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INDONESIA
Indonesian Journal of Statistics and Its Applications
Published by Institut Pertanian Bogor
ISSN : 25990802     EISSN : 25990802     DOI : -
Core Subject : Science, Education,
Computer Science & IT Mathematics Other
Indonesian Journal of Statistics and Its Applications (eISSN:2599-0802) (formerly named Forum Statistika dan Komputasi), established since 2017, publishes scientific papers in the area of statistical science and the applications. The published papers should be research papers with, but not limited to, the following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education. All papers were reviewed by peer reviewers consisting of experts and academicians across universities and agencies
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 192 Documents
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Analysis of Net Enumeration Rate of Senior High School Using Fixed-Effect Clustered-Robust Standard Error Model: Analisis Angka Partisipasi Murni Sekolah Menengah Menggunakan Model Fixed-Effect Clustered Robust Standard Error Metanda, Leonita Amara Husna; Oktora, Siskarossa Ika
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p270-286

Abstract

The Net Enumeration Rate (NER) of senior high school (SHS) in Indonesia in 2017-2019 always be the lowest than the other education levels and cannot fulfill the target of the 2014-2019 National Medium-Term Development Plan (RPJMN). This study aims to analyze the determinants of NER of SHS in Indonesia 2017-2019 using the panel data regression method. The independent variables include child labor, child marriage, Smart Indonesia Program (PIP), repeat rates, and poverty. The NER of SHS is the dependent variable. Based on the modeling, heteroscedasticity and autocorrelation problems are found. The fixed-effect clustered-robust standard error method is used to solve these problems. The results show that the NER of SHS increased every year, and poverty decreased every year. Meanwhile, other variables fluctuate during 2017-2019. Furthermore, it is found that child labor and poverty significantly affect the NER of SHS in Indonesia. Meanwhile, child marriage, PIP, and repeat rates have no significant effect. This study can be used by local government to implement more effective policies based on the factor that do have significant effects on NER of SHS in Indonesia in 2017-2019.
GSTARIMA Model with Missing Value for Forecasting Gold Price Fadhlul Mubarak; Atilla Aslanargun; İlyas Sıklar
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p90-100

Abstract

Gold is one of the investments that be a great demand. Selecting and applying the best GSTARIMA model for gold price forecasting was the aim of this study. However, the gold price data that has been obtained missing values. Missing value data has been imputed by the last data before the missing value and moving average techniques. The GSTAR (1) and GSTARI (1, 1) models have been combined with an imputation technique solved this problem. Based on the smallest RMSE value, the GSTARI (1, 1) model which has been combined with the imputation technique that used the last value was the best method because it produced the smallest RMSE when compared to other methods. Forecasting results shown that gold prices in the United States, United Kingdom, and Indonesia increased but gold prices in Turkey actually decreased. Forecasting gold prices in each of these countries become one of the references in investing in gold. Based on the results of gold price forecasting, gold prices changed but not significantly.
Modeling Dengue Fever by using Conditional Autoregressive Bessag-York-Mollie: Pemodelan Demam Berdarah dengan Menggunakan Conditional Autoregressive Bessag-York-Mollie Jajang Jajang; Budi Pratikno; Mashuri Mashuri
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p101-113

