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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 175 Documents
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Implementation of AHP Algorithm for Design and Development Halal Food Recommendation System at Cirebon Regional Geneva, Erick Abraham; kusnadi, adhi; Tobing, Fenina Adline Twince
IJNMT (International Journal of New Media Technology) Vol 10 No 2 (2023): IJNMT
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v10i2.3491

Abstract

Cirebon is one of the cities in Indonesia that has a variety of unique culinary delights. One of the most famous Cirebonese halal culinary delights is nasi jamblang. However, the many choices of halal Cirebonese food can make tourists struggle to choose food that suits their taste and preferences. This research aims to design and build a halal Cirebonese food recommendation system using the Analytical Hierarchy Process (AHP) method. The AHP method is used to determine the weights of the factors that influence the selection of halal Cirebonese food. This recommendation system is built using PHP and JavaScript programming languages, as well as Laravel, React, and MySQL frameworks. This recommendation system has been tested by distributing questionnaires using End User Computing Satisfaction method with google form to 35 respondents The test results show that this recommendation system produces a user satisfaction value of 87.92%. This value indicates that this recommendation system has met user expectations.
Comparison of Linear and Non-Linear Machine Learning Algortima for Predicting the Effectiveness of Plant Extracts as Corrosion Inhibitors Mulyana, Yudha; Akrom, Muhamad; Trisnapradika, Gustina Alfa
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (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.v11i1.3572

Abstract

This research aims to develop a Machine Learning (ML) model that can predict the corrosion inhibitor potential of plant extracts with high accuracy. Corrosion is a serious problem in industry because it can reduce the service life of materials and cause economic losses. This research focuses on the use of green inhibitors, especially plant extracts, which are considered environmentally friendly and have high anticorrosion efficiency. The dataset used includes molecular and physicochemical features of plant extracts. The ML model development process involves data normalization, selection of linear and non-linear ML algorithms, model training with k-fold crossvalidation, and model performance evaluation using regression metrics such as MSE, RMSE, MAE, and R2. Experiments compare various ML algorithms and show that the AdaBoost Regressor (ABR) model exhibits the best prediction performance with the highest R2 value of 0.993 and a low MSE of 0.002. These results provide new insights into the potential of ML models to predict effective corrosion inhibitors from plant extracts. The ABR model had a low prediction error, indicating high accuracy in predicting corrosion inhibition efficiency. In addition, the analysis of important features shows that two features, Conc and LUMO, have a significant influence on the ABR model. This research makes an important contribution to the development of effective prediction methods in the corrosion control industry. The ABR model can serve as a basis for designing more effective and environmentally friendly corrosion inhibitor materials, as well as a reference for other researchers in developing ML models that accurately predict the corrosion inhibition efficiency of plant extracts.
The Effect of Using the Al-Mumtaz Application on Student Learning Outcomes UIN Mahmud Yunus Batusangkar Hidayat, Rahmat; Abidin, Munirul; Hilmi, Danial
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.v11i2.3614

Abstract

Abstract” In today's digital era, the utilization of technology in the learning process is becoming increasingly important. Online learning applications such as Al-Mumtaz have the potential to overcome the limitations of conventional learning methods and increase student interest and motivation to learn. However, the effect of using the Al-Mumtaz application on student learning outcomes at Mahmud Yunus State Islamic University Batusangkar has not been widely studied. This study aims to identify and analyze the effect of using the Al-Mumtaz application on student learning outcomes at UIN Mahmud Yunus Batusangkar and provide evidence-based recommendations regarding the effectiveness of digital learning applications. This study used a quantitative pre-experiment design with a sample of Arabic language education students who used the Al-Mumtaz application. Data were collected through initial and final tests, and analyzed using validity, reliability, normality, homogeneity, and paired t tests. The analysis showed that 10 out of 15 questions were valid and reliable. The data were normally distributed and met the assumption of homogeneity of variance. The paired t-test revealed a significant mean difference between the pre-test (62.50) and post-test (89.00) scores after the use of Al-Mumtaz application, with an increase of 23.50.The use of Al-Mumtaz application in Arabic language learning at UIN Mahmud Yunus Batusangkar provides significant results.This study confirms the positive effect of Al-Mumtaz application on student learning outcomes and highlights the importance of technology in improving the effectiveness of learning in the academic environment.
Convolutional Neural Network Implementation in BISINDO Alphabet Sign Language Recognition System Kinanti, Aning Aning; Maulana, Donny; Edora, Edora
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (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.v11i1.3629

