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Indonesian Journal of Statistics and Its Applications

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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.

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Articles 17 Documents

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Search results for , issue "Vol 4 No 1 (2020)" : 17 Documents clear

ANALISIS PENGARUH PENGETAHUAN TENTANG SEKS TERHADAP PERILAKU SEKSUAL REMAJA DI INDONESIA MENGGUNAKAN REGRESI LOGISTIK MULTINOMIAL Muhammad Ricky Pranata; Ray Sastri
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Sexual impulse will begin to appear in a person when entering adolescent age. The adolescent does different things to fulfill their sexual impulse such as holding hands, hugging, kissing, touching and even having sex. Because this is a new experience to them, they need a lot of information about sexuality such as the reproductive system, sexually transmitted diseases, and others. They can get it in school, the internet, or discuss it with others. The way they deal with their sexual impulse is largely determined by their individual characteristics, knowledge, and discussion partners. This study aims to determine the effect of individual characteristics, knowledge, and information sources on adolescent sexual behavior. This study uses data from the Indonesian Demographic and Health Survey (SDKI) in 2012 with a unit of analysis adolescence age of 15─19 years and is never married. The method of analysis uses multinomial logistic regression with adolescent sexual behavior as response variables divided into three categories; quiet (ignore it), minor sexual activity, and serious (touching the sensitive area and or having sex). The conclusion is the individual's background, sexual knowledge, and sources of information influence sexual behavior both in boy and girl. Serious sexual behavior tends to occur in adolescents who do not attend school, a man who understands about contraception, girls who misunderstand about pregnancy, and those who discuss sexuality with friends.

ON GENERALISATION OF GOMPERTZ-MAKEHAM DISTRIBUTION Akinlolu Olosunde; Tosin Adekoya
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

In this paper an exponentiated generalised Gompertz-Makeham distribution. An exponentiated generalised family was introduced by Codeiro, et. al., which allows greater flexibility in the analysis of data. Some Mathematical and Statistical properties including cumulative distribution function, hazard function and survival function of the distribution are derived. The estimation of model parameters are derived via the maximum likelihood estimate method.

PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK Hasnita Hasnita; Farit Mochamad Afendi; Anwar Fitrianto
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.

KLASIFIKASI FAKTOR-FAKTOR PENYEBAB PENYAKIT DIABETES MELITUS DI RUMAH SAKIT UNHAS MENGGUNAKAN ALGORITMA C4.5 Dewi Rahma Ente; Sri Astuti Thamrin; Samsul Arifin; Hedi Kuswanto; Andreza Andreza
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Diabetes mellitus (DM) is one of the chronic and deadly diseases that are widely observed in various countries today. This disease continues and is increasing to a very alarming stage. This study aims to identify and see the relationship between factors that influence DM disease. The method used in this research is C4.5 algorithm which is one of the algorithms used to make predictive classifications. Classification is one of the processes in data mining that aims to find patterns in relatively large data that use the representations in the form of decision trees. This method is applied to data from medical records of patients with DM in 2014-2018 taken from the Hasanuddin University Teaching Hospital. The results obtained indicate that there are four factors that influence the prediction of a patient's DM status namely; Fasting Blood Glucose (GDP), LDL Cholesterol, Triglycerides, and Body Weight.

ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN GENDER (IPG) JAWA TENGAH TAHUN 2011-2015 Intan Lukiswati; Anik Djuraidah; Utami Dyah Syafitri
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

The Gender Development Index (GDI) is a measure of the level of achievement of gender-based human development in Indonesia. Central Java Province is the largest province in Java with a GDI rate which tends to increase during the period of 2011 to 2015. Central Java's GDI, when compared to other provinces on Java Island, ranks third after DKI Jakarta and DI Yogyakarta. Central Java’s GDI consists of several observations for a certain period of time so that panel data regression analysis can be used. The purpose of this study was to model the GDI of women in Central Java with panel data regression and find out which explanatory variables significantly affected women's GDI in Central Java from 2011 to 2015. The results of this study indicate that explanatory variables that significantly influence women's GDI in Central Java are life expectancy, primary school enrollment rates, high school enrollment rates, and per capita expenditure.

