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Jurnal Statistika Universitas Muhammadiyah Semarang
Published by Universitas Muhammadiyah Semarang
ISSN : 23383216     EISSN : 25281070     DOI : -
Core Subject : Science,
Decision Sciences, Operations Research & Management
Focus and Scope a. Statistika Teori, Statistika Komputasi, Statistika terapan b. Matematika Teori dan Aplikasi c. Design of Experiment
Arjuna Subject : Matematika - Pemodelan dan Simulasi
Articles 213 Documents
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COMPARISON OF THE KAPLAN-MEIER METHOD AND THE TARONE-WARE TEST BASED BY GENDER ON STUNTING DATA AT PUSKESMAS LEGOK Naufal Fadhlullah; Asthagina Delia Putri; Wiwik Wiyanti
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 14, No 1 (2026): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.14.1.2026.22-31

Abstract

Stunting is one of the problems of acute malnutrition due to the consumption of unnutritious food for a long period, at least the first 1000 days of life. Biological differences between males and females can be a risk in the detection of stunting time. Therefore, this study was conducted to determine whether there is a significant difference in the detection time of stunting in male and female toddlers at Puskesmas Legok. Comparison data between groups by sex were analyzed using Kaplan-Meier and Tarone-Ware test. The results of the study showed that there was a significant difference in the detection time of stunting in toddlers based on gender. The average stunting detection time in female toddlers is longer than in male toddlers. The Tarone-Ware test also showed significant results with a Chi-Square value of 7.802 and p = 0.005. therefore, this study reveals that there are differences in the time of stunting detection based on gender.
CLUSTERING OF REGENCIES IN WEST KALIMANTAN BASED ON FINANCIAL RATIOS USING THE AVERAGE LINKAGE METHOD Hazwani Dhiya' Atiq Viatmaja; Annisa Auliarahmi; Gabriella Simarmata
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 14, No 1 (2026): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.14.1.2026.43-60

Abstract

Regional financial management serves as a crucial framework for assessing fiscal viability and the impact of policies on development. In West Kalimantan, identifying patterns in budget performance is essential to support targeted financial policy decisions, particularly regarding fiscal solvency and flexibility. This study aims to group regencies in West Kalimantan based on budget ratios derived from the 2024 Audited Examination Result Reports and evaluate the quality of the formed clusters. The research employs a quantitative descriptive method using Hierarchical Cluster Analysis with the Average Linkage approach and Manhattan distance. Five financial ratios were analyzed across twelve regencies, with cluster validity tested using Silhouette, Davies-Bouldin, and Dunn indices. The results indicate that the optimal number of clusters is two. Cluster 1 consists solely of the Bengkayang Regency, characterized as an outlier with an extremely high financial independence ratio, indicating strong fiscal autonomy. Cluster 2 comprises the remaining eleven regencies, characterized by low financial independence and high dependency on central government transfers, despite demonstrating relatively good revenue effectiveness. The study concludes that significant fiscal disparity exists in West Kalimantan. These findings suggest that policy planning should focus on enhancing local revenue generation and fiscal independence for the majority of regencies to approach optimal performance.
FORECASTING THE NUMBER OF BREAST CANCER AMONG WOMEN IN INDONESIA BASED ON TIME SERIES MODELS Wulanova Romadhona; Syasya Qonita Azizah; Vivin Vivin
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 14, No 1 (2026): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.14.1.2026.61-70

Abstract

Breast cancer is one of the leading causes of death among women in Indonesia, requiring a mathematical prediction model to support health policy and planning. This study uses two time series forecasting methods with an autocorrelation approach, namely Autoregressive Integrated Moving Average (ARIMA) and Exponential Smoothing State Space (ETS), to predict the number of new breast cancer cases among women in Indonesia. The data used is secondary data from Gapminder for the period 1990-2021 and analyzed using accuracy metrics such as AIC, BIC, RMSE, MAE, and MAPE. The best ARIMA model obtained was ARIMA (0,2,2), with AIC (358.16) and BIC (362.37) values, as well as smaller RMSE and MAE values compared to the ETS (M,A,N) model. Diagnostic results showed good model fit with ARIMA model residuals being white noise. The forecast results for 2022-2031 show a consistent upward trend in the number of cases, from around 26,218 cases in 2022 to 20,616 cases in 2031. These findings confirm that the ARIMA model is effective in capturing long-term linear patterns and can be used as a basis for formulating strategies for the prevention and early detection of breast cancer in Indonesia.
APPLICATION OF BINARY LOGISTIC REGRESSION WITH TIME BASED SAMPLING IN ANALYSIS OF RISK FACTORS FOR MOTORCYCLE TRAFFIC VIOLATIONS IN MEDAN CITY Graceya Zagita Manik; Irgie Attaurrazaq; Donni Ramadhan Siregar; Katrin Jenny Sirait
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 14, No 1 (2026): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.14.1.2026.32-42

