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Jurnal Statistika dan Aplikasinya
Published by Universitas Negeri Jakarta
ISSN : -     EISSN : 26208369     DOI : https://doi.org/10.21009/JSA.041
Core Subject : Economy, Science, Education, Agriculture, Social,
Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Social Sciences Other
Jurnal Statistika dan Aplikasinya JSA is dedicated to all statisticians who wants to publishing their articles about statistics and its application. The coverage of JSA includes every subject that using or related to statistics.
Arjuna Subject : Matematika - Statistik dan Probabilitas
Articles 169 Documents
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Pemodelan Regresi Logistik Ordinal Backward dengan Imputasi K-Nearest Neighbour pada Indeks Pembangunan Manusia di Indonesia Tahun 2021 Muftih Alwi Aliu; Anwar Fitrianto; Erfiani; Indahwati; Khusnia N. K.
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07105

Abstract

The human development index (HDI) is one of the important things to note in Indonesia today. The growth of HDI in Indonesia in 2021 is not evenly distributed in all regencies/cities and has high disparities. This study aims to find out the description of HDI data, get the best model to determine the factors that significantly affect the HDI of regencies/cities in Indonesia in 2021 and identify the classification accuracy results of the best model. The independent variables used in this study are average years of schooling, open unemployment rate, population growth rate, population density, percentage of poor people and sex ratio. The independent variables in this study contained missing values, so they were handled using k-nearest neighbour (KNN) imputation and continued modelling using ordinal logistic regression using the backward elimination technique to obtain significant factors. The results showed that the proportion of the low HDI category was 4.28%, the medium HDI category was 48.64%, and the high HDI category was 47.08%. Based on logistic regression modeling using backward elimination which has the smallest AIC value of 293.387, a model with independent variables of average years of schooling (X1), population density (X4), percentage of poor people (X5) and sex ratio (X6) is a variable that significantly affects the HDI of regencies/cities in Indonesia in 2021. The accuracy value of the classification accuracy of training data and test data from the ordinal logistic regression model of HDI of regencies/cities in Indonesia in 2021 is 83.46% and 86.61%, respectively, which means that the model is good for prediction.
Proporsi Perempuan yang Bekerja pada Posisi Manajerial di Indonesia Tahun 2015-2021: Pendekatan Feasible Generalized Least Square Aghnia Ussyarovi; Siskarossa Ika Oktora
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07106

Abstract

The fifth goal of the SDGs is to achieve gender equality and empower women. One indicator of women's empowerment is to give women the opportunity to be in managerial positions. The proportion of women working in managerial positions tends to increase from year to year but still lagging behind and there are gaps compared to men. This study aims to determine the effect of the percentage of women with the highest diploma who had a minimum of Diploma IV, female AHH, female LFPR, the number of women who attended training, and the percentage of women working with married status on the proportion of women working in managerial positions in Indonesia 2015-2021. The data used is secondary data obtained from the Central Statistics Agency. The method used is the fixed effect panel data regression method with the Feasible Generalized Least Square estimation method. The results of this study indicate that the percentage of women with the highest diploma who had a minimum of Diploma IV, female AHH, and the percentage of women working with married status have a significant effect, while female LFPR and the number of women who attended training did not significantly affect the proportion of women working in managerial positions.
Penerapan Metode Support Vector Machines (SVM) dan Metode Naïve Bayes Classifier (NBC) dalam Analisis Sentimen Publik terhadap Konsep Child-free di Media Sosial Twitter Dania Siregar; Faroh Ladayya; Naufal Zhafran Albaqi; Bintang Mahesa Wardana
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07109

