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Syarif Abdullah
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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.
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Articles 17 Documents
 1   2 
Robust Quality Control Implementation for Nickel Pig Iron Using Median Absolute Deviation Estimators Aditya Rahadian Fachrur; Chyntia Devi Octaviany; Wiwien Suzanti; Midia Rahma; Mariana Feronica Damanik; Ferdian Bangkit Wijaya
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.v1i1.31481

Abstract

Nickel Pig Iron (NPI) is one of the main products of Indonesia's nickel-based industry, which is still expanding. Reducing production costs and guaranteeing product quality depends on maintaining a constant nickel content. The Median Absolute Deviation (MAD) estimator, which is resistant to outliers and non-normal distributions, is used in this study's control charts and process capability analysis. After 15 days of production, data was gathered and examined using capability indices and control charts based on MAD. According to the results, the process mean is not statistically in control since many points exceed the control borders, even while process variation stays within the control limits. Acceptable process precision was indicated by the process capability index CpMAD being above 1. However, the CpkMAD value below 1 suggests that the mean process output does not consistently meet the target specifications. These findings highlight the need for further investigation and process improvement to enhance quality consistency in NPI production.
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.
Cluster Analysis of District/City Welfare in Aceh Province: An Application of the K-Means Method Alifah Alyana; Hasya Zayyan Haziqah; Rini Safariani; Riska Mulyani; Wanda Surianto
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.v1i2.35948

Abstract

Community well-being is a critical determinant of regional development success, particularly in Aceh Province, which exhibits unique social and economic characteristics. This study aims to classify districts and municipalities in Aceh Province based on welfare indicators using the K-Means Clustering method. The analysis employed secondary data from the Central Bureau of Statistics (BPS) for 2023, covering 23 districts/municipalities and 10 indicators: population density, labor force size, labor force participation rate, open unemployment rate, average years of schooling, life expectancy, per capita expenditure, poverty rate, GRDP distribution, and gender development index. The findings reveal that the optimal number of clusters is two. The first cluster comprises regions with relatively lower welfare indicators, while the second cluster consists of regions with higher levels of socio-economic development but facing internal disparities. The silhouette coefficient of 0.309 and the Davies-Bouldin Index (DBI) of 1.065 indicate that the model is reasonably effective in capturing welfare differences across regions. These results provide valuable insights for formulating more targeted and efficient regional development policies
Analyzing of Consumer Price Index Influence on Inflation in Cilegon City for the Years 2023-2024 Atira Keisha Belva Armanda Fadhila; Patricia Pingkan Kumenap; Stella Caroline Roma Ito; Zilda Ainun Tazkia; Isnaini Mahuda; Yanyan Dwiyanti
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.v1i1.31294

Abstract

The Consumer Price Index (CPI) in Cilegon City is an important barometer for measuring the inflation rate in the region. Inflation itself is an economic phenomenon that reflects the general increase in the prices of goods and services over a certain period. As a crucial indicator, inflation affects purchasing power, the cost of living, and overall economic stability. Therefore, monitoring the CPI in Cilegon is highly relevant for understanding local economic dynamics and formulating appropriate policies. This study aims to determine whether the Consumer Price Index impacts inflation in Cilegon City. The study results indicate that the CPI has a significant and negative influence on inflation in Cilegon City for the years 2023-2024. Furthermore, the coefficient of determination shows that approximately 57.76% of the variability in the CPI in Cilegon can be explained by the inflation rate, while the remaining 42.24% comes from variables not accounted for in the model.
The Effect of Canva App-Assisted Interactive Learning Media on Elementary School Grade IV Math Learning Interest Miftakul Nur Wahyu Ning Tias; Dya Ayu Agustiana Putri; Eka Yuliana 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.v2i1.38307

