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All Journal Journal Industrial Servicess Industri Inovatif : Jurnal Teknik Industri Jurnal Abdidas JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Abdimas Mandalika Integrasi : Jurnal Ilmiah Teknik Industri Journal of Systems Engineering and Management Journal of Mathematics, Computation and Statistics (JMATHCOS) Theta: Journal of Statistics
Atia Sonda
Universitas Sultan Ageng Tirtayasa
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Public Health Social Sciences Transportation Other

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Journal : Theta: Journal of Statistics

 1 
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.
Co-Authors Achmad Bahauddin Achmad Bahaudin Ade Irman Ade Irman Saeful Mutaqin Ade Sri Mariawati Agung Satrio Wicaksono Alisya, Regina Dwirahma Amelia Amelia Ani Umyati Anting Wulandari Aprilianti, Widia Arina, Faula Asep Ridwan Asep Ridwan Asih Machfuzhoh Asshiddieqie, Rafi Ramadhan Atira Keisha Belva Armanda Fadhilla Aulia Ikhsan Bagaskara, Rafli Priyantama Ramadhan Bobby Kurniawan Bobby Kurniawan Damasa G Desmiani Delano, M Fabian Reinhard Dewantari, Nustin Merdiana Dyah Lintang Trenggonowati Erien Amelia Evi Febianti Faiz, Syukron Faula Arina Fellek Getu Tadesse Gunawan, Akbar Gustriza Erda Hadi Setiawan Herlina, Lely - Ichsan, Andhika Muhamad Kulsum Kulsum Lely Herlina Mahuda, Isnaini Majainus, Muhammad Dzacky Alief Putra Maria Ulfah Miftahus Sholihin Miftahus Sholihin Muhammad Adha Ilhami Muhammad Fabian Reinhard Delano Muhtar Muhtar Purbasari, Intan Putri Dina Sari Putro Ferro Ferdinant Putro Ferro Ferdinant Rahma, Midia Ridwan, Asep Rurry Patradhiani Saeful Mutaqin, Ade Irman Sari, Febrianti Permata Sari, Putri Dina Syarif Abdullah Syukron Faiz Udiansyah, Naufal Arrafi ulfah, Maria Vicky Arya Wahyu Susihono WICAKSONO, AGUNG SATRIO Yadi, Yayan Hary Yusraini Muharni Yusuf, Vira Aleyda Zahra Mahendra Putri Zilda Ainun Tazkia