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All Journal INTEGER: Journal of Information Technology Network Engineering Research Operation [NERO] Prosiding Seminar Nasional Sains dan Teknologi Terapan JURIKOM (Jurnal Riset Komputer) Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Ilmu Komputer dan Sistem Informasi
Sulistyowati Sulistyowati
Institut Teknologi Adhi Tama Surabaya
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science

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Penerapan Metode K-NN untuk Klasifikasi Pencari Kerja pada Dinas Tenaga Kerja Budanis Dwi Meilani; Mochammad Reza Dwi Afriansyah; Sulistyowati Sulistyowati; Zuli Maulidati; Resa Uttungga
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.328

Abstract

The Pasuruan Manpower Office is one of the agencies that tries to assist companies in the selection process for job seekers who match the criteria for available job vacancies. A common problem in the Job Placement section is the inefficiency in determining suitable job classifications for job seekers. In addition to the lack of human resources in the job selection process, there is also no system that helps the workforce sector in selecting suitable jobs for job seekers. The purpose of this study is to design and analyze a system that can assist the Pasuruan Regency Manpower Office in finding Fields / Classes in Placement according to the criteria of job seekers. The job seeker criteria used are Education Level, Work Experience, Language Proficiency, Last Educational Grade, Major, Expertise, and Training Certificate. The method used in this study is the K-NN method. The K-NN method is a method for classifying objects based on learning data that is closest to the object, by selecting the appropriate K data can be classified based on the nearest neighbor. From the results of the tests carried out 12 times on the testing system against 50 data with training data parameters of 25 data to 50 data and the K values ​​used were 1, 2, 3, 4, 5 and 6, the average classification had an accuracy value of 81.17% and an error value of 18.87%.
Co-Authors Abdul Kholiq Al Anam Amalia Anjani Arifiyanti Andi Wijaya Andy Rachman Anggi Yhurinda Perdana Putri Anwar Sodik, Anwar Bagus Eko Prasetyo Basma Eno Ketherin Bayu Fahmi Agustin Budanis Dwi Meilani Dynda Cendikya Di Putri Edy Santoso Hendra Thesna Prayoga Hendro Nugroho Ilham Agus Mardiko Isa Albanna Isa Albanna M. Anandi Ferdiansyah Mochammad Reza Dwi Afriansyah Muhammad Ainul Rozi Nanang Fakhrur Rozi Pakarbudi, Adib Pascal Brilliandy Kristianto Pratama Sandi Alala Pratama Sandi Alala Putri, Anggi Yhurinda Perdana Rachman Arief Resa Uttungga Ridlwan Rabbani Nugroho Shah Khadafi Wahyu Widodo Yanuar Efendi Zuli Maulidati