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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 201 Documents
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PENGGEROMBOLAN TWEET BADAN NASIONAL PENANGGULANGAN BENCANA INDONESIA PERIODE AGUSTUS 2018 FEBRUARI 2019 MENGGUNAKAN TEXT MINING Windyana Pusparani; Agus M Soleh; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 4 (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.v4i4.525

Abstract

Twitter is a popular social media platform for communicating between its users by writing short messages in limited characters, called tweets. Extracting data information that has non-structured form and huge-sized, usually known as text mining. Badan Nasional Penanggulangan Bencana Indonesia (@BNPB_Indonesia) is the official twitter account of the government agency in the field of disaster management that uses twitter to share much information about disasters that have occurred in Indonesia. This study aims to determine the characteristics of all tweets and to group the types of tweets that they shared based on the similarity of its content. The data used in the study came from BNPB Indonesia's tweets with the period of taking tweets 6th of August 2018 to 16th of February 2019. The cluster result obtained by the k-Means method was 4 groups. The characteristics of the first cluster contained information about the weather conditions in Yogyakarta, the second cluster was about the source and magnitude of an earthquake, and the third group was about the occurrence of earthquakes in Lombok. However, the fourth group characteristic couldn’t be specifically identified because there was no clear distinction between other tweets in its members.
ON THE MODELLING OF LEPROSY PREVALENCE IN SOUTH SULAWESI USING SPATIAL AUTOREGRESSIVE MODEL Rezki Melany Sabil; Ray Sastri
Indonesian Journal of Statistics and Applications Vol 4 No 2 (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.v4i2.529

Abstract

The prevalence of leprosy is the number of leprosy cases per 10.000 peoples. Based on data from the Ministry of Health, the highest prevalenece of leprosy was in South Sulawesi. This is needs a special attention because leprosy is a contagious disease. The number of leprosy cases in an area may be influenced by the number of leprosy case in the neighbor area due to the movement of the air. So that, the location of area need to be included in analysis of leprosy. The aim of this study is to identify the variables that spatially affect the prevalence of leprosy in South Sulawesi and modelling it. This study uses data from the Ministry of Health for year 2016. The method of analysis is Spatial Autoregressive Model (SAR). The results is There is a positive spatial autocorrelation in the prevalence of leprosy in district level, which means that regions with high prevalence of leprosy are surrounded by areas with high prevalence of leprosy, and vice versa. The prevalence of leprosy in an area is influenced by the prevalence of leprosy in neighbor districts, the percentage of BCG vaccines recipient and the percentage of households with healthy lifestyle.
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.
KAJIAN VALIDITAS INSTRUMEN PENGUKURAN SKALA PENGALAMAN KERAWANAN PANGAN DI INDONESIA Herlina Herlina; Bagus Sartono; Budi Susetyo
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.543

Abstract

The results of the FAO study since 2013 through the Voices of Hungry Project (VoH-FAO) have produced measures of the Food Insecurity Experience Scale (FIES). FIES is a global reference scale that becomes a reference for comparing the prevalence of food insecurity between countries and regions. The challenge of using the FIES instrument, each country must carry out linguistic adaptations that are appropriate to the culture and national language. This study aims to analyze the validity of FIES measurements in Indonesia, including internal and external analysis. The Rasch model (RM) used for internal validity analysis. Measurement of the validity and reliability of Indonesian FIES items was calibrated with a global reference scale. Differences in the scale of calibration items with a global reference scale of less than 0.35 indicate that they are standard items. FIES measurements require at least five common items. External analysis of FIES measurements uses the Pearson correlation between district-level aggregation on each FIES item that is answered "yes" and determinant characteristics of household food insecurity. The expected correlation coefficient indicated the direction of a positive correlation and observed the correlation coefficient of item 1501 to 1508, which is getting smaller. Internal analysis of FIES measurements in Indonesia shows the achievement of unidimensional and local independence assumptions. However, item 1501 has identified as an outlier. Then identify unique issues are 1501 and 1504, while unique items in rural subsamples are 1503 and 1508. Unique item differences founded in food expenditure 60 percent or more, i.e., 1502. This shows a discordance with items assumption of parameter invariance. The reliability of the FIES item is 0.78, and this reflects the suitability of the model quite well. External analysis of the FIES measurement identifies item 1501 and 1504 as invalid items (unique items).
COVARIANCE BASED-SEM ON RELATIONSHIP BETWEEN DIGITAL LITERACY, USE OF E-RESOURCES, AND READING CULTURE OF STUDENTS Reny Rian Marliana; Leni Nurhayati
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.552

Abstract

In this paper, a relationship model among latent variables using Covariance Based-Structural Equation Modeling (CB-SEM) is studied. The latent variables are digital literacy, use of e-resources and reading culture of students. The goal of the study is to build a simultaneously model between those three variables, determine the influence of digital literacy on the use of e-resources and reading culture of students, and the influence of the use of e-resources on reading culture of students. The parameters of the model are estimated by the Maximum Likelihood method. This study took data from 256 questionnaires of students at STMIK Sumedang. Results showed that digital literacy significantly influences the use of e-resources and the reading culture of students. In contrast, there are no significant influences on the use of e-resources on the reading culture of the student.
PEMODELAN POISSON RIDGE REGRESSION (PRR) PADA BANYAK KEMATIAN BAYI DI JAWA TENGAH Wulandari Wulandari
Indonesian Journal of Statistics and Applications Vol 4 No 2 (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.v4i2.555

