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All Journal Journal on Education JOMLAI: Journal of Machine Learning and Artificial Intelligence
P.A.M. Zidane R.W.P.P. Zer
STIKOM Tunas Bangsa
Computer Science & IT Education Engineering

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Peran Pendidikan dan Pelatihan Manajemen Keuangan dalam Meningkatkan Kapabilitas Karyawan Bank BCA Pematangsiantar Zulia Almaida Siregar; Roger Susilo Napitupulu; Dimas Prayogi; P.A.M. Zidane R.W.P.P. Zer; Michael Kevin Artado Sihombing; Abdi Rahim Damanik
Journal on Education Vol 7 No 1 (2024): Journal on Education: Volume 7 Nomor 1 Tahun 2024
Publisher : Departement of Mathematics Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/joe.v7i1.6950

Abstract

This study aims to analyze the role of financial management education and training in enhancing the capabilities of employees at Bank BCA Pematangsiantar. Education and training are crucial components in human resource development, which not only increase knowledge and skills but also strengthen employees' competencies in managing complex financial tasks. This study employs a quantitative approach using a survey method to collect data from employees at Bank BCA Pematangsiantar. The data are analyzed using descriptive and inferential statistical techniques to measure the impact of education and training on employee capabilities.The results of the study show that financial management education and training programs have a significant impact on improving employee capabilities. Employees who participated in these programs demonstrated improvements in financial analysis, risk management, and better financial decision-making. Additionally, the training also enhanced employees' confidence in performing their daily tasks.This study concludes that investing in financial management education and training is an effective strategy to enhance employee capabilities and, ultimately, the overall performance of Bank BCA Pematangsiantar. Recommendations are given to the bank management to continue supporting and developing relevant education and training programs, as well as creating a work environment that fosters continuous learning.
Analysis of Open Unemployment Rate Prediction Using Backpropagation Method P.A.M. Zidane R.W.P.P. Zer; Dimas Prayogi; M Arif Y Sinaga; Olivia Diwani Saragih; Victor Asido Elyakim P
JOMLAI: Journal of Machine Learning and Artificial Intelligence Vol. 4 No. 2 (2025): Juni 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jomlai.v4i2.5957

Abstract

The open unemployment rate (TPT) is one of the important indicators in assessing the economic health of a region. This study aims to develop an accurate prediction model for the open unemployment rate using the backpropagation algorithm, as well as to evaluate the factors that influence the prediction. Accurate TPT prediction can help the government and policy makers in designing strategies to alleviate unemployment based on the results of the analysis of the developed model. This study aims to analyze and predict the Open Unemployment Rate (TPT) in various provinces in Indonesia in 2024 to 2026 using an artificial neural network model with the Backpropagation algorithm. Based on the test results, the 3-6-1 architecture model showed a prediction ability with 100% accuracy, while other architectures also gave very good results, with 100% accuracy for the 3-3-1 model and 97.06% for the 3-12-1 model. The TPT prediction results show that the unemployment rate is predicted to continue to increase from year to year, indicating the potential for an increase in the number of unemployed in the future. On the other hand, the accuracy analysis shows that each architecture produces different results, with the 3-6-1 architecture producing a longer time for the testing process, but still providing optimal accuracy. This finding illustrates that choosing the right architecture greatly affects the accuracy and efficiency in predicting TPT, which can be an important basis in formulating policies to eradicate unemployment in Indonesia.
Co-Authors Abdi Rahim Damanik Dimas Prayogi M Arif Y Sinaga Michael Kevin Artado Sihombing Olivia Diwani Saragih Roger Susilo Napitupulu Victor Asido Elyakim P Zulia Almaida Siregar