Abstract

This research develops a system for recognizing finger spelling gestures in Indonesian Sign Language (BISINDO) using Convolutional Neural Network (CNN). The objective of this research is to apply the Convolutional Neural Network (CNN) method to the BISINDO finger spelling gesture recognition system to improve its accuracy. The method employed is Convolutional Neural Network (CNN), an effective method for processing image data for pattern recognition. Based on the test results, the system demonstrates that the developed CNN model is capable of recognizing BISINDO finger spelling gestures with an accuracy of 97.5%. This indicates that the BISINDO finger spelling gesture recognition system performs well in pattern recognition. The implementation of the system for real-time prediction via a web interface using Flask also enhances its accessibility. However, there is still room for improvement, particularly in recognizing one of the 26 letters that has not been predicted accurately. For further development, it is recommended to consider collecting a larger dataset and incorporating more complex gesture variations to improve recognition accuracy.
The Effect of Video Games Towards the Students' Academic Performance Lala, Yohanes Brian Caesaryano; Oetama, Raymond Sunardi; Lvina, Kimberly
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.v11i2.3638

Abstract

Video games have remained popular ever since the video game industry boom in the 1980s. It has remained popular, especially for children, teenagers, and adults. However, video games have also sparked controversies among the population. Concerns have been raised regarding the harmful effects of video games, particularly regarding addictions. Video games have been accused by many of being the cause of lowering academic performance. Therefore, this study aims to explore the relationship between video games and the overall academic performance of university students in depth. We applied several statistical methods using a questionnaire, which 100 university students filled out. The insights uncovered from this study may help determine if and how much video games affect the academic performance of university students.
Implementation of Support Vector Machine Method for Twitter Sentiment Analysis Related to Cancellation of u-20 World Cup in Indonesia Armanda, Muhammad; Tobing, Fenina Adline Twince
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (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.v11i1.3673

Abstract

The cancellation of the U-20 world cup in Indonesia in 2023 has become a hot debate among the Indonesian people because the reasons for the cancellation are still unclear. The number of pro and con opinions uploaded by the Indonesian people on twitter social media makes these opinions can be used as data to assess opinions which are divided into three categories, namely positive, negative and neutral. After being divided into three categories, sentiment analysis will then be carried out using the SVM method and comparing linear, polynomial and rbf kernels to get the best performance of existing kernels in the support vector machine method. By using confusion matrix to measure the performance of the classification, accuracy, precision, recall and f1-score can be assessed. It was found that the 80:20 data ratio had the highest accuracy of the linear, polynomial, rbf kernel and the rbf kernel had better results than the linear and polynomial kernels, namely Accuracy 78.15%, F1-Score, 76.30%, Precision 77.37% and Recall 75.58%. In addition, the data obtained also succeeded in analyzing Indonesian texts that were input externally and categorized into positive, neutral and negative. From the results that have been obtained, the support vector machine method has been successfully implemented in sentiment analysis of the U-20 world cup cancellation in Indonesia in 2023 on twitter social media
Avia Saga: A Gamified Mobile-Based Learning Management System Oswald, Putra Aldo; Gunawan, Dennis
IJNMT (International Journal of New Media Technology) Vol 11 No 1 (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.v11i1.3675

Abstract

The usage of Learning Management Systems (LMS) has increased since the Covid-19 pandemic. LMS have drawbacks despite the advantages they provide. To fully support the advantages they provide, students must be motivated and involved. Adding gamification to the LMS is one way to potentially solve this issue. The MDA framework and Octalysis are combined in this research's gamification approach. The application, named Avia Saga, was designed and built using Flutter and Spring Boot as a mobile application. A trial of the application was conducted with 38 students majoring in Informatics. The evaluation of the application was done using the Hedonic-Motivation System Adoption Model (HMSAM) with a Likert scale. The research results revealed a 7% increase in the behavioral intention to use category, suggesting a greater inclination for reusing the application, and an 11.7% increase in the immersion category, indicating elevated sentiments of users being carried away by the ambiance while using the application.
Approach Convolutional Neural Network LeNet-5 for Interactive Learning of Korean Syllables (Hangul) Al Fitra Yudha, Vasyilla Kautsar; Kurniasari, Arvita Agus; Arifianto, Aji Seto; Afriansyah, Faisal Lutfi
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.v11i2.3705

