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All Journal Theta: Journal of Statistics
Putri Dina Sari
Universitas Sultan Ageng Tirtayasa

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Journal : theta journal of statistics

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Customer Segmentation Analysis of Maxim Application Based on RFM Model and K-Means Clustering as the Basis for Marketing Strategy Zilda Ainun Tazkia; Zahra Mahendra Putri; Atira Keisha Belva Armanda Fadhilla; Atia Sonda; Aulia Ikhsan; Putri Dina Sari
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.v2i1.39360

Abstract

The rapid development of online transportation services requires a data-driven understanding of customer behavior. This study aims to segment Maxim application customers using the Recency, Frequency, and Monetary (RFM) model combined with the K-Means clustering method among students of the Faculty of Engineering, Sultan Ageng Tirtayasa University. This research employs a descriptive quantitative approach with a sample of 100 respondents. The optimal number of clusters was determined using the Elbow method, resulting in four customer segments: Inactive Customers, Occasional Customers, Loyal Customers, and Priority Customers. The segmentation analysis was conducted separately for Maxim Bike and Maxim Car services. The results indicate that the Priority cluster has the highest transaction frequency and expenditure value despite consisting of relatively few customers, while the Inactive cluster shows the lowest level of transaction activity. In the Maxim Bike category, the Priority cluster represents the largest proportion of customers and shows the most recent transaction activity. In addition, the distribution of study programs indicates the dominance of Statistics students in the Loyal and Priority clusters across both service categories. Descriptive statistical analysis further shows that respondents' perceptions of Maxim services fall into the positive category, with average indicator scores above 3.20.
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.
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.
Co-Authors Agung Satrio Wicaksono Arina, Faula Atia Sonda Atira Keisha Belva Armanda Fadhilla Aulia Ikhsan Gustriza Erda Isnaini Mahuda Miftahus Sholihin Muhammad Fabian Reinhard Delano Patricia Pingkan Kumenap Regina Dwirahma Alisya Syarif Abdullah Syukron Faiz Zahra Mahendra Putri Zilda Ainun Tazkia