1,720,977 research outputs found
Meta Analytic Second Order Confirmatory Factor Analysis Dengan Two Stage-SEM Dan Generalized Method Of Moments Pada Faktor-Faktor Yang Mempengaruhi Infrastruktur Daerah Tertinggal Di Pulau Jawa
Full text linkFakta lain dari pesatnya pembangunan di Pulau Jawa adalah masih terdapat enam kabupaten di Pulau Jawa yang masuk kriteria daerah tertinggal. Suatu daerah digolongkan menjadi daerah tertinggal diukur berdasarkan enam kriteria utama, yaitu ekonomi, sumber daya manusia, infrastruktur, kemampuan keuangan daerah, aksesibilitas, dan karakteristik daerah. Oleh karena itu ketersediaan data yang akurat mengenai daerah tertinggal merupakan salah satu aspek penting untuk mendukung program strategis penanggulangan daerah tertinggal. Dengan menggunakan metode meta-analytics second-order confirmatory factor analysis (Meta-SOCFA). Penelitian ini bertujuan untuk mendapatkan bentuk estimasi effect size model Meta-SOCFA dengan pendekatan generalized method of moments (GMM), mendapatkan model second-order CFA dari faktor-faktor yang mempengaruhi infrastruktur daerah tertinggal di Pulau Jawa, dan untuk melakukan Meta-SOCFA dengan pendekatan pendekatan twostage structural equation modeling (TSSEM) pada faktor-faktor yang mempengaruhi infrastruktur daerah tertinggal di pulau Jawa. Hasil penelitian menunjukkan bahwa effect size penelitian tidak homogen, sehingga estimasi effect size gabungan dilakukan dengan model random effect. Hasil penelitian ini menunjukkan bahwa analisis model pengukuran dapat diterima untuk menjelaskan infrastruktur daerah tertinggal di Jawa berdasarkan hasil Goodness of Fit Indicates. Hasil lain dari penelitian ini juga menunjukkan bahwa dimensi ekonomi, aksesibilitas dan SDM dengan estimasi MLE dan GMM menunjukkan hasil signifikan memiliki hubungan dengan infrastruktur daerah tertinggal, sementara itu dimensi karakteristik daerah tidak signifikan memiliki hubungan dengan infrastruktur daerah tertinggal dengan menggunakan kedua estimasi MLE maupun GMM.
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Another fact of the rapid development on the island of Java is that there are still six districts on the Java Island that fall under the criteria of underdeveloped areas. An area is classified as an underdeveloped area measured based on six main criteria, namely economy, human resources, infrastructure, regional financial capacity, accessibility, and regional characteristics. Therefore, the availability of accurate data regarding underdeveloped areas is an important aspect to support strategic programs aimed at overcoming underdeveloped areas. Using the metaanalytic second-order confirmatory factor analysis (Meta-SOCFA) method. This study aims to obtain an estimate of the effect size of the Meta-SOCFA model with the generalized method of moments approach, to obtain a second-order CFA model of the factors that affect the infrastructure of underdeveloped areas, and to carry out Meta-SOCFA with a two-stage structural equation modeling (TSSEM) approach on the factors that influence the infrastructures of the underdeveloped areas of the Java Island. The results showed that the study effect size was not homogeneous, so estimation of the effect size was done using the random-effects model. The results of this study indicate that measurement model analysis can be accepted to explain the infrastructure of underdeveloped areas in Java Island based on the results of Goodness of Fit Indicates. Other results of this study also show that the dimensions of economy, accessibility, and human resources with MLE and GMM estimates show a significant relationship with infrastructure in underdeveloped areas, while the dimensions of regional characteristics have no significant relationship with infrastructure in underdeveloped areas using both MLE and GMM estimates
Analisis Perilaku Hidup Bersih Dan Sehat (PHBS) Rumah Tangga Penderita TB Di Wilayah Pesisir Kota Surabaya Menggunakan Pendekatan Regresi Logistik Biner
Full text linkProvinsi Jawa Timur merupakan salah satu dari tiga provinsi di Indonesia dengan jumlah kasus TB terbesar yakni mencapai 23.487 kasus dimana angka penderita TB yang tertinggi di Jawa Timur adalah di Kota Surabaya, sedikitnya 4.739 warga bermukim di Surabaya yang terkena penyakit TB. Penyakit ini banyak ditemukan di permukiman padat penduduk dengan sanitasi yang kurang baik, kurangnya ventilasi dan pencahayaan matahari dan kurangnya istirahat seperti diwilayah pesisir. Penyakit TB yang diderita masyarakat tersebut mempengaruhi perilaku hidup masyarakat dalam menjaga kesehatan dan kebersihan. Padahal dengan berperilaku hidup bersih dan sehat tersebut dapat mengurangi resiko penularan TB sehingga dapat menurunkan jumlah penderita TB, oleh karena itu Dinas Kesehatan menyelenggarakan program PHBS bagi masyarakat yang menderita TB Penelitian ini bertujuan untuk menentukan faktor-faktor yang mempengaruhi status PHBS rumah tangga penderita TB di pesisir Surabaya Metode yang digunakan adalah regresi logistik biner. Hasil analisis menunjukkan bahwa kebiasaan membuka pintu dan jendela, kebiasaan merokok dan minum alkohol, kebiasaan olahraga, makanan bergizi, kebiasaaan cuci tangan dengan sabun dan air bersih, istirahat cukup, pemisahan peralatan mandi dan makan berpengaruh signifikan (α=5%) terhadap PHBS rumah tangga dengan penderita TB di pesisir Surabaya.
