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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): diterbitkan berkala 2 (dua) kali dalam setahun yang memuat tulisan ilmiah yang berhubungan dengan bidang statistika dan aplikasinya. Artikel yang dimuat berupa hasil penelitian bidang statistika dan aplikasinya dengan topik (tapi tidak terbatas): rancangan dan analisis percobaan, metodologi survey dan analisis, riset operasi, data mining, pemodelan statistika, komputasi statistika, time series dan ekonometrika, serta pendidikan statistika.
Arjuna Subject : -
Articles 10 Documents
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Search results for , issue "Vol 4 No 3 (2020)" : 10 Documents clear
ANALISIS TINGKAT KESEHATAN DAN EFISIENSI PERBANKAN TERHADAP PROFITABILITAS BANK MENGGUNAKAN REGRESI BERGANDA DAN ANOVA: Studi kasus pada tahun 2014 – 2017 Dita Anggun Lestari; Sarini Abdullah
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.538

Abstract

In this digital era, the competitiveness of small banks has decreased, and many bank consolidation phenomena have occurred. This study aims to examine the effect of bank soundness and efficiency on profitability in the face of competition and the current bank consolidation or merger phenomenon. Determination of variables refers to Bank Indonesia standards in measuring bank performance using the RGEC method approach consisting of the ratio of LDR, NIM, BOPO, NPL, CAR, and prime lending rate (SBDK), while bank profitability is represented by ROA. The research object is the bank category BUKU 1 - 4 which is supervised by OJK and listed as issuers on the Indonesia Stock Exchange during 2014 - 2017. The sampling technique used is purposive sampling so that from 102 banks 34 banks were obtained which were used as research objects. The data analysis technique used is multiple regression analysis and Anova comparison test. Based on the results of data testing, it is known that simultaneously and partially the ratios of LDR, NIM, BOPO, NPL, CAR, and SBDK have an effect on ROA. In comparison to the average BOPO, prime lending rate, and ROA variables, there are significant differences with bank categorization BUKU 1-4.
PENGGEROMBOLAN SUBSEKTOR INDUSTRI BERDASARKAN PERKEMBANGAN INDEKS PRODUKSI MENGGUNAKAN PREDICTION-BASED CLUSTERING Agustin Faradila; Utami Dyah Syafitri; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.585

Abstract

Statistics Indonesia (BPS) noted that there has been a decrease in the contribution of the industrial sector to the national GDP even though it had provided a significant multiplier effect on national economic growth. Therefore, it is necessary to cluster the industrial subsector based on its growth patterns so that the optimization of development results can be achieved. Prediction-based clustering is part of time series clustering (TSclust) which aims to form clusters based on prediction characteristics so that it can be used to choose a cluster that will become a mainstay industry in the future. This study focused on applying prediction-based clustering in the large and medium industrial sub-sector for a prediction period of 1 month, 1 quarter, and 1 semester. The data used in this study was the production index data from January 2010 to December 2018. The results showed that the best cluster for 1 month consisted of 5 groups, for 1 quarter consisted of 4 groups and for 1 semester consisted of 2 groups. Thus, it was concluded that the food industry; leather industry, leather goods, and footwear; and the pharmaceutical industry, chemical drug products, and traditional medicine could be chosen to be the mainstay industry in the future.
PENGGUNAAN PROPENSITY SCORE STRATIFICATION-SUPPORT VECTOR MACHINE UNTUK MENGESTIMASI EFEK PERLAKUKAN AKTIVITAS OLAHRAGA PADA PENDERITA DIABETES MELITUS Ernawati Ernawati; Bambang Widjanarko Otok; Sutikno Sutikno
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.653

Abstract

Randomized Controlled Trial (RCT) is not possible to do in observational studies, mainly in health cases, because it is directly related to human life. Actually, good randomization is needed to make the treatment and control groups have no large differences in the observed variables, so it results from unbiased treatment One alternative method that is increasingly used in statistical analysis in the field of health is the Propensity Score (PS). If the propensity score had estimated using the SVM method and divided into groups of strata that have a similar propensity score, it is known as the Propensity Score Stratification (PSS-SVM). The purpose of the PSS-SVM is to balance the observed variables between the treatment group and the control group by dividing them into several strata groups so that a balanced trend is obtained or the propensity score is called balance. This eliminates the influence of the confounding variables and unbalance of the treatment and control groups and obtain an unbiased estimation of the treatment effect. In this Research, the PSS-Method applied in case of disease complication in patients with Diabetes Mellitus Type 2 at the Regional Public Hospital of Pasuruan with respondents who counted 96 patients. The purpose of using PSS-SVM, in this case, is to reduce the confounding effects (sports activity) that influence disease complications. In strata of 4 reduce the largest bias with the percent bias reduction (PBR) is 86.39% with the smallest standard error is 0.103.
ANALYSIS OF DESIGN EFFECT FOR INDONESIAN NATIONAL LABOUR FORCE SURVEY Adhi Kurniawan
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.659

