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International Journal of New Media Technology
Published by Universitas Multimedia Nusantara
ISSN : 23550082     EISSN : 25811851     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering
International Journal of New Media Technology (IJNMT) is a scholarly open access, peer-reviewed, and interdisciplinary journal focusing on theories, methods, and implementations of new media technology. IJNMT is published annually by Faculty of Engineering and Informatics, Universitas Multimedia Nusantara in cooperation with UMN Press. Topics include, but not limited to digital technology for creative industry, infrastructure technology, computing communication and networking, signal and image processing, intelligent system, control and embedded system, mobile and web based system, robotics.
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Articles 191 Documents
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Singular Value Decomposition for Deep Space Image Compression: A Hubble Case Study: A Hubble Case Study Themy Sabri Syuhada
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4714

Abstract

Modern deep space exploration generates massive volumes of high-resolution imagery, creating a significant bottleneck for data transmission over limited bandwidths. This research addresses this challenge by evaluating Singular Value Decomposition (SVD) as a numerical Low-Rank Approximation technique for compressing astronomical data. Unlike data-hungry deep learning models, this study applies matrix factorization directly to raw 16-bit .TIF images from the Hubble Space Telescope, varying the rank (k) to analyze the trade-off between storage efficiency and reconstruction fidelity. Experimental results demonstrate that SVD offers a stable compression mechanism, effectively filtering sensor noise while preserving coherent celestial structures. The analysis identifies rank k=100 as the optimal threshold, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 30.81 dB with a Compression Ratio of 12.87 times. These findings suggest that SVD provides a computationally efficient and mathematically deterministic alternative to complex neural networks for onboard satellite data processing, successfully balancing scientific accuracy with transmission constraints.
Integrating Generative Artificial Intelligence in Higher Education: Opportunities, Challenges, and Pedagogical Implications Sabri Zulkifli; Zulfahmi; M. Bayu Wibawa
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4719

Abstract

The increasing use of digital technology has changed many aspects of higher education, particularly in teaching and learning activities. In recent years, Generative Artificial Intelligence (GenAI) has attracted considerable attention because of its ability to generate content and assist various academic tasks. This study examines the role of GenAI in supporting learning transformation in higher education, focusing on its benefits, challenges, and broader educational implications. A qualitative approach was applied through a literature review of recent scholarly publications related to artificial intelligence in education. The findings suggest that GenAI can improve learning efficiency, support more adaptive and personalized learning experiences, and assist students in academic activities such as research and writing. At the same time, several concerns remain important, including academic integrity, excessive dependence on technology, and the absence of clear institutional regulations. The study also indicates that the successful use of GenAI depends not only on technological capability but also on pedagogical readiness and institutional support. Overall, GenAI offers significant opportunities for higher education; however, its implementation should be managed carefully to ensure that technological innovation remains aligned with ethical and educational values.
A Hierarchical Two Stage BERT Model for Cyberbullying Detection Muhamad Syukron; Nayya Safitri Ramadani; Muhammad Ilham Firmansyah; Asi Emilia Putri
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4730

Abstract

Cyberbullying has emerged as a persistent social issue in tandem with the rapid proliferation of social media usage, particularly in developing nations such as Indonesia. Online bullying extends beyond explicit insults to encompass more nuanced forms, including denigration and social exclusion, which are often subtle and context-dependent. This diversity presents significant challenges for automated detection systems, especially when cyberbullying is treated as a single, homogeneous category. To address this challenge, this study proposes a two-stage classification framework for detecting cyberbullying in the Indonesian language using the IndoBERT model. In the first stage, a binary classification model is employed to differentiate between bullying and non-bullying content. In the second stage, cyberbullying tweets are further categorized into three specific types: harassment, denigration, and exclusion. The model is trained using a curated dataset of Indonesian tweets collected from publicly available social media content. The results indicate that the proposed framework achieves strong and consistent performance, particularly in fine-grained cyberbullying classification, with an overall accuracy of approximately 92% in the second stage. These findings suggest that hierarchical classification can yield robust performance and effectively aid in the detection of cyberbullying.
Optimization of Posyandu Data Collection using Web Information System and Plan Do Check Act Model Adriyendi
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4731

