Article Per Year (5 Year)
p-Index From 2021 - 2026
2.127
P-Index
This Author published in this journals
All Journal Technomedia Journal Jurnal Nasional Komputasi dan Teknologi Informasi Aptisi Transactions on Management CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics ADI Bisnis Digital Interdisiplin (ABDI Jurnal) JINAV: Journal of Information and Visualization IAIC Transactions on Sustainable Digital Innovation (ITSDI) Jurnal Manajemen Retail Indonesia (JMARI) International Journal for Applied Information Management Startupreneur Business Digital (SABDA Journal)
Rakhmansyah, Mohamad
Universitas Raharja
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Neuroscience Social Sciences Transportation

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : Technomedia Journal

 1 
Evaluasi Keberhasilan Implementasi Sistem Informasi Manajemen Proyek dalam Meningkatkan Efektivitas Tim Kerja: Evaluating the Success of Project Management Information System Implementation to Enhance Work Team Effectiveness Rakhmansyah, Mohamad; Sunarjo, Richard Andre; Lutfiani, Ninda; Riskhandini, Dinda Putri; Shepard, Anjani
Technomedia Journal Vol 9 No 3 (2025): February
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v9i3.2419

Abstract

This research aims to} evaluate the success of the implementation of the Project Management Information System (SIMP) in increasing the effectiveness of work teams, by utilizing key performance indicators (KPI) as a measuring tool. In the era of digitalization, the use of SIMP is increasingly widespread in various organizations to manage projects and optimize team collaboration. However, although many companies adopt SIMP, implementation results often vary, depending on the effectiveness of using the system in daily practice. The approach used in this research is quantitative descriptive, with data collection through questionnaire surveys to team members who have actively used SIMP in their work for a minimum of six months. Some of the main performance indicators evaluated include time efficiency, quality of work results, team satisfaction, and effectiveness of communication between team members. Data analysis was carried out descriptively to assess the performance of each indicator and identify roles SIMP in supporting overall team performance. The research results show that SIMP plays a significant role in increasing the efficiency of task completion times, improving the quality of project results by reducing errors, increasing team member satisfaction with better collaboration, and facilitating more effective communication between members. Based on these results, it is concluded that SIMP implementation is able to support the effectiveness of the work team as a whole, although there are challenges such as the need for initial adaptation and training. Recommendations are given that companies carry out regular training and system maintenance to ensure the continued effectiveness of SIMP use. It is hoped that this research will provide guidance for other organizations in evaluating and optimizing use SIMP at their workplace.
Integration of Business Intelligence and Predictive Analytics for Student Success Based on Blockchain: Integrasi Business Intelligence dan Analitik Prediktif untuk Keberhasilan Mahasiswa Berbasis Blockchain Rahardja, Untung; Rakhmansyah, Mohamad; Wijaya, Surta; Anjani, Sheila Aulia; Davies, Mary
Technomedia Journal Vol 10 No 1 (2025): June
Publisher : Pandawan Incorporation, Alphabet Incubator Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/tmj.v10i1.2389

Abstract

In the digital era, education is undergoing a significant transformation, with predictive analytics becoming an important approach to increasing student success. Rapid advancements in technology have enabled institutions to collect and analyze diverse datasets, yet challenges remain in ensuring data accuracy, transparency, and reliability. This research explores the integration of blockchain technology to address data integrity challenges, with a focus on its application in predictive analytics. The objective is to enhance the reliability of student-related data while improving the effectiveness of academic performance predictions. Specifically, this research examines the relationship between Academic Performance Metrics (APM), Student Engagement Data (SED), Socioeconomic Factors (SEF), Blockchain-Enabled Data Integrity (BDI), and Predictive Algorithm Efficiency (PAE). Using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, data were collected through structured surveys and institutional records involving higher education students. The constructs were validated through measurement model testing before proceeding to structural path analysis. The results show the significant influence of socio-economic factors and blockchain-based data integrity on academic outcomes, while student engagement and predictive algorithm efficiency also demonstrate moderate effects. The study also identifies areas that require improvement in predictive models, particularly regarding the alignment of input variables with algorithm design. These findings emphasize the importance of leveraging technology to develop more equitable and effective educational strategies, while underscoring the need for continued improvements in construct design to increase the reliability and validity of models. This research contributes to the growing field of educational data science by offering a blockchain-enhanced framework for predictive analytics in education.
Co-Authors Abdul Rahman, Abdul Wahab Abudaqa, Anas Adam Faturahman Alfiah Khoirunisa Andwiyan, Denny Anjani, Sheila Aulia Davies, Mary Dlamini, Sipho Eduard Hotman Purba Fitrianto, Taufiq Nur Gulo, Nitema Hermawan, Pandu I Ketut Gunawan Ikraman, Ikraman Ira Geraldina Komara, Maulana Arif Maulana, Imam Ryan Miftah, Mohammad Mulyati Mulyati Ninda Lutfiani Nuke Puji Lestari Santoso Nur Azizah Po Abas Sunarya Riskhandini, Dinda Putri Sari, Meri Mayang Shepard, Anjani Srenggini, Abdullah Dwi Sunarjo, Richard Andre Supriyanti, Dedeh Susanto, Sugeng Tri Wahyuningsih Untung Rahardja Widiyatmoko, Tri Wijaya, Surta Wijayanti, Fitri Dwi