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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal Information System Development JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) METIK JURNAL Building of Informatics, Technology and Science REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer Jurnal Pengabdian kepada Masyarakat Nusantara DEVICE Jurnal Sains Teknologi dan Sistem Informasi Jurnal Pengabdian Kepada Masyarakat Prosiding Konferensi Nasional PKM-CSR Jurnal Sains dan Teknologi Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi SATIN - Sains dan Teknologi Informasi International Journal of Multidisciplinary Approach Research and Science
Pangaribuan, Jefri Junifer
Universitas Pelita Harapan
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Journal : Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)

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Classification of Hearing Loss Degrees with Naive Bayes Algorithm Okky Putra Barus; Romindo; Jefri Junifer Pangaribuan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4683

Abstract

According to the World Health Organization (WHO), hearing loss is one of the fourth leading causes of disability. The number of people with hearing loss continues to increase yearly. This increase occurred due to delays in recognizing hearing loss, leading to delays in providing treatment. To solve this problem, one solution to deal with this is early identification to detect the degree of hearing loss. This research will use machine learning to classify the degree of hearing loss. The algorithm implemented in this study is naive Bayes. This study uses a data set from the Zenodo open access repository with 3105 raw data and 19 features. This study evaluates the performance of overall accuracy, precision, recall, and f1-score and classified four classes: mild, moderate, moderately severe, and severe. The methodology classification stages in this study include data preprocessing, data training, data testing, and evaluation. From evaluating the performance of the Naive Bayes algorithm, the classification results obtained the highest impacts in the form of 94% overall accuracy, 100% precision, 100% recall and 97% f1-score in classifying the degree of hearing loss.
Co-Authors Ade Maulana Ade Maulana Anggara, Alfin Angkasa, Vincent Aulia Rizky Muhammad Hendrik Noor Asegaff Barus, Okky Putra Calista Chandra Chandra, Wenripin Chilwin Chilwin Chilwin Elrico Tanto Jaya Erick Suliegna Erlanie Sufarnap Faisal Nadjar Ferawaty, Ferawaty Henry Tanjaya Ilhami, Mirza Iskandar Muda Jaclyn Tjuarsa Jepronel Saragih Jovita Zesty Jusin Jusin Jusin Kenichi Kenichi Kenichi, Kenichi Kevin Bastian Sirait Leonie, Valerie Lestari, Megawaty Megawaty Lestari Mirza Ilhami Mirza Ilhami Muhammad Thafa Kurniawan Okky Barus Putra, Alexander Reynaldo Fang Riche Riche Ricky Ricky Ricky Robin - Romindo Romindo Romindo Romindo Sabrina Jovanka Sabrina Jovanka Shendy Dennison Shendy Dennison Sherin Karisa Tanjaya, Henry Triandes Sinaga Vincent Angkasa Wiliem, Evelyn William William Yudhistira A. Pratama Yudhistira Adhitya Pratama Yudhistira Adhitya Pratama