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All Journal Abdimas Talenta : Jurnal Pengabdian Kepada Masyarakat Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal EK dan BI Internet of Things and Artificial Intelligence Journal Jurnal Bersama Pengabdian Kepada Masyarakat
Erwin Sirait, Erwin
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Journal : Internet of Things and Artificial Intelligence Journal

 1 
Best Employee Selection Using The Additive Ratio Assesment Method Siregar, Victor Marudut Mulia; Sirait, Erwin; Sihombing, Lasminar Lusia; Siregar, Ivana Maretha
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 1 (2023): Vol. 3 No.1 (2023): Volume 3 Issue 1, 2023 [February]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i1.589

Abstract

This study aims to solve the problem of selecting the best employees at Café Alvina. In order for employee performance to be further improved and more motivated in doing their work, the leadership gives awards to employees who have a good reputation in it so that all employees are motivated to be able to improve the quality of their respective work. The problem of selecting the best employees is done by building a decision support system. The DSS was built using the ARAS (Additive Ratio Assessment method) method. The criteria used consisted of discipline, responsible, diligent, and cooperation with the weight of each criterion being 0.28, 0.11, 0.19, 0.31, 0.11. The results obtained from this study are the best employee recommendations consisting of employee_004 with a score of 0.9246 ranked 1st, employee_006 with a score of 0.8244, ranked 2nd, and employee_002 with a score of 0.5446 ranked 3rd. Through this decision support system, Alvina's café manager was greatly assisted because it becomes easier to decide on the selection of the best employees at the Café.
Classification of Customer Satisfaction Through Machine Learning: An Artificial Neural Network Approach Siregar, Victor Marudut Mulia; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi; Yunus, Muhammad
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 3 (2023): Vol. 3 No. 3 (2023): Volume 3 Issue 3, 2023 [August]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i3.643

Abstract

This study aims to classify customer satisfaction data from Café Alvina using Machine Learning, specifically by implementing the Backpropagation Artificial Neural Network. The data used in this study consists of 70 training data and 30 testing data, with the input layer of the Artificial Neural Network having 5 neurons and the output layer having 2 neurons. The tested Artificial Neural Network models include the 5-5-2 model, 5-10-8-8-2 model, 5-5-10-2 model, and 5-8-10-2 model. Among the four models used in the testing process of the Backpropagation Artificial Neural Network system using Matlab, the 5-10-8-8-2 architecture model performed the best, achieving an MSE (Mean Squared Error) of 0.000999932 during training with 2920 epochs and a testing MSE of 0.000997829. After conducting the testing, the performance of the Artificial Neural Network models was as follows: the 5-5-2 model achieved 81%, the 5-10-8-8-2 model achieved 100%, the 5-5-10-2 model achieved 98%, and the 5-8-10-2 model achieved 96%. Through the implementation of Backpropagation Artificial Neural Network, the classification of customer satisfaction can be effectively performed. The trained and tested data demonstrate that the Artificial Neural Network can accurately recognize the input data in the system.
Decision Support System for Selecting Social Assistance Recipients using The Preference Selection Index Method Parapat, Eka Pratiwi Septania; Sinaga, Kalvin; Sirait, Erwin; Manalu, Andi Setiadi
Internet of Things and Artificial Intelligence Journal Vol. 3 No. 4 (2023): Vol. 3 No. 4 (2023): Volume 3 Issue 4, 2023 [November]
Publisher : Association for Scientific Computing, Electronics, and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/iota.v3i4.662

Abstract

This research aims to solve the problem of selecting social assistance recipients in the Nagori Moho area, Java Marajah Bah Subdistrict, Jambi, Simalungun District; in order to obtain the right targeted recipients of social assistance, the Nagori office carries out the selection of its residents, this selection is carried out by implementing a computer-based decision support system (DSS). The decision support system uses the PSI method. The criteria used in this method consist of economic condition, income, jobs, age, and dependents of the school children. The results obtained from this research are recommendations for the population receiving aid with results consisting of rank 1 with the alternative value S_Purba with a value of 0.9286, then rank two with the alternative F_Azhar with a value of 0.7599, and rank 3 is Jumiati with a value of 0.7163. This decision support system can make it easier for the Nagori office to select residents worthy of assistance.
Co-Authors Darwin Lie Doris Yolanda Saragih Hengki Mangiring Parulian Simarmata Manalu, Andi Setiadi manik, ria retno Muhammad Yunus Parapat, Eka Pratiwi Septania Purba, Arifin Tua Saragih, Doris Yolanda Saragih, Roy Syahputera Siagian, Gunaria Sihite, Sandi Maiko Sihombing, Lasminar Lusia Silalahi, Mastiur Verawaty Simarmata, Hengki Mangiring Parulian Sinaga, Kalvin Sirait, Debora Siregar, Ivana Maretha Siregar, Victor Marudut Mulia Situmorang, Masni Veronika