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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.
Arjuna Subject : -
Articles 175 Documents
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Data Quality Issues : Case Study of Claim and Insured in Indonesia Insurance Company Solontio, Chris; Hidayanto, Achmad Nizar
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3755

Abstract

Data has become an asset for insurance companies that have many benefits and management needs to realize the importance of data quality to avoid the impact of poor data quality. In this study, data quality measurement will be carried out by observation to see the total amount of invalid data from data dimensions, namely, accuracy, completeness and consistency of the relationship between claim data and insured, and findings from each data fields in this case study. In addition, researchers conducted interviews to find out the obstacles faced by IT, Customer Retention, Operational, and Actuary teams where they are directly related to data flow and data processing. From the results of the analysis, there is invalid data that will affect the analysis and cause obstacles faced by users according to the interview results. In the conclusion, management needs to form a data govenance team to avoid poor data quality that has responsibility for data flow and maintains data quality in order to provide a positive impact such as providing the right data accuracy in data analysis and user time to be more effective in data processing, assisting in making data warehouses, applying AI and digital transformation as a form of improvement in the services provided.
Evaluating the Impact of Particle Swarm Optimization Based Feature Selection on Support Vector Machine Performance in Coral Reef Health Classification Bastiaans, Jessica Carmelita; Hartojo, James; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3761

Abstract

This research explores improving coral reef image classification accuracy by combining Histogram of Oriented Gradients (HOG) feature extraction, image classification with Support Vector Machine (SVM), and feature selection with Particle Swarm Optimization (PSO). Given the ecological importance of coral reefs and the threats they face, accurate classification of coral reef health is essential for conservation efforts. This study used healthy, whitish, and dead coral reef datasets divided into training, validation, and test data. The proposed approach successfully improved the classification accuracy significantly, reaching 85.44% with the SVM model optimized by PSO, compared to 79.11% in the original SVM model. PSO not only improves accuracy but also reduces running time, demonstrating its effectiveness and computational efficiency. The results of this study highlight the potential of PSO in optimizing machine learning models, especially in complex image classification tasks. While the results obtained are promising, the study acknowledges several limitations, including the need for further validation with larger and more diverse datasets to ensure model robustness and generalizability. This research contributes to the field of marine ecology by providing a more accurate and efficient coral reef classification method, which can be applied to other image classifications.
Enhancing Support Vector Machine Classification of Nutrient Deficiency in Rice Plants Through Particle Swarm Optimization-Based Feature Selection Hartojo, James; Bastiaans, Jessica Carmelita; Pramunendar, Ricardus Anggi; Andono, Pulung Nurtantio
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3762

Abstract

The research focuses on the classification of nutrient deficiencies in rice plant leaves using a combination of Support Vector Machine (SVM) and Particle Swarm Optimization (PSO) methods for feature selection. Image features are extracted using Histogram of Oriented Gradients (HOG), which is then optimized with PSO to select the most relevant features in the classification process. Indonesia is one of the largest rice producers in the world, with food security as a major issue that requires sustainable solutions, especially in the agricultural sector. The growth and yield of rice plants are highly dependent on the availability of nutrients such as Nitrogen (N), Phosphorus (P), and Potassium (K). However, traditional observation methods to detect nutrient deficiencies in plants become inefficient as the scale of production increases. The dataset used includes images of rice leaves showing nitrogen (N), phosphorus (P), and potassium (K) deficiencies. Experiments show that the SVM model optimized with PSO provides a classification accuracy of 83.19% and a runtime of 129.63 seconds with 1150 best feature combinations out of 2303 extracted features, which is higher accuracy and faster runtime than the model that does not use PSO. These results show that the integration of PSO in the feature selection process not only improves the accuracy of the model, but also reduces the required computation time. This research makes an important contribution to the development of an automated system for the classification of nutrient deficiencies in crops, which can be implemented in large farms or other agricultural fields.
Cross-Platform Mobile Based Crowdsourcing Application for Sentiment Labeling Using Gamification Method Elaine, Elaine; Putri, Farica Perdana; Suryadibrata, Alethea
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): 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.v11i2.3935