KAJIAN REGRESI KEKAR MENGGUNAKAN METODE PENDUGA-MM DAN KUADRAT MEDIAN TERKECIL Khusnul Khotimah; Kusman Sadik; Akbar Rizki
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Regression is a statistical method that is used to obtain a pattern of relations between two or more variables presented in the regression line equation. This line equation is derived from estimation using ordinary least squares (OLS). However, OLS has limitations that are highly dependent on outliers data. One solution to the outliers problem in regression analysis is to use the robust regression method. This study used the least median squares (LMS) and multi-stage method (MM) robust regression for analysis of data containing outliers. Data analysis was carried out on generation data simulation and actual data. The simulation results of regression analysis in various scenarios are concluded that the LMS and MM methods have better performance compared to the OLS on data containing outliers. MM method has the lowest average parameter estimation bias, followed by the LMS, then OLS. The LMS has the smallest average root mean squares error (RMSE) and the highest average R2 is followed by the MM then the OLS. The results of the regression analysis comparison of the three methods on Indonesian rice production data in 2017 which contains 10% outliers were concluded that the LMS is the best method. The LMS produces the smallest RMSE of 4.44 and the highest R2 that is 98%. MM's method is in the second-best position with RMSE of 6.78 and R2 of 96%. OLS method produces the largest RMSE and lowest R2 that is 23.15 and 58% respectively.

PENERAPAN ANALISIS LASSO DAN GROUP LASSO DALAM MENGIDENTIFIKASI FAKTOR-FAKTOR YANG BERHUBUNGAN DENGAN TUBERKULOSIS DI JAWA BARAT Stephan Chen; Khairil Anwar Notodiputro; Septian Rahardiantoro
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Tuberculosis is the deadliest infectious disease in Indonesia, and West Java is a province with the largest number of tuberculosis cases in Indonesia. This research was conducted to identify variables and groups of variables that could explain the number of tuberculosis cases in West Java. The data used has many explanatory variables, and these variables form groups. LASSO and group LASSO analysis can be used for variables selection and handle data that has many explanatory variables, and group LASSO analysis can be used on data with grouped variables. The results of the LASSO analysis, variables that can explain the number of tuberculosis cases in West Java are the number of people with disabilities, the number of pharmacy staff, the number of malnourished people, the number of people working and the number of cities. According to the group LASSO analysis, the variables that can explain the number of tuberculosis cases in West Java are variables in the health and environmental groups. The government can focus on these factors if they want to reduce the number of tuberculosis cases in West Java.

ANALISIS KURVA ROC PADA MODEL LOGIT DALAM PEMODELAN DETERMINAN LANSIA BEKERJA DI KAWASAN TIMUR INDONESIA Muhammad Rizqi Fachrian Nur; Siskarossa Ika Oktora
Indonesian Journal of Statistics and Applications Vol 4 No 1 (2020)
Publisher : Departemen Statistika, IPB University dengan Forum Perguruan Tinggi Statistika (FORSTAT)

Binary logistic regression is used for probability modeling or to predict binary response variables (Success / Failure) from one or more explanatory variables that are continuous or categorical. In carrying out this analysis, there are several ways to test the suitability of the resulting model, and one of them is the area under the ROC curve. The application of the analysis method in this study is the determinant of the elderly population to work. The population of the elderly in Indonesia is increasing every year. Many views that the elderly depend on other residents, especially in terms of the economy. However, if seen from the percentage of elderly working in Indonesia, it is increasing, including the elderly in KTI. The purpose of this study is to determine the characteristics of the elderly in KTI, know the factors that influence the decision of the elderly population to work in KTI and find out the tendency of variables that affect the decision of the elderly to work in KTI. The data used are raw data from Badan Pusat Statistik (BPS) was Survei Sosial Ekonomi Nasional (Susenas) Kor March 2018. This study using descriptive analysis methods and binary logistic regression. The results are that the variables that significantly influence the decisions of the elderly to work are residence, gender, age, education, family status, marital status, health complaints, and health insurance. Elderly who has characteristics residing in rural, male sex, classified as young elderly (60-69 years old), has the highest level of elementary school education, has the status of KRT in his family, is married, has no complaints health, and not having health insurance will have a greater tendency to decide to work.

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)

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)

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.

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