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Muhammad Athoillah
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kampus II Universitas PGRI Adi Buana Surabaya Jl. Dukuh Menanggal XII, Surabaya 60234 Jawa Timur, Indonesia.
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INDONESIA
J Statistika : Jurnal Ilmiah Teori Dan Aplikasi Statistika
Published by Universitas PGRI Adi Buana Surabaya
ISSN : 20890028     EISSN : 26547511     DOI : https://doi.org/10.36456/jstat.vol16.no2
Core Subject : Economy, Science,
Decision Sciences, Operations Research & Management Economics, Econometrics & Finance
This journal publishes scientific articles in the form of research results, case studies, or literature reviews on various aspects related to the field of statistics, scientific data and their applications. Such as Computing, Time Series, Multivariate, Data Mining, Biostatistics, Survival Analysis, Econometrics, Spatial Analysis, Actuarial, Quality Control, Bayesian Analysis, Development Research in Statistics, Natural Language Processing, Applied Mathematics, Applied Statistics. However, the editorial team does not rule out other topics in the fields of statistics and scientific data.
Arjuna Subject : -
Articles 278 Documents
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Optimization of Raw Material Supply Scheduling to Maximize Storage Utilization at Company X
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 17 No 2 (2024): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol17.no2.a9837

Abstract

This study focuses on optimizing the raw material supply scheduling at Company X, a business in the food and beverage industry, to maximize storage utilization. Company X faces challenges in balancing raw material demand with limited storage capacity, leading to issues such as stock shortages or overstocking. The current inventory management practices have proven ineffective in meeting consumer demand efficiently. This research utilizes a quantitative methodology, focusing on numerical data analysis to address the research objectives. Through this approach, measurable data are collected and examined to provide clear insights into patterns, relationships, and trends. The quantitative method ensures objectivity and precision, making it ideal for evaluating factors such as raw material supply schedules, storage capacity, and demand fluctuations, thereby supporting data driven decision-makingThrough the analysis of sales data and raw material requirements, this research aims to develop a more effective scheduling strategy that aligns supply with demand while optimizing storage space. The findings of this study offer potential solutions for improving operational efficiency, reducing waste, and enhancing the overall supply chain performance. By implementing optimized supply scheduling, Company X can ensure smoother operations and better meet consumer needs, thus maximizing value across the entire supply chain.
Comparison of Geographically Weighted Generalized Poisson Regression (GWGPR) and Geographically Weighted Negative Binomial Regression (GWNBR) Methods in Determining Factors Affecting Tuberculosis Cases in Indonesia
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10073

Abstract

The findings of this study demonstrate that both the Geographically Weighted Generalized Poisson Regression (GWGPR) and Geographically Weighted Negative Binomial Regression (GWNBR) models are effective in modeling tuberculosis (TB) incidence data characterized by overdispersion and spatial heterogeneity. Although both models yield comparable fit statistics—as indicated by nearly identical Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) values—GWGPR exhibits a higher sensitivity to regional variability, as evidenced by the formation of four distinct provincial clusters based on significant predictor variables, compared to only two clusters identified by the GWNBR model. This suggests that GWGPR may offer a more nuanced understanding of spatial effects in epidemiological data. Furthermore, several covariates; namely smoking prevalence, average annual humidity, number of rainy days, reported health complaints, and TB case detection and treatment coverage, emerged as consistently significant across all provinces in both modeling approaches. The recurrence of these variables across spatially disaggregated models highlights their fundamental role in influencing TB transmission dynamics at a national scale. Accordingly, the use of spatially adaptive models such as GWGPR can support more targeted and effective disease control strategies by aligning health policy responses with the localized determinants of TB burden.
Geographically Weighted Negative Binomial Regression (GWNBR) Modeling In Infant Mortality Rate Cases In South Sulawesi
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10093

