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muhammad Muhajir
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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 73 Documents
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Spatial Analysis of Earthquake Intensity Distribution in Java Using the Interpolation Method (2022–2024) Cahyani, Laras Niken Dwi; Pradana, Wahyu Aji; Ariyadi, Fandy Akhmad; Fauzan, Achmad; Primatika, Roza Azizah
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.art5

Abstract

Java, situated in the Pacific Ring of Fire, is one of the most seismically active regions in the world, with frequent earthquakes posing significant risks to its dense population and critical infrastructure. This study aimed to analyze the spatial distribution and intensity patterns of earthquakes in Java from 2022 to 2024 using data from the Meteorology, Climatology, and Geophysics Agency (Badan Meteorologi, Klimatologi, dan Geofisika, BMKG). Spatial interpolation techniques—inverse distance weighted (IDW), nearest neighbor, and Thiessen polygon—were applied to evaluate their effectiveness in mapping earthquake intensity patterns. The dataset included the earthquake magnitude, location, and occurrence time, with performance evaluated using mean absolute percentage error (MAPE) and mean absolute error (MAE). Results showed that the nearest neighbor method achieved the highest accuracy (MAPE of 12.27%, MAE of 0.37), followed by IDW, while the Thiessen polygon method demonstrated limited suitability for continuous seismic phenomena. These findings underscore the importance of selecting appropriate interpolation methods for seismic risk mapping, providing actionable insights for disaster preparedness and urban planning in Java.
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.
A Zero-Inflated Ordered Probit Approach to Modeling Household Poverty Levels Yudhani, Nidya Putri; Vita Ratnasari; Santi Puteri Rahayu
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.art6

Abstract

This research addressed the limitations of the ordered probit (OP) regression model in handling data that contains an excessive number of zero responses. The zero-inflated ordered probit (ZIOP) model was employed to overcome this issue. This model separates the estimation of structural zeros and ordinal outcomes through two distinct components: a binary probit for zero inflation and an OP for ordered categories. Due to the absence of closed-form solutions, parameter estimation was conducted using the maximum likelihood estimation (MLE) method with the Berndt-Hall-Hall-Hausman (BHHH) iterative algorithm. The analysis was based on 4,067 household-level observations from Indonesia’s National Socio-Economic Survey, incorporating indicators of health, education, and standard of living derived from the multidimensional poverty index (MPI) framework. The result of the Vuong test (4.56) confirmed that the ZIOP model significantly outperformed the conventional OP model for zero-inflated ordinal data. Therefore, the ZIOP model is considered more appropriate for analyzing household poverty classifications with a high prevalence of zero observations.
Analysis of Multinomial Logistics Regression on the Students Faith Data Mutijah; Rohmad; Kholid Mawardi; Suparjo; Muhamad Slamet Yahya; Ifada Novikasari
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.art4

Abstract

It is essential for the prospective teacher students of Islamic education to have a high faith level because it will influence their behavior. In addition, it positively impacts their social life. The level of a person’s faith will have a different impact; hence, it needs to be measured. The faith concepts and their measurement have been widely developed recently. One of them is a faith concept, which is built by two dimensions or variables, i.e., belief and behavior or feeling. Both variables can fluctuate between very high, high, moderate, low, or very low, each influencing faith. Students who study in an Islamic Religious Education study program and at the same time attend Islamic boarding school are predicted to have high faith. This paper aims to describe the level of student faith and find a suitable multinomial logistic regression model through analysis of its method. Data was collected using questionnaires filled out by 52 students. The results showed that the percentage of students’ faith levels with very high level was 5.8%, high was 36.5%, moderate was 38.5%, low was 13.5%, and very low was 5.8%. Meanwhile, the model accuracy was 94.2%.
Evaluation of Biclustering Imputation Methods for Glioblastoma Gene Expression Data Silalahi, Agatha; Titin Siswantining; Setia Pramana
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.art7

Abstract

Glioblastoma is a highly aggressive primary brain tumor with a low survival rate. One of the main challenges in analyzing glioblastoma gene expression data is the presence of missing values, which can reduce biclustering accuracy and affect biological interpretation. This research compared six imputation methods k-nearest neighbors (KNN), mean imputation, singular value decomposition, nonnegative matrix factorization, soft impute, and autoencoderon the GSE4290 gene expression dataset with missing values ranging from 5% to 50%. An evaluation using root mean square error (RMSE), mean absolute error (MAE), and structural similarity index measure (SSIM) showed that soft impute provided the best performance at all levels of missing values, with RMSE of 0.0076, MAE of 0.0073, and perfect SSIM of 1.0000 at 50% missing values. Meanwhile, deep learning-based autoencoder experienced significant performance degradation at high missing values. These findings indicate that more complex models are not always superior, and regularization-based approaches like soft impute are more effective in preserving the biological structure of the data. The results of this research contribute to the optimization of imputation strategies to improve the accuracy of biclustering analysis in glioblastoma studies.
Analysis of Industrial Waste Quality Control Using Generalized Variance and Hotelling’s T2 Control Diagram Methods Hamidah, Isna; Hamid, Abdulloh; Khaulasari, Hani
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.art8