Abstract

Dengue fever is a tropical disease caused by the dengue virus.  The small proportional of this dengue fever disease can develops into a more severe dengue hemorrhagic fever (DHF). This research discussed about the model for disease mapping in Ciamis District.   The purpose of this research is to characterize relative risk and factors correlated with case DHF.  Independent variables used in this research are population density, attitude of region, and the number of health worker. To analysis this data, we used conditional autoregressive Bessag-York-Mollie (CAR-BYM) model.  Based on descriptive statistic, the maximum and minimum DHF cases are Ciamis and Sukamantri, respectively.  Furthermore, basedon model results, we found that the maximum and minimum relative risk are Cijeungjing and Sukamantri, respectively. Furthermore, there were 7 sub districts which relative risk are greater than one and 20 sub districts which relative risk are less than one.  The sub districts which relative risk are greater than one show that DHF cases in these sub districts are greater than expectation.  Based on the CAR-BYM model result showed that Each increase the population density by one unit contributes to the addition of DHF cases by 0.0012 units. Each additional health worker one unit, it will reduce the number of DHF cases by 0.0675 units. Each additional altitude of one unit will reduce the number of dengue cases by 0.0011 units.  Based on relative risk (RR) value of the CAR-BYM model we found that the Cijengjing and Ciamis Districts have darkest color.  The RR values in the two sub-districts are 3,449 and 3,240, respectively. The RR values of the two sub-districts are more than expected values.
Identification of Factors Affecting Smoking Prevalence in West Java using Spatial Modeling: Identifikasi Faktor-Faktor Yang Memengaruhi Prevalensi Merokok di Jawa Barat Menggunakan Pemodelan Spasial Aditya Firman Baktiar; Toza Sathia Utiayarsih
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p114-131

Abstract

Smoking behavior is certainly a serious problem that needs to be resolved immediately in Indonesia. It because smoking has been shown to trigger various diseases. More than that, smoking can also causes death. Based on the results of Riskesdas in 2013 and 2018, the province with the highest smoking prevalence in Indonesia is Jawa Barat. Moreover, the prevalence of smoking in Jawa Barat also shows a stable trend and has always been above the national prevalence since 2001. If we look at the spatial distribution, the prevalence of smoking in Jawa Barat shows a grouping where close districts/cities have a similar values to each other. It indicates spatial dependencies that need to be accommodated in the modeling. Therefore, this study was conducted to determine the factors that influence the prevalence of smoking in Jawa Barat by using spatial analysis. Based on the spatial lag model, it was found that the percentage of the population graduating from high school and the percentage of the highland area had a significant effect on smoking prevalence in Jawa Barat. While the percentage of the married population, the percentage of the working population, and tobacco production had no significant effect.
Application of Adaptive Synthetic Nominal and Extreme Gradient Boosting Methods in Determining Factors Affecting Obesity: A Case Study of Indonesian Basic Health Research Survey 2013: Aplikasi Metode Adaptive Synthetic Nominal dan Extreme Gradient Boosting dalam Menentukan Faktor yang Memengaruhi Obesitas: Studi Kasus Riset Kesehatan Dasar Indonesia 2013 Rombe, Yoris; Thamrin, Sri Astuti; Lawi, Armin
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p309-317

Abstract

Obesity is the accumulation of excessive body fat and can be harmful to health. According to recent studies, several factors that contribute to the increasing prevalence of obesity in Indonesia include poor diet, lack of consumption of vegetables and fruits, high consumption of fast food, area of residence, and lack of physical activity. In addition, psychological factors, high consumption of alcohol and cigarettes, cultural differences, and stress factors also trigger obesity. The rapid development of the medical field cannot be separated from the availability of data that is increasingly easy to access and increasing knowledge in the medical field. This makes machine learning increasingly needed for pattern recognition from very large medical data, including obesity data. In this study, the factors that influence obesity status in Indonesia will be determined. In order to achieve this, Extreme Gradient Boosting (XGBoost) was used. This method is one of the classification methods that has better scalability and more efficient over its previous methods. Besides that, to overcome the imbalanced data, Adaptive Synthetic Nominal Algorithm (ADASYN-N) is used in order to balance the data and improve its prediction accuracy. Both the ADASYN-N and XGBoost methods will be applied to obesity data from the Indonesian Basic Health Research Survey in 2013. This study shows that female is more at risk in determining obesity status in Indonesia based on the highest gain value (37%). In addition, age 35-54 years, strenuous activity, and eating vegetables for 6 days are also risk factors of obesity.
Identification Pharmacodynamic Interactions of Active Compounds of Diabetes Mellitus Type 2 Herbal Plants Using the Random Forest Method: Identifikasi Interaksi Farmakodinamik Senyawa Aktif Tanaman Jamu Diabetes Melitus Tipe 2 Menggunakan Metode Random Forest Askari, M. Aiman; Afendi, Farit M.; Fitrianto, Anwar; Wijaya, Sony Hartono
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p245-260

