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Irfan Santiko
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Journal of Multimedia Trend and Technology
Published by Universitas Amikom Purwokerto
ISSN : -     EISSN : 29641330     DOI : https://doi.org/10.35671
Core Subject : Science, Education, Art,
Arts Computer Science & IT Education Languange, Linguistic, Communication & Media
The Journal of Multimedia Trend and Technology is an online journal organised and managed independently by a consortium of multimedia and visual communication design lecturers. JMTT is an open-access journal that is provided for researchers, lecturers, and students who will publish research results in the field of all things about digitalized multimedia and its process. Currently, JMTT is under the auspices of the Amikom University Purwokerto higher education organisation, with the management of the Multimedia, Game, and Mobile Apps Study Centre together with Educollabs.
Arjuna Subject : Ilmu Komputer (semua kategori) - Interaksi Komputer-Manusia
Articles 6 Documents
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Search results for , issue "Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology" : 6 Documents clear
Exploring the Impact of Evolving Consumer Behavior on Purchase Intention in Social Commerce Environments Budianto, Philip Antonius; Halim, Erwin; Setiawan, Ariiq Naufal Arrafi; Effendi, Hafizd Rasya
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.81

Abstract

This Preliminary study explores the influence of evolving consumer behavior on purchase intention in the context of social commerce. In the contemporary landscape, where the convergence of social media and online shopping has become pervasive, a comprehensive understanding of the pivotal factors that influence purchasing decisions has become imperative. The research study will examine five primary variables: social media marketing content, consumer behavior change, consumer engagement, data security, and conversion rate. The findings indicate that marketing content, behavior change, data security, and conversion rate have a substantial impact on the growth of social commerce and purchase intention. However, consumer engagement does not demonstrate a significant effect. The data were collected from 50 respondents, and the survey was initiated in March 2025. Most of the participants are women (57.5%) and between the ages of 12 and 27 years (97.1%), which suggests that young consumers are actively involved. Most people live in Jakarta and the surrounding cities. Most of them have a diploma or a bachelor's degree, and 57% are currently studying. These statistics highlight the significant presence of young, educated people in the world of social commerce. The study gives businesses ideas to improve their strategies. These ideas include creating interesting content, building trust, and adapting to new consumer behaviors.
Drug Use Pattern Analysis Model Using the K-Means Algorithm as a Basis for Stocktaking Decision Making Tania, Ayesha
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.90

Abstract

Analysis of drug usage patterns using the K-Means algorithm aims to group drug usage based on its usage level and determine the most optimal number of clusters as a basis for more appropriate and efficient stock management recommendations. This study only focuses on drug usage data for the period of January 1, 2025, to June 30, 2025, with the variables used being the number of drugs used and their frequency of use. The approach used is data mining with the K-Means Clustering method, as well as cluster evaluation using the Elbow Method and Silhouette Coefficient. Using the Elbow method, the appropriate number of clusters is 3. This is indicated by the elbow point at k = 3, where the decrease in the WCSS value begins to decrease significantly. Evaluation of clustering quality using the Silhouette Score and Daevis-Bouldin Index (DBI) shows that the formed cluster structure has good quality. The Silhouette Score value reaches 0.61, and the DBI value is 0.53. This indicates that the data in each cluster is quite homogeneous, and the separation between clusters is quite optimal. The analysis results show that the most optimal number of clusters is three clusters, representing drug categories with high (fast-moving), medium (medium-moving), and low (slow-moving) usage levels. Each cluster has different but consistent usage characteristics. These findings provide a clear picture of the distribution pattern and drug needs at the Purwokerto Utara II Community Health Center,  and help identify the possibility of deadstock and stockouts. Thus, it can be concluded that the application of the K-Means algorithm is very effective in supporting drug stock management decision-making so that drug procurement planning can be carried out more accurately, efficiently, and sustainably.
Comparison of Support Vector Machine and XGBoost Algorithms in Sentiment Analysis of Visitor Reviews of Baturraden Tourism Forest Utami, Catur Risma; Santiko, Irfan
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.91

