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Theta: Journal of Statistics
Published by Universitas Sultan Ageng Tirtayasa
ISSN : 31091903     EISSN : 31089895     DOI : http://dx.doi.org/10.62870/tjs.v1i1
Core Subject :
Theta: Journal of Statistics is a double-blind peer-reviewed journal in the field of statistics. This Journal is published by the Department of Statistics, Faculty of Engineering, Universitas Sultan Ageng Tirtayasa in collaboration with Badan Kerja Sama Perguruan Tinggi Negeri (BKS PTN) Wilayah Barat Bidang Teknik (State University Cooperation Agency Western Region for Engineering) and Forum Pendidikan Tinggi Statistika Indonesia (Indonesian Statistics Higher Education Forum), or abbreviated as FORSTAT. Theta: Journal of Statistics publishes its journal issues in March and September. Theta Journal has P-ISSN: 3109-1903 (print version) and E-ISSN: 3108-9895 (online version). We accept submissions from all over the world. Our Editorial Board members are prominent and active international researchers in the statistics field who ensure efficient, fair, and constructive peer-reviewed processes. All accepted articles will be published and freely available (no charge) to all readers with worldwide visibility and coverage.
Arjuna Subject : -
Articles 17 Documents
 1   2 
Analysis of the Spatial Distribution Pattern of Poverty Percentage in Central Java in 2024 Using the Spatial Autocorrelation Approach Miftahus Sholihin; Gustriza Erda; Putri Dina Sari; Agung Satrio Wicaksono; Atia Sonda; Muhammad Fabian Reinhard Delano; Syukron Faiz
Theta: Journal of Statistics Vol 1, No 1 (2025): Available Online in March 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i1.31320

Abstract

Poverty remains a critical socio-economic issue in Central Java, Indonesia, exhibiting significant regional disparities. This study aims to analyze the spatial distribution pattern of poverty rates in Central Java in 2024 using a spatial autocorrelation approach with an inverse distance weight matrix. Secondary data from the Central Bureau of Statistics (BPS) of Central Java is utilized, covering poverty percentages across regencies and cities. The analysis method involves Moran’s I to assess global spatial autocorrelation and Local Indicators of Spatial Association (LISA) to identify local spatial clusters. The findings indicate a positive Moran’s I value, suggesting a significant spatial dependence in poverty distribution. Several high-poverty clusters are identified in specific regions, confirming spatial concentration patterns. The study highlights that regional proximity influences poverty rates, where areas with high poverty tend to be surrounded by regions with similar conditions. These results provide empirical evidence for policymakers to design targeted poverty alleviation programs based on spatial characteristics. The study concludes that understanding spatial autocorrelation in poverty distribution is crucial for formulating effective regional development policies and reducing socio-economic disparities.
Analysis of Gojek Service User Segmentation Among FT UNTIRTA Students Using the RFM Method Dinda Dwi Anugrah Pertiwi; Regina Dwirahma Alisya; Andhika Muhamad Ichsan; Faula Arina; Isnaini Mahuda
Theta: Journal of Statistics Vol 2, No 1 (2026): Available Online in March 2026
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v2i1.39312