Abstract

This study aims to optimize Posyandu data management through a Web-Based Information System (WBIS) integrated with the PDCA (Plan–Do–Check–Act) cycle to improve the efficiency and sustainability of Maternal and Child Health (MCH) services. A qualitative descriptive approach combined with a Research and Development (R&D) method was employed. Data were collected through observations, interviews, documentation, and literature review. The system was developed using a client–server architecture with a relational database and evaluated through functional testing and user acceptance testing. The results indicate that the prototype effectively integrates digital registration, health record management, and automated reporting. The PDCA integration supports continuous improvement in planning, data recording, evaluation, and intervention processes. User testing demonstrates that the system is user-friendly, reduces data entry errors, and accelerates reporting compared to manual procedures. Although limited to Posyandu in Nagari Pagaruyung, the study provides practical implications by reducing administrative workload for health volunteers and strengthening data-driven decision-making. The novelty lies in embedding the PDCA cycle into a web-based Posyandu Information System as a socio-technical model for continuous improvement in community health data governance.
An Analysis of the Knowledge-AttitudeBehavior Approach to Personal Data Security Resilience Among Students in West Java Eka Hidayat
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4739

Abstract

Digital transformation in the higher education sector poses significant risks to students' personal data security. Data breach incidents within campus environments demonstrate that state-of-the-art technological infrastructure will not be effective unless complemented by user security awareness. This study aims to analyze the personal data security resilience of students in West Java through the Knowledge-Attitude-Behavior approach. A quantitative survey method was applied to collect and evaluate students' empirical responses regarding their cognitive understanding of cyber risks, emotional responses to privacy policies, and concrete actions in protecting sensitive information. The evaluation results of these three dimensions are expected to measure the readiness and effectiveness of students as the first line of defense, or a human firewall, against various cybercrime threats. The findings of this study are projected to provide an empirical foundation for higher education institutions and policymakers in designing mitigation strategies and comprehensive, sustainable cybersecurity literacy programs.
An IoT-Based Intelligent Waste Segregation System with Real-Time Capacity Monitoring I Gede Wiryawan; Muhammad Nauval Hamdhani; Mohammad Abdul Azis; Muhammad Lukmanul Hakim; Achmad Sofyan Hakiki; M. Is’adul Ikhwan; Tiara Agustina Putri Wulandari; Nency Elvaretta Ardelia
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4743

Abstract

Waste management in Indonesia is confronting a critical crisis, marked by the accumulation of 85% of waste in nearly full landfills and persistently low rates of source segregation, particularly in university campus environments. This study presents the development of RecyClean Smart Bin, a prototype IoT-based intelligent waste bin that integrates an ultrasonic sensor for real-time bin capacity monitoring, proximity and capacitive sensors for automatic classification of organic, inorganic, and metallic waste, and a web-based application for remote monitoring. The methodology encompasses hardware–software co-design, firmware programming, system implementation, and functional validation conducted over five days using fifteen representative waste samples. Results indicate excellent ultrasonic sensor accuracy (maximum deviation of 0.6 cm) and an overall sorting success rate of 80% (12 out of 15 samples), with particularly high precision in metallic waste detection. The proposed system offers a practical contribution toward advancing the circular economy and enabling sustainable smart environments.
Enhancing Operational Efficiency in FMCG Customer Care: Design and Implementation of a High-Performance Web-Based Knowledge Management System Using Next.js with Hybrid Rendering Architecture Alghifari Rasyid Zola; Meredita Susanty
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4744

Abstract

In the highly competitive Fast-Moving Consumer Goods (FMCG) sector, the speed and accuracy of customer support are critical differentiators. This study addresses a significant operational bottleneck at PT Paragon Technology and Innovation, where reliance on fragmented, static documentation led to high information retrieval latency and service inconsistencies. To mitigate these issues, we designed and developed an integrated Knowledge Management System (KMS) serving as a Single Source of Truth (SSOT). Engineered using an Agile methodology, the system utilizes a modern Full-stack architecture based on the Next.js framework. It employs a hybrid rendering strategy, leveraging Server-Side Rendering (SSR) to optimize load performance and Client-Side Rendering (CSR) for interactive interfaces. The backend integrates a PostgreSQL database managed via Prisma ORM to ensure data integrity. Key features include a unified global search engine and a transparent audit trail mechanism for governance. Post-implementation evaluation indicates that the system significantly reduces agent search time and enhances data validity through real-time tracking. These findings demonstrate the efficacy of modern web architectures in transforming legacy workflows into a responsive, data-driven customer service ecosystem.
Determinants of Student Test Scores: Academic, Family, and Learning Environment Factors a Multiple Linear Regression Analysis Rena Nainggolan; Fenina Adline Twince Tobing
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4762