Abstract

Child-free is a concept in which a person chooses not to have children or places and situations that are without the presence of a child. Along with the rapid flow of information, this child-free concept began to be discussed virally, especially on Indonesian social media, such as Twitter. Sentiment analysis is the mining of all people’s expressions and views on a phenomenon or product online in the form of text. Through a large sample collection of opinions and expressions, we can capture the voices or views of society, understand the dynamics that are taking place, and even know the extent to which the issue begins to touch aspects of people’s social life. This study aims to conduct sentiment analysis by comparing the performance of two different methods used to classify people’s views in the form of text data crawled tweets from Twitter. The two methods compared are Support Vector Machines (SVM) and Naive Bayes Classifier (NBC). Another purpose of this study is to provide an overview of public sentiment on social media Twitter about the concept of child-free. The results of this study showed that the data experienced an imbalance so to overcome this problem, SMOTE is used, SMOTE managed to increase the sensitivity of the prediction of minor data. The classification method that produces the best prediction on test data using the F1-weighted average criterion is SMOTE-SVM with a value of 60.45%. The opinions that support child-free mostly have to do with parents' unpreparedness to take care of children, while opinions that reject child-free think that it is contrary to religious advice and child-free decisions will make it difficult for old age because no one takes care of them.
Front Matter Jurnal Statistika dan Aplikasinya Volume 7 Issue 1, June 2023 Journal Editor JSA
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07100

Abstract

Back Matter Jurnal Statistika dan Aplikasinya Volume 7 Issue 1, June 2023 Journal Editor JSA
Jurnal Statistika dan Aplikasinya Vol 7 No 1 (2023): Jurnal Statistika dan Aplikasinya
Publisher : Program Studi Statistika FMIPA UNJ

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07111

Abstract

Regresi Nonparametrik Spline Truncated untuk Memodelkan Tingkat Pengangguran Terbuka di Pulau Kalimantan Andi Nohe, Darnah
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07211

Abstract

Nonparametric regression is a statistical technique employed to discern the relationship pattern between a predictor variable and a response variable in the absence of prior information about the form of the regression function or when the pattern of the regression curve is unknown. Truncated spline nonparametric regression represents an approach for aligning data, considering the curve's smoothness. It possesses continuous segmented characteristics, offering flexibility and adeptly accommodating the explanation of local data function features. The study aims to identify the influencing factors on the open unemployment rate in Kalimantan Island during 2020. Additionally, it seeks to derive a spline truncated nonparametric regression model for Open Unemployment Rate data in Kalimantan Island for the same year. The study employs a nonparametric regression model with a spline truncated method, determining optimal knot points based on the minimum Generalized Cross Validation (GCV) value. The study reveals that the most effective spline truncated nonparametric regression model features two knot points. Significant factors influencing the open unemployment rate include the labor force participation rate, school year expectations, regional gross domestic product at current prices, and the human development index.
Perbandingan Metode K-Means Clustering dengan Self-Organizing Maps (SOM) untuk Pengelompokan Provinsi di Indonesia Berdasarkan Data Potensi Desa Iyohu, Lisa Rianti; Ismail Djakaria; La Ode Nashar
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07208

Abstract

K-Means is a method of grouping data into several different groups so that data that has similar characteristics is made into one group while data that has different characteristics is made into a different group, where this method works by minimizing the distance between the data and the cluster center. In addition to K-Means clustering, there is also the Self Organizing Maps (SOM) method which is an undirected method, meaning that layers consisting of neurons are arranged into groups based on input values, where each data grouping process is based on the characteristics or features of the data. Clustering is carried out in Provinces in Indonesia based on village potential data in 2021 with the aim of knowing the performance comparison of K-Means clustering and Self Organizing Maps (SOM). Determination of the optimal number of clusters is carried out using the Elbow method, the results in the study obtained 3 clusters for both K-Means clustering and Self Organizing Maps (SOM). The clustering results are evaluated using the Davies Bouldin Index (DBI) value and show that clustering using the Self Organizing Maps (SOM) method provides better results than using the K-Means clustering method where the DBI value is 0.1829366. The clustering results using the Self Organizing Maps (SOM) method for cluster 1 consist of 31 province members, cluster 2 consists of 1 province member and cluster 3 consists of 2 province members.
Analisis Faktor-Faktor yang Menjelaskan Tingkat Kematian Akibat Bunuh Diri pada Negara-Negara di Benua Asia dan Eropa Yekti Widyaningsih; Setjiadi, Nerissa Netanaya
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07207

