and Sustainable Technology) Jurnal Aplikasi Sistem Informasi dan Elektronika International Applied Science Journal Of Humanities Community Empowerment Jurnal Edukasi Pengabdian Masyarakat: EDUABDIMAS ABDIMASTEK Teknologi : Jurnal Ilmiah Sistem Informasi
Irawan, Dudi
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.
PENERAPAN ALGORITMA APRIORI UNTUK MENGIDENTIFIKASI LOKASI STRATEGIS PROMOSI SEKOLAH SWASTA Sulistyo, Henny Wahyu; Hardian Oktavianto; Irawan, Dudi
J-ENSITEC Vol. 11 No. 02 (2025): June 2025
Publisher : Universitas Majalengka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31949/j-ensitec.v11i02.13377

Abstract

Schools must carry out effective promotions and target the right markets to attract prospective students efficiently. This study applies data mining using Association Rule and Apriori algorithms to uncover patterns for identifying promising promotional areas. It consists of four stages: data collection, preprocessing, implementation and testing, and evaluation, aiming to determine strategic locations for school promotion. The Apriori algorithm analyzed student data and produced 16 association rules, all with 100% confidence. Findings show that Kaliwates District appeared eight times, Sumbersari four times, and Tempurejo and Patrang twice each, indicating most students come from Kaliwates. The ‘Public Elementary School’ itemset appeared 12 times, revealing that most students enrolled from public rather than private schools. The study concludes that educational data plays a crucial role in shaping promotion strategies, especially through association analysis. Rules with the ‘District’ itemset in the antecedent are particularly useful as a promotional reference. These insights help decision-makers identify high-potential regions and school origins, supporting more targeted and effective promotional efforts.
Co-Authors Aditia, Daffa Hibran Ari Eko Wardoyo Ari Eko Wardoyo Auliq, Muhammad A'an Aurelia Bahri, Mokh Hairul Bahri, Mokh. Hairul Brahma, Aji Brian, Muhammad Daffa, Clarence Daffa Rizqullah Fitriana Fitriana Ginanjar Abdurrahman Hakim, Muhammad Amirul Idris Mahmudi ISWAHYUDI ISWAHYUDI Iswahyudi yudi Izzati Muhimmah Lintang Setyo Kurniawati Moh Khoirul Anam Mokh Hairul Bahri Muhammad Ainul Wafi Muharom, Lutfi Ali Nogroho, Aji Brahma Oktavianto, Hardian Qodariyah , Nur Rahman, Miftahur Sofia Ariyani Sulistyo, Henny Wahyu Sutikno Sutikno Taufiq Timur Warisaji Tito Yuwono, Tito Umilasari, Reni Warisaji, Taufiq Timur Wijaya, Guruh ZAINUL ARIFIN