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Gedung Giri Santika, Program Studi Sains Data, Fakultas Ilmu Komputer, Universitas Pembangunan Nasional "Veteran" Jawa Timur, Surabaya
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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
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A Quantitative Analysis of Economic Strategy and Its Influence on Final Ranking in Magic Chess Game Using Machine Learning Hazza Fitrah; Dafa Zain Musyafa; Nauval Theo Jovaldi; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (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.v2i1.25

Abstract

Economic management is a fundamental strategic pillar in auto-battler games such as Magic Chess, but its quantitative impact on player performance has not been extensively studied. This research aims to empirically measure the predictive ability of economic variables on players' final rankings. We analyzed a dataset consisting of 57 match records from players at the ‘Grandmaster’ ranking level. Two modeling approaches, Multiple Linear Regression and Random Forest, were used to predict players' final rankings (values 1–8) based on three primary economic features: total gold spent, re-roll frequency, and average economic bonus. The results from the Linear Regression model showed a Mean Squared Error (MSE) of 0.5496. However, the most significant finding was the R-squared value, which was only 0.016. This extremely low R-squared value indicates that the economic variables analyzed could only explain 1.6% of the total variance in players' final rankings. The conclusion of this study is that economic metrics alone are insufficient to build a reliable model for accurately predicting final rankings. This strongly suggests that other strategic factors, such as synergy composition, item allocation, and tactical decisions on the game board, have a far more dominant influence in determining a player's success in high-level Magic Chess.
Dijkstra's Algorithm for Optimizing Humanitarian Aid Distribution Routes to Flood Victims in Cerme District, Gresik Datia Putri Nabila Br Tarigan; Desi Tristianti; Erika Fatimatul Hidayanti; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (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.v2i1.32

Abstract

This study presents the development and analysis of a system designed to optimize the distribution routes of social aid during flood emergencies in the Cerme District, Gresik Regency. The primary objective is to ensure that logistical operations, particularly the delivery of aid to affected villages, are carried out in the most efficient and timely manner. To achieve this, Dijkstra’s Algorithm is employed due to its well-established reliability in computing the shortest path between nodes in a weighted graph. The graph used in this research is constructed based on real-world spatial data, with each node representing a village and the edges representing actual road distances obtained from mapping services. The system is implemented using an Object-Oriented Programming (OOP) paradigm in Python, which ensures modularity and scalability of the codebase. For graph modeling and shortest path computation, the NetworkX library is utilized, while the graphical user interface (GUI) is built using Tkinter to provide an interactive and user-friendly experience. The application enables users to select starting and destination points from dropdown menus, compute the shortest route dynamically, and visualize it on an interactive graph complete with route details and distances. Experimental trials were conducted by simulating various flood scenarios, and the results demonstrated that the system successfully identified optimal aid routes with minimized travel distances. These outcomes confirm the practicality and effectiveness of the proposed method. Moreover, the ability to update the graph dynamically allows the system to adapt to changes in road accessibility due to flooding. This makes the tool highly applicable in real-world disaster response scenarios. In conclusion, the developed application offers a valuable solution for both local government agencies and humanitarian volunteers, helping to improve coordination, reduce delivery time, and ensure that aid reaches flood-affected communities as efficiently as possible.
Comparative Analysis of Hierarchical Clustering and K-Medoids for Clustering Cases of Childhood Respiratory Diseases in Lamongan Regency Adelia Yuandhika; Nezalfa Sabrina; Cahya Eka Melati; Dwi Arman Prasetya; Prismahardi Aji Riyantoko
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (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.v2i1.37