Abstract

Geographically Weighted Negative Binomial Regression (GWNBR) is a method used to model count data that exhibit overdispersion and spatial heterogeneity. South Sulawesi is one of the provinces experiencing an increase in infant mortality cases. Therefore, this study aims to obtain a better model for mapping the factors that influence infant mortality cases in South Sulawesi Province. The method used in this study is GWNBR with an Adaptive Tricube Kernel as the weighting function. The results show that the GWNBR model with Adaptive Tricube Kernel weighting produces the smallest AIC value, which is 223.4447, making it more effective for modeling infant mortality cases in South Sulawesi Province. The variables significantly affecting infant mortality cases include X1 (Percentage of Exclusive Breastfeeding), X2 (Percentage of Early Initiation of Breastfeeding), X3 (Complete Baby Visit Coverage), X4 (Percentage of Vitamin A Supplementation), X5 (Number of Community Health Centers), X6 (Percentage of Low Birth Weight Babies), X7 (Delivery Coverage in Health Service Facilities), and X8 (Iron Tablet Supplementation to Pregnant Women).
The Modeling of The Poverty Rate In Indonesia From 2018 to 2023 Using A Panel Data Regression Approach
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10139

Abstract

Indonesia was ranked sixth out of eleven Southeast Asian countries with the highest poverty rate, highlighting the need for effective strategies to address poverty issues. The governmental strategy was aligned with the Sustainable Development Goals (SDGs), with the primary objective of achieving zero poverty. In this study, the poverty rate in Indonesia from 2018-2023 exhibited fluctuating trends, marked by both increases and decreases over the years. This phenomenon reflects the dynamic nature of poverty levels in the country. Poverty rates are assumed to be related to education and the economy. Referring to the statement, this study involves the percentage of poverty as the dependent variable and access to clean water access, gini ratio, open unemployment rate, and literacy rate as independent variables. Based on the structure of the research data, the dynamics of the poverty rate from 2018-2023 involve both cross-sectional and time series data structures. In this case, the panel data regression method based on the Random Effects Model is appropriate and can accommodate the identification process to the conclusion. This study aims to identify the factors that influence the poverty rate. Furthermore, the findings indicate that all independent variables have simultaneous and partial effects on the poverty rate.
Cluster Analysis Using the Ward Algorithm for Grouping Regency / City in Central Java Province Based on Poverty Indicators 2023
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10160

Abstract

Poverty is still a major issue in Central Java Province, with the poverty rate recorded at 10.23% in 2023. Uneven poverty reduction in various regions adds to the complexity of this problem. This study aims to classify districts/cities in Central Java based on poverty indicators, using a hierarchical cluster analysis approach through Ward's algorithm. The data used is secondary data from the Central Bureau of Statistics (BPS), which includes six variables that affect poverty. One them is the percentage poor people (X1), which represents the proportion of the population below the poverty line and is an important indicator in assessing the welfare of a region. The results show that the application of hierarchical cluster analysis through Ward's algorithm produces four clusters of districts/cities based on poverty indicators in 2023, each with different characteristics. The first cluster consists regions with high poverty rates and limitations in education, health, and economic infrastructure. Regions in this cluster require special attention in policy formulation. Based on the characteristics obtained from each cluster, these findings can be used to design more targeted and data-based development policies, such as budget allocation, poverty reduction programs, and improving access to basic services according to the conditions each cluster.
Forecasting Stock Prices Using a Nonlinear Approach with the Exponential Smooth Transition Autoregressive (ESTAR) Model
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10213

Abstract

Increased interest among Indonesians in investing in a single financial asset has driven significant growth in the capital market. However, high market volatility brings the risk of loss that needs to be anticipated. One of the relevant models in dealing with these problems is using the nonlinear Exponential Smooth Transition Autoregressive (ESTAR) model. ESTAR is an extension of the Autoregressive (AR) model that uses smoother transitions to handle nonlinear time series data. This study aims to predict stock prices using the ESTAR nonlinear model to help investors deal with market uncertainty and manage short-term risk. The data used is the daily closing price of PT Bank Central Asia Tbk shares, for the period January 2022 to December 2024. The research methodology includes stationarity test, AR(p) parameter estimation, ESTAR(p,d) model parameter estimation, and prediction accuracy evaluation using Mean Absolute Percentage Error (MAPE). The results show that the AR(1) model is the best order model and the ESTAR(1,1) model is the final optimal model. Evaluation of the prediction results for the next one month period, shows that the MAPE value is 2.79% which indicates the model's performance in predicting stock prices is very good.
Implementation of K-Means Cluster for Districts or Cities in West Java Province Based on Unemployment Indicators
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10311

