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INDONESIA
STATISTIKA
Published by Universitas Islam Bandung
ISSN : 14115891     EISSN : 25992538     DOI : https://doi.org/10.29313/statistika.v23i1.1628
Core Subject : Science, Education,
Decision Sciences, Operations Research & Management Mathematics
STATISTIKA published by Department of Statistics, Faculty of Mathematics and Natural Sciences, Bandung Islamic University as pouring media and discussion of scientific papers in the field of statistical science and its applications, both in the form of research results, discussion of theory, methodology, computing, and review books. Published biannually in May and November each.
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Articles 9 Documents
 1 
Search results for , issue "Vol. 25 No. 1 (2025): Statistika" : 9 Documents clear
Law of Total Probability of Aftershocks in Earthquake Insurance Darwis, Sutawanir; Hajarisman, Nusar; Suliadi; Fatiha Nurfauzan, Arsyi; Aulia, Githa
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.3886

Abstract

Abstract. Seismic hazard expressed in annual rate of exceedance of a peak ground acceleration traditionally refers to mainshock. A similar seismic hazard, APSHA, has been adopted for aftershock probabilistic seismic hazard. Probabilistic seismic hazard assessment (PSHA) refers to a homogeneous Poisson process to describe mainshock while APSHA models aftershock occurrence as a nonhomogeneous Poisson process whose rate modeled as Omori law. It shown that the combination of PSHA and APSHA results seismic hazard for mainshock-aftershock seismic sequence/cluster (SPSHA/CPSHA). This study shows how to combine results of APSHA and PSHA and proposes a method for earthquake insurance. The study was carried out for West Java region with 206 occurrences consist of 11 clusters. One cluster with 74 aftershocks was chosen for further study. The parameters of SPSHA/CPSHA was estimated using maximum likelihood. The results of SPSHA/CPSHA combined with damage probability matrix (DPM) yields an expected annual damage ratio (EADR) as an indicator of earthquake insurance. The proposed method in this study can be used as a method for computing earthquake insurance premium. Due to limited data further study is needed to obtain accurate and reasonable results.
The Effect of The Mortality Rate Multiplier on Determination of Contribution to Sharia Group Life Insurance Using TMI III and TMI IV at PT Asuransi Jiwa ABC Jihan Khafidhotin Najah; Hikmah, Yulial; Karin Amelia Safitri; Fia Fridayanti Adam
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.4123

Abstract

Abstract. The human mortality rate is an important factor in determining premiums. Information about mortality rates can be obtained through mortality tables that describe the probability or the probability of individual deaths. This study examines the effect of the mortality rate multiplier on gross premium determination in sharia-term life insurance using TMI III and TMI IV at PT Asuransi Jiwa ABC. This research method includes data analysis in the form of claim estimation data and claim realization data for several years from members in the Sharia group term life insurance products. The results of the analysis show that the difference in the mortality rate multiplier value between TMI III and TMI IV affects the gross premium value, especially in certain age ranges, there is a mortality rate value in TMI III that is greater than that in TMI IV, but the resulting premium value is the opposite: the premium in TMI IV is greater than the premium value in TMI III.
Forecasting Foreign Tourist Visits in North Sumatra Province Using the SARIMA Model with Step Function Intervention Debora Sebrina Br. Simanjuntak; Alvionita S., Mika; Achmad Syaiful
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.4629