Abstract

This study aims to examine the influence of interactive learning media assisted by the Canva application on the interest in learning mathematics in grade IV in elementary school. This study is a type of quantitative research using Quasi-Experimental Design with Nonequivalent Control Group Design. Data were obtained from 53 students who were divided into an experimental group (who participated in the interactive learning media assisted by the Canva app) and a control group (who did not participate in the interactive learning media assisted by the Canva application). The instrument used is a learning interest questionnaire with validity and reliability that has been tested using the JAMOVI 2.7.12 application. The analysis showed that the instrument had high reliability with a Cronbach's Alpha coefficient of 0.974 and adequate validity. Based on the results of the statistical test, there was a significant increase in student learning interest in the experimental group compared to the control group after the implementation of interactive learning media assisted by the Canva application (p< 0.001). These results show that interactive learning media assisted by the Canva application has a positive influence on increasing interest in learning mathematics. This research supports the importance of the application of interactive learning media in elementary schools to increase students' interest in learning mathematics.
Customer Segmentation of GrabBike Users Based on RFM Analysis Using K-Means Clustering: A Case Study of Engineering Faculty Students Kinanthi Trah Asmaraningtyas; Rafly Priyantama Ramadhan Bagaskara; Rafi Ramadhan Asshiddiqie; Agung Satrio Wicaksono; Syarif Abdullah; Himmatul Mursyidah
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.v2i1.39395

Abstract

The development of online transportation services has changed the mobility patterns of society, including among students who have high activity levels. GrabBike, as one of the application-based transportation services, is widely used by students to support their activities. Differences in intensity and usage patterns require a deeper understanding of user behavior through customer segmentation. Therefore, this study aims to segment GrabBike service users among students of the Faculty of Engineering, Sultan Ageng Tirtayasa University, using the RFM model with the application of the K-Means algorithm. This study uses a quantitative survey method with data collection through an online questionnaire (Google Form). The data used is primary data from 86 respondents who are students of the Faculty of Engineering who are GrabBike users. The stages of research include data collection, data preprocessing (cleaning, RFM transformation, and standardization using Standard Scaler), application of the K-Means algorithm, and analysis of segmentation results. The optimal number of clusters was determined using the Elbow and Silhouette methods. The results of the study show that the optimal number of clusters is three. Segmentation using the K-Means algorithm produces three user segments, namely Top Class Users, Ordinary Users, and Low Users. The Top Class Users segment has the highest frequency of use and expenditure, making them potential loyal users. The Ordinary Users segment is the largest segment with moderate usage levels and has the potential to be increased through targeted marketing strategies. Meanwhile, the Low Users segment has low usage levels and requires reactivation strategies. Overall, the K-Means-based RFM approach has proven effective in grouping GrabBike users based on usage behavior and can be used as a basis for formulating more targeted online transportation service marketing strategies.
Alternative Evaluation of Nonparametric Analysis of Covariance (Ancova) Methods: Simulation Study for Violation of Parametric Assumptions Pardomuan Robinson Sihombing
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.v1i2.36271

Abstract

Analysis of Covariance (ANCOVA) is a fundamental statistical method in experimental research, but it often faces violations of parametric assumptions that can compromise the validity of statistical inferences. This study aims to evaluate the relative performance of various alternative nonparametric ANCOVA methods through simulation. The simulation study generated a dataset of 75 observations (25 per group) using normal, gamma, and lognormal distributions to simulate violations of normality and homoskedasticity assumptions. Seven alternative methods were evaluated: Quade ANCOVA, ANCOVA Rank Transformation, Aligned Rank Transform, van Elteren Test, NANCOVA Resampling, ANCOVA Permutation, and ANCOVA Bootstrap. Assumption testing revealed significant violations of residual normality and homoskedasticity, while homogeneity of slopes was met. ANCOVA's Rank Transformation yields an F-statistic of 54.54 (p
Forecasting the Open Unemployment Rate in Banten Province Using the FB Prophet Method in Python Programming Language Ferdian Bangkit Wijaya; Deananta Pramudia Putra; Mahsa Azzahra; Faula Arina; Fajri Ikhsan
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.v1i1.31309

