Jurnal Teknologi dan Manajemen Informatika
Journal Title Jurnal Teknologi dan Manajemen Informatika Initials JTMI Abbreviation J. Teknologi dan Manajemen Informatika Frequency 2 Issues every year (June and December) DOI Prefix 10.26905 by Crossref Print ISSN 1693-6604 Online ISSN 2580-8044 Editor in Chief Kiswara Agung Santoso Deputy Editor Devita Maulina Putri Publisher Faculty of Information Technology, University of Merdeka Malang Citation Analysis SINTA 3 | Dimensions | DOAJ | Google Scholar | Garba Rujukan Digital (GARUDA) licenced Journal Summary: Jurnal Teknologi dan Manajemen Informatika (JTMI) is published by the Faculty of Information Technology, University of Merdeka Malang. JTMI publishes 2 issues per year in June and December with the scope of computer science covering information technology, information systems, and information management
Authors who publish with this journal agree to the following terms: (1)Â Copyright of the published articles will be transferred to the journal as the publisher of the manuscripts. Therefore, the author confirms that the copyright has been managed by the journal. (2)Â Publisher of JTMI: Jurnal Teknologi dan Manajemen Informatika isÂ University of Merdeka Malang. (3) The copyright follows Creative Commons Attributionâ€“ShareAlike License (CC BY SA): This license allows to ShareÂ â€” copy and redistribute the material in any medium or format, AdaptÂ â€” remix, transform, and build upon the material, for any purpose, even commercially.
Comparative Analysis of LSTM-Based Models for Daily Gold Price Forecasting Using Time Series and Sentiment Features
This study aims to improve the accuracy of gold price forecasting by combining statistical and deep learning methods with sentiment analysis. Three models were developed and compared: (1) a pure Long Short-Term Memory (LSTM) model, (2) a hybrid LSTM + Prophet model, and (3) a hybrid LSTM + Prophet + Sentiment model. The datasets consisted of daily gold prices and financial news sentiment from 2013 to 2023. Each model was evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), R-squared (R2), and Mean Absolute Percentage Error (MAPE). The pure LSTM model achieved an R2 of 0.9375, while the hybrid LSTM + Prophet model improved performance to 0.9394 with lower error rates. The integration of sentiment data resulted in stable but not significantly higher accuracy. Overall, the hybrid LSTM + Prophet model produced the best results, confirming that combining statistical trend decomposition with deep learning effectively enhances forecasting stability and interpretability for financial time series data such as gold prices.
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Comparative Analysis of SMOTE, WMOTE, and ADASYN Oversampling Methods on Multinomial Naive Bayes Performance in Classifying Toddlers Nutritional Status
Class imbalance in toddler nutritional status data often reduces the ability of classification models, especially in predicting minority classes. This study aims to analyze the impact of three oversampling techniques, namely SMOTE, WMOTE, and ADASYN, on improving the performance of the Multinomial Naive Bayes (MNB) algorithm. A dataset of 243 data was processed through a preprocessing stage by converting categorical variables using numeric labels. To meet the MNB algorithm's requirement for non-negative data, continuous numeric features (such as birth weight, birth height, weight, height, and age) were normalized using the Min-Max Scaler to the range [0, 1]. This process discretizes continuous values onto a probability scale to ensure feature compatibility with the Multinomial distribution. Data balancing was performed only on the training dataset, where the SMOTE method produced 374 data, ADASYN produced 375 data, and WMOTE produced 373 data. The evaluation results show that although all three oversampling methods experienced a slight decrease in global accuracy, the model's ability to detect minority classes improved, as evidenced by increases in G-Means and Balanced Accuracy. The test results concluded that MNB-ADASYN was the best model for prioritizing high sensitivity to all class labels, while MNB-WMOTE provided the most consistent global accuracy stability while maintaining performance on minority classes.
Application of VGG16 Deep Learning Architecture and K-Means for PCOS Severity Clustering Based on Ovarian Ultrasound Images
Polycystic Ovary Syndrome (PCOS) is a complex hormonal disorder that significantly impacts the reproductive and metabolic health of women. A primary challenge in PCOS analysis is the reliance on subjective manual observations of ultrasound (USG) images, which are prone to inconsistency due to the lack of objective standards. This study proposes an automated, unsupervised framework to stratify the morphological characteristics of PCOS ultrasound images without predefined clinical labels. The method integrates feature extraction using the VGG16 deep learning architecture with K-Means clustering. The structural integrity of the computational clusters was quantitatively evaluated, achieving a Silhouette Score of 0.4248 and a Davies-Bouldin Index (DBI) of 0.9175, indicating the successful formation of distinct morphological groupings. Furthermore, an evaluation using a Support Vector Machine (SVM) to assess the internal consistency and linear separability of the extracted features yielded a test accuracy of 97.54%. The results demonstrate that the integration of VGG16 and K-Means effectively partitions PCOS ultrasound images into objective, computationally stable morphological groups. This approach is designed to function as a standardized clinical decision support tool, mitigating visual subjectivity and providing a measurable baseline for women's reproductive health management.