Abstract

The high disparity of open unemployment rate among regions in West Java Province is an issue that requires mapping based on regional socioeconomic characteristics. The purpose of this study is to group districts/cities in West Java based on the open unemployment rate and its influencing factors using the K-Means Cluster method. The data used is secondary data for the year 2023 obtained from the Central Bureau of Statistics. Assumption test was conducted using KMO and Bartlett's Test to ensure sample adequacy and feasibility of data structure. The results of the analysis show that the regions in West Java are divided into three clusters. Cluster 1 consists of regions with high development but also high unemployment rates, such as Bandung City and Bekasi Regency. Cluster 2 includes regions with medium socioeconomic conditions, such as Depok City and Bandung Regency. Cluster 3 consists of regions with low development, low HDI, and high poverty, such as Ciamis, Garut, and Pangandaran. These findings indicate the existence of significant inequality among regions and can serve as a basis for the formulation of more targeted and region-based unemployment reduction policies.
Bonus-Malus Premium for Third Party Liability Insurance with Poisson-Lindley Distribution Claim Frequency and Exponential-Inverse Gamma Distribution Claim Severity
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10327

Abstract

Insurance is a form of mutual cooperation that provides protection against unforeseen risks. With insurance, individuals can feel more secure about potential future losses, whether related to themselves or their property. As the number of motor vehicles in Indonesia increases, so does the risk of traffic accidents, making motor vehicle insurance particularly Third Party Liability (TPL) insurance increasingly important. To enhance fairness, insurance companies implement premium systems based on claim history, one of which is the bonus-malus system. This study discusses premium calculation in a bonus-malus system for TPL insurance, assuming that claim frequency follows a Poisson-Lindley distribution and claim severity follows an exponential-inverse gamma distribution. The data used are secondary data obtained from PT. XYZ for the 2019 underwriting year, focusing on policyholders in category two. The analysis results indicate that the selected distributions fit the data well. The optimal bonus-malus system determines that the initial pure premium to be paid by new policyholders is Rp22,970. Premiums in subsequent years are adjusted based on claim activity: increasing if a claim is made and decreasing if no claim occurs.
Implementation of Geographically and Temporally Weighted Regression with Cross Validation and Generalized Cross Validation Methods for Deforestation Modeling in Kalimantan
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10331

Abstract

Deforestation in Indonesia has received national and international attention for its ecological, economic and social impacts. Kalimantan is an area with high deforestation rates triggered by various factors that vary between locations and time. This study aims to model the deforestation rate in Kalimantan during the period 2014 to 2022 using the Geographically and Temporally Weighted Regression (GTWR) method. The model was tested with a combination of Fixed and Adaptive Gaussian Kernel weighting functions and Cross Validation (CV) and Generalized Cross Validation (GCV) bandwidth determination methods. The results show that the best model is GTWR with Fixed Gaussian Kernel and GCV based on R2 and AIC values. Spatial-temporal analysis shows that neither variable is significant in 2014, forest fires are significant in 2019, and in other years both variables are broadly significant. The findings provide insights into the spatial and temporal dynamics of deforestation factors to support area-based policies.
Evaluating Patient Satisfaction in Surabaya Public Health Centers Using an Integrated IPA–Kano Framework
J STATISTIKA: Jurnal Imiah Teori dan Aplikasi Statistika Vol 18 No 1 (2025): Jurnal Ilmiah Teori dan Aplikasi Statistika
Publisher : Faculty of Science and Technology, Univ. PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/jstat.vol18.no1.a10449

Abstract

Quality healthcare services at community health centers (Puskesmas) are crucial for enhancing public satisfaction and trust, particularly in large cities like Surabaya. This study aims to identify and prioritize service attributes using Importance Performance Analysis (IPA), classify them using the Kano method, and integrate both approaches to formulate a comprehensive service improvement strategy. A survey method with questionnaires was administered to 85 patients at Puskesmas Surabaya, and the data were analyzed using IPA and Kano methods. The IPA results show that four variables fall into the low-priority quadrant: registration speed, the attitude of the staff in providing service, waiting room conditions, and the availability of adequate parking spaces. Meanwhile, the Kano method classified all attributes as One Dimensional (O), meaning that improving the quality of these attributes will proportionally enhance patient satisfaction. Conversely, satisfaction will decrease if the performance of these attributes declines. The integration of IPA and Kano methods highlights the need to improve the speed of registration, staff service attitudes, waiting room conditions, and parking availability. These findings emphasize the importance of focusing on these aspects to improve overall service quality and patient satisfaction.

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