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INDONESIA
Enthusiastic : International Journal of Applied Statistics and Data Science
Published by Universitas Islam Indonesia
ISSN : 2798253X     EISSN : 27983153     DOI : 10.20885
Core Subject : Economy, Science, Education, Engineering,
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics
ENTHUSIASTIC is an international journal published by the Statistics Department, Faculty of Mathematics and Natural Sciences, Universitas Islam Indonesia. ENTHUSIASTIC publishes original research articles or review articles on all aspects of the statistics and data science field which should be written in English. ENTHUSIASTIC has the vision to become a reputable journal and publish good quality papers. We aim to provide lecturers, researchers both academic and industry, and students worldwide with unlimited access to be published in our journal. Specifically, these scopes of the ENTHUSIASTIC journal are: 1. Statistical Disaster Management 2. Actuarial Science 3. Data Science 4. Statistics of Social and Business 5. Statistics of Industry
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 78 Documents
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Spatio-Temporal Modeling of Crime Rates Using Geographically and Temporally Weighted Regression Putra, Robiansyah Putra; Zai, Fidelis Nofertinus
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art4

Abstract

This study analyzes the spatio-temporal modeling of crime rates in 35 regencies and cities in Central Java using the geographically and temporally weighted regression (GTWR) method. The objective is to investigate how socio-economic factors, including the open unemployment rate, percentage of the poor population, population density, average years of schooling, job vacancies, labor force participation rate, and labor wage, influence crime rates across different regions and periods. The goodness-of-fit test results indicateed that the GTWR model had an R-squared value of 93.51%, higher than the 88.64% of the geographically weighted regression (GWR) model, demonstrating GTWR’s ability to explain crime data variations that were heterogeneous both spatially and temporally. Partial significance tests and mapping results showed that the influence of variables differed across years and regions, with population density and labor-related factors consistently being the main predictors. These findings highlight the importance of designing crime prevention policies that are locally tailored and based on spatio-temporal evidence.  
Analyzing Sentiments on IISMA Discontinuation Rumors with SVM, Random Forest Classifier, and XGBoost Classifier Handa, Michelle Intan; Sampe, Maria Zefanya; Syafrudi
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art5

Abstract

Indonesian International Student Mobility Award (IISMA) is a government-run student exchange program. Recently, rumors regarding its discontinuation have sparked various public opinions. This study aims to analyze these public sentiments and evaluate which machine learning model is most suitable for classifying sentiment labels in the dataset. The models tested included support vector machine (SVM), random forest classifier (RFC), and extreme gradient boosting (XGBoost) classifier. The dataset consisted of 630 tweets scraped from Twitter and was split into an 80:20 ratio, with 80% allocated for training and 20% for testing. The results indicated that both SVM and RFC were the most effective models, achieving the highest accuracy of 85.44%. Sentiment analysis reveals that the majority of public opinion is positive, suggesting that most people agree with the discontinuation of the IISMA program because the program is perceived as nonurgent and not a current national priority. These findings provide insights into public sentiment and highlight the utility of machine learning models in classifying such sentiment data effectively.
Log-Linear Analysis of the Association among Hematological Variables in Dengue Hemorrhagic Fever Cases Irfan, Miftahul; Hayati, Ma’rufah; Madonna, Nora; Dewi, Wardhani Utami
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art6

Abstract

Health data are often analyzed in their continuous form through approaches such as linear, logistic, or survival models. In this study, hematological variables were dichotomized based on established clinical cut-offs to enable log-linear analysis of associations among categorical variables, acknowledging the potential loss of information from this transformation. A log-linear model was applied to evaluate independence, dependence, and interaction patterns among leukocyte, hemoglobin, and hematocrit categories in a dengue hemorrhagic fever (DHF) patient dataset. Previous analyses using survival models identified these variables as factors associated with recovery rates; however, these models did not capture their interaction structure. Log-linear analysis was therefore employed to examine these associations more comprehensively. The best-fitting model was identified as , which included two-factor interactions between leukocyte–hematocrit and hemoglobin–hematocrit. This model demonstrated a good fit (Pearson , , ), including a three-factor interaction resulted in a saturated model (= 0) and did not improve model performance. These findings highlight significant interaction patterns among hematological variables in DHF patients, providing a more detailed understanding of their joint associations.
Tourist Preference Analysis Based on Google Reviews Using the DBSCAN Method Qori'atunnadyah, Marita; Murni, Cahyasari Kartika; Choiri, Achmad Firman; Marianto, Hadi; Yazid, Muhammad
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art7

Abstract

Tourism is a strategic sector contributing to regional economic growth. Although Lumajang Regency offers prominent natural destinations, data-based insights into tourist preferences remain limited. This study analyzed tourist preferences using Google Reviews through a text mining approach that integrated the density-based spatial clustering of applications with noise (DBSCAN) algorithm and lexicon-based sentiment analysis. Data were collected via web scraping from six major destinations, yielding 16,904 reviews, of which 9,800 contained analyzable text. The text data were preprocessed using the term frequency-inverse document frequency (TF–IDF) to generate numerical representations prior to clustering. Using DBSCAN with parameters ε = 0.8 and MinPts = 4, one main cluster comprising 9,353 reviews and 447 outliers was identified. The main cluster was dominated by keywords such as waterfall, beautiful, and scenery, emphasizing the visual appeal of Tumpak Sewu as Lumajang’s tourism icon, while the outliers reflected reviews from international visitors and practical travel information. Sentiment analysis showed that most reviews were positive (68.0%), followed by neutral (24.1%) and negative (7.9%). These findings indicate a predominantly positive perception of Lumajang tourism, though accessibility and facilities require improvement. The study demonstrates the potential of digital review data for developing data-driven tourism management and promotion strategies.
Recommending E-Commerce Platforms for MSMEs: A Sentiment Analysis Approach Adiyana, Imam; Kurniawan, Angga; Rahmatika, Alfilia Hilda; Setiono, Nisrina Hanifa; Gumelar, Satya Fajar
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 5 Issue 2, October 2025
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol5.iss2.art8