Abstract

The implementation of multistage sampling design is a good strategy to achieve the gain in efficiency of survey cost. However, in terms of sampling efficiency, it leads to the loss of precision indicated by the higher sampling variance compared to SRS design. Design effect measures the ratio of actual variance to the variance of SRS and can be decomposed to the effect of sample weight and the effect of clustering. This study aims to analyse the effect of sample weight and the effect of clustering on the estimation of labour variables resulted from the National labour Force Survey of Indonesia. The analysis is provided at the national level, stratum level, and province level. In general, the study finds that the design effect varies between labour variables. The effect of clustering is higher than the effect of the sample weight. There is also a high variability of the clustering effect between provinces and between strata (urban-rural). In contrast, the design effect due to the sample weight is similar between provinces, but it differs between strata. Allocating sample size proportionally to each stratum could be a good strategy for dealing with the high effect of weighting. On the other hand, for the future specific survey that measures the variable with a high clustering effect and high rate of homogeneity, the alternative strategy is increasing the sample size of the cluster and declining the sample size of households per cluster
PENDEKATAN OPSI CASH-OR-NOTHING UP AND IN BARRIER UNTUK PENENTUAN NILAI PREMI ASURANSI PERTANIAN Yunita Wulan Sari; Gunardi Gunardi
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.660

Abstract

Crop insurance is a type of insurance that provides protection to farmers who hold an insurance policy for losses due to crop failure. Extreme weather, especially rainfall, has been the main cause of the crop failure. Therefore, the type of crop insurance based on weather or rainfall must be developed and applied. This paper will discuss the cash-or-nothing up and in barrier option approach for determining insurance premiums where the risk of loss in terms of high rainfall, then compare it to the Black-Scholes option approach. In this approach, the claim limit is based on the rainfall index and the value of the barrier is determined according to the size of the extreme rainfall. We use cumulative rainfall data in the first subround in Sleman regency as a case study. The conclusions obtained are barrier value has a negative effect on the value of insurance premiums and claim limit value has a positive effect. Besides the premium value with this barrier option approach is cheaper than the Black-Scholes option approach, this approach method more interesting to apply because of the barrier value addition.
PENDUGAAN CURAH HUJAN DENGAN TEKNIK STATISTICAL DOWNSCALING MENGGUNAKAN CLUSTERWISE REGRESSION SEBARAN TWEEDIE Riza Indriani Rakhmalia; Agus M Soleh; Bagus Sartono
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.667

Abstract

Rainfall prediction is one of the most challenging problems of the last century. Statistical Downscaling Technique is one of the rainfall estimation techniques that are often used. The goal of this paper is to develop the modeling of cluster-wise regression with rainfall data set that has Tweedie distribution. The data used in this paper were the precipitation from Climate Forecast System Reanalysis (CFSR) version 2 as the predictor variables and rainfall from BMKG as the response variable. Data were collected from January 2010 to December 2019 on the Bogor, Citeko, Jatiwangi, and Bandung rain posts. The best result of this study is a Cluster-wise Regression model with 4 clusters and using Tweedie distribution in each rain post. The best model was evaluated by the Root Mean Square Error Prediction. RMSEP value on Bogor rain post is 17.11 (three clusters), Citeko rain post 14.85 (two clusters), Jatiwangi rain post 15.26 (three clusters), and Bandung rain post 14.33 (two clusters). This model was able to make models and clusters well on daily rainfall application.
PENENTUAN FAKTOR-FAKTOR POTENSIAL YANG MEMPENGARUHI KEJADIAN MALARIA DI PROVINSI PAPUA DENGAN EPIDEMIOLOGI SPASIAL Siswanto Siswanto; Sri Astuti Thamrin
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.681