Abstract

Traffic violations by motorcyclists are a major contributor to accidents in Medan City. This research sought to examine the frequency of violations and the factors affecting the likelihood of such violations among riders at three intersections in Medan City: Dr. Mansyur, Setia Budi, and Fly Over Jamin Ginting. A cross-sectional quantitative design and time-based sampling was used. Observations were conducted over three days in two sessions (daytime 02:00–03:00 PM and afternoon 04:00–05:00 PM WIB), with a sample of 540 motorcyclists. The dependent variable was violation status; independent variables included gender, motorcycle type, rider status, and observation time. Binary logistic regression was applied. Results showed a violation rate of . Simultaneously, all independent variables had a significant effect (p<0.001). Partially, only rider status was significant (p<0.001; OR=2.728), meaning riders with a passenger were 2.789 times more likely to violate than solo riders. Gender, motorcycle type, and observation time were not significant. The model fitted well (Hosmer–Lemeshow test, ). In conclusion, rider status is the main factor, so supervision should focus on riders with passengers.
ASSESSING CLUSTER VALIDITY AND STABILITY OF HIERARCHICAL WARD’S LINKAGE AND NON-HIERARCHICAL K-MEANS ON THE EBGS INDEX OF REGENCIES/MUNICIPALITIES IN SOUTH SULAWESI Elisabeth Evelin Karuna; Mahrani Mahrani; Atiqa Azza El Darman
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 14, No 1 (2026): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.14.1.2026.01-21

Abstract

Abstract: Cluster analysis is a statistical method used to group objects based on similar characteristics. In general, there are two main categories in cluster analysis, namely hierarchical methods (such as Ward's linkage) and non-hierarchical methods (such as K-Means). This study aims to compare the performance of these two methods in grouping the Electronic-Based Government System (EBGS) Index of districts/cities in South Sulawesi Province. The results of the analysis show that both methods produce identical validity index values, namely a Silhouette Coefficient of 0.67, a Davies-Bouldin Index (DBI) of 0.39, and a Calinski-Harabasz Index (CHI) of 83.02. These values indicate that the clusters formed have high internal compactness and clear separation between clusters. In addition, the Adjusted Rand Index (ARI) value of 1.00 indicates perfect agreement between the results of Ward's linkage and K-Means, signifying a very high level of stability. Thus, the results of this study show that the grouping of the SPBE Index in South Sulawesi is valid, stable, and able to represent the natural structure of the data consistently. Keywords:Cluster; K-Means; Ward's Linkage; Validity; Stability; EBGS
PANEL DATA MODELING OF THE POOR POPULATION IN EAST NUSA TENGGARA: THE ROLE OF HUMAN DEVELOPMENT INDEX AND GROSS REGIONAL DOMESTIC PRODUCT Esra Rombeallo; Risky Meyranti; Dwi Wahyuni
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 2 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.2.2025.98-109

Abstract

Kemiskinan adalah masalah yang kompleks dan multidimensi, yang mencakup masalah pendapatan dan ketidakmampuan untuk mengakses hak-hak dasar (misalnya pendidikan, perawatan kesehatan, perlindungan sosial). Analisis regresi data panel merupakan metode yang tepat untuk memeriksa masalah kemiskinan, karena mengintegrasikan data cross-sectional dan time series. Penelitian sebelumnya biasanya hanya menggunakan satu jenis metode regresi data panel. Penelitian ini bertujuan untuk memodelkan pengaruh Indeks Pembangunan Manusia (IPM) dan pertumbuhan Produk Domestik Regional Bruto (PDRB) terhadap jumlah orang miskin di kabupaten/kota di provinsi Nusa Tenggara Timur menggunakan panel data regresi selama periode 2022-2024. Common Effect Model (CEM), Random Effect Model (REM), dan Fixed Effect Model (FEM) dilakukan dalam penelitian ini. Model dipilih menggunakan uji Chow, uji Hausman, uji Lagrangian Multiplier. Hasil penelitian menunjukkan bahwa Random Efffect Model (REM) adalah metode yang paling tepat dengan persamaan yang diestimasi. Berdasarkan metode REM, IPM mempunyai pengaruh negatif dan signifikan terhadap jumlah orang miskin, yang menunjukkan bahwa peningkatan pembangunan manusia berkontribusi terhadap kemiskinan. Sebaliknya, pertumbuhan PDRB memiliki efek positif namun tidak signifikan secara statistik, yang menunjukkan bahwa pertumbuhan ekonomi itu sendiri tidak mungkin dapat mengurangi kemiskinan tanpa kebijakan yang adil dan pembangunan yang inklusif. Studi-studi mendatang dapat mempertimbangkan untuk mengintegrasikan variabel-variabel sosioekonomi tambahan guna memberikan wawasan yang lebih mendalam tentang dinamika kemiskinan, serta mengeksplorasi pendugaan alternatif dalam metodologi REM.
ORDINAL LOGISTIC REGRESSION MODEL FOR HUMAN DEVELOPMENT INDEX DATA IN PAPUA AND WEST PAPUA PROVINCES Nabilla Rida Tri Nisa; Novia Amilatus Solekha; Abdullah Fahmi; Panji Jilblathar; Purhadi Purhadi
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 2 (2024): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.2.2024.%p

