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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal UNITEK KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Algoritma J-COSCIS : Journal of Computer Science Community Service ABDINE Jurnal Pengabdian Masyarakat Jurnal Teknologi Komputer dan Informasi Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Journal Of Artificial Intelligence And Software Engineering Jurnal Komputer dan Teknologi (JUKOMTEK) Smart Dedication: Jurnal Pengabdian Masyarakat
Gellysa Urva
Sekolah Tinggi Teknologi Dumai
Arts Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Other

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Journal : Journal Of Artificial Intelligence And Software Engineering

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Optimization of Employee Reward Schemes Using Genetic Algorithm: A Multi Criteria Performance Based Approach Urva, Gellysa; Desriyati, Welly
Journal of Artificial Intelligence and Software Engineering Vol 5, No 2 (2025): June
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i2.7105

Abstract

Employee reward distribution plays an important role in increasing motivation and retention. Conventional employee reward models often contain elements of subjectivity and do not reflect the overall contribution of employees. This can lead to unfairness and reduce work motivation. Traditional models in reward allocation often fail to incorporate a comprehensive evaluation of employee performance based on various criteria. This study develops a multi-criteria performance-based reward allocation model using Genetic Algorithm (GA) as an optimization approach. The model is designed to consider various performance indicators such as performance, attendance, tenure, and innovation in the process of fair and proportional bonus distribution. The optimization results show a very strong positive correlation (r = 0.99) between the employee's composite score and the amount of bonus allocated. In addition, the simulation of the evolution of fitness values shows a constant increase in both the average and the best values of the solution population, confirming the effectiveness of the genetic algorithm exploration and convergence process. This model produces a bonus distribution that is proportional to employee contributions, reflecting the principles of fairness, meritocracy, and transparency in the reward system. In addition, this model is flexible to budget changes and can be replicated for real implementation. The scientific contribution of this research lies in the application of a heuristic approach to multi-criteria optimization in the context of human resource management, complementing the literature that has so far been dominated by linear models.
A Classification Model of Children’s Digital Device Dependency Based on the Learning Vector Quantization (LVQ) Algorithm Urva, Gellysa; Nazir, Refdinal
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember (On Progress)
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8244

Abstract

Digital device dependency among children has become a critical issue in the modern era, influencing cognitive, social, and health aspects. Excessive use of digital devices may lead to decreased concentration, academic performance, and social interaction. The identification of children's digital dependency levels has often relied on manual observation by parents or teachers, which tends to be subjective. Therefore, this study aims to develop a classification model for children's digital device dependency using the Learning Vector Quantization (LVQ) algorithm. The data were collected through a questionnaire distributed to 110 respondents, consisting of parents of elementary school students in Dumai City. The questionnaire contained 34 items measured using a five-point Likert scale (1–5). The data were processed using Python with supporting libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and Neupy. The experimental results showed that the LVQ algorithm successfully classified children's dependency levels into three categories low, moderate, and high with an accuracy of 87.5%, an average precision of 85.4%, and an average recall of 86.2%. The findings revealed that most children belong to the moderate dependency category, with an average score of 3.03. The main factors influencing digital dependency include usage duration, habits of using devices while eating or before sleeping, and decreased social interaction. The application of the LVQ algorithm proved effective in identifying children’s digital usage patterns and can serve as a foundation for developing early detection systems and promoting digital literacy policies within elementary education environments
Co-Authors Amallia Amiroel Oemara Syarief Anisa Rahmawati Aulia Sari Burelia, Ulfhi Desyanti - Desyanti Halimahtussadiyah Indradewa, Rhian Julanos Khairul Azmi Marpaung, Roki Melingga Milyani Merina Pratiwi Novia Ulfa Ravie, Muhammad Refdinal Nazir Savitry, Eka Sellyana, Ari Siti Aminah Suhaidi, Mustahzzihim Syahrul Ramadhani Syamsiar, Syamsiar Tina Wati Manalu Tri Handayani Tri Handayani Tri Yuliati Welly Desriyati Welly Desriyati Yuliati, Tri Yuliati, Tri