Decision Support System for Employee Bonus Recommendation Using Fuzzy Logic Sistem Pendukung Keputusan Rekomendasi Bonus Karyawan Menggunakan Logika Fuzzy
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Abstract
A decision support system is indeed something that should be used to make it easier for organizations to determine a policy. With the existence of information technology, all data analysis and calculation is carried out automatically through computers. Similarly, in making recommendations to give bonuses to an employee in a company or institution. To speed up the decision-making process, a system is needed that can provide recommendations like calculations made by human intelligence. The system was developed using the fuzzy logic method that expresses classical logic into linguistic forms. The advantage offered by this logic is that it produces a more just and humane decision such as a decision that results from human feelings and thoughts. This system uses four variables used to determine the receipt of bonus wages, namely the age of the employee, the length of service, the amount of salary and productivity in one month. Each of these variables has a linguistic variable that is used to represent a certain state or condition that utilizes natural language. This research produces a system that can provide recommendations for organizations or companies to use in determining the receipt of bonus wages in accordance with the rules applied.
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References
[2] Ching-Torng Lina, Hero Chiub, Yi-Hong Tsenga. “Agility evaluation using fuzzy logic”. Int. J. Production Economics. 2006.
[3] John Lee, Ian Cameron, Maureen Hassall. “Improving process safety: What roles for Digitalization and Industry 4.0?”. Process Safety and Environmental Protection. 2019.
[4] Zaenuddin, Imam & Ade Bani Riyan. Perkembangan Kecerdasan Buatan (AI) Dan Dampaknya Pada Dunia Teknologi. Jitu: Jurnal Informatika Utama. Vol.2 No.2 November 2024. e-ISSN 2988-7631.Hal: 128-153
[5] Melanie A. Meyer. “Healthcare data scientist qualifications, skills, and job focus: a content analysis of job postings”. Journal of the American Medical Informatics Association. 2019.
[6] Erkam Guresen, Gulgun Kayakutlu, Tugrul U. Daim. “Using artificial neural network models in stock market index prediction”. Expert Systems with Applications. 2011.
[7] Russell, S. J., & Norvig, P. Artificial intelligence: a modern approach. thuvienso. hoasen.edu.vn. 2016.
[8] A Ibrahim, R A Surya. “The Implementation of Simple Additive Weighting (SAW) Method in Decision Support System for the Best School Selection in Jambi”. IOP Conf. Series: Journal of Physics: Conf. Series. 2019.
[9]J. Benítez, X. Delgado-Galván, J. Izquierdo, R. Pérez-García. “Consistent completion of incomplete judgments in decision making using AHP”. Journal of Computational and Applied Mathematics. 2015.
[10] Silaban, Kando Narodo. “Penerapan Metode Tsukamoto (Logika Fuzzy) Dalam Sistem Pendukung Keputusan Untuk Menentukan Besarnya Gaji Karyawan Pada Hotel Grand Antares”. Journal of Informatics, Electrical and Electronics Engineering. ISSN 2807-9507. Vol 1, No 1, September 2021. Hal 20-26
[11] Ayan Chaki, T. Chattopadhyay, Member, IEEE. “An Automatic decission support system for medical instrument suppliers using fuzzy multifactor based approach”. 5th Annual IEEE Conference on Automation Science and Engineering. 2009.
[12] Jafar Rezaei, Roland Ortt. “Supplier segmentation using fuzzy logic”. Industrial Marketing Management. 2013.
[13] S. G. Li, S. M. Sharkh, F. C. Walsh, C. N. Zhang. “Energy and Battery Management of a Plug-In Series Hybrid Electric Vehicle Using Fuzzy Logic”. IEEE Transactions on Vehicular Technology. Vol 60(8). 2011.
[14] El Hassan Ait Laasri, Es-Saïd Akhouayri, Dris Agliz, Daniele Zonta, Abderrahman Atmani. “A fuzzy expert system for automatic seismic signal classification”. Expert Systems with Applications. 2015.
[15] Heinrich Rommelfanger. “The Advantages of Fuzzy Optimization Models in Practical Use”. Fuzzy Optimization and Decision Making. Vol 3. 2004.