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East Java Province is one of three provinces in Indonesia with the largest number of TB cases, reaching 23 487 cases in which the highest number of TB patients in East Java in Surabaya, at least 4,739 residents living in Surabaya affected by TB. The disease is commonly found in the densely populated settlements with poor sanitation, lack of ventilation and daylighting, and lack of rest as the coastal region.TB disease suffered by these communities influence the behavior of people living in maintaining health and hygiene. In fact, by behaving clean and healthy living can reduce the risk of TB transmission so as to reduce the number of tuberculosis patients, therefore the Health Service organizes Clean And Healthy Behavior (CHB) for people suffering from TB This study aims to determine the factors that affect the status of PHBs household TB patients in Surabaya coast method used is a binary logistic regression. The analysis showed thatopen doors and windows habits, smoking and drinking alcohol, exercise, nutritious food, a habit of washing hands with soap and clean water, adequate rest, shower and eat separation equipment significant effect (α = 5%) to PHBS households with TB patients in Surabaya coast
Classification of Underdeveloped Areas in Indonesia Using the SVM and k-NN Algorithms
Full text linkThe determination or classification of underdeveloped areas essentially consists of classifying several observations taking into account existing indicators. The classification method used is K-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). This study aims to analyze the accuracy of the classification between SVM and k-NN algorithms in the classification of underdeveloped areas in Indonesia. The data source used in this study is secondary data obtained from the Central Bureau of Statistics (BPS). The data used are 514 districs and municipalities of Indonesia. After analysis, the conclusion is that there are 122 districs and municipalities that are left behind out of a total of 514 districs and municipalities in Indonesia. The most underdeveloped areas are on the island of Papua, followed by the areas of the islands of Bali and Nusa Tenggara, and Sulawesi. Based on the results of the classification of underdeveloped areas using the method SVM with the kernel RBF has the best results with the parameters C = 1 and γ = 0.05 while the results of the classification of underdeveloped areas using the method k-NN obtains the best results with k = 15 Based on the results of classification of underdeveloped areas using the SVM and the k-NN method, including the level of classification is very good. The two methods compared have the same precision value of 92.2% and can be used to determine the classification of underdeveloped areas. Keywords: classification, machine learning, supervised learning, underdeveloped areas
Pemodelan Pengaruh Imunisasi DPT Terhadap Angka Kematian Bayi di Jawa Timur Tahun 2016 Menggunakan Pendekatan Regresi Nonparametrik Spline
Full text linkEast Java is one of the provinces with a high IMR level. Based on the District / City report in East Java, in 2006 it was 0.035 live births and became 0.0032 live births in 2008. Identification of factors that influence both indicators correctly can be done by modeling, namely by nonparametric regression analysis. The nonparametric regression approach used is Spline, with its strengths the model tends to look for estimates wherever the data moves. This is because there is a knot point which is a joint fusion point which indicates a change in data behavior patterns. Based on the results of analysis and discussion using Spline analysis, it is known that the factors that influence the incidence of IMR in East Java are toddlers receiving type 3 DPT immunization. The best Spline nonparametric regression model is a linear Spline model with three point knots. The GCV value produced was 51.34. Factors of children under five obtained immunizations affecting infant mortality rates in districts / cities in East Java in 2016. This research still uses linear spline regression program with a combination of one, two, and three knots with R square of 65.92%. The need to develop programs into quadratic and cubic orders using a combination of knots.
Jawa Timur merupakan salah satu provinsi dengan tingkat AKB yang tinggi. Berdasarkan laporan Kabupaten/Kota di Jawa Timur, pada tahun 2006 sebesar 0,035 kelahiran hidup dan menjadi 0,0032 kelahiran hidup pada tahun 2008. Jika suatu daerah dengan AKB yang tinggi, maka terdapat kemungkinan bahwa daerah sekitarnya akan memiliki beban AKB yang sama pula. Identifikasi faktor-faktor yang mempengaruhi kedua indikator secara tepat dapat dilakukan dengan pemodelan, yaitu dengan analisis regresi nonparametrik. Pendekatan regresi nonparametric yang digunakan adalah Spline, dengan kelebihannya model cenderung mencari estimasinya kemanapun data tersebut bergerak. Hal ini dikarenakan terdapat titik knot yang merupakan titik perpaduan bersama yang menunjukkan terjadinya perubahan pola perilaku data. Berdasarkan hasil analisis dan pembahasan dengan menggunakan analisis Spline diketahui bahwa faktor yang berpengaruh terhadap kejadian AKB di Jawa Timur adalah balita memperoleh imunisasi DPT tipe 3. Model regresi nonparametrik Spline terbaik adalah model Spline linear dengan tiga titik knot. Nilai GCV yang dihasilkan adalah 51,34. Faktor balita memperoleh imunisasi mempengaruhi angka kematian bayi di kabupaten/kota di Jawa Timur pada tahun 2016. Penelitian ini masih menggunakan program regresi spline linier dengan kombinasi satu, dua, dan tiga knot dengan R square sebesar 65,92%. Perlu adanya pengembangan program menjadi orde kuadratik dan kubik dengan menggunakan kombinasi knot.