Abstract

The Google Maps platform provides a platform for visitors to express their opinions through reviews. This study aims to compare the performance of the Support Vector Machine and XGBoost algorithms in sentiment analysis of Baturraden Tourism Park visitor reviews. Data were collected using scraping techniques and obtained 4,096 reviews. After going through preprocessing stages including cleaning, tokenization, normalization, stopword removal, and stemming, the data used in the analysis process amounted to 2,912 reviews. Word weighting was carried out using the TF-IDF method, and the SMOTE technique was applied to address class imbalance. The results showed that the Support Vector Machine algorithm performed better than XGBoost with an accuracy rate of 94.52% before SMOTE and 94.86% after SMOTE, while XGBoost obtained an accuracy of 92.80% before SMOTE and 93.15% after SMOTE. These findings indicate that the Support Vector Machine is more effective in classifying positive and negative sentiments. This study is expected to contribute to the application of machine learning methods to understand visitor opinions on the Google Maps platform. Especially in the context of tourist attractions.
Predicting Greater Jakarta Area House Prices Using Random Forest and Linear Regression Agustin, Firli Firmansyah; Zaki, Fariz Nur Fikri
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.104

Abstract

This study focuses on analyzing and predicting house prices in the Greater Jakarta Area using a machine learning approach, specifically comparing the performance of random forest regression and multiple linear regression. the increasing demand for adequate housing in Greater Jakarta Area, coupled with fluctuating house prices influenced by factors like land size, building size, number of bedrooms, bathrooms, and other facilities, necessitates an accurate price prediction system to assist both the public and businesses in decision-making. data was collected from Rumah123.com via Kaggle, followed by pre-processing and exploratory data analysis (EDA). the models were built using both algorithms and evaluated through 10-fold cross-validation, with an 80% training and 20% testing data split. the results demonstrate that random forest regression outperforms multiple linear regression, achieving a correlation coefficient of 0.5043 and a mean absolute error of 157,698,532. in contrast, multiple linear regression (m5p) yielded a correlation coefficient of 0.4895 and a mean absolute error of 209,890,933. therefore, random forest regression is recommended as a superior model for house price prediction in the Greater Jakarta Area region.
Promoting Hydroponics Learning with Gamification Approach Wang , Gunawan; Sugianto , Ananda Dwi
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.138

Abstract

Hydroponics is a farming technique without using soil media instead of water, it has many advantages compared to conventional general techniques in terms of resource efficiency. The use of hydroponics has gained wide popularity due to space optimization. Due to its simplicity, hydroponics method has attracted many new entrepreneurs especially from youngsters. Although it is quite popular nowadays, the hydroponics adoption for Indonesian farmers is quite limited due to lack of training exposure and access. The current training is still conducted in traditional classrooms with high costs and limited participants. The article proposes a new learning method using gamification method to empower new hydroponics entrepreneurs. The gamification method enables to deliver material in a more convenient way and reach a wider audience through mobile application. The article uses a common MDA framework to provide systematic material design and links to objectives. The outcome is expected to be used as a reference for hydroponics entrepreneurs to educate a larger audience.
Understanding GenAI Adoption in Education: A Systematic Literature Review Hidayat, Alifiansyah Arrizqy; Rospricilia, Tita Ayu; Rosidah, Nur Azizah; Fauzia, Ramadhani Vanva; Putra, Ian Mahendra
Journal of Multimedia Trend and Technology Vol. 4 No. 2 (2025): Journal of Multimedia Trend and Technology
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/jmtt.v4i2.139

Abstract

This study conducts a systematic literature review of 40 peer-reviewed articles to investigate behavioral factors influencing the adoption of Generative Artificial Intelligence (GenAI) in education. Using PRISMA guidelines, the review identifies key constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT) including performance expectancy, effort expectancy, social influence, and facilitating conditions as consistent predictors of GenAI usage. Additionally, complementary variables such as trust, perceived risk, self-efficacy, hedonic motivation, and ethical concerns are found to significantly shape user engagement. The integration of UTAUT with models like TAM, TPB, and SCT enhances explanatory depth, offering a multidimensional framework for understanding GenAI adoption. The study proposes a conceptual model and highlights the importance of inclusive, context-sensitive approaches to support responsible GenAI integration in academic settings.

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