Abstract

In Indonesia malaria is found to be widespread in all islands with varying degrees and severity of infection. Based on the Annual of Parasite Incidence (API) in Eastern Indonesia, Malaria is a disease that has a high incidence rate. The three provinces with the highest APIs are Papua (42.64%), West Papua (38.44%) and East Nusa Tenggara (16.37%). Spatial aspects are considered important to be studied because the spread of disease through mosquitoes is strongly influenced by fluctuating climate. The purpose of this study is to determine the potential factors that influence the incidence of Malaria disease in the province of Papua in 2013 by looking at aspects that are the focus of attention in spatial epidemiology. The methods used in analyzing the area are Simultaneous Autoregressive (SAR) and Conditional Autoregressive (CAR) models with a spatial weighting matrix up to second order. The result shows the average monthly wind velocity, average monthly rainfall, and malaria treatment with government program drugs by getting ACT drugs are substantial factors in determining the incidence number of Malaria in Papua based on the lowest AIC value for the second-order of CAR model. While the SAR model, in this case, has no spatial influence. By knowing the potential factors that influence the incidence of malaria, the Papua Province through the Health Office can take more effective preventive measures to reduce the number of malaria incidents.
KAJIAN SIMULASI OVERDISPERSI PADA REGRESI POISSON DAN BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA BALITA GIZI BURUK Puput Cahya Ambarwati; Indahwati Indahwati; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.684

Abstract

One type of geographically weighted regression (GWR) that can be used to explain the relationship between the response variables in the form of count data and explanatory variables is the geographically weighted Poisson regression (GWPR). In the GWPR, there is an assumption that should be fulfilled called equidispersion, a condition where the variance equals the mean. If that condition is ignored, overdispersion will occur. Overdispersion is a condition when the variance is greater than the mean. The use of GWPR analysis in an overdispersion situation will produce a smaller standard error than it should be (underestimate). This may produce a significant test result leading to the rejection of the null hypothesis. One of the classic approaches commonly used to handle overdispersion in GWR is geographically weighted negative binomial regression (GWNBR). GWNBR is derived from a mixture of Poisson and Gamma distributions which is similar to the negative binomial distribution. Simulation data and real data were used in this study. The results showed that the application of GWPR on overdispersion data could increase the number of rejections of H0 or the number of p-values. The application of GWNBR on the East Java malnutrition toddler data in 2017 showed that the GWNBR model is better than GWPR based on the comparison of AIC, Pseudo R2, and RMSE.
ANALISIS SPASIAL KETERTINGGALAN DAERAH DI INDONESIA TAHUN 2018 MENGGUNAKAN GEOGRAPHICALLY WEIGHTED LOGISTIC REGRESSION Tata Pacu Maulidina; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.690

Abstract

Development inequality in Indonesia has led the developed and underdeveloped regions. Regional backwardness caused by high inequality must be handled properly to prevent negative impacts on national stability. But in fact, the handling of underdeveloped regions is only effective in Western Indonesia, while in Eastern Indonesia tends to be not optimal. This study aims to explore regional backwardness in Indonesia and examines the factors that influence it. Based on data, underdeveloped regions tend to cluster in eastern Indonesia, and the independent variables have large variations between regions. This indicates dependence and spatial heterogeneity. Therefore, this study applies spatial analysis using the Geographically Weighted Logistic Regression (GWLR) method. GWLR shows better performance in modeling the regional backwardness in Indonesia compared to its global model (binary logistic regression). This study provides a local model for each district/city that can be used by local governments to implement more effective policies based on factors that do have significant effects on regional backwardness.
ANALISIS INFLASI MENGGUNAKAN DATA GOOGLE TRENDS DENGAN MODEL ARIMAX DI DKI JAKARTA Newton Newton; Anang Kurnia; I Made Sumertajaya
Indonesian Journal of Statistics and Applications Vol 4 No 3 (2020)
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.v4i3.694

Abstract

Inflation is an important economic indicator in showing the economic symptoms of a region's price level. DKI Jakarta is the capital of Indonesia chosen as the center of the economic barometer because it can provide the greatest contribution and influence on the Indonesian economy. The ARIMAX model was used for forecasting by adding independent variables in the Google trends data. Google trends data were explored based on seven expenditure groups published by IHK. The purpose of this study was to determine the effect of forecast Google trends using BPS inflation data in DKI Jakarta. The result of the exploration of Google Trends data was forecasted to get the best forecast model results. The result of data analysis indicates that the forecast results approached the original BPS data with the best forecast model is ARIMAX (2.0.3) all variables X. Google Trends data can be used as forecasting but cannot be used as a reference policy decision.

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