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All Journal BAREKENG: Jurnal Ilmu Matematika dan Terapan Enthusiastic : International Journal of Applied Statistics and Data Science
Utari, Dina Tri
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Energy Engineering Mathematics Mechanical Engineering Physics Transportation

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THE IMPACT OF SOCIAL MEDIA MARKETING ACTIVITIES ON SKINTIFIC PRODUCTS: PLS-SEM APPROACH Afifah, Nanda Nasywa; Utari, Dina Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1157-1168

Abstract

The swift expansion of the internet has markedly facilitated communication, thereby evolving social media from a tool for personal interaction into a formidable platform for product promotion. This research aims to investigate the effects of social media marketing endeavors on brand awareness, brand image, and brand loyalty about Skintific, a well-regarded skincare brand. Considering its growing significance, formulating a robust marketing strategy—especially within social media—is essential for enhancing brand visibility and cultivating customer loyalty. This study analyzes data from online questionnaires collected from June to July 2024 using Structural Equation Modeling (SEM) through Partial Least Squares (PLS). The sample for this investigation comprises 170 consumers of Skintific residing in Yogyakarta, with data gathered through questionnaires disseminated on platforms including Instagram, Twitter, LINE, and WhatsApp. The results indicate a positive and statistically significant correlation between social media marketing activities and brand metrics: awareness, image, and loyalty.
Temperature Prediction in Norway Using GRUs: A Machine Learning Approach Andrie Pasca Hendradewa; Utari, Dina Tri
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 1, April 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss1.art2

Abstract

Accurate temperature forecasting in Norway is significant for environmental stewardship and disaster management, in addition to providing essential support for critical sectors, including agriculture, urban development, and energy resource management. This study employed the gated recurrent unit (GRU) to augment the precision of temporal temperature forecasts. After that, it was used to project temperatures for seven days. The dataset, obtained from https://www.yr.no/nb, comprised records of minimum and maximum temperatures spanning from February 1, 2018, to December 31, 2024. The data was partitioned, with 80% allocated for training and 20% designated for testing. Utilizing a training regimen of 20 epochs alongside a three-day lookback interval, the model attained R² scores of 0.82 for minimum temperature predictions and 0.86 for maximum temperature forecasts. These results underscore the GRU model’s capacity to accurately capture daily temperature variations and produce dependable predictions. Given its commendable performance on training and testing datasets, the GRU model is particularly suitable for temperature forecasting.
COMPARISON OF K-MEANS AND GAUSSIAN MIXTURE MODEL IN PROFILING AREAS BY POVERTY INDICATORS Wahidah, Zumrotul; Utari, Dina Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0717-0726

Abstract

The Covid-19 pandemic has led to income degradation of the Indonesia population which potentially triggers poverty. According to the Indonesian Central Statistics Agency, the Province of Central Java is one of the areas that is most affected by Covid-19 especially on the economic aspect. In 2020, the percentage of poor people has increased by 0.6% from 2019. If this condition is ignored for the long term, it will have a negative impact on hampering national development. As a first step in designing a strategy for mitigating the impact of poverty, it is necessary to carry out an appropriate profiling of the areas affected on the economic aspect based on poverty indicators. This study compares the K-Means Clustering and Gaussian Mixture Model (GMM) in providing the best data grouping based on clustering indexes, including: connectivity, Dunn, and silhouette. GMM is a generalization of K-Means clustering to include information about the covariance structure of the data as well as latent Gaussian centers. We used poverty indicators data from Central Statistics Agency of Central Java, such as poverty line, percentage of poor population, poverty depth index, and poverty severity index. The results obtained from this study indicate that the GMM gives the best results with the 3 clusters, with the number of members for the first, second, third is 10, 19, and 6 respectively.
INTEGRATION OF SVM AND SMOTE-NC FOR CLASSIFICATION OF HEART FAILURE PATIENTS Utari, Dina Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 4 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss4pp2263-2272