Abstract

Drug-drug interactions is defined as the modification of the effect of a drug as a result of another drug given simultaneously or with an interval or when two or more drugs interact so that the effectiveness or toxicity of one or more drugs changes. Pharmacodynamic interactions are one type of interaction that needs special attention because these interactions work directly on the body's physiological systems and compete on the same receptors so that they can be antagonistic, additive, or synergistic. The use of medicinal plants is becoming an alternative because in addition to their relatively safer side effects, medicinal plants consisting of active compounds are appropriate in treating degenerative metabolic diseases triggered by mutations in many genes. As in the case of polypharmacies, interactions of active compounds in medicinal plants can also lead to phapharmodynamic interactions. Therefore, it is also necessary to identify the active compounds so that it can then be known whether the interaction of the compounds will be beneficial or detrimental. In this study, pharmacodynamic identification was applied to Diabetes Mellitus Type 2 medicinal plant compounds by using the independent variables Target Protein Connectedness (TPC), Side Effect Similarity (SES), and Chemical Similarities (CS) using Random Forest classification method. From a search of various databases, 21 active compounds were obtained and then only 100 compound interactions could be calculated as independent variables. With an accuracy value and AUC of 0,96, there were 93 pairs of compounds that interacted pharmacodynamically and the remaining 7 did not interact.
Identification of Social Support and Knowledge of Covid-19 Survivors with Structural Equation Modeling in R Rahmi, Nur Silviyah; Masruro Pimada, Laila; Yesica, Reza; Nur Cahaya Ningsih, Devi
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p287-295

Abstract

COVID-19 cases in Indonesia have finally reached a second peak amounting to 4 million cases. A number of the death rate was 3.4 percent, yet the recovery rate was 95.9 percent. The Health Ministry of Republic Indonesia through the Covid-19 Task Force has issued guidelines for preventing and controlling Covid-19 to decrease the death rate and increase the recovery rate. According to the guidelines, a person who undergoes quarantine needs to be provided with health care, and social and psychosocial support. This study seeks to identify the influence of external factors including social support, as well as internal factors including patient motivation, and knowledge on the recovery rate of Covid-19 survivors. The research methods use Structural Equation Modelling to determine the indicators that have the most significant influence on the latent variables of social support, knowledge, and motivation for healing Covid-19. Primary data collection was carried out online with a sample of 176 Covid-19 survivors across Indonesia in August 2021. The methods of the Shapiro-Wilk test for normal multivariate show the p-value at 0.00 significantly satisfies the assumption. The result shows that social support has a significant effect on knowledge with a regression coefficient is 0.263. Knowledge has a regression coefficient is 0.645 for the Healing of Covid-19. In conclusion, the higher social support provided by the patient's external parties: family, surrounding environment, and public health center officers, will impact the higher patient's knowledge and healing of Covid-19 disease. Meanwhile, social support has no significant effect on healing actions.
Comparison of The SARIMA Model and Intervention in Forecasting The Number of Domestic Passengers at Soekarno-Hatta International Airport Anistia Iswari; Yenni Angraini; Mohammad Masjkur
Indonesian Journal of Statistics and Applications Vol 6 No 1 (2022)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i1p132-146

