cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Amri Muhaimin
Contact Email
amri.muhaimin.stat@upnjatim.ac.id
Phone
+6285132556260
Journal Mail Official
jasid@upnjatim.ac.id
Editorial Address
Gedung Giri Santika, Program Studi Sains Data, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
Location
Kota surabaya,
Jawa timur
INDONESIA
Jurnal Aplikasi Sains Data
Published by Universitas Pembangunan Nasional Veteran Jawa Timur
ISSN : -     EISSN : 3108947X     DOI : 10.33005
Core Subject : Economy, Science, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering
Jurnal Aplikasi Sains Data (JASID) is a peer-reviewed scientific journal published by the Data Science Study Program at Universitas Pembangunan Nasional "Veteran" East Java (UPN "Veteran" Jatim). Serving as a dedicated platform, JASID facilitates the dissemination of knowledge and practical experiences among researchers, practitioners, and academics in the field of data science applications. The journal covers a broad spectrum of topics within data science, including but not limited to machine learning, data mining, data analysis, data visualization, and natural language processing. It also emphasizes real-world applications of data science across diverse sectors such as business, finance, healthcare, and education. Through a rigorous peer review process, JASID upholds high standards of quality and originality in its published works. The journal aims to be a valuable resource for data science professionals and enthusiasts in Indonesia, fostering interdisciplinary collaboration and enhancing public understanding of the transformative potential of data science applications. Jurnal Aplikasi Sains Data (JASID) is a peer-reviewed scientific journal dedicated to advancing knowledge and innovation in the diverse field of data science applications. The journal welcomes original research articles, comprehensive reviews, and practical case studies that contribute to the understanding and implementation of data science across various domains. JASID’s scope encompasses, but is not limited to, the following areas: Machine Learning Data Mining Data Analysis Data Visualization Natural Language Processing Cloud Computing Big Data Internet of Things (IoT) Artificial Intelligence (AI) Robotics Ethical Considerations in Data Science Application of Data Science in sectors such as Business, Finance, Healthcare, Education, and Government Objectives JASID strives to: Foster and disseminate cutting-edge research and development in data science applications, both within Indonesia and globally. Serve as a reputable platform for researchers, practitioners, and academics to exchange knowledge, experiences, and innovative ideas in data science. Promote interdisciplinary collaboration among academia, industry, and government institutions to accelerate the advancement and practical adoption of data science. Raise awareness of the transformative potential and societal benefits of data science applications.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 26 Documents
 1   2   3 
Analysis of Music-Listening Habits and Mood Changes During Daily Activities Using Multiple Linear Regression Erida Dwi Cahyanti; Miftakhul Aulia Zuhrotun Nisa; Alya Aziza Khairunnisa; Andini Mei Nuraini; Nadifa Alya Nur Atika Sari; Dyah Linda Aulia
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i2.84