Abstract

Abstract. Time series analysis is a method for identifying trends, patterns, and fluctuations in data. Its interpretation can be used for forecasting, such as the number of foreign tourist visits in the tourism sector. The Indonesian tourism sector contributes positively to the national economy with a contribution value of 3.79% of the total foreign exchange worth 146.6 billion USD in December 2023. North Sumatra Province as one of the provinces that contributes to the local economy, through landscape diversity and easy access through Kuala Namu Airport (KNO) as a Passenger Exit Survey (PES) makes North Sumatra Province a priority scale destination. Foreign tourist visits to North Sumatra from January 2017 to March 2020 fluctuated, but in April 2020 there was a significant decline due to Covid-19 and social restriction policies. The purpose of this study was to forecast foreign tourist arrivals in North Sumatra from August 2023 to March 2024 using the SARIMA model with step function intervention analysis. The results showed that the number of tourist visits will increase according to the ARIMA (0,1,1)(1,0,1)12 model with the intervention orders b = r = s = 0. The forecasting evaluation obtained is AIC 447.38 and MAPE 9.91%.
Comparison of Generalized Poisson Regression and Negative Binomial Regression Models Based on Akaike Information Criterion Values Sinta Qorri Aina; Darnah; Meirinda Fauziyah; Wiwit Pura Nurmayanti
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5402

Abstract

Abstract. Poisson regression models discrete data and assumes equidispersion, where the variance equals the mean. It is frequently observed that discrete data exhibits a variance exceeding its mean, a phenomenon known as over-dispersion. Over-dispersion may be addressed through various methodologies, such as Generalized Poisson Regression (GPR) and Negative Binomial Regression (NBR). Model selection is predicated on the smallest Akaike Information Criterion (AIC) value. This study aimed to identify the best model in the comparison of models between GPR and NBR based on the smallest AIC value so that it can be known what factors influence the number of cases of pulmonary tuberculosis (TB) in Indonesia in 2022. The results of the study showed that the NBR model was the best model, with an AIC value of 688.49. Factors that influence cases of pulmonary TB in Indonesia in 2022 are the percentage of households that have access to proper sanitation, nursing staff, and the percentage of education levels completed are high school or equivalent.
Analysis of International Tourist Visits Based on Nationality and Tourism Travel Characteristics Using Complete Linkage Handayani, Vitri Aprilla; Sulistyono, Eko; Arrafi, Adamsyam; Hayati, Nahrul
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5801

Abstract

Abstract. This research aims to analyze the characteristics of international tourists in Indonesia using Clustering Method: Complete Linkage. The study successfully identified 5 distinct clusters based on nationality and tourism travel characteristics. The analysis showed significant differences between clusters in terms of country of origin, travel patterns, preferences, expenditure, and tourist activities. Cluster 1 was dominated by ASEAN and Middle Eastern countries with stable visitation patterns influenced by geographical proximity and cultural and business relationships. Cluster 2 was the largest group, encompassing various countries with holiday and business tourism characteristics, longer stays, and higher expenditure. Clusters 3, 4, and 5 each consisted of a single country: Timor Leste, Hong Kong, and Papua New Guinea respectively, with unique visitation patterns. Each cluster showed differences in travel purposes, length of stay, expenditure, and activities of interest. A deep understanding of each tourist group’s characteristics was crucial for developing more targeted tourism marketing strategies. The clustering results could be utilized for infrastructure planning, resource allocation, promotional strategies, and service improvements tailored to each group’s characteristics, thereby enhancing tourist experiences and Indonesia’s overall tourism competitiveness.
Geographically Weighted Logistic Regression Modeling on the Spread of Dengue Fever in Bali Province Safitri Pratiwi, Luh Putu; I Made Pasek Pradnyana Wijaya
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.5852

Abstract

Abstract. One of the statistical methods that can be used for data analysis by taking spatial factors into account is Geographically Weighted Logistic Regression (GWLR). GWLR is a model where there are parameters that are influenced by location (Geographically varying coefficient) and parameters that are not influenced by location. Research continues to be carried out to understand the factors that influence the spread of dengue fever and to develop more effective strategies for controlling this disease as well as the best model for data on the spread of dengue fever in Bali Province based on AIC values. The variables used are the response variable (Y) which is the case of dengue fever. The variables studied are the number of dengue fever sufferers in 2022 as the response variable and the predictor variables are number of drinking water facilities (X1), population density (X2), number of doctors (X3), number of health workers, namely nurses (X4), and number of adequate sanitation facilities (X5). The GWLR model is better used to analyze dengue fever data in Bali compared to the Logistic Regression model seen from the low AIC value of 29.4481. The variable number of doctors (X3) is the only variable that significantly affects the probability of DHF occurrence in Bali Province at α = 10%. The positive coefficient for β3 indicates that an increase in the number of doctors increases the probability of DHF occurrence in the region.
Modeling of Maternal Mortality Risk Factors Using Negative Binomial Regression Approach in Southeast Sulawesi Province in 2022 Alfia Mutmainah; Ruslan; Irma Yahya
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.6137