 
GWRPCA ALGORITHMIC FRAMEWORK: ANALYZING SPATIAL DYNAMICS OF POVERTY IN EAST JAVA PROVINCE
Full text linkThis study employs Regression Principal Component Analysis (RPCA) and Geographically Weighted Regression Principal Component Analysis (GWRPCA) algorithms to analyze poverty in East Java Province, using data from Statistics Indonesia (BPS). The research investigates regency/city-level poverty percentages and identifies influential factors such as education, literacy rates, housing conditions, and economic indicators. The results reveal that GWRPCA, with an 85.10% R2 value, outperforms RPCA, highlighting its effectiveness in capturing spatial diversity and providing a nuanced portrayal of poverty characteristics across regencies/cities in East Java. In conclusion, GWRPCA emerges as a powerful algorithmic tool for informing targeted poverty alleviation policies, offering insights into spatial variations. The study suggests future research directions to explore evolving spatial patterns and consider additional variables for a more comprehensive analysis. The findings emphasize the significance of spatial considerations in devising effective, context-specific strategies for each regency/city in East Jav
THE RELATIONSHIP BETWEEN PUBLIC INFORMATION OPENNESS AND ICT DEVELOPMENT
Full text linkThe relationship between Information and Communication Technology (ICT) development and the level of Public Information Openness (KIP) holds significant implications for inclusive and sustainable societal development. This study employs statistical analysis, including Pearson correlation, to examine this relationship across Indonesian provinces in 2022. Findings indicate a positive correlation between ICT development and KIP. Access to ICT infrastructure and ICT usage show significant correlations with IKIP levels across various provinces. Provinces with better ICT development generally exhibit higher KIP levels. However, the relationship with ICT skills is comparatively weaker, indicating other influencing factors on ICT literacy within the community. The conclusion drawn from this research is that ICT development positively contributes to enhancing Public Information Transparency in Indonesia. Therefore, further efforts are needed to support equitable ICT development, enhance digital literacy, and strengthen public information transparency, enabling the population to effectively harness information and communication technolog
AI-Based Models for Identifying Underdeveloped Villages in Indonesia's Rural Development
Full text linkThis study improves the prediction and classification of underdeveloped villages in Indonesia using Artificial Intelligence (AI) and machine learning. It identifies key factors driving underdevelopment to inform policy interventions that support Sustainable Development Goals (SDGs), particularly SDG 1 (No Poverty), SDG 10 (Reduced Inequality), and SDG 11 (Sustainable Communities). Using data from 75,261 villages based on Indonesia’s Village Development Index (IDM), the Decision Tree model achieved the highest classification accuracy at 99.5%. Analysis of feature importance revealed the Economic Resilience Index (IKE) as the most significant factor, followed by the Ecological Resilience Index (IKL) and the Social Resilience Index (IKS). These results align with the SDGs’ focus on economic, social, and environmental resilience. The research offers a data-driven approach to advancing rural development and guiding effective policy decisions in Indonesia
Understanding Gender Inequality in Indonesia: an AI Approach to Evaluating Socio-Economic Factors for Sustainable Development
Full text linkGender inequality in Indonesia remains a significant challenge, especially in achieving gender parity across various development indicators. This study evaluates the impact of different socioeconomic and demographic variables on the Gender Inequality Index (GII) across all regencies and cities in Indonesia in 2023, focusing on aligning with the fifth Sustainable Development Goal (SDG) of Gender Equality. The variables considered include Women's Income Contribution, Women in Professional Occupations, Women's Human Development Index, Women's Life Expectancy, Women's Labor Force Participation Rate, Women's Expected Years of Schooling, and Adjusted Per Capita Expenditure for Women. Three machine learning models, Support Vector Regression (SVR), AdaBoost Regressor, and Random Forest Regressor are applied to determine the relationship between these factors and the GII. Random Forest Regressor demonstrated the best performance with a Mean Squared Error (MSE) of 0.0109. The feature importance analysis reveals that the Women's Human Development Index has the highest impact at 43.92%, followed by Women's Life Expectancy at 23.25%, and Adjusted Per Capita Expenditure for Women at 10.27%. These findings highlight the pivotal role of human development and economic factors in shaping gender inequality in Indonesia, providing valuable insights for formulating targeted policies aimed at reducing gender disparities and promoting equitable development across the country
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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