Abstract

The increasing popularity of South Korean culture among Indonesian society has led to a growing interest in gaining a deeper understanding of the country, including a desire to master the Korean language. However, learning the Korean alphabet (hangul) often presents challenges due to its characters being unfamiliar to the Indonesian people. Therefore, engaging and interactive learning media are needed to assist in the learning process. Within this endeavor, a learning website called Learn Hangul was developed, focusing on two main features: learning hangul characters and their arrangement, as well as practicing writing syllables using Korean letters. This website was developed using the Convolutional Neural Network (CNN) LeNet-5 to facilitate learning, with black box testing results indicating good functionality. Model performance evaluation yielded satisfactory values, with model accuracy at 89.2%, precision at 89.7%, recall at 88.8%, and an F1-score of 89.2%. Direct testing with users also showed a high success rate, with 80% of respondents experiencing an increase in their knowledge of Korean characters (Hangul) after trying to learn them on the Learn Hangul website. Thus, the Learn Hangul website serves as a useful learning tool for those interested in studying the Korean alphabet (hangul).
Ensemble Learning - Random Forest Algorithm to Classify Obesity Level Abigail, Tesalonika; Overbeek, Marlinda Vasty
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.v11i2.3709

Abstract

Obesity is one of the serious global health problems caused by excessive accumulation of body fat. According to the World Health Organization (WHO), the prevalence of obesity has tripled in the last 40 years, with 650 million out of 1.9 billion overweight adults suffering from obesity. Obesity is a non-communicable disease that increases the risks of more dangerous diseases, such as heart disease and cancer. Therefore, early detection of obesity level is crucial. Currently, Body Mass Index (BMI) serves as a measurement indicator, but it tends to overestimate obesity for those with high muscle mass and vice versa, making it ineffective as it only relies on height and weight, without considering body composition and daily activities. To solve this, the best Random Forest model has been developed, selected based on the results of model selection after comparisons using feature selection and hyperparameter tuning. The selected model successfully improved accuracy by 1.4%, which then implemented into a web-based system to classify obesity levels. Evaluation of the model resulted in Precision, Recall, F1-Score, and Accuracy of 97%, 97%, 97%, and 96.8% respectively. Based on these evaluation results, it can be concluded that this system is highly effective in classifying obesity levels.
IMPLEMENTATION OF HEURISTIC EVALUATION METHOD FOR EVALUATION AND RECOMMENDATIONS UI/UX DESIGN IMPROVEMENTS ON THE CINEPOLIS WEBSITE Aristawati, Cindy; Tobing, Fenina Adline Twince; Surbakti, Eunike Endariahna; Peranginangin, Jimmy; Pinem, Anjar
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.v11i2.3736

Abstract

UI/UX is one of the most important elements of a website. One of the tasks of UI/UX is to make it easier to achieve a goal that the user wants. Cinépolis is a cinema that has been established in Indonesia since 2014. Cinépolis then launched its own website to make it easier for users to view movie information and order tickets. Based on the questionnaires that have been distributed and calculated using the System Usability Scale or SUS method, the Cinépolis website gets a score of 54.03 and is below the SUS standard of 68. The predicate obtained from the Cinépolis website is grade D with the predicate Poor. Heuristics are methods for finding interface problems to improve usability and user experience. The joint evaluation of 2 evaluators showed that there were 20 problems on the Cinépolis website based on 10 heuristic principles, while the evaluation of the Cinépolis website improvement prototype with 1 other evaluator found 5 problem findings based on 10 heuristic principles on the Figma prototype. The prototype that has been implemented gets a final score of 88.01 using the SUS calculation based on the questionnaire data that has been distributed. The final predicate obtained from the Cinépolis repair website is grade A with the predicate of Excellent.

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