Abstract

This study aims to analyze the influence of academic, family, and learning environment factors on student exam scores. The study employs a quantitative approach using secondary data in the form of the Student Performance Factors dateset comprising 6,607 observations. The dependent variable in this study is student exam scores (Exam_Score) [1], while the independent variables include study hours (Hours_Studied), attendance (Attendance), previous scores (Previous_Scores), tutoring sessions (Tutoring_Sessions), parental involvement (Parental_Involvement), and access to learning resources (Access_to_Resources) . The analytical techniques used were descriptive statistics and multiple linear regression. The results indicate that the regression model is statistically significant simultaneously with an F-value of 1,601 and p < 0.001, and explains 66.0% of the variation in students’ exam scores (R² = 0.660). Partially, study hours, attendance, previous scores, and tutoring sessions have a positive and significant effect on test scores. Additionally, students with higher levels of parental involvement and access to learning resources tend to achieve better test scores [2] . These findings indicate that students’ academic achievement is not solely determined by individual study effort but is also influenced by family support and the availability of an adequate learning environment.
Immersive Culinary Simulation With FSM-Based NPCs in a 3D Game for Promoting Sundanese Restaurants Sri Rahayu; Egha Satria Bagaskara; Dewi Tresnawati; Yosep Bustomi
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4769

Abstract

The development of a 3D interactive game entitled “Sapiring Rasa” aims to promote traditional Sundanese restaurants while supporting the preservation of Indonesia’s culinary heritage through digital media. The game is designed for desktop platforms with a top-down perspective, allowing players to manage a traditional eatery, serve customers, and address various resource management challenges. The development process employed the Unity3D Engine and applied the Multimedia Development Life Cycle (MDLC), which includes the stages of Concept, Design, Material Collecting, Assembly, Testing, and Distribution. The main contribution of this study lies in the practical implementation of artificial intelligence–based non-player characters (NPCs) using a Finite State Machine (FSM) approach to create dynamic and responsive customer interactions, thereby improving gameplay immersion and user experience. System functionality was validated through alpha testing using black-box methods, while usability evaluation was conducted through beta testing using the System Usability Scale (SUS) involving 30 participants. The results showed an average SUS score of 73.5, classified as “Good” and “Acceptable.” These findings indicate that the proposed game is usable and well received by users. Overall, the integration of local cultural elements into interactive gameplay demonstrates the potential of digital games as effective media for cultural promotion and educational purposes.
CNN-Transformer Fusion for Indonesian Traditional Cake Recognition: An EfficientNet-ViT Approach with Grad-CAM Explainability Tasya Yustira; Aswan Supriyadi Sunge
IJNMT (International Journal of New Media Technology) Vol 13 No 1 (2026): Vol 13 No 1 (2026): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v13i1.4775

Abstract

Visual recognition of Indonesian traditional confectionery is an underexplored problem in deep learning research, partly due to high inter-class visual ambiguity and the scarcity of well-curated local food benchmarks. We address this gap by fusing Efficient-Net with a Vision Transformer (ViT) encoder into a unified classification network. The rationale for this pairing is straightforward: EfficientNet’s compound-scaled convolutional stack efficiently encodes low and mid-level texture cues, while the ViT’s self-attention layers then relate those cues across distant image regions-a capability that convolution alone cannot replicate. Post-hoc explainability, is provided through Grad-CAM, which produces class-discriminative spatial maps confirming that activations concentrate on cake surfaces rather than background. We train and evaluate on a publicly available eight-class Kaggle corpus of 1,833 images, applying a two-stage fine-tuning regimen totaling 25 epochs. The resulting system attains 94.37% accuracy, 94.57% precision, 94.37% recall, and 94.31% F1 on the reserved test split. Beyond the metrics, the Grad-CAM evidence suggests the network learns genuinely food-discriminative features, lending credibility to deployment in culinary archiving and nutrition-monitoring applications. Index Terms-deep learning; EfficientNet; food image classification; Grad-CAM; Vision Transformer

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