Abstract

Abstract— Respiratory diseases affecting children remain a significant health issue in Indonesia, including in Lamongan Regency. The region faces challenges related to pediatric respiratory illnesses, particularly Childhood Tuberculosis, Pneumonia in toddlers, and Cough in toddlers, which impact children's quality of life and development. Therefore, understanding the spatial distribution and correlation patterns among these diseases is essential to support more targeted health intervention planning. This study analyzes the distribution patterns of pediatric respiratory diseases in Lamongan Regency and clusters regions based on similarities in the number of cases using an unsupervised learning approach. The method employed is Hierarchical Clustering with four distance calculation techniques: single, complete, average, and ward linkage and K-Medoids with two distance calculation techniques: euclidean and manhattan distance. The data, sourced from the Lamongan District Health Office, include four numerical variables related to respiratory diseases, aggregated by sub-districts. Data normalization was carried out using standardization, and cluster quality was evaluated using three internal metrics: Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The analysis results indicate that the optimal number of clusters is three. Among all methods tested, the Hierarchical Clustering with ward linkage method yielded the best performance, with a Silhouette Score of 0.5447, a DBI of 0.5884, and a CHI of 20.3018. These results demonstrate that the ward linkage method is the most effective in clustering regions based on the characteristics of pediatric respiratory disease cases and can be used for mapping priority health intervention areas in Lamongan Regency.
Spatial Modeling of Factors Determining Active Family Planning Participation in East Java: A Geographically Weighted Regression and Elastic Net Approach Ardiana Fatma Dewi; I Nyoman Kresna Wira Yudha; Muhammad Nasrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (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.v2i1.58

Abstract

This research identifies regional variations and determinants of active family planning (KB) participation in East Java using spatial modeling. The study utilizes data from the 2024 Family Information System (SIGA) of BKKBN East Java. To address multicollinearity and high-dimensional data, the Elastic Net method—combining Ridge and Lasso penalties—was employed for variable selection, retaining 6 out of 10 initial variables. Global modeling through Ordinary Least Squares (OLS) showed an Adjusted of 0.668. However, a Moran’s I test on the residuals revealed significant spatial autocorrelation (Z-score = 2.5677, p = 0.0102), justifying the use of Geographically Weighted Regression (GWR). The GWR model, using a Fixed Gaussian kernel with a bandwidth of 103.63, improved performance with an Adjusted of 0.7348. The results demonstrate spatial heterogeneity, where factors such as unmet need, households with children, and welfare levels have varying impacts across different districts. This spatial visualization helps identify priority areas for strategic resource allocation to enhance KB program efficiency
NEXT-DAY RAINFALL CATEGORY PREDICTION USING ENSEMBLE MACHINE LEARNING AND SHAP Ulfa Emi Rahmawati; Muhammad Galang Satrio Wicaksono
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

Rainfall intensity varies substantially over time and is difficult to classify using simple linear approaches because meteorological variables interact nonlinearly. This study develops a day-ahead rainfall-category classification framework using meteorological observations from BMKG for 2025. The dataset contains 366 daily observations of rainfall, temperature, humidity, wind speed, and wind direction. Temporal feature engineering was applied through three-, five-, and seven-day rainfall accumulation as well as rolling humidity and wind-speed features. Random Forest, XGBoost, and LightGBM were trained and compared using accuracy, weighted precision, weighted recall, and weighted F1-score. Hyperparameter tuning was performed using cross-validation, and SHAP was employed to interpret the selected model. XGBoost produced the best balance between precision and recall, with accuracy of 0.8356, weighted precision of 0.7710, weighted recall of 0.8356, and weighted F1-score of 0.8010. The five-fold cross-validation score was 0.8528. SHAP analysis identified humidity as the most influential feature, followed by seven-day rainfall accumulation and five-day rainfall accumulation. These findings indicate that short-term meteorological history and atmospheric humidity are important signals for day-ahead rainfall-category classification.
A Comparative Analysis of K-Nearest Neighbors and Logistic Regression for Classifying the Effectiveness of the Do Not Disturb Feature in Supporting Student Focus and Productivity Nadhifa Syakira Amanda; Aurelia Citra Dewi Permadani; Maulidya Miftahul Magfiroh; Ramya Kinanthi; Aisyah Aszahra Alfani; Murad Ali
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

