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p-Index From 2021 - 2026
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This Author published in this journals
All Journal Journal of Economic, Bussines and Accounting (COSTING) bit-Tech
Alvin Lesmana
Universitas Widya Dharma Pontianak
Computer Science & IT Economics, Econometrics & Finance

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Financial Fitness: A Guide to Boosting Your Money Management Skills Hendy Budianto; Andre Prasetya Willim; Alvin Lesmana
Journal of Economic, Bussines and Accounting (COSTING) Vol 7 No 4 (2024): Journal of Economic, Bussines and Accounting (COSTING)
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/costing.v7i4.11093

Abstract

This study investigates the relationships between financial literacy, budgeting practices, financial behavior, and money management skills among customers of Bank Mandiri Pontianak Diponegoro. Using a quantitative research design with a sample of 90 customers selected via random sampling, data was collected using structured questionnaires. The analysis employed Smart PLS (Partial Least Squares Structural Equation Modeling) to examine direct and indirect effects. The findings reveal significant direct effects, where higher levels of financial literacy and effective budgeting practices are associated with improved financial behavior and enhanced money management skills. Indirect effects further underscore the role of financial behavior as a mediator between financial literacy/budgeting practices and money management skills. These results highlight the importance of promoting financial education and supporting disciplined budgeting practices to foster better financial behaviors and enhance overall financial well-being among bank customers.
Safety Stock Forecasting using ARMA and DR-ARMA under Different Sparsity Levels Jasmine Putri Halim; Jimmy Tjen; Alvin Lesmana
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3154

Abstract

Accurate demand forecasting is vital in supply chain management, particularly in the fast-moving consumer goods (FMCG) industry that experiences rapid stock turnover and fluctuating demand. The Auto-Regressive Moving Average (ARMA) has been the standard approach for time series forecasting, however it often underperforms under sparse and fluctuating data. This study contributes to the literature by applying Demand Response-ARMA (DR-ARMA) that was initially developed to address data sparsity and fluctuations under more complete and lower-sparsity data conditions. Using three primary datasets with varying sparsity levels from an FMCG distributor of bottled water products in West Borneo, DR-ARMA was benchmarked against classical ARMA. The results show that DR-ARMA consistently outperforms the classical ARMA model even under more complete, lower sparse data conditions. In lower sparsity datasets, DR-ARMA achieved average Mean of Percentage Error (MAPE) values of 22.64% and 6.41% respectively compared to the baseline ARMA model (235.60% and 180.86%). However, its best performance was observed in higher sparse condition (70.45%), achieving an average MAPE value of 1.79% across all datasets, suggesting the model remains most effective when applied to sparse data as originally intended. These improvement enables more precise safety stock planning, lower holding costs, and position DR-ARMA as a practical forecasting tool that connects analytical performance with real operational impact.
Co-Authors Andre Prasetya Willim Hendy Budianto Jasmine Putri Halim Jimmy Tjen