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All Journal INTECOMS: Journal of Information Technology and Computer Science International Journal of Law Social Sciences and Management
Qolbi, Rofika
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Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice Social Sciences

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Penggunaan Machine Learning dalam Sistem Informasi SDM untuk Prediksi Turnover Karyawan fazhar laundry dengan algoritma Decission Tree Lestari, Sri; Qolbi, Rofika
INTECOMS: Journal of Information Technology and Computer Science Vol. 8 No. 4 (2025): INTECOMS: Journal of Information Technology and Computer Science
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/bhe4by81

Abstract

Turnover karyawan merupakan tantangan signifikan dalam industri laundry yang dapat berdampak pada efisiensi operasional dan kualitas layanan. Penelitian ini bertujuan untuk mengembangkan sistem prediksi turnover karyawan menggunakan pendekatan machine learning dengan algoritma Decision Tree yang diintegrasikan ke dalam sistem informasi sumber daya manusia (SDM). Data yang digunakan mencakup faktor-faktor seperti usia, masa kerja, kinerja, kehadirandan gaji. Algoritma Decision Tree dipilih karena kemampuannya dalammenghasilkan model yang mudah diinterpretasikan serta efektif dalam menangani data kategorikal dan numerik. Hasil penelitian menunjukkan bahwa modelprediksi yang dikembangkan mampu mengidentifikasi potensi karyawan yang berisiko tinggi untuk melakukan turnover dengan tingkat akurasi yang memadai. Sistem ini diharapkan dapat membantu manajemen dalam mengambil keputusan strategis untuk menurunkan tingkat turnover serta meningkatkan retensi karyawan.  
Application of the K-Nearest Neighbor Method in Determining Laptop for Classes Qolbi, Rofika; Tundo, Tundo; Putri Wibowo, Salsabila; Akbar, Yuma
International Journal of Law Social Sciences and Management Vol. 1 No. 3 (2024): International Journal of Law Social Sciences and Management
Publisher : Yayasan Meira Visi Persada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69726/ijlssm.v1i3.31

Abstract

Laptops are one of the basic needs in today's modern life. Laptops are used in a wide variety of activities such as work, study, and entertainment. This research aims to be able to predict the class of laptops in the Ilda Computer store. In this process, the K-Nearest Neighbor Algorithm (KNN) method will be applied. There are 2 types of data that will be used in this study, namely training data totaling 80 data and test data as many as 6 data. In the data, there are 7 criteria that will be used, namely Price, Screen Size, Resolution, OS, RAM, Processor Type, and Laptop Class. In this study, it was obtained that the application of the KNN Algorithm can help in determining the prediction of the Laptop Class. And also the application of the KNN algorithm with K=3 obtained the best performance results with an accuracy value of 50%, a presicion of 50%, and a recall of 66%. Meanwhile, with K=4, the best performance results were obtained with an accuracy value of 50%, presicion of 66%, and recall of 50%. Finally, the K=5 obtained the best performance with an accuracy value of 66%, a presicion of 33%, and a recall of 100%.
Co-Authors Akbar, Yuma Putri Wibowo, Salsabila Sri Lestari Tundo, Tundo