Abstract

The rapid growth of e-commerce in Indonesia presents significant opportunities for micro, small, and medium enterprises (MSMEs), yet the diversity of marketplace platforms complicates the selection of an optimal sales channel. This study addressed this challenge by developing a data-driven recommendation system based on sentiment analysis of user reviews. Utilizing a dataset of 80,000 reviews scraped from four major platforms on the Google Play Store (Shopee, Tokopedia, Lazada, and Blibli), two classification approaches were implemented and compared: support vector machine (SVM) and long short-term memory (LSTM). Both models demonstrated a competitive performance, enabling effective sentiment categorization. Furthermore, multinomial logistic regression was employed to analyze the influence of key variables rating, number of likes, and marketplace brand on sentiment outcomes. The analysis revealed that Shopee yielded the highest probability of receiving positive reviews (97.82%) and showed no significant association with negative sentiment. Consequently, this study recommends Shopee as the primary platform for MSMEs to enhance their digital presence and sales performance. The primary contribution lies in integrating machine learning-based sentiment analysis with statistical modelling to generate actionable, evidence-based marketplace recommendations for MSMEs.
Sharpe Ratio-Based Dynamic Crypto Asset Allocation with Trend Filtering Using SMA Fauzan Adziima, Andri; Wara, Shindi Shella May; Nasrudin, Muhammad; Pratama, Alfan Rizaldy
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art1

Abstract

This paper proposes a dynamic cryptocurrency asset allocation strategy that combines Sharpe Ratio-based weighting with trend filtering using the Simple Moving Average (SMA) of Bitcoin (BTC). The model reallocates capital among a portfolio of seven major cryptocurrencies (BTC, ETH, BNB, SOL, TON, TRX, XRP) every three days, conditional on BTC trading above its respective SMA threshold (50-day, 100-day, or 200-day). When BTC trends below the SMA, the strategy shifts fully to USDT to minimize downside risk. Using historical data from January 1, 2024, to January 1, 2025, the study evaluates performance across three SMA configurations and benchmarks against a buy-and-hold baseline. Results show that the SMA-50 strategy achieved the highest cumulative return (+231.51%) and Sharpe Ratio (2.51), significantly outperforming both the longer SMA-based models and the baseline average return (+132.14%). Risk analysis indicates that shorter SMA windows allow more responsive exposure during market uptrends but increase short-term volatility. Overall, the findings support the use of hybrid strategies combining trend-following filters and risk-adjusted allocation for managing crypto portfolios in volatile environments.
Determination Premiums Motor Vehicle Insurance Using Bonus-Malus Optimal Listiani, Amalia; Patricia, Mitha; Yulita, Tiara
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art2

Abstract

The increasing number of motor vehicles in Sumatera has heightened accident risks, emphasizing the need for motor vehicle insurance to distribute risk between policyholders and insurers. Determining fair and risk-based premium requires consideration of each policyholder’s claim history. This study aimed to determine motor vehicle insurance premiums using the optimal bonus-malus system based on claim data for the minibus category with comprehensive coverage in Sumatera during 2022. The proposed model extended the Bayesian bonus-malus framework by incorporating the trust region reflective (TRR) method for estimating claim severity and the Newton-Raphson method for estimating claim frequency, thereby enhancing parameter estimation accuracy and numerical stability. This approach offers a more equitable and precise premium adjustment mechanism aligned with individual risk levels, contributing to improved risk-based pricing, reduced underwriting losses, and greater transparency for policyholders. The results showed that the claim frequency followed the Poisson-Lindley distribution, while claim severity followed the lognormal-gamma distribution. Based on these models, the premium was computed by multiplying the basic premium by the relative value of the subsequent year and dividing it by the base relative value. Premium decrease in the absence of claims and increase when claims occur.
Utilizing Geographically Weighted Regression with a Gaussian Kernel to Analyze Unemployment Hayati, Ma'rufah; Madonna, Nora; Simanjuntak, Erica Grace; Nikmah, Rohmatun
Enthusiastic : International Journal of Applied Statistics and Data Science Volume 6 Issue 1, April 2026
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/enthusiastic.vol6.iss1.art3

Abstract

Unemployment is a major challenge in economic development, reflecting an imbalance between labor supply and available job opportunities. This study aimed to examine the spatial variation of factors influencing the open unemployment rate (OUR) in Lampung Province, Indonesia, and to compare the performance of a global regression model with the geographically weighted regression (GWR) model in explaining these variations. The GWR method, using a fixed Gaussian kernel, was applied to capture spatial heterogeneity across regions. Secondary data were obtained from the Statistics Indonesia of Lampung Province in 2023, including economic growth (EG), human development index (HDI), and labor force participation rate (LFPR). The results showed that in the global regression model, LFPR was the only variable that significantly reduced unemployment, while EG and HDI were not statistically significant. The Breusch–Pagan test confirmed spatial heterogeneity, supporting the use of the GWR. The GWR model performed better, with Akaike information criterion (AIC) of 40.8262 and R² of 0.6059. Spatial analysis indicated that EG and HDI positively affected unemployment in several districts, suggesting limited job absorption and possible skill mismatches, whereas LFPR consistently showed a negative relationship with the open unemployment rate (OUR) across regions.

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