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International Journal of Basic and Applied Science
Published by Institute of Computer Science
ISSN : 23018038     EISSN : 27763013     DOI : https://doi.org/10.35335/ijobas
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Physics
International Journal of Basic and Applied Science provides an advanced forum on all aspects of applied natural sciences. It publishes reviews, research papers, and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. Electronic files and software regarding the full details of the calculation or experimental procedure, if unable to be published in a normal way, can be deposited as supplementary electronic material.
Arjuna Subject : Umum - Umum
Articles 3 Documents
 1 
Search results for , issue "Vol. 14 No. 2 (2025): Sep (In Progress)" : 3 Documents clear
Distribution cost optimization: Comparison of NWC, MODI, and Stepping Stone methods in transportation problems Riandari, Fristi; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.688

Abstract

Solving transportation problems is essential in minimizing distribution costs in logistics and supply chains. Three classical methods North West Corner (NWC), Modified Distribution Method (MODI), and Stepping Stone are frequently used, but few studies offer a comprehensive comparison. This study fills this gap by evaluating their performance using simulated data representing real-world distribution scenarios. This study applies a structured comparative framework to analyze NWC (a cost-agnostic initial allocation technique), MODI (a dual-variable-based optimization approach), and Stepping Stone (a closed-loop path evaluation method). Each method was tested on a simulated cost matrix using Python. Evaluation metrics included total distribution cost, number of iterations, and computation time. The NWC method yielded a feasible but suboptimal solution with a cost of 540 units. Optimization using MODI reduced the cost to 425, while Stepping Stone further minimized it to 410 after three iterations. MODI showed greater computational efficiency, while Stepping Stone offered visual traceability of cost reductions. This study contributes methodologically by combining heuristic and iterative optimization techniques in one analytical framework. Practically, it provides decision-makers with insights into selecting appropriate solution methods based on trade-offs between simplicity, efficiency, and cost minimization.
A System dynamics quantitative model for enhancing e-government maturity in the indonesian education sector Yulistiawan, Bambang Saras; Widyastuti, Rifka; Mulianingtyas, Rr Octanty; A, Galih Prakoso Rizky; Sihotang, Hengki Tamando
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.693

Abstract

This study develops a deterministic mathematical model integrated with system dynamics to measure key success factors driving e-government maturity in Indonesia’s education sector. Addressing the gap in previous research, which mainly relied on descriptive methods, the model quantitatively examines causal relationships among leadership commitment, budget support, digital infrastructure, human capital, service quality, and feedback mechanisms. The methodology involves three stages: (1) constructing a causal loop diagram based on theoretical and empirical insights, (2) converting these relationships into a linear system of equations normalized on a [0–1] scale, and (3) performing simulations and sensitivity analyses to evaluate policy scenarios. Simulation results indicate that even relatively high leadership commitment (K=0.75) only produces moderate maturity levels (M≈0.409). The greatest improvement occurs when feedback loops are reinforced and service quality investments are prioritized. Sensitivity analysis reveals the model is particularly responsive to changes in feedback effectiveness and service quality weighting, identifying these as critical leverage points for accelerating transformation. Under optimal conditions, maturity can increase from 0.41 to 0.48, reflecting a 7% gain over the baseline. The study contributes a replicable quantitative framework for evidence-based policymaking, while noting limitations in parameter assumptions and empirical calibration for future refinement.
Classification of stunting for early childhood in indramayu using machine learning methods Krisnanik, Erly; Cholil, Widya; Adrezo, Muhammad; DP, Catur Nugrahaeni; Binti Mohamad, Mumtazimah
International Journal of Basic and Applied Science Vol. 14 No. 2 (2025): Sep (In Progress)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/ijobas.v14i2.833

Abstract

The stunting prevalence rate in 2020 of the Ministry of Health of the Republic of Indonesia was 38.9%. The stunting prevalence rate in Central Java itself is 33.9%, of which 17.0% are stunted and 16.9% are very short. The purpose of the study is to obtain valid data on the factors causing stunting and carry out the classification process quickly. The method used in this study is machine learning by comparing three algorithms, namely: SVM, KNN and Random Forrest. The results of this study are said that the average calculation of the accuracy level of early childhood stunting data using SVM and KNN is above 80% and Random Forrest is below 80%. While the calculation results of the average precision value of 84% and recall value of 80% using SVM, the average precision value of 95% and the recall value of 91% using KNN with K = 1, and the average precision value of 87% and the recall value of 52% using Random Forrest.  The conclusion of the comparison between SVM, Random Forest and KNN methods to calculate precision and recall values can be said that KNN is better with K = 1 close to 100%.

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