Abstract

Sentiment analysis is the application of natural language processing which aims to identify the sentiment of texts. To carry out sentiment analysis, data which has been labeled sentiment is needed to be included in the training model. Crowdsourcing is considered as the most optimal method to label data because it has a high level of accuracy at a relatively low cost. However, the use of crowdsourcing platforms has its own challenge, which is to increase user interest and motivation. A solution which can be applied is to design and build a crowdsourcing platform or application using the gamification method. The definition of gamification is an effort to increase one's intrinsic motivation for an activity by applying game elements to it. Therefore, a cross-platform mobile based crowdsourcing application for sentiment labeling using gamification method was carried out. The gamification design process was done based on the 6D framework and the application was developed using the Ionic-React framework. Application was examined through black box testing and the result showed that the application was functioning properly and according to the design requirements. There was also an evaluation carried out by distributing Intrinsic Motivation Inventory questionnaires to users who had used the application for 2 weeks. From a total of 40 respondents, the result showed that the level of user motivation and interest in using the application was high with a percentage of 83.10%.
News Management Application Development Syahputra, Muhammad Adryan; Bintana, Rizqa Raaiqa; Lestari, Dewi
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.3870

Abstract

This study aims to develop a news management application at institution X that has been running previously, into an interactive application that can be used as a means of writing, reviewing and collecting news articles which are then published to daily newspaper pages. The application development model used in this study is the waterfall model. The waterfall model involves stages such as communication, planning, modeling, construction, and deployment. This study succeeded in developing a news management application from the previous version into a new application with the implementation of all its functional requirements. Testing was carried out using the black box method and system usability scale (SUS). The results of the black box test showed that the news management application had successfully carried out all functions correctly and in accordance with expectations. In the SUS test, two measurements were taken, namely before and after development. The SUS score before development was 71.3, while after development it increased to 78.8. This shows an increase in user perception of the usability of the application after development was carried out. Structured work and a clear flow from initiation to end can minimize errors because it has clear details and final descriptions.
Evaluation of the Effect of Practicum in Improving Computer Networking Course Learning in Infomatics Major FAUZAN, AHMAD DHAVI; NOVARIQA, PUTERI; HALIM, FRANSISCUS ATI
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.4003

Abstract

Abstract - This research examines the effect of practicum sessions on improving learning outcomes in Computer Networking courses for Informatics students. The main issue explored is whether practical learning enhances theoretical understanding and technical skills better than traditional methods. A quantitative comparative approach was applied, analyzing secondary data of Midterm and Final Exam scores from two groups (one with practicum integration and one without). The results revealed significant performance differences, with the practicum group achieving higher scores, improved consistency, and lower variance compared to the non-practicum group. These findings highlight the positive impact of practical sessions on knowledge retention and skill development. The research concludes that practicum sessions play a critical role in effective learning for Computer Networking courses, suggesting the integration of hands-on activities in theoretical subjects. Further research could explore broader data sources to assess additional influencing factors.
Design and Development of a Web-Based Network Device Inventory Information System at PT. XYZ Fitriana, Desi; Frendiana, Viving; Utami, Budi
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.4369

Abstract

PT. XYZ is an internet service provider thatowns various network devices such as routers, switches,and servers. Device management is still performedmanually without digital record-keeping, leading toinefficiencies, particularly in the process of requestingand checking device availability. This study aims todevelop a web-based inventory information system tomanage network devices systematically and efficiently.The system is developed using the Laravel frameworkand PHP programming language, and it is equipped witha dynamic QR Code feature that is automaticallygenerated for each recorded device. The inventoryinformation system website was tested through aContract Acceptance Test involving three user roles:Admin, NOC, and CTO, with an average evaluation scoreof 97.5. In addition, system functionality testing of thesystem was conducted using the black-box testing methodon 75 scenarios, all of which were successfully executed.Security testing results indicated a low security risk level.These overall testing results show that the systemfunctions as intended and capable of meeting user needsoptimally.
Waste Processing and Recycling Product Marketplace Application Using Tensorflow and Midtrans API Technology Setiawan, Eko Budi; Afriyadi, Bayu
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.4384