Abstract

Many Indonesians still view mental health as a taboo subject and people with mental disorders are treated like a disgrace. As a result, they have difficulty getting the help they need and can end in suicide. The objects of research are countries in Asia and Europe. The purposes of this research are to analyze factors explaining death rate due to suicide and to work out the grouping results of Asian and European countries. The methods used are multiple linear regression, Ward’s method clustering, and Biplot mapping. Based on the analysis result, it is obtained that factors of having no religion, alcohol consumption, and psychiatrists’ availability have significant positive relationships with suicide rate. Factors of income and unemployment have significant negative relationships with suicide rate. Factor of education level has no significant effect with suicide rate. Two groups of countries are formed, namely group 1 consisting of 46 countries and group 2 consisting of 44 countries. Result of mapping based on the groups using the Biplot method is able explain 63,7% of data diversity. Group 1 is a group of countries that have a high unemployment rate and low values in: suicide rate, proportion of irreligious people, Gross Domestic Product (GDP) per capita, number of psychiatrists, and education level. Group 2 is a group of countries that have high values in: suicide rate, proportion of irreligious people, GDP per capita, number of psychiatrists, and education level while the unemployment rate is low.
Klasterisasi Desa di Provinsi Jawa Barat Berdasarkan Indeks Pembangunan Desa (IPD) Tahun 2021 Menggunakan Algoritma K-Prototypes Irsyifa Mayzela Afnan; Siti Hasanah; Anwar Fitrianto; Erfiani; Alfa Nugraha
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07206

Abstract

Cluster analysis is a method used to group data with similar characteristics. There are various clustering methods adapted to different types of data. K-Prototypes is a clustering method that can be applied to mixed numerical and categorical data. The data used in this study are mixed numerical and categorical data derived from the Village Potential data in 2021. The aim of this research is to group villages in West Java based on variables from the Indeks Pembangunan Desa (IPD). Clustering using three clusters adapted to village status according to IPD resulted in 931 villages in cluster-1, 1880 villages in cluster-2, and 2104 villages in cluster-3. The characteristics of cluster-1 villages are villages that have adequate health and education facilities and good infrastructure conditions. Cluster-2 has an average numeric variable lower than cluster-1 but higher than cluster-3.
Determinan Kejahatan Siber (Cybercrime) di ASEAN Tahun 2015-2020: Pendekatan Panel Data Regression with Random Effect Daffa Dhiaulhaq, Hafsha; Siskarossa Ika Oktora
Jurnal Statistika dan Aplikasinya Vol. 7 No. 2 (2023): Jurnal Statistika dan Aplikasinya
Publisher : LPPM Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/JSA.07205

Abstract

The use of information and communication technology in ASEAN is massive and growing. This development not only has a positive impact, but also a negative one. In addition to advancements in various fields, criminality is also increasing. ASEAN is the fastest growing digital market in the world. As digital technology becomes more integrated into our lives, cybercrime will increase exponentially. This study aims to determine the overview and variables that affect cybercrime in ASEAN in 2015-2020. This research uses secondary data from ITU, World Bank, UNDP, and Transparency International the Global Coalition Against Corruption. Through descriptive analysis, it is found that the value of the Global Cybersecurity Index (GCI) in ASEAN has increased, but there is still a cybersecurity gap between countries in ASEAN. Through the panel data regression equation formed, it is found that economic growth, mobile broadband users, and average years of schooling significantly affect the GCI in ASEAN in 2015-2020. Meanwhile, the variables of mobile cellular users, technology exports, and corruption perception index do not have a significant effect. Therefore, governments in ASEAN countries are also expected to continue to increase economic growth and allocate budgets for technical implementation of cybersecurity. In addition, the government should maintain the stability of the number of mobile broadband users, and increase the average number of mobile broadband users.

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