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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 85 Documents
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Black-Scholes Method for Rainfall Index-Based Agricultural Insurance Premiums Lestari, Fuji; Julianty, Dila Tirta; Vikarti, Maulida Magdalena
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.art7

Abstract

Agriculture plays an important role in national economic development. However, it has the highest risk of loss due to its dependence on climate conditions. One of the efforts to reduce the risk of crop failure is through an agricultural insurance program. This study aimed to analyze the value of the rainfall climate index used and the calculation of agricultural insurance premiums based on it. The method used to determine the rainfall climate index was the historical burn analysis method, while the method used to calculate agricultural insurance premiums was the Black-Scholes method. The study showed significant spatial variation in rainfall index-based agricultural insurance premiums across Sumatra. Premiums rose with higher percentiles, with North Sumatra the highest (IDR 3.28–3.55 million) and Aceh the lowest (IDR 100–137 thousand). The inclusion of all rainfall stations revealed a more detailed spatial pattern. Overall, premiums strongly reflect local climatic conditions and can aid risk assessment and insurance planning.
Household Electricity Demand Forecasting in Batam from 2023 to 2047 Using Multilayer Perceptron Neural Network Ginting, Tiffani Giofanta; Hermansah; Hanggara, Yudhi
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.art6

Abstract

The rapid growth of electricity demand in Batam, driven by increasing household and industrial consumption, necessitates accurate long-term energy forecasting. This study aimed to forecast household electricity demand in Batam from 2023 to 2047 using the multilayer perceptron (MLP) artificial neural network (ANN) model. Secondary data from PT PLN Batam (2013-2022), including customer numbers, electricity sales volume, and revenue, were analyzed. A total of 200 MLP models were trained, varying the number of hidden layers and nodes, with algorithms including BACKPROP, RPROP+, RPROP−, SAG, and SLR. The partial autocorrelation function (PACF) was used to determine the number of input layer nodes. The optimal model, using the smallest learning rate (SLR) algorithm with four hidden layers and ten nodes, achieved the best performance with the lowest mean squared error (MSE) of 35.93 and mean absolute percentage error (MAPE) of 0.47%. The projection results show a consistent increase in electricity demand, with a peak forecast of 2,114 GWh by 2047. These findings provide valuable insights for long-term energy planning and policy-making, ensuring adequate electricity supply and infrastructure development in Batam.  
Identification of Sexual Harassment Comment on Tiktok Platform Using Indobert Embedding and Long Short-Term Memory Mumtaz, Najla Lathifah; Kesumawati, Ayundyah; Primandari, Arum Handini
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.art8

Abstract

Sexual harassment in online comment sections is a growing concern on social media platforms like TikTok, where informal language makes manual moderation ineffective. This study developed an automated detection model using a hybrid Indonesian bidirectional encoder representation from transformers (IndoBERT)-long short-term memory (LSTM) architecture, employing IndoBERT as a static feature extractor and an LSTM network to model sequential dependencies. Given the high-class imbalance in social media data, this study specifically evaluated the impact of the synthetic minority over-sampling technique (SMOTE) on classification performance. Experimental results showed that the base IndoBERT-LSTM model achieved a high overall accuracy of 89.04% but struggles with a low recall (0.40) for the minority harassment class. While applying SMOTE improved the model’s sensitivity (recall) for harassment to 0.54, it resulted in a significant decrease in precision, and an overall accuracy drop to 87.79%. These findings indicate that while oversampling can modestly enhance the detection of harassment instances, it introduces a substantial trade-off by increasing false positives. This study concludes that for highly informal and imbalanced TikTok data, standard oversampling techniques such as SMOTE may be less effective, suggesting the need for more advanced contextual augmentation or cost-sensitive learning approaches in future digital safety research.  
Implementation of Multivariate Small Area Estimation on Socio-Economic Indicators Firdaus, Firdaus; Nugroho, Sigit; Ayu Lestari, Wina; Pandu Winata, Aji
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.art9

Abstract

The welfare of a region’s population is generally measured through socio-economic indicators such as per capita expenditure, access to education, and access to safe drinking water. This study aimed to estimate socio-economic indicators in Bengkulu Province at the small-area level using the multivariate small area estimation (SAE) approach under the empirical best linear unbiased prediction (EBLUP) framework. The analysis was based on secondary data obtained from BPS-Statistics Indonesia, covering the 2024 observation period, with indicators aggregated at the district level. Area-level auxiliary variables derived from the Village Potential Statistics (Potensi Desa, PODES) were incorporated to improve estimation accuracy by borrowing strength across correlated indicators and areas. Results showed that indirect estimation substantially improved the precision of direct estimates. The mean squared error (MSE) for the school participation rate decreased from 0.260 to 0.040, while the MSE for access to safe drinking water decreased from 0.00550 to 0.00098, indicating efficiency gains of approximately 85% for both indicators. Overall, the multivariate EBLUP approach yielded more stable and reliable estimates than direct estimation. Therefore, this approach is recommended for generating small area estimates of correlated welfare indicators when sample sizes are limited, thereby providing more accurate information for regional planning and policy formulation.
Analyzing the Determinants of the Gender Development Index (GDI) with an Ordinal Logistic Regression Approach Izza, Ananda Nur; Farida, Yuniar
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.art10

Abstract

Regional disparities in the gender development index (GDI) across districts and cities in East Java reflect unequal gender development outcomes despite provincial progress. These disparities are associated with differences in access to health, education, and economic opportunities. This study identified the determinants of GDI using a gender-specific ordinal logistic regression model based on district/city-level data. The analysis included indicators representing health, education, and economic dimensions. Results showed that the determinants of GDI differed by gender. For males, life expectancy and expected years of schooling significantly increased the likelihood of a region being classified into a higher GDI category. For females, expected years of schooling and mean years of schooling were the most influential factors, emphasizing the importance of educational access and attainment. The model demonstrated moderate explanatory power for male data (Nagelkerke R-squared = 0.414) and strong explanatory power for female data (Nagelkerke R-squared = 0.654). However, the models exhibited relatively high classification error rates, with misclassification rates of 39.47% for males and 52.63% for females. These findings provide evidence that the drivers of gender development vary by gender and offer practical insights for designing targeted, gender-responsive policies to reduce regional disparities and promote inclusive development in East Java.

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