Abstract

Environmental pollution is an unsettling problem for everyone and the ecosystem which can be caused by poorly managed waste originated from the final output of industrial production processed. It can negatively impact the surrounding environment if it is not handled properly. Therefore, the waste must be processed until it meets the predetermined characteristic standards before being disposed of. Among the actions that can be taken is carrying quality control. This study aims to evaluate and characterize the quality of the waste produced. The methods used were the generalized variances and Hotelling’s T2 control charts. The data used for this research was the characteristics of liquid waste from a sugar factory industry, taken from May to September 2023. The quality control results, which were obtained using the generalized Variance control chart, could be statistically controlled after eight improvements. Then, Hotelling’s T2 control chart was successfully controlled after one test. The capability index value obtained was > 1, indicating that the quality control process in liquid waste at the Pesantren Baru sugar factory is capable or controlled.
Clustering of Provinces in Indonesia Based on Environmental Health Indicators Using K-Medoids Agustin, Widya Saputri; Mardiyyah, Safwah Ayu; Zahra , Qolbiyatus Syifa Az; Anggreany, Anggun Nur; Widodo, Edy
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.art9

Abstract

According to the Ministry of Health of the Republic of Indonesia, key environmental health indicators include access to safe drinking water, adequate sanitation, and healthy living environments. As of 2023, only 10.21% of Indonesian households had access to safe sanitation, far from the government’s 2045 target of 70%. Indonesia’s ranking at 164th out of 180 countries in the 2022 environment performance index (EPI), with a score of 28.20 out of 100, further underscores the need for targeted interventions. This study aims to classify Indonesian provinces based on environmental health indicators, thereby supporting more effective policy prioritization. The k-medoids clustering algorithm was employed due to its robustness to outliers and flexibility in handling mixed data types, making it well-suited for this context. This study utilized data from 34 provinces in 2023, sourced from the Ministry of Health. These provinces were grouped into two clusters, with cluster 2 representing provinces with stronger environmental health performance. The clustering results were validated using the silhouette coefficient, confirming the quality of the groupings. Provinces in cluster 1 require greater policy attention to improve environmental health conditions. This study demonstrates the potential of robust medoids-based clustering for guiding targeted environmental health strategies in developing countries.
Implementation of Hotelling’s T2 Method in Quality and Capability Control of Newlab Collagen Production Processes Indrani , Rahmadana Kadija; Kariyam, Kariyam
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.art1

Abstract

Every company has quality standards that are determined for the production process. However, there are factors that occur in the production process that causes defects in the product. From these problems, this research was conducted to analyze the quality control, causal factors, and performance of the production process on Newlab Collagen products. The methods used in production quality control were Hotelling’s T2 control chart, fishbone diagram, and process capability analysis. In the Hotelling’s T2 control chart, the multivariate observation data was divided into two phases, with five quality indicators. The results of the first phase of the Hotelling’s T2 control map showed that the quality indicators of the Newlab Collagen production were out of control, which caused by unstable machine factors. Based on control chart, the second phase showed that the quality indicators of the Newlab Collagen production process were still out of control. This condition was evidenced by the process capability value in phase I and phase II being less than one. These findings suggest that the company needs to make improvements, optimization, and quality control in the production.
Genetic Cluster Analysis of Insulin Resistance Using KNN Imputation and FABIA-CCA Biclustering Soemarso, Ditoprasetyo Rusharsono; Siswantining, Titin; Pramana, Setia
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.art10

Abstract

Type 2 diabetes mellitus (T2DM) is a metabolic disorder primarily driven by insulin resistance, involving complex genetic regulation. Understanding the molecular mechanisms underlying insulin resistance is crucial for identifying therapeutic targets. This study compared the performance of two biclustering algorithms, factor analysis for bicluster acquisition (FABIA) and the Cheng and Church algorithm (CCA), in analyzing gene expression data associated with insulin resistance. Using the GSE19420 dataset, simulated missing values were introduced to evaluate the robustness of both methods. Results showed that CCA consistently achieved lower mean squared error (MSE) in reconstructing gene expression patterns, suggesting higher accuracy in capturing co-expression structures. Nevertheless, FABIA effectively detected sparse, biologically relevant clusters. Notably, key genes such as MYO5B, DLG2, AXIN2, and PTK7 were identified within the biclusters, supporting their involvement in insulin signaling and metabolic regulation. These findings underscore the need to select biclustering methods that align with specific analytical goals and offer insights into gene networks involved in insulin resistance.
Analysis of Factors that Influence Maternal Mortality Rates Using Generalized Poisson Regression pratiwi, Yuniar Ines; Khaulasari, Hani; Farida, Yuniar; Ferdani, Ayu
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.art2

Abstract

Maternal Mortality Rate (MMR) is the number of deaths of women within 42 days after childbirth or during pregnancy. Objective: This study aims to identify factors affecting MMR in East Java and compare the performance of the Generalized Poisson Regression (GPR) model with Poisson regression. The method used is Generalized Poisson Regression, a regression model for count data, which extends Poisson regression to overcome the problem of overdispersion or underdispersion with data derived from the East Java Health Office, including MMR as the dependent variable, as well as five variables that are thought to affect it in 38 districts/cities. The GPR model proved superior to Poisson regression with an Akaike Information Criterion (AIC) value of 239.515 to identify factors affecting maternal mortality. Factors such as delivery handled by health workers, K6 visits by pregnant women, provision of diphtheria-tetanus immunization, and obstetric complications affect MMR in East Java in 2022.

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