Article Per Year (5 Year)
p-Index From 2021 - 2026
0.751
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Education Policy and Development (EPD)
Titik Khawa Abdul Rahman
Asia e University
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
Model to Evaluate Hierarchical Organizations Performance in Implementing Higher Education Information Systems Reni Haerani; Titik Khawa Abdul Rahman
Education Policy and Development Vol. 2 No. 2 (2024): Education Policy and Development
Publisher : Research Synergy Foundation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/epd.v2i2.2459

Abstract

Adopting and successfully implementing information systems in higher education is essential for improving administrative processes and communication and supporting academic activities. However, the hierarchical nature of such organizations poses unique challenges that must be addressed for the effective adoption of information systems. This study proposes a framework to assess the performance of hierarchical organizations in effectively implementing information systems in universities. The proposed framework evaluates various dimensions that influence the successful adoption of information systems in hierarchical organizations. This dimension includes leadership support, communication channels, organizational culture, and resource allocation. The conceptual framework provides a holistic assessment of an institution’s ability to effectively adopt and utilize information systems. A structural equation model and Smart Partial Least Squares (Smart PLS) were used for data analysis. Using a sample of 121 respondents, data were collected using a questionnaire instrument using the Google Form link at Banten Province higher education leadership levels. This framework provides a structured approach to assessing the performance of hierarchical organizations in terms of the adoption of information system success in higher education institutions. By leveraging this framework, institutions can enhance their information system adoption processes and ultimately improve their effectiveness in using information systems for academic and administrative purposes. The results indicate that hierarchical organizations can optimize performance when implementing higher education information systems, focusing not only on the technical aspects of the system but also on organizational culture, communication, and leadership involvement.
Analysis of Information Systems Acceptance and Success Models in Higher Education Reni Haerani; Titik Khawa Abdul Rahman; Dwi Ismiyana Putri; Rika Apriani; Mardi Yudhi Putra
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 10 No 1 (2026)
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/intensif.v10i1.24969

Abstract

Background: The integration of Information Systems (IS) in higher education has transformed interactions among students, lecturers, and administrative staff, making system acceptance and success essential for effective academic processes. Various evaluation frameworks have been developed, with the DeLone and McLean Information System Success Model being one of the most widely applied. Objective: This study aims to analyze factors influencing the adoption of academic information systems in higher education using the DeLone and McLean model and to evaluate system success from the perspectives of lecturers, students, and administrative personnel. Methods: A quantitative research approach was employed using questionnaire-based data collection. Data analysis was conducted using SmartPLS 3.0 to assess validity, reliability, and structural relationships among variables. A total of 252 respondents were selected using the Slovin formula and proportional stratified random sampling. The evaluated constructs included system quality, information quality, service quality, system use, user satisfaction, and benefits. Results: The results show that system quality, information quality, and service quality have a positive and significant effect on system use and user satisfaction. Furthermore, system use and user satisfaction contribute to perceived net benefits, such as improved learning outcomes, increased management efficiency, and academic productivity. High service quality also supports continued system usage. All measurement constructs met validity and reliability criteria, with loading factors above 0.7 and Average Variance Extracted (AVE) values exceeding 0.50. Conclusion: In conclusion, the DeLone and McLean model effectively explains academic information system success in higher education, highlighting the importance of system quality, user satisfaction, and generated benefits.
Integrating multi-criteria decision making and public sentiment analysis for sustainable urban green space planning Muhammad Syafaat S. Kuba; Muhammad Faisal; Nurnawaty Nurnawaty; Titik Khawa Abdul Rahman; Andi Makbul Syamsuri; Muhyiddin AM Hayat; Rizki Yusliana Bakti
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11168

Abstract

Sustainable planning of green open spaces (GOS) requires decision-making models that combine expert evaluation with public input. This study proposes a novel hybrid framework that integrates multi-criteria group decision making (MCGDM) with public sentiment analysis to support community-based and data-driven urban planning. The workflow consists of evaluating 25 community-proposed GOS locations using stepwise weight assessment ratio analysis (SWARA) for criteria weighting and MABAC-BORDA for multi-criteria ranking, resulting in 11 feasible alternatives. To incorporate community perspectives, a term frequency-inverse document frequency-support vector machine (TF-IDF–SVM) classifier was applied to 1500 public comments, where SVM achieved the highest accuracy (0.80–0.96). The integrated approach improves ranking stability, reduces decision ambiguity, and strengthens alignment between expert judgment and community sentiment. This study contributes a transparent, participatory decision-support model that unifies MCGDM and sentiment analysis to enhance the effectiveness of sustainable GOS planning.
Comparison algorithms with optimization for clustering multi-criteria Ida Mulyadi; Titik Khawa Abdul Rahman; Muhammad Faisal
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3476-3491

Abstract

In higher education, forecasting student graduation is crucial for early intervention development, policy formulation, and class planning. This categorizes the factors that affect student graduation according to both academic and non-academic traits. To compare various clustering techniques, such as K-means, fuzzy C-means (FCM), K-medoids, density-based spatial clustering of applications with noise (DBSCAN), spectral clustering, Gaussian mixture model (GMM), and deep learning (DL), as well as particle swarm optimization (PSO) and genetic algorithm (GA) optimization for K-means and FCM, this article applies the hybrid elbow + silhouette optimization prior to the clustering process. Two clusters were found in the pre-cluster optimization research results. K-means, FCM, GMM, and DL clustering all demonstrated more distinct centroid separation; K-means and GMM were the most visually stable and comprehensible. Inter-cluster separation is the main goal of post-cluster optimization, and the K-means + PSO method is the best option. The cross-dataset validation findings indicate that the clustering model exhibits moderate consistency on the new dataset, with an adjusted Rand index (ARI) of 0.478 and a normalized mutual information (NMI) of 0.433.
Co-Authors Andi Makbul Syamsuri Bakti, Rizki Yusliana Dwi Ismiyana Putri Ida Mulyadi, Ida Muhammad Faisal Muhammad Syafaat S. Kuba Muhyiddin A.M Hayat Nurnawaty Reni Haerani Rika Apriani