Comparative Analysis of Unsupervised Methods for Anomaly Detection in IoT-Based Pharmaceutical Cold Chain Temperature
Maintaining vaccines within the 2°C–8°C range throughout cold chain storage and distribution is essential, since temperature excursions can degrade their biological potency. The growth of IoT-enabled sensors now allows continuous temperature data collection, opening the door to automated anomaly detection via time-series analysis. This research compares several unsupervised approaches for spotting temperature anomalies in an IoT-based pharmaceutical cold chain setup, benchmarking Isolation Forest against three deep learning architectures: Autoencoder, LSTM, and LSTM-Attention. Using roughly 8,640 temperature readings collected at 5-minute intervals over 30 days, the data were normalized with Min-Max Scaling and structured into sequences via a sliding window technique. Performance was assessed using precision, recall, and F1-score, alongside MAE and RMSE for prediction accuracy. Results showed Isolation Forest outperforming the other models (precision: 0.686, recall: 0.418, F1-score: 0.520) while also being the fastest to train and run. The deep learning models underperformed, likely limited by dataset size, making Isolation Forest the more practical choice for balancing detection accuracy with computational cost.
Traditional Snack Image Classification Using ResNet50 and EfficientNetB0: A Comparative Study for Smart Culinary Tourism in Jakarta
This study is motivated by the significant potential of Jakarta’s traditional culinary sector in tourism, which is not yet supported by interactive digital identification media for tourists. This research aims to implement a Convolutional Neural Network (CNN) algorithm to automatically classify traditional snacks as part of a smart tourism system. A primary dataset consisting of 2,600 images across 14 snack classes was collected from Ciracas Market and Setu Babakan in April 2025. Data preprocessing was conducted using augmentation techniques, followed by model development using transfer learning with two architectures, ResNet50 and EfficientNetB0. The experimental results show that ResNet50 achieved the highest accuracy of 99.08% with a loss value of 0.0239, outperforming EfficientNetB0, which decreased to 94.48% accuracy. The best model was deployed in a Streamlit-based web application that provides interactive information, including the name, history, and ingredients of traditional snacks. This system facilitates tourists in recognizing Jakarta’s local culinary heritage and supports the implementation of smart tourism.
Analysis of the Relationship Between TikTok Engagement and SME Brand Awareness Using Pearson Correlation and Linear Regression
The increasing use of social media, particularly TikTok, has strengthened its role as a digital marketing platform that encourages customer interaction and enhances brand awareness. This study investigates the relationship and influence of TikTok engagement on brand awareness in SME Fanny’s Lapis Labu Samarinda. A quantitative approach was implemented using questionnaire responses obtained from 100 participants. The collected data were analyzed through Pearson correlation and simple linear regression techniques. The findings reveal that TikTok engagement has a very strong and statistically significant positive relationship with brand awareness, as indicated by a correlation coefficient of 0.839. In addition, TikTok engagement explains 70.5 percent of the variation in brand awareness. These results confirm the substantial contribution of TikTok engagement to strengthening brand awareness among SMEs. Furthermore, user interaction data generated through TikTok can be utilized as strategic information to support more effective data-driven marketing decisions.
Optimizing Endless Runner Game Player Performance Using a Hybrid GMF-MLP Recommendation System Based on Neural Collaborative Filtering
Endless Runner games feature exponentially increasing difficulty as distance grows, often causing character failure and player frustration, largely because players struggle to select power-ups suited to their current difficulty context. While prior recommender-system research has mostly focused on purchase prediction for monetization, this study instead builds a personalized item recommendation system aimed at reducing failure and maximizing scores. We propose a hybrid Neural Collaborative Filtering (NCF) architecture combining General Matrix Factorization (GMF), which captures linear preferences, with a Multi-Layer Perceptron (MLP), which models non-linear interactions between playstyle and failure context (cause of death). The model was trained on 10,000 gameplay activity logs containing features such as jump count, obstacles avoided, and death cause. Over 20 training epochs, both training and validation accuracy converged to approximately 0.88–0.90, with a negligible gap between the two curves, indicating minimal overfitting. These results demonstrate that integrating GMF and MLP effectively produces recommendations adaptive to dynamic gameplay conditions.
Attitude Does Not Moderate Influence Perceived Ease of Use, Perceived Usefulness, and Perceived Risk on The Adoption of Fintech Technology
This study aims to determine the effect of Perceived Ease of Use, Perceived Usefulness, and Perceived Risk on the adoption of fintech technology with attitude as a moderating variable. The type of research used is explanatory research. The sample in this study were 91 respondents, students of STIE Malangkucecwara who have businesses. The research sample is a saturated sample. The research instrument used a questionnaire and observation. The analysis used Partial Least Squares Structural Equation Modeling (PLS-SEM), where X1 refers to the variable Perceived Ease of Use, X2 to the variable Perceived Usefulness, X3 to the variable Perceived Risk, Z to the variable Attitude and Y to the variable Adoption of fintech technology. Based on the results of the study, it was concluded that Perceived Ease of Use, Perceived Usefulness, and Perceived Risk were proven to have a positive and significant effect on the adoption of Byond by BSI fintech technology. Attitude was not proven to moderate the influence of Perceived Ease of Use, Perceived Usefulness, and Perceived Risk on the adoption of Byond by BSI fintech technology.