cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Rusliadi
Contact Email
garuda@apji.org
Phone
+6285642100292
Journal Mail Official
fatqurizki@apji.org
Editorial Address
Jln. Perum Cluster G11 Nomor 17 Jl. Plamongan Indah, Pedurungan, Semarang, Provinsi Jawa Tengah, 50195
Location
Kota semarang,
Jawa tengah
INDONESIA
International Journal of Applied Mathematics and Computing.
Published by Asosiasi Riset Ilmu Matematika dan Sains Indonesia
ISSN : 30481988     EISSN : 3047146X     DOI : 10.62951
Core Subject : Science, Education,
Computer Science & IT Mathematics
This Journal accepts manuscripts based on empirical research, both quantitative and qualitative. This journal is a peer-reviewed and open access journal of Mathematics and Computing
Arjuna Subject : Matematika - Matematika Komputer
Articles 54 Documents
 2   3   4   5   6 
A Systematic Literature Review The Impact of ERP and DSS on Organizational Performance: The Mediating Role of Decision-Making Quality Julianto, Dimas Gibran Julianto; Hamdani, Hamdani; Achmad, Gusti Noorlitaria; Ramadhani, Herry; Arifin, Zainal
International Journal of Applied Mathematics and Computing Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i3.334

Abstract

The adoption of Enterprise Resource Planning (ERP) and Decision Support Systems (DSS) is increasingly widespread to improve organizational performance, yet the underlying mechanisms remain fragmented. This study aims to conduct a Systematic Literature Review (SLR) to identify the impact of ERP and DSS on organizational performance, highlighting the mediating role of decision-making quality. A systematic search was performed on four sources (Scopus, Web of Science, Google Scholar, and ProQuest) for the 2021–2025 period. From 217 initial records, 34 empirical studies were analyzed using thematic synthesis. The results show that both ERP and DSS positively affect organizational performance, directly and through the enhancement of decision-making quality. Decision quality encompassing speed, accuracy, and information completeness – acts as a significant mediator. The mediation effect is determined by factors including top management support, organizational culture, data quality, and user competency. The findings emphasize that IT investments only yield superior performance when accompanied by strengthened decision-making capabilities
Utilization of Management Information Systems in Strategic Decision Making in the Human Resources Sector Dharmawan, Ade Putra; Hamdani, Hamdani; Achmad, Gusti Noorlitaria
International Journal of Applied Mathematics and Computing Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i3.336

Abstract

This study employs a systematic literature review and bibliometric analysis to examine trends, patterns, and conceptual developments in Human Resource Management Information Systems (HR-MIS). The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was applied to ensure the literature review process was conducted systematically, transparently, and reproducibly. Selected articles met key inclusion criteria: published before April 16, 2026, written in English, and specifically discussing HR Management Information Systems. The Scopus database, an abstract and citation database of peer-reviewed literature, served as the primary data source for this literature review. A visual mapping analysis was performed using VOSviewer, software for constructing and visualizing bibliometric networks, to map citation networks, collaborations, and keyword co-occurrences, uncovering the intellectual structure and evolutionary trajectory of the human resource field. The findings indicate that the use of HR-MIS is strongly correlated with improved efficiency in strategic decision-making and resource allocation. The literature is divided into two primary clusters: strategic information governance and the operational impact on organizational behavior. By integrating bibliometric and systematic methodologies, this study successfully provides a comprehensive identification of key contributors, research trends, benefits, challenges, and future research agendas.
The Role of Human Resource Information Systems (HRIS) in Improving Employee Performance: A Systematic Literature Review AS, Falga Bara Prima Abadi; Hamdani, Hamdani; Lestari, Dirga
International Journal of Applied Mathematics and Computing Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i3.337

Abstract

As modern enterprises embrace digital transformation, Human Resource Information Systems (HRIS) have become essential for managing organizational talent. This systematic literature review synthesizes empirical evidence from 30 academic papers and three foundational texts published between 2021 and 2026, explicitly examining how digital HR infrastructures elevate individual workforce achievements. Utilizing the PRISMA protocol, the study extracts data across multiple global databases to map the direct impacts and underlying mechanisms linking system utilization to employee productivity. Comprehensive analysis confirms that digital HR platforms significantly boost staff effectiveness by optimizing administrative routines, elevating data precision, and upgrading evaluation protocols. Crucially, these technological advantages translate into individual success primarily through intermediate channels, including workplace contentment, active engagement, and superior strategic judgments. Furthermore, organizations only realize these benefits when supported by an accommodating corporate culture and flawless technical integration. By assembling previously disconnected findings, this review bridges the gap between technological capabilities and behavioral outcomes, equipping executives with evidence backed strategies for technological governance while outlining a rigorous agenda for future multilevel and longitudinal research.
A Comparison of SVM and ELM Algorithms Based on SMOTE for Anemia Classification Using Hematology Data Dharmaesa, Dio; Hamdani, Hamdani; Suyatno, Addy
International Journal of Applied Mathematics and Computing Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing
Publisher : Asosiasi Riset Ilmu Matematika dan Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijamc.v3i3.376

Abstract

Anemia remains a significant global health concern, and its diagnosis through manual interpretation of Complete Blood Count (CBC) results is susceptible to bias and misinterpretation. Machine learning techniques offer a promising solution for identifying complex patterns in medical data. however, their performance is often affected by class imbalance issues commonly found in healthcare datasets. Therefore, this study aims to evaluate and compare the performance of Support Vector Machine (SVM) and Extreme Learning Machine (ELM) algorithms enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) for anemia classification. The proposed approach employs SVM and ELM classifiers with parameter optimization using K-Fold Cross Validation, while SMOTE is applied to address the imbalance in class distribution. The study utilizes a secondary CBC dataset consisting of 364 patient records categorized into Anemia and Non-Anemia classes. Experimental results indicate that the SMOTE-based SVM model achieved an accuracy of 94.52%, precision of 97.14%, recall of 91.89%, and an F1-score of 94.44%, with a computation time of 0.013 seconds. In comparison, the SMOTE-based ELM model attained an accuracy of 91.78%, precision of 89.74%, recall of 94.59%, and an F1-score of 92.11%, while requiring only 0.002 seconds of computation time. The findings suggest that SVM delivers more stable performance and the highest precision, making it highly effective in reducing false positive predictions. On the other hand, ELM demonstrates greater sensitivity to the incorporation of synthetic samples but outperforms SVM in terms of recall and computational efficiency, making it a suitable alternative when rapid processing and higher sensitivity are prioritized.

Page 6 of 6 | Total Record : 54

 2   3   4   5   6 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 3 (2026): July: International Journal of Applied Mathematics and Computing Vol. 3 No. 2 (2026): April: International Journal of Applied Mathematics and Computing Vol. 3 No. 1 (2026): January: International Journal of Applied Mathematics and Computing Vol. 2 No. 4 (2025): October : International Journal of Applied Mathematics and Computing Vol. 2 No. 3 (2025): July : International Journal of Applied Mathematics and Computing Vol. 2 No. 2 (2025): April: International Journal of Applied Mathematics and Computing Vol. 2 No. 1 (2025): January: International Journal of Applied Mathematics and Computing Vol. 1 No. 4 (2024): October: International Journal of Applied Mathematics and Computing Vol. 1 No. 3 (2024): July : International Journal of Applied Mathematics and Computing Vol. 1 No. 2 (2024): April : International Journal of Applied Mathematics and Computing Vol. 1 No. 1 (2024): January: International Journal of Applied Mathematics and Computing More Issue