Abstract

The Open Unemployment Rate is a key indicator in measuring labor market imbalances, reflecting the economic dynamics of a region. Banten Province has consistently ranked among the top three provinces with the highest Open Unemployment Rate in Indonesia over the past decade, indicating structural challenges in employment. To address this issue, a forecasting model is needed to provide accurate predictions that support more effective labor policy planning. This study uses the Prophet method, an additive regression approach developed by Facebook, to forecast the Open Unemployment Rate in Banten Province over the next 10 semesters (February 2025-August 2029). The data used is sourced from the Statistics Indonesia (BPS) for the period 2005-2024, collected every semester (February and August). The model's performance is evaluated using the Mean Absolute Percentage Error (MAPE) as the primary evaluation metric. The results show that the Prophet model effectively captures trend and seasonal patterns. With a MAPE value of 5.3910%, the model demonstrates very good accuracy (MAPE < 10%), making it suitable for medium-term forecasting. The predictions indicate a downward trend in the Open Unemployment Rate in Banten over the next five years. The conclusion of this study suggests that the Prophet model can be a reliable tool for projecting the Open Unemployment Rate and supporting labor policy planning in Banten. Future research is expected to incorporate external factors or use hybrid modeling approaches to improve prediction accuracy.
Comprehensive Evaluation Of Proportion Hypothesis Testing: Integration Of Manual Calculations And Effect Sizes In The "New Statistics" Framework Pardomuan Robinson Sihombing
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.v2i1.38576

Abstract

A Conventional statistical inference often relies solely on the use of p-values, thereby overlooking the practical significance of results, particularly in categorical data analysis, where the reporting of effect sizes is frequently neglected. This study aims to evaluate the application of proportion hypothesis tests in five scenarios (one Population to k dependent and independent populations) by integrating manual calculations, effect size estimates, and post-hoc tests. The research method involved deconstructing the Z, McNemar, Chi-Square, and Cochran's Q test formulas, which were compared with effect metrics including Cohen's g, Cohen's h, Cramer's V, and Serlin's . The results of the analysis reveal an apparent discrepancy between statistical and practical significance. In the one-population test, statistically significant results (p < 0.05) turned out to have a negligible effect (Cohen's g = 0.12), whereas in the Cochran's Q test, the threshold p-value (p = 0.0497) was reinforced by a substantial effect size (Serlin's R² = 0.30). This study concludes that integrating omnibus tests with post-hoc procedures (such as Marascuilo and Pairwise McNemar) and reporting effect sizes is imperative to avoid misleading binary interpretations and enhance the validity of scientific conclusions. The main contribution of this paper is providing a consolidated, practical framework for researchers to transition into the "New Statistics" paradigm when dealing with diverse categorical data structures.
Comparative Analysis of Univariate and Spatio-Temporal Models for Forecasting Micro and Small Industries in Java Ferdian Bangkit Wijaya; Weksi Budiaji; Aulia Ikhsan; Agung Satrio Wicaksono; Aditya Rahadian Fachrur; Dinda Dwi Anugrah Pertiwi
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.v1i2.37067

Abstract

Micro and Small Industries (MSEs) serve as the backbone of the regional economy in Java, Indonesia, characterized by high volatility and potential spatial interdependencies. While advanced spatio-temporal models like GSTARIMA are theoretically superior in capturing inter-regional spillover effects, their empirical effectiveness on limited annual aggregate data remains questionable. This study evaluates the forecasting performance of the univariate ARIMA model against the multivariate GSTARIMA model for predicting the number of MSE business units across six provinces in Java from 2013 to 2024. Using a Queen Contiguity spatial weight matrix and Root Mean Square Error (RMSE) as the evaluation metric, the study rigorously tests the Principle of Parsimony. Preliminary analysis using Moran’s I indicates non-significant spatial autocorrelation, suggesting that business growth is predominantly driven by internal temporal inertia rather than immediate spatial propagation. Consequently, the results demonstrate that the simpler ARIMA model outperforms the complex GSTARIMA model in 10 out of 12 testing scenarios (83.3%), with accuracy improvements reaching up to 94% in specific provinces. The study concludes that adding spatial complexity to short-term annual time series (N=12) leads to over-fitting without proportional gains in accuracy. The ARIMA-based forecast for 2025–2029 identifies three distinct regional growth typologies: expansive growth, market saturation, and structural correction, providing critical insights for differentiated regional policy planning.

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