Abstract

The development of transportation and application-based services highlights the importance of user behavior analysis as a basis for data-driven marketing strategies. This study analyzes the segmentation of GOJEK service users (GoRide, GoCar, and GoFood) among students of the Faculty of Engineering, Sultan Ageng Tirtayasa University (FT UNTIRTA) using the Recency, Frequency, and Monetary (RFM) approach. Primary data were collected through questionnaires distributed to 105 active GOJEK users using purposive sampling. Data were analyzed through pre-processing, standardization, determination of the optimal number of clusters using the Elbow method, and clustering using the K-Medoids algorithm, which was selected over K-Means and K-Median due to its robustness against outliers, suitability for non-normally distributed RFM data, and use of actual data points as cluster centers for more interpretable segmentation results. The results showed that the optimal number of clusters for each service was three, classified as loyal, active, and passive customers. In GoRide, the distribution was 15 loyal, 32 active, and 16 passive users; in GoCar, 16 loyal, 10 active, and 35 passive users; and in GoFood, 25 loyal, 1 active, and 52 passive users. Loyal clusters are characterized by low recency and high frequency and monetary values, active clusters show medium usage rates, and passive clusters exhibit low frequency and transaction values. These results demonstrate that the RFM and K-Medoids combination is effective in identifying behavioral differences among GOJEK users, as validated by the Silhouette Score and Davies-Bouldin Index confirming cluster compactness and separation quality, and can serve as a basis for formulating more targeted marketing strategies in the student environment.
Human Price Index of Lampung Province using Multiple Linear Analysis Rohimatul Anwar; Mika Alvionita Sitinjak
Theta: Journal of Statistics Vol 1, No 2 (2025): Available Online in September 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i2.31717

Abstract

This study aims to determine the effect of the number of poor people, average years of schooling, open unemployment rate, and per capita expenditure on the human development index in Lampung Province. The data used are secondary data sourced from the Central Statistics Agency of Lampung Province. The data used is cross-sectional data from 2023 by district/city in Lampung Province. The analysis used is a multiple regression analysis of cross-sectional data. The results obtained indicate that the average years of schooling and per capita expenditure significantly influence the human development index in Lampung Province. In addition, 98.8% of the human development index variables in Lampung Province can be explained simultaneously by the number of poor people, average years of schooling, open unemployment rate, and per capita expenditure.
Similarity Analysis of the Default Transition of Bond Issuer in Indonesia using Euclidean Distance Aulia Ikhsan; Fikri C Permana; Ayu Nurulhaq Putri; Rifki Hamdani; Mukhtar Mukhtar; Syarif Abdullah; Rifqy Hafizh; Muhammad Hikam Adiguna; Dinda Dwi Anugrah Pertiwi; Kinanthi Trah Asmaraningtyas
Theta: Journal of Statistics Vol 1, No 1 (2025): Available Online in March 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i1.31335

Abstract

The debt instrument (bond) as one of the investment instruments in the Capital Market has a main risk known as default. Default risk can be mitigated if investors assess the credit quality of the bond and its issuer, as measured by rating. In this research, the initial rating of issuers was investment grade (BBB or higher) and valid for at least 1 year, with their business operations based in Indonesia. The observation period was from 2007 to 2023. A Markov Chain was used to create a transition matrix to analyze transitions and default. The probability of AAA staying over 1 year is 0.9858 whereas the likelihood of AA, A, and BBB staying in the same rating is 0.9203, 0.8825, and 0.8630, respectively. The BBB in a 5-year transition has the highest probability of default by 0.0370. The Euclidean distance was used to measure the similarity of default durations. The 1-year and 3-year transition have the shortest distance, at  0.00939. The conclusion of this research is a higher rating has a higher probability of staying at the same rating and carries lower risk. Furthermore, 1-year and 3-year transitions show similarities based on their probability of default.
Analysis of Student Purchasing Patterns with Market Basket Analysis (MBA) Using the Apriori Algorithm in the FT UNTIRTA Canteen Patricia Pingkan Kumenap; Stella Caroline Roma Ito; Muhammad Fabian Reinhard Delano; Syukron Faiz; Aulia Ikhsan; Miftahus Sholihin; Atia Sonda
Theta: Journal of Statistics Vol 2, No 1 (2026): Available Online in March 2026
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v2i1.39319