Abstract

The Human Development Index (HDI) is a key indicator used to measure the quality of economic development, particularly the degree of human development. In 2019, the HDI for Papua Province was 60.84, while West Papua Province recorded a value of 64.70. According to the Central Statistics Agency, these figures indicate that Papua and West Papua are the provinces with the lowest HDI in Indonesia. This research aims to identify the factors influencing the HDI in Papua and West Papua Provinces in 2019 using an ordinal logistic regression approach. The study utilizes secondary data from the Central Statistics Agency for both provinces. The results indicate that the model developed is appropriate, with the Open Unemployment Rate (TPT) and average per capita expenditure being significant factors influencing HDI. The model's effectiveness is evidenced by an Akaike Information Criterion (AIC) value of 28.978
WASTE GENERATION MODELING BASED ON SOCIOECONOMIC AND SOCIODEMOGRAPHIC FACTORS IN WEST JAVA USING GEOGRAPHICALLY WEIGHTED REGRESSION Sri Pingit Wulandari; Sri Mumpuni Retnaningsih; Nimas Ayu Prabawani; Apsarini Pradipta
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 2 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.2.2025.110-122

Abstract

Waste has become a national concern, reflecting the increasing consumption patterns within society. The rise in consumption contributes to the growing volume and diversity of waste generated. This issue is also aligned with Sustainable Development Goals (SDGs) Goal 12, which emphasises responsible consumption and production. West Java Province is one of the provinces with the highest total waste generation in Indonesia. Several factors likely influence the annual increase in waste generation in West Java. Therefore, this study aims to model the factors affecting waste generation in West Java Province by incorporating spatial aspects using the Geographically Weighted Regression (GWR) method. Based on the analysis, the GWR model was applied using an adaptive bisquare kernel function, achieving a model fit of 96.65%. The factors found to have a significant influence on waste generation in West Java Province include life expectancy of schooling (HLS), the percentage of the population living in poverty, and the Gross Regional Domestic Product (GRDP) at constant prices.
Hildreth-Lu Model for Autocorrelation Correction in Time-Series Regression of Shrimp Growth Perfomance Febriyani Eka Supriatin; Aulia Rahmawati; Muhammad Dailami
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 13, No 2 (2025): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.13.2.2025.123-133

Abstract

Autocorrelation frequently occurs in time-series regression models, leading to inefficient estimators and biased inference when ignored. This study analyzed the relationship between water quality parameters and shrimp growth performance by applying the Hildreth–Lu iterative method to correct autocorrelation. The dataset consisted of Specific Growth Rate (SGR) as the dependent variable and three water-quality parameters—temperature, pH, and dissolved oxygen (DO)—as explanatory variables, with pond type included as a dummy factor. The initial Ordinary Least Squares (OLS) estimation revealed that temperature and pH significantly affected SGR, while the Durbin–Watson (DW) value of 0.878 indicated positive autocorrelation in the residuals. After applying the Hildreth–Lu correction, the estimated autocorrelation coefficient (ρ) was 0.64, and the DW statistic improved to 2.03, confirming that serial correlation had been successfully removed. The corrected model provided more efficient and unbiased parameter estimates without requiring data transformation or loss of observations. The results confirm that temperature is the most influential factor in shrimp growth, while pH, DO, and pond type showed no significant effects. The study highlights the importance of autocorrelation diagnostics in regression analysis and demonstrates that the Hildreth–Lu method is an effective and reliable approach for improving model efficiency in small-sample time-series data
MODELING OF POVERTY INDICATORS IN EAST JAVA PROVINCE USING BOOTSTRAP AGGREGATING MULTIVARIATE ADAPTIVE REGRESSION SPLINE (BAGGING MARS) danu priambodo; Rochdi Wasono; M. Al Haris
Jurnal Statistika Universitas Muhammadiyah Semarang Vol 12, No 2 (2024): Jurnal Statistika Universitas Muhammadiyah Semarang
Publisher : Department Statistics, Faculty Mathematics and Natural Science, UNIMUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jsunimus.12.2.2024.19-28

Abstract

Poverty is a situation where a person is below the minimum standard value line. The view regarding poverty can be said that poverty is a multidimensional phenomenon where there are many indicators that influence poverty, so modeling needs to be carried out to find out what indicators influence poverty. This research uses The Multivariate Adaptive Regression Spline (MARS) with Bootstrap Aggregating. MARS is a nonparametric regression method that can handle high- dimensional data. The best model produced by MARS is a combination of BF=24, MI=1, MO=0 with a GCV of 9.231184. Then Bagging was carried out on the initial dataset with 35, 45, 50, 75 and 100 bootstrap replications. The best model was produced by MARS Bagging on 45 replications with a GCV of 3.84492. The GCV value obtained by Bagging MARS is smaller than MARS. This shows that Bagging can reduce GCV and increase accuracy, so this method can be used in this research.

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