Abstract

The Covid-19 pandemic has had a massive effect on the air transportation sector. Soekarno-Hatta International Airport (Soetta) skilled a lower variety of passengers because of the Covid-19 pandemic, even though Soetta Airport persisted to perform normally. Forecasting the number of passengers needs to be done by the airport to decide the proper policy. Therefore, the airport wishes to estimate the range of passengers to determine the right coverage and prepare the facilities provided if there may be a boom withinside the range of passengers throughout the Covid-19 pandemic. Forecasting the number of domestic passengers at Soetta Airport on this examination makes use of the SARIMA model and intervention. This examination compares the SARIMA model and the intervention in forecasting the number of domestic passengers at Soetta Airport. The effects confirmed that the best SARIMA model became ARIMA ARIMA(0,1,0)(1,0,0)12 with MAPE and RMSE of 55,18% and 588887.4, respectively. The best intervention model  became ARIMA0,1,1) (1,0,0)12 b = 0, s = 5, r = 1  with MAPE of 35,25% and RMSE of 238563,4. The MAPE and RMSE values acquired suggest that the intervention model is better than the SARIMA model in forecasting the number of domestic passengers at Soetta Airport throughout the Covid-19 pandemic.
Comparison of Hierarchical Clustering, K-Means, K-Medoids, and Fuzzy C-Means Methods in Grouping Provinces in Indonesia according to the Special Index for Handling Stunting: Perbandingan Metode Hierarchical Clustering, K-Means, K-Medoids, dan Fuzzy C-Means dalam Pengelompokan Provinsi di Indonesia Menurut Indeks Khusus Penanganan Stunting Suraya, Ghina Rofifa; Wijayanto, Arie Wahyu
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p180-201

Abstract

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in the world and has a bad impact on children's future. In 2018, Indonesia was ranked the 31st highest stunting in the world and ranked 4th in Southeast Asia. About 30.8% (roughly 3 out of 10) of children under 5 years suffer from stunting in Indonesia. To support the government policy making in handling stunting, it is undoubtedly necessary to classify the levels of stunting handling in regions in Indonesia. In this work, the hierarchical agglomerative and non-hierarchical clustering is compared and evaluated to perform clustering on stunting data. The agglomerative hierarchical cluster uses Single Linkage, Average Linkage, Complete Linkage, and Ward Method, while the non-hierarchical cluster uses K-Means, K-Medoids (PAM) Clustering, and Fuzzy C-Means. This study uses data from 12 IKPS indicators in 34 provinces in Indonesia in 2018. Based on the results of the evaluation using the Connectivity Coefficient, Dunn Index, Silhouette Coefficient, Davies Bouldin Index, Xie & Beni Index, and Calinski-Harabasz Index, the results show that the Average Linkage is the best cluster method with the optimal number of clusters is four clusters. The first cluster is a cluster with a good level of stunting management which consists of 28 provinces. The second cluster consists of only one province, DI Yogyakarta with a very good level of stunting handling. The third cluster consists of four provinces with poor stunting handling rates. Finally, the last cluster consisting of one province, Papua, has a very poor level of stunting handling.
Handling Multicollinearity Problems in Indonesia's Economic Growth Regression Modeling Based on Endogenous Economic Growth Theory: Penanganan Masalah Multikolinieritas pada Pemodelan Pertumbuhan Ekonomi Indonesia Berdasarkan Teori Pertumbuhan Ekonomi Endogenous Yanke, Aldino; Zendrato, Nofrida Elly; Soleh, Agus M
Indonesian Journal of Statistics and Applications Vol 6 No 2 (2022)
Publisher : Statistics and Data Science Program Study, IPB University, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v6i2p214-230

Abstract

One of the multiple linear regression applications in economics is Indonesia’s economic growth model based on the theory of endogenous economic growth. Endogenous economic theory is the development of classical theory which cannot explain how the economy grows in the long run. The regression model based on the theory of endogenous economic growth used many independent variables, which caused multicollinearity problems. In this study, the multiple linear regression model using the least-squares estimation method and some methods to handle the multicollinearity problem was implemented. Variable selection methods (backward, forward, and stepwise), principal component regression (PCR), partial least square (PLS), and regularization methods (Ridge, Lasso, and Elastic Net) were applied to solve the multicollinearity problem. Variable selection method with backward, forward, and stepwise has not been able to overcome the problem of multicollinearity. In contrast, Principal Component Regression, PLS regression, and regularization regression methods overcame the multicollinearity problem. We used "leave one out cross-validation" (LOOCV) to determine the best method for handling multicollinearity problems with the smallest mean square of error (MSE). Based on the MSE value, the best method to overcome the multicollinearity problem in the economic growth model based on endogenous economic growth theory was the Lasso regression method.

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