Abstract

Both developed and developing countries face waste problems worldwide. The recycling process can not only help reduce the amount of waste but also produce new products with economic value. In order to support this effort, information and communication technology, especially Android-based applications, can be an effective solution. This application can provide education, recommendations, and a marketplace platform for recycled products, hoping to allow the public to access information and market their products more efficiently. Previous research supports the development of this application, showing great potential in helping the community manage waste effectively and economically. This study shows that Android-based waste processing and recycled product marketplace applications have great potential in helping to overcome waste problems. The test results of the application features show that all functions work well, which is my expectation. In addition, the user acceptance test shows that the majority of respondents agree that this application is effective in reducing waste accumulation (88%), facilitating the recycling process (93.36%), and providing economic value for recycled products (89.6%). This application has become a practical solution for educating the public and increasing participation in waste management while providing economic benefits through recycling.
Triangulation Approach Using K-Means, Hierarchical Clustering, and DBSCAN for Beef Production Analysis Syamsiah, Nurfia Oktaviani; Purwandani, Indah; Rosmiati, Mia; Nurwahyuni, Siti
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.4481

Abstract

This study implements a methodological triangulation approach for clustering highly skewed data using three algorithms with different paradigms: K-Means (partitional-based), Agglomerative Hierarchical Clustering with Ward Linkage (hierarchical-based), and DBSCAN (density-based). Applied to beef production data from 38 Indonesian provinces in 2024, the dataset exhibited extreme characteristics with a coefficient of variation of 171.89%, skewness of 2.87, and a maximum-minimum ratio of 664:1. Data were standardised using Z-score transformation to address scale differences. Evaluation using the Silhouette Score for K-Means and Hierarchical Clustering, alongside qualitative outlier detection with DBSCAN, revealed high consistency across all algorithms in identifying k=2 as the optimal structure, with a Silhouette Score of 0.9155. K-Means and Hierarchical Clustering produced identical groupings, separating three observations (7.89%) from 35 observations (92.11%), while DBSCAN confirmed this by explicitly labelling the three provinces as outliers. Robustness analysis via bootstrap resampling (100 iterations) demonstrated clustering stability with membership consistency of 99.7-100% and standard deviation of 0.0089. Sensitivity analysis validated the stability of outlier detection across the epsilon range 0.5-0.55. This research demonstrates that algorithmic triangulation provides robust cross-validation for data with extreme outliers, yielding consistent and stable clustering structures across sampling variation and parameter changes.
Expert System for Diagnosing Human Psychological Disorders Using the Forward Chaining Method Hesti; Hetty Rohayani
IJNMT (International Journal of New Media Technology) Vol 12 No 2 (2025): Vol 12 No 2 (2025): 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.v12i2.4512

Abstract

This study aims to design an expert system capable of providing early diagnosis of human psychological disorders using the Forward Chaining method. The research was conducted through a literature review, the development of a knowledge base, and the formulation of rule-based diagnostic structures derived from psychological references, including the DSM-5. The system is designed to identify eight common psychological disorders using twenty primary symptoms that are mapped into IF–THEN rules. The inference process operates by matching user-selected symptoms with the rules contained in the knowledge base. The results indicate that the Forward Chaining method can systematically and logically generate early diagnostic indications for disorders such as depression, anxiety, bipolar disorder, PTSD, and others. A case simulation demonstrates that the reasoning mechanism is able to produce accurate conclusions based on the combination of symptoms entered. Although the system has not yet been implemented as a software application, this study confirms that the conceptual design of an expert system using Forward Chaining can serve as an effective tool for early mental-health detection and has strong potential for further development.

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