Abstract

SMOTE (Synthetic Minority Over-sampling Technique) and SMOTE-NC (SMOTE for Nominal and Continuous features) are variations of the original SMOTE algorithm designed to handle imbalanced datasets with continuous and nominal features. The primary difference lies in their ability to generate synthetic examples for the minority class when dealing with continuous and nominal features. We employed a dataset comprising continuous and nominal features from heart failure patients. The distribution of patients' statuses, either deceased or alive, exhibited an imbalance. To address this, we executed a data balancing procedure using SMOTE-NC before conducting the classification analysis with SVM. It was found that the combination of SVM and SMOTE-NC methods gave better results than the SVM method, seen from the higher level of accuracy and F1 score. F1 gives less sensitivity to class imbalance compared to accuracy. Suppose there is a significant imbalance in the number of instances between classes. In that case, the F1 score can be a more informative metric for evaluating a classifier's performance, especially when the minority class is of interest.
ADVANCEMENTS IN ALZHEIMER’S DIAGNOSIS THROUGH MRI USING BAYESIAN CONVOLUTIONAL NEURAL NETWORKS AND VARIATIONAL INFERENCE Nareswari, Alifia Ardha; Utari, Dina Tri
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 18 No 4 (2024): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol18iss4pp2423-2434

Abstract

Alzheimer’s disease is one of the brain disorders that can be deadly in older. The disease is less treated and less recognized, but Alzheimer’s disease is now a significant public health problem. Early detection of the disease can significantly reduce symptoms. However, the lack of medical personnel makes handling this disease complex. Therefore, an automatic diagnosis of Alzheimer’s disease is needed with a Magnetic Resonance Imaging (MRI) examination to get an accurate diagnosis of the disease. This study classified the type of Alzheimer’s disease with deep learning methods using the Bayesian Convolutional Neural Network (BCNN) and the Variational Inference (VI) technique. It aims to determine image classification and accuracy level at the level of Alzheimer’s disease by using 2,400 brain MRI images, divided into three classes (non-demented, very mild demented, and mild demented) based on severity. The data was acquired from the kaggle.com website. We use a dataset scenario of 80% for training and 20% for testing, 100x100 pixels, kernel size 3x3, and optimizer Adam with epoch 200. The accuracy of the image classification process is 80%. The non-demented label predicts that the uncertainty is 0.371, and the other uncertainty prediction is 0.002. The ability to anticipate uncertainty enables clinicians to make informed decisions regarding the reliability of the model’s output and the need for additional validation or confirmation.
COMPARATIVE ANALYSIS OF MACHINE LEARNING MODELS FOR RAINFALL CLASSIFICATION IN YOGYAKARTA Utari, Dina Tri; Palage, Ghalang Rambu Putera; Fadhlirobby, Faiz; Nuswantoro, Artheta Bimo
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 4 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss4pp2765-2776

Abstract

Precise rainfall classification is most important for meteorological forecasting and disaster risk mitigation, particularly in regions such as Yogyakarta, which are vulnerable to extreme weather events. Although previous studies have examined rainfall classification through the lens of meteorological variables, a notable lack of research has systematically evaluated the effectiveness of diverse machine learning algorithms for categorizing rainfall types within this specific locale. This study aims to rectify this gap by incorporating essential weather variables, specifically temperature, humidity, atmospheric pressure, and precipitation, into predictive models that utilize K-Nearest Neighbors (KNN), decision trees, and logistic regression techniques. Among the evaluated models, the decision tree demonstrated the highest degree of accuracy across both training and testing datasets. An examination of feature significance indicated that precipitation emerged as the most pivotal variable, aligning with the fundamental physical mechanisms associated with rainfall. This study contributes significantly to the evolving field of weather informatics by illustrating the utility of machine learning approaches in classifying regional rainfall. However, the parameters of this research are limited to specific meteorological variables and do not account for spatial or temporal variations, which could potentially influence the model’s broader applicability. Future research endeavors could augment this framework by integrating remote sensing data and methodologies for spatiotemporal modeling.
Co-Authors Afifah, Nanda Nasywa Andrie Pasca Hendradewa Fadhlirobby, Faiz Nareswari, Alifia Ardha Nuswantoro, Artheta Bimo Palage, Ghalang Rambu Putera Wahidah, Zumrotul