Abstract

Music is commonly used during daily activities for entertainment, concentration, and mood regulation. However, the relationship between music-listening habits and perceived mood changes may vary across individuals and listening contexts. This study aims to explore music-listening patterns and examine the association between selected listening habits and perceived mood changes using Exploratory Data Analysis (EDA) and multiple linear regression. The study used survey data from 150 respondents. Data preprocessing included removing personal identification columns, checking missing values and duplicate records, inspecting potential outliers using the Interquartile Range (IQR) method, and encoding categorical variables using one-hot encoding. EDA was conducted using descriptive statistics, distribution plots, scatter plots, and Pearson correlation analysis. The initial regression model included 11 predictors and produced an R² of 0.235 and an adjusted R² of 0.174, with an overall F-statistic of 3.863 and a p-value of 7.19 × 10⁻⁵. Diagnostic testing indicated non-normal residuals and heteroskedasticity. Therefore, the final model used heteroskedasticity-consistent HC3 standard errors and included two predictors: the percentage of daily activities perceived as more comfortable with music and the indicator for listening without a specific time. The final model achieved an R² of 0.1503 and an adjusted R² of 0.1387. Both predictors were statistically significant in the final model. These findings suggest that perceived comfort when listening to music and listening-time patterns are associated with reported mood changes. Nevertheless, the observational survey design and limited explanatory power mean that the results should not be interpreted as evidence of causation. Keywords: exploratory data analysis, music-listening habits, mood changes, multiple linear regression, HC3 robust standard errors
Analysis of Campus Facility Availability and Academic Community Satisfaction Using Logistic Regression and Random Forest Nurul Azizah; Moch. Rizky Ramadani; Nadia Siti Ramadhani; Ramdhan Giovanni Syarif Rizan; Najma Syahidah; Jameel Ahmed
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Campus facilities play an important role in supporting academic activities and student satisfaction. This study aimed to classify student satisfaction with campus facilities using Binary Logistic Regression and Random Forest. Survey data from 150 student respondents were used, with overall satisfaction scores of 1–3 classified as lower satisfaction and scores of 4–5 as higher satisfaction. Five facility-availability predictors were selected using Pearson correlation with a threshold of (|r| 0.20). The dataset was divided into training and testing sets using an 80:20 stratified split. Class weighting, five-fold stratified cross-validation, and GridSearchCV were applied to address class imbalance and optimize model hyperparameters. On the testing set, the tuned Random Forest achieved 93.33% accuracy, 83.33% precision, 83.33% recall, an F1-score of 83.33%, and a ROC-AUC of 86.81%. Logistic Regression achieved 90.00% accuracy, 71.43% precision, 83.33% recall, an F1-score of 76.92%, and a ROC-AUC of 92.36%. Random Forest was selected as the final model because it achieved higher test-set accuracy, precision, and F1-score than Logistic Regression. Feature importance analysis identified parking availability as the most important predictor, with an importance value of 0.3899. These findings suggest that Random Forest can classify student satisfaction in this dataset, although further validation using larger samples is needed to assess model generalizability.
Applying Principal Component Regression to Factors Associated with College Students’ Frequency ofCooking Their Own Breakfast Aurellia Putri Magani Trihapsari; Bunga Shafa Kireina; Lale Midad Haqiqia; Firstia Nabila Ariyani
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cooking breakfast independently is associated with healthier eating habits, but its frequency among college. Several interrelated factors influence students. This study aimed to model the weekly frequency of cooking breakfast independently (Y) using principal component regression (PCR) with four predictors: confidence in cooking ability (X1), frequency of not cooking because food ingredients had run out (X2), frequency of cooking because food outside the home was more expensive (X3), and frequency of preparing breakfast to take as a packed meal (X4). The four predictors were selected from 15 candidate questions based on conceptual relevance and examined using Spearman’s correlation across 12 numerical indicators; X1 and X3 were significantly correlated with Y, while X2 and X4 were retained despite not being significant. The data came from 150 college students who completed an online form, with no missing values. The analysis included descriptive statistics, Pearson’s correlation, standardisation, principal component analysis (PCA), and PCR, with the number of components selected through 5-fold cross-validation based on the minimum RMSE. Two principal components explained 61.01% of the variance in the predictors and produced a cross-validation RMSE of 1.5882. The model was jointly significant (F = 18.65; p = 6.03×10−8), with an R² of 0.2024, an adjusted R² of 0.1916, and a cross-validated R² of 0.1556. On the original scale, X1, X3, and X4 were positively associated with Y, whereas X2 was negatively associated with Y. The model’s explanatory power was low to moderate; therefore, interpret the results as associations rather than causal relationships.
Analysis of Preference Clustering and Strategies for Utilizing Sources and Media in Accessing Academic References by College Students Using the K-Means and DBSCAN Algorithms Putri Varila; Rizka Ariyanti; Nayaka Adyatma Nugroho; Saniatun Hazanah; Muhammad Fajar Kurniawan; Najwa Andity Khairani
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The increasing availability of academic information sources has led to diverse preferences and strategies among university students in searching for and utilizing academic references. This study aims to identify patterns in students’ academic reference-seeking behavior by applying Principal Component Analysis (PCA) and comparing the clustering performance of K-Means and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Data were collected through an online questionnaire involving 150 university students, with ten numerical variables used in the analysis. The data were preprocessed, standardized using StandardScaler, and reduced using PCA before clustering. The results showed that PCA reduced the ten variables to six principal components while retaining 83.53% of the cumulative variance. K-Means produced two clusters containing 123 and 27 respondents, achieving a Silhouette Score of 0.4543, a Calinski-Harabasz Index (CHI) of 52.2985, and a Davies-Bouldin Index (DBI) of 1.4225. Meanwhile, DBSCAN, with eps = 2.3 and min_samples = 2, identified two clusters consisting of 137 and 2 respondents, along with 11 noise points. DBSCAN achieved a Silhouette Score of 0.5469, a CHI of 15.3191, and a DBI of 0.4818. Therefore, DBSCAN was selected for further analysis to identify differences in students’ academic reference-seeking behaviors, particularly in source verification, reference usage, and manual and AI-assisted searching.
A Comparative Analysis of K-Medoids and DBSCAN for Clustering Student Consumptive Behavior Profiles: Penelitian Perilaku Konsumtif Mahasiswa Saktio Nur Najwan; Muhammad Irbabul Salas; Muhammad Fauzan Kamaludin; Razzan Nafis Wahyudi Wahyudi; Nasrul Ali Masyrufi; Ahsanta Khuluqi Dafi
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i2.90