Abstract

Abstract. This study aims to model the risk factors for maternal mortality in Southeast Sulawesi Province in 2022 using a negative binomial regression approach. This analysis is used to overcome the overdispersion problem in count data. The data used is secondary data obtained from the Southeast Sulawesi Provincial Health Service. The variables studied included the number of health centers, the percentage of births in health service facilities, the percentage of pregnant women who implemented the K4 program, the percentage of pregnant women who implemented the K1 program, and the number of midwives. The research results showed that the variables percentage of births in health service facilities, percentage of pregnant women implementing the K4 program, and number of midwives significantly affected maternal mortality. The negative binomial regression model illustrates that increasing the percentage of births in health service facilities and implementing the K4 program can reduce maternal mortality. On the other hand, increasing the number of midwives did not have a significant effect on reducing maternal mortality.
Analysis of Variables that Influence Digital Entrepreneurship in the Western Region of Indonesia Using Panel Data Regression Erina Herwindalita
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.6287

Abstract

Abstract. Technological developments and digitalization provide a new space to support various entrepreneurial and productivity activities. However, digital-based entrepreneurship in Indonesia is not yet running optimally, especially in the western region of Indonesia, which has great potential. Thus, this research aims to analyze the variables influencing digital entrepreneurship in western Indonesia in 2020-2024 using a panel data regression model. Among the three model estimation approaches, it was found that the fixed effect model with cross-sectional weight was the most effective. The analysis results show that digital entrepreneurship in the western region of Indonesia experiences growth every year. Household internet access and the high school/equivalent net enrollment rate (NER) have a significant positive effect on digital entrepreneurship and productivity scores in the western region of Indonesia in 2020 – 2024. Meanwhile, the construction cost index and the gender empowerment index have no significant effect on digital entrepreneurship and productivity scores in the western region of Indonesia in 2020 – 2024.
Clustering Analysis for Regional Variations in Beef Production: A Comparative Study of Hierarchical and Non-Hierarchical Yunna Mentari Indah; Kuswandi, Wawan
Statistika Vol. 25 No. 1 (2025): Statistika
Publisher : Department of Statistics, Faculty of Mathematics and Natural Sciences, Universitas Islam Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/statistika.v25i1.6386

Abstract

Abstract. Cluster analysis is vital for identifying regional disparities in beef production and guiding targeted livestock development, particularly on Java Island. This study aimed to classify 119 regencies/cities in Java based on 2023 beef production data using both hierarchical (single linkage, average linkage, complete linkage, centroid linkage, Ward's method) and non-hierarchical (k-means, k-medoid, fuzzy c-means) clustering techniques. Performance evaluation through the Davies-Bouldin index and silhouette score revealed that the centroid linkage method delivered the most accurate results. The analysis identified three distinct clusters: Cluster 1 consisted of 90 regencies/cities with moderate beef production, Cluster 2 comprised 6 regencies/cities with high production, and Cluster 3 included 16 regencies/cities with low production. West Java records the highest production volume, while East Java has the largest cattle population. These results highlight regional differences in production capacity, and suggest the need for targeted policy interventions. To bridge potential gaps in beef availability and address regional imbalances, policies could focus on improving beef production in low-output areas and optimizing practices in high-production regions. This study also underscores the importance of considering production levels and livestock populations in regional development strategies. The findings are expected to contribute to efforts aimed at increasing domestic beef availability and reducing reliance on imports, thereby helping to meet the nutritional and protein needs of the population.

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