Smartphone notifications can interrupt students’ concentration during academic activities, making the effectiveness of the Do Not Disturb (DND) feature relevant to focus and productivity. This study compares K-Nearest Neighbors (KNN) and Logistic Regression to classify perceived DND effectiveness using eleven predictors related to digital device use, study focus, DND usage, and perceived productivity. Data were collected through an online questionnaire involving 150 students at Universitas Pembangunan Nasional “Veteran” Jawa Timur. The target variable was grouped into effective and ineffective categories. The dataset was divided into training and testing sets using a stratified 70:30 split, and five-fold cross-validation was applied during model development. KNN achieved a cross-validation accuracy of 89.52% with k = 17, while Logistic Regression obtained 82.86%. On the 45-observation test set, Logistic Regression achieved an accuracy of 84.44%, precision of 88.24%, recall of 90.91%, F1-score of 89.55%, and ROC-AUC of 0.8990. KNN achieved 80.00% accuracy, 81.58% precision, 93.94% recall, 87.32% F1-score, and 0.9318 ROC-AUC. Thus, Logistic Regression performed better in accuracy, precision, and F1-score, whereas KNN had higher recall for the effective class and a higher ROC-AUC. Logistic Regression coefficients also showed that perceived DND assistance in maintaining focus was positively associated with the predicted effective category. Overall, Logistic Regression provided more balanced classification performance on the test set, although neither algorithm outperformed the other across all metrics. These findings concern students’ reported perceptions and do not establish a causal effect of DND on focus or productivity.
ANALYSIS OF THE EFFECTS OF DISCOUNTS AND PROMOTIONS ON IMPULSE BUYING Auzan Azinuddin Wazidiatama; Ananda Deva Dwi Jaya; Aldha Nurhaliza Putri; Hera Aprilia Noradinda; Tarra Afifah Zahra Gaida; Yusuf Java Abimadia
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.v2i2.77

Abstract

This study aims to analyze the effects of discount and promotional strategies on impulse buying tendencies. The development of consumer behavior, particularly on digital platforms such as Shopee, has made impulse buying an increasingly relevant phenomenon to investigate, especially in relation to price incentives offered by sellers. To achieve this objective, this study employs a quantitative approach by analyzing data from 150 respondents. The analytical methods include Principal Component Analysis (PCA) to reduce ten independent variables into a simpler set of components and Principal Component Regression (PCR) to examine the effects of these components on impulse buying tendencies. The results indicate that PCA successfully reduced the dimensionality of the data. The first principal component (PC1) explained 33.49% of the total variance, while the first two components (PC1 and PC2) jointly explained 49.01%. By including the sixth component (PC6), the cumulative explained variance reached 83.48%, indicating that most of the information in the data could be represented by six principal components. In the model diagnostics, 10 out of 150 respondents had Cook's Distance values exceeding the established threshold, indicating the presence of several influential observations that should be considered when interpreting the stability of the regression model. Based on these findings, discount and promotional strategies were found to have a statistically significant relationship with impulse buying tendencies, although the presence of influential observations should be taken into account when interpreting the model results. Keywords: Principal Component Analysis, Principal Component Regression, Diskon, Promo, E-Commerce
A Comparative Analysis of Learning Outcomes in Online and In-Person Courses for College Students in East Java Indana Zulfa Azzahra Roziki; Sultan Ekdan Arifqi; Farah Adawiyah Aini; Natalya Sutoyo; Afifah Fikria Dzakiroh; Kalyssa Bianca
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (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.v2i1.80

