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All Journal International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science International Journal of Visual and Performing Arts Frontier Energy System and Power Engineering
Agung Bella Putra Utama
Universitas Negeri Malang
Arts Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Journal : international journal of advances in intelligent informatics

 1 
An integrative review of computational methods for vocational curriculum, apprenticeship, labor market, and enrollment problems Ahmad Dardiri; Felix Andika Dwiyanto; Agung Bella Putra Utama
International Journal of Advances in Intelligent Informatics Vol 6, No 3 (2020): November 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v6i3.581

Abstract

Computational methods have been used extensively to solve problems in the education sector. This paper aims to explore the computational method's recent implementation in solving global Vocational education and training (VET) problems. The study used a systematic literature review to answer specific research questions by identifying, assessing, and interpreting all available research shreds of evidence. The result shows that researchers use the computational method to predict various cases in VET. The most popular methods are ANN and Naïve Bayes. It has significant potential to develop because VET has a very complex problem of (a) curriculum, (b) apprenticeship, (c) matching labor market, and (d) attracting enrollment. In the future, academics may have broad overviews of the use of the computational method in VET. A computer scientist may use this study to find more efficient and intelligent solutions for VET issues.
Fixed sherwood duel optimization for time series imputation Agung Bella Putra Utama; Aji Prasetya Wibawa; Anik Nur Handayani; Andrew Nafalski
International Journal of Advances in Intelligent Informatics Vol 12, No 1 (2026): February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v12i1.2396

Abstract

Missing values remain a persistent challenge in time-series data, particularly within large-scale monitoring systems where reliable forecasting and evaluation are essential. Incomplete records often arise from irregular reporting, infrastructure limitations, or system failures, leading to biased analyses and inaccurate predictions. Traditional imputation methods, such as mean, median, and mode substitution, provide computational efficiency but oversimplify temporal structures. At the same time, more advanced approaches, including Multiple Imputation by Chained Equations (MICE) and K-Nearest Neighbors (KNN), offer improvements yet remain sensitive to data distribution and model configuration. To address this gap, this study introduces Sherwood Duel Optimization (SDO). This socio-inspired framework reconceptualizes imputation as a deterministic duel-based optimization problem. In its fixed form, SDO generates multiple candidate imputations and selects the most robust replacement value using a composite multi-metric scoring mechanism that integrates forecasting accuracy and explanatory power. The framework was evaluated using multivariate educational time-series data and further validated across heterogeneous SDG-related domains, and compared against classical and advanced baselines across three forecasting models. Experimental results demonstrate that SDO consistently outperforms existing methods, reducing forecasting error (MAPE) by more than 40%, achieving the lowest RMSE, and producing R² values exceeding 0.95. Statistical testing confirms that these improvements are significant across experimental configurations. These findings highlight the potential of SDO as a reliable, interpretable, and computationally efficient optimization-based imputation framework. By strengthening data reliability at the reconstruction stage, SDO enhances the credibility of downstream forecasting and decision-making in institutional and sustainability-oriented monitoring systems.
Co-Authors Ade Kurnia Ganesh Akbari Adjie Rosyidin Ahmad Dardiri Aji Prasetya Wibawa Akhmad Fanny Fadhilla Alfiansyah Putra Pertama Triono Andien Khansa’a Iffat Paramarta Andrew Nafalski Anik Nur Handayani Faradini Usha Setyaputri Felix Andika Dwiyanto Felix Andika Dwiyanto Fitriana Kurniawati Gwinny Tirza Rarastri Harits Ar Rosyid Jehad A.H. Hammad Leonel Hernandez Nastiti Susetyo Fanany Putri Suyono Suyono Wako Uriu Zaeni, Ilham Ari Elbaith