Abstract

The decline of infant mortality is one of the targets of the Indonesian government in the health sector, including the Government of Central Java. To achieve this goal, it is necessary to identify factors that affect many infant mortalities in the district/city of Central Java. Infant mortalities are count data, so Poisson regression is commonly used. The data in the study showed the existence of multicollinearity in several predictor variables, so an appropriate model was needed. Poisson Ridge Regression (PRR) is a Poisson modeling that accommodates multicollinearity. In this study, the PRR model was used to model infant mortality in Central Java district/city. The results showed that the parameter estimation of the PRR model was slightly different than the estimated Poisson regression model. Modeling infant mortality with the PRR model, out of five predictor variables, three variables harmed many infant deaths, while the other two variables had a positive effect on many infant deaths.
PEMODELAN GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA PERSENTASE KRIMINALITAS DI PROVINSI JAWA TIMUR TAHUN 2017 Dessy Wulandari Syahputri Yusuf; Elvira Mustikawati Putri Hermanto; Wara Pramesti
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.557

Abstract

Crime is everything that exists in Indonesia. Based on BPS data in 2018, East Java Province ranks first in the Province of North Sumatra and the Special Capital Region of Jakarta. This research was conducted to determine the factors that support crime in each Regency / City of East Java Province. The method used in this research is Weighted Geographic Regression (GWR). Geographically Weighted Regression (GWR) is one of the statistical methods used to model variable responses with regional or area-based predictor variables. Based on the GWR results, it is recognized as a variable Population Density Percentage (X1), Open Unemployment Rate (X2), Poor Population (X3), Population who are Victims of Drug Abuse (X4), Human Development Index (X5), and Married Human Population (X6) ) importance in the city of Surabaya. The coefficient of determination (R2) and AIC from GWR is better than the OLS model. This refers to the optimal R2 and AIC values ​​of 91.40% and 129.293.
KLASIFIKASI PENYAKIT PNEUMONIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK DENGAN OPTIMASI ADAPTIVE MOMENTUM Lingga Aji Andika; Hasih Pratiwi; Sri Sulistijowati Handajani
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
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.v3i3.560

Abstract

Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.
PENGGUNAAN ANALISIS KLASTER K-MEANS DALAM PEMODELAN REGRESI SPASIAL PADA KASUS TUBERKULOSIS DI JAWA TIMUR TAHUN 2017 Hardani Prisma Rizky; Wara Pramesti; Gangga Anuraga
Indonesian Journal of Statistics and Applications Vol 4 No 1 (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.v4i1.563

Abstract

Tuberculosis (TB) is a contagious infectious disease caused by the bacterium Mycobacterium tuberculosis which can attack various organs, especially the lungs. TB if left untreated or incomplete treatment can cause dangerous complications to death. East Java Province has the second-highest TB case after West Java Province. Therefore we need statistical modeling to analyze the factors that influence TB in East Java Province. The data used in this study were sourced from data from BPS and East Java Provincial Health Offices in 38 districts/cities in East Java Province in 2017. Analysis of data using the OLS regression approach only looked at variable factors but was unable to know the effects of territory. So to overcome this, a spatial regression approach is used by comparing the weight of Queen Contiguity and the results of the k-means cluster analysis to obtain the best model. Based on the results of the analysis, the spatial aspects of the data have met the assumptions of spatial dependencies using the Moran's I test with a p-value of 0.000001295. The weighting matrix used is the k-means cluster weighting matrix k = 2. The test results obtained by the Spatial Autoregressive Moving Average (SARMA) model selected as the best model with the value of the deterrence coefficient (R2) and Akaike Info Criterion (AIC), 87.10% and 586.69. The factors that significantly influence the number of Tuberculosis patients in each district/city in East Java are population density (X2) and the number of healthy houses (X9).
THE BEST GLOBAL AND LOCAL VARIABLES OF THE MIXED GEOGRAPHICALLY AND TEMPORALLY WEIGHTED REGRESSION MODEL Nuramaliyah Nuramaliyah; Asep Saefuddin; Muhammad Nur Aidi
Indonesian Journal of Statistics and Applications Vol 3 No 3 (2019)
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.v3i3.564

Abstract

Geographically and temporally weighted regression (GTWR) is a method used when there is spatial and temporal diversity in an observation. GTWR model just consider the local influences of spatial-temporal independent variables on dependent variable. In some cases, the model not only about local influences but there are the global influences of spatial-temporal variables too, so that mixed geographically and temporally weighted regression (MGTWR) model more suitable to use. This study aimed to determine the best global and local variables in MGTWR and to determine the model to be used in North Sumatra’s poverty cases in 2010 to 2015. The result show that the Unemployment rate and labor force participation rates are global variables. Whereas the variable literacy rate, school enrollment rates and households buying rice for poor (raskin) are local variables. Furthermore, Based on Root Mean Square Error (RMSE) and Akaike Information Criterion (AIC) showed that MGTWR better than GTWR when it used in North Sumatra’s poverty cases.

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