Abstract

This study aims to compare students’ experiences and perceptions of learning outcomes in online and offline courses based on survey data from 150 respondents. The analysis includes five paired metrics—duration of focus, percentage of material understood, number of notes taken, effectiveness rating, and assignment volume rating—as well as five categorical preference questions. The Shapiro–Wilk test was applied to the differences between offline and online scores to assess normality. Since all metrics yielded p-values below 0.05, differences in numerical metrics were analyzed using the Wilcoxon signed-rank test. Categorical preferences were analyzed using the exact binomial test against a reference proportion of 50%, while the p-values for all ten tests were corrected using the Holm–Bonferroni method. K-Means clustering was applied to the five standardized offline–online difference features. The results show that the offline group had significantly higher medians for focus duration, comprehension percentage, number of notes, and effectiveness ratings, while task volume ratings did not differ significantly after Holm’s correction. Preference for the offline method was also dominant in terms of preferred method, most effective method, ease of discussion, and impact on health; preferences regarding scheduling did not show significant dominance. Segmentation yielded two clusters consisting of 81 and 69 respondents, respectively. These results illustrate patterns within the survey sample and do not, by themselves, represent all students in East Java.
Comparison of Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) Methods in Classifying University Students’ Sleep Quality Based on Academic Assignment Workload Mukhammad Rasya Azaka Makruf MAKRUF; Muhammad Iqbal Hafidz; Oktasae Lawusya Emka; Alfina Muna Maitsa; Najwa Lailatun Nisa; Mohammad Fadhel Putra Naue
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

A high academic assignment workload often directly reduces the duration and quality of students’ sleep, triggering sleep debt and a decline in both physical and psychological performance. This study aims to analyze the effect of academic assignment volume on students’ sleep duration and to classify their sleep quality using machine learning algorithms. Data were collected through a questionnaire from 150 respondents with 15 variables covering assignment work duration, sleep hours, and assignment pile-up factors. Exploratory Data Analysis (EDA) showed a negative correlation of -0.43 between assignment work hours and sleep duration on active lecture days. Classification modeling was carried out using the Support Vector Machine (SVM) algorithm with an optimized Radial Basis Function (RBF) kernel (C=100, γ=0.01) and K-Nearest Neighbors (KNN) with the best number of neighbors (k=25). The test results show that the SVM model was slightly better overall, with an accuracy of 56.67% and a macro F1-score of 56.62%, compared with the KNN model, which achieved an accuracy of 53.33% and a macro F1-score of 51.39%. In addition, SVM proved better at detecting the poor sleep class, while KNN was more sensitive to the restful sleep class. This study demonstrates that time management and the amount of assignment workload affect students’ rest duration, and that a machine learning approach is able to map these classification patterns despite the challenge of overlapping data.
Analysis of Study Space Comfort Levels in Coffee Shops Using Decision Tree and Random Forest Algorithms Fitri Nur Wahyuni; Putri Ayu Dita Widya; Fitriany Aisyah Nurvella
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.v2i2.83

Abstract

Coffee shops are no longer just places to enjoy drinks, they have also become workspaces and study spaces for university students. This study examines this shift through Exploratory Data Analysis (EDA) of questionnaire data from 150 valid respondents, covering 22 variables such as visit frequency and duration, spending, number of devices, seating area, preferred time, noise disturbance, Wi-Fi and power outlet problems, and the percentage of tasks completed. The data were cleaned and checked for missing values and outliers using the IQR method. The results show that most respondents feel comfortable, with scores of 4 and 5 being the most common, particularly among students from Computer Science and Engineering and Science, in the late afternoon, and in workspace areas. Comfort is positively related to productivity, with the percentage of tasks completed rising from 52.0% for Uncomfortable respondents to 64.9% for Comfortable respondents and 74.1% for Very Comfortable respondents. However, Wi-Fi and power outlet problems remain common, experienced occasionally by 41.3% and often by 33.3% of respondents. Decision Tree and Random Forest models reached 50.0% and 56.7% accuracy respectively in predicting comfort, so they are not yet reliable without further tuning. Overall, the findings confirm that coffee shops now serve as student workspaces, and facility quality is a factor owners should address.

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