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All Journal Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal EK dan BI Internet of Things and Artificial Intelligence Journal
Erwin Sirait, Erwin
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering

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PENGEMBANGAN APLIKASI MOBILE UMKM UNTUK MENINGKATKAN PENJUALAN DAN JANGKAUAN PASAR Sirait, Erwin; Purba, Arifin Tua
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 7 No 2 (2024)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v7i2.1848

Abstract

Micro, small, and medium enterprises (MSMEs) play a crucial role in the Indonesian economy, including in Pematangsiantar City, which has more than 29,000 MSMEs. However, MSMEs face various challenges, such as tight competition, limited market access, and lack of adoption of digital technology. This study aims to develop a mobile application for MSMEs to improve operational efficiency, expand market reach, and increase competitiveness. The methods used include literature studies, data collection from local MSMEs, flowchart and ERD-based system design, and application testing to ensure its functionality. The application developed has key features such as product management, digital transactions, and integration with logistics partners and payment systems. The results of the study show that digitalization through mobile applications can increase market reach, accelerate transactions, and provide convenience in business management for MSME actors. Thus, digital transformation through this application has the potential to be an effective solution in increasing MSME growth and supporting business sustainability in the digital era.
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.
ENHANCING STATISTICAL LEARNING MOTIVATION THROUGH LECTURER COMPETENCY: A STUDY AT UNIVERSITAS MURNI TEGUH, INDONESIA Simarmata, Hengki Mangiring Parulian; Saragih, Doris Yolanda; Sirait, Erwin; Lie, Darwin
Jurnal Ekonomi dan Bisnis (EK&BI) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/ek&bi.v8i1.2247

Abstract

This study aims to examine the influence of statistics lecturer competence and teaching strategies on students’ interest in learning statistics at Universitas Murni Teguh PSDKU Pematangsiantar. Using a quantitative approach with a sample of 105 undergraduate students, data were collected through structured questionnaires and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that lecturer competence has a strong and significant positive effect on students’ interest (β = 0.684; p < 0.001), indicating that pedagogical skills, subject mastery, and communicative ability play a crucial role in motivating students to engage with statistical content. Conversely, teaching strategies showed a significant yet negative effect (β = -0.188; p < 0.05), suggesting a potential mismatch between current instructional approaches and students’ learning preferences. Validity and reliability tests confirmed that all constructs met convergent validity (outer loading > 0.70), internal consistency (Cronbach’s alpha > 0.88), and model fit criteria (SRMR = 0.069; NFI = 0.830). The results highlight the importance of competent and engaging lecturers in fostering positive attitudes toward learning statistics, while also emphasizing the need for adaptive and student-centered teaching strategies. This study contributes to the growing body of knowledge on educational quality and student engagement in quantitative disciplines, and offers practical implications for improving teaching practices in higher education, particularly in contexts where statistics is perceived as a challenging subject
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN MAHASISWA PKL TERBAIK MENGGUNAKAN METODE MOOSRA Purba, Arifin Tua; Manalu, Andi Setiadi; Sirait, Erwin
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2137

Abstract

Politeknik Bisnis Indonesia is a vocational higher education institution committed to producing graduates who are not only academically competent but also equipped with practical skills required in the workforce. One of the essential programs in its curriculum is the Internship (PKL), designed to allow students to apply their knowledge in real-world work environments. However, the selection of the best internship students has been conducted manually, leading to inefficiencies and potential subjectivity in the evaluation process. This study aims to design a Decision Support System (DSS) using the MOOSRA (Multi-Objective Optimization on the Basis of Simple Ratio Analysis) method to support a more objective and systematic selection process. The evaluation involves five main criteria: discipline, teamwork, skill, work quality, and attendance, with six student candidates as alternatives. The research stages include problem identification, criteria and weight determination, data collection, data processing with the MOOSRA method, system design, and system testing. The results show that the MOOSRA method effectively ranks the students, with student A4 selected as the best internship participant with the highest Yi score of 6.12347. This research demonstrates that the MOOSRA method can significantly improve decision-making accuracy and fairness in multi-criteria selection processes.
Co-Authors Darwin Lie Manalu, Andi Setiadi Muhammad Yunus Parapat, Eka Pratiwi Septania Purba, Arifin Tua Saragih, Doris Yolanda Sihombing, Lasminar Lusia Simarmata, Hengki Mangiring Parulian Sinaga, Kalvin Siregar, Ivana Maretha Siregar, Victor Marudut Mulia