Abstract

The rapid adoption of digital payment technologies, such as e-wallets and mobile banking, has significantly altered the financial habits of university students, often blurring the psychological boundaries of spending compared to conventional cash transactions. This study aims to objectively map and cluster the multidimensional consumptive behavior profiles of university students based on their payment preferences and living status. A quantitative approach was employed using a dataset of 150 respondents evaluated across 15 financial and behavioral variables. To address the complexity of the high-dimensional data, preprocessing techniques including One-Hot Encoding, Min-Max Scaling, and Principal Component Analysis (PCA) were applied prior to modeling. The study utilized a comparative clustering approach to evaluate the performance of the K-Medoids and DBSCAN algorithms. The evaluation metrics demonstrate that DBSCAN (, MinPts ) outperformed K-Medoids, achieving an optimal Silhouette Score of 0.5438, a Davies-Bouldin Index of 0.7151, and a Calinski-Harabasz Index of 101.57. The DBSCAN model successfully mapped the data into six distinct consumer clusters. The profiling results reveal a significant behavioral divide: students preferring digital payments exhibit notably higher consumptive tendencies, impulsivity, and reliance on PayLater credit facilities compared to highly conservative cash users. Furthermore, living status dictates fixed expense allocations, compounding the financial vulnerabilities of specific groups. These empirical findings provide a robust data-driven foundation for developing targeted financial literacy strategies to help students manage digital financial ecosystems responsibly Keywords: Consumptive Behavior, Clustering, DBSCAN, K-Medoids, Digital Payment.
Analysis of the Impact of Gadget Use on Student Academic Productivity Vahrel Vebrian Maulana; Alfilansyah Idham Chalid Mawardi; Rahmat Fajar Bagaskara; Malika Arya Janaloka; Mochamad Fadil Ivansyah Bachtiar; Muhammad Fat'han
Jurnal Aplikasi Sains Data Vol. 2 No. 2 (2026): Journal of Data Science Applications
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i2.92

Abstract

The increasing use of gadgets among students provides high flexibility in supporting academic activities, but on the other hand, it has the potential to trigger distractions that have an impact on reducing learning productivity. This research aims to analyze the influence of gadget usage patterns on the duration of student learning productivity using a quantitative approach. Data collection was carried out using a Likert scale questionnaire, which was distributed to students from various faculties at the National Development University "Veteran" East Java. Data analysis included multiple linear regression to test the influence of quantitative variables and attitude scales simultaneously or partially, as well as the One-Way ANOVA test to evaluate differences in average productivity based on groups of categorical variables. Before modeling was carried out, all data had gone through the cleaning stages and testing of classical prerequisite assumptions, which included tests for normality, multicollinearity, heteroscedasticity, and autocorrelation. The results of the regression analysis show that together the variables of perceived duration of task work, level of lost time, and ability to ignore social media have a significant effect on the duration of learning productivity. Partially, the perception of lost time due to the use of irrelevant devices is the main obstacle to productivity, while the ability to limit oneself from social media and focus on completing tasks makes a real positive contribution. On the other hand, the results of the One-Way ANOVA test prove that there is no significant difference in the average duration of learning productivity, both in terms of variations in the time when distractions occur and the main reason why students open their devices. These findings show that the impact of technology is universal, where student learning productivity is determined more by internal self-control and time management than conditional factors such as gadget use.

Page 3 of 3 | Total Record : 26

 1   2   3 

Filter by Year

2025 2026


Reset
Filter By Issues
All Issue Vol. 2 No. 2 (2026): Journal of Data Science Applications Vol. 2 No. 1 (2026): Journal of Data Science Applications. Vol. 1 No. 2 (2025): Journal of Data Science Applications. Vol. 1 No. 1 (2025): Journal of Data Science Applications.