Abstract

Sales activities have an important role in business sustainability, especially in the food and beverage sector, where understanding consumer purchasing behavior and effective inventory management are crucial. This research aims to analyze student purchasing patterns in the canteen of the Faculty of Engineering, Sultan Ageng Tirtayasa University using the Market Basket Analysis method based on the Apriori algorithm. The data used is primary transaction data using a purposive sampling technique of 322 valid transactions. Analysis was carried out using association rule mining with minimum support and confidence parameters to identify relationships between items. The results show that the strongest association rule involves the combination of lime leaf rice, jumbo iced tea, and grilled chicken with a support value of 0.047, confidence 0.75, and lift 2.95. Apart from that, the rule also found was lime leaf rice grilled chicken with support 0.096, confidence 0.66, and lift 2.59. Several other rules have high confidence but low lift due to the dominance of white rice items. These findings indicate that students tend to buy a combination of main food, side dishes and drinks in one transaction. The Apriori algorithm has been proven to be able to identify significant purchasing patterns and can support product structuring, promotion and inventory management strategies.
Application of the TARCH Model for Stock Price Prediction: Evidence from PT Bank Rakyat Indonesia (BRI) Tbk Putri Dina Sari; Faula Arina; Aulia Ikhsan; Isnaini Mahuda; Syarif Abdullah; Patricia Pingkan Kumenap; Regina Dwirahma Alisya
Theta: Journal of Statistics Vol 1, No 2 (2025): Available Online in September 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i2.35930

Abstract

Stock price volatility is a crucial aspect in capital market analysis because it can influence investment decisions. The GARCH model is commonly used to model volatility, but this model assumes that positive and negative shocks affect volatility symmetrically. In practice, particularly in banking stocks, asymmetric effects are often observed, with negative shocks having a greater impact on volatility than positive shocks. To address this issue, this study uses the Threshold ARCH (TARCH) model, which is capable of capturing asymmetric effects. The research data consists of the daily closing prices of PT Bank Rakyat Indonesia (BRI) Tbk shares from January 2, 2015, to September 12, 2025. The results show that the TARCH model is more appropriate than the symmetric GARCH model, as the asymmetry coefficient is significant, indicating the presence of leverage in BRI shares. Therefore, the TARCH model can be used to forecast BRI stock volatility and provide more accurate information for investors and analysts in anticipating market risks.
Comparing Best Subset and Lasso Regression in the Customer Loyalty Prediction in a Restaurant Dataset Aulia Anggitanniradi; Weksi Budiaji; Juwarin Pancawati; Sri Mulyati
Theta: Journal of Statistics Vol 1, No 1 (2025): Available Online in March 2025
Publisher : Faculty of Engineering, Univesitas Sultan Ageng Tirtayasa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62870/tjs.v1i1.31171

Abstract

Independent variables such as attributes related to the product, service quality, and purchase satisfaction are often correlated with one another in a customer loyalty research case. For instance, product attributes may overlap with service quality, and both factors jointly influence purchase decisions. To address multicollinearity, models such as best subset and Lasso regression can be employed. These models will be applied to a restaurant customer loyalty dataset. This study was conducted at Warung Tuman Restaurant in South Tangerang, Indonesia, from April to June 2022. We analyze responses from 100 purposively sampled consumers, with loyalty as the dependent variable and  (product attributes),  (service quality), and  (purchase satisfaction) as predictors. Correlation analysis revealed strong positive relationships (r = 0.44, p < 0.00) among predictors, confirming multicollinearity and justifying the use of best-subset and Lasso. The dataset was split into a 60% training set and a 40% test set, with the training set used to develop predictive models, which were then evaluated for accuracy using the test set. All correlation values demonstrate a significant positive relationship between the independent variables, indicating the suitability of the best subset and Lasso regression applications. The best subset and Lasso regression generate models with two independent predictor variables, i.e. product attributes and purchase satisfaction. The best subset regression exhibits a lower Sum of Squared Errors (SSE), thereby indicating its superior performance compared to the Lasso regression model. To effectively sustain and improve customer loyalty, restaurant managers should prioritize optimizing product attributes and purchase satisfaction factors.

Page 2 of 2 | Total Record : 17

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