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All Journal Xplore: Journal of Statistics
Gerry Alfa Dito
Department of Statistics, IPB University
Decision Sciences, Operations Research & Management Engineering Mathematics

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Pendekatan Metode CHAID dan Regresi Logistik dalam Menganalisis Faktor Berpengaruh pada Kejadian Stunting di Provinsi Jawa Barat Fitri Dewi Shyntia; Anang Kurnia; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (886.259 KB) | DOI: 10.29244/xplore.v11i1.857

Abstract

Stunting is a chronic nutritional disorder characterized by short or very short height compared to the average child of his age. Data on the prevalence of stunting under five collected by the World Health Organization (WHO) in 2018 stated that Indonesia was the third-highest contributor to stunting in the South-East Asia Regional (SEAR) after Timor Leste and India. Indonesia's national stunting prevalence is 29,6%. West Java Province has the 12th the highest prevalence in Indonesia is one of the priority areas in stunting management, with the stunting prevalence rate most similar to the Indonesian national stunting prevalence of 29,2%. This study aims to examine the variables that are indicated to affect the incidence of stunting in children aged 0-59 months based on data obtained from the 2018 Basic Health Research (Riskesdas). Eighteen variables are categorized into child characteristics, nutritional fulfillment, socio-demographic, socialeconomic, and environmental characteristics. The analysis was performed using the logistic regression method and the Chi-Square Automatic Interaction Detection (CHAID) method. The analysis results show that the probability of stunting will increase significantly in children under five with several criteria. These Criteria are mothers with low education, sex of male toddlers, toddlers who do not carry out immunizations, toddlers who are not given additional food (PMT), and infants with households that have a safe place to eat and the disposal of wastewater from the kitchen is not suitable.
Penerapan Binary Particle Swarm Optimization Support Vector Machine untuk Klasifikasi Komentar Cyberbullying di Instagram Dewi Fortuna; Itasia Dina Sulvianti; Gerry Alfa Dito
Xplore: Journal of Statistics Vol. 11 No. 1 (2022)
Publisher : Department of Statistics, IPB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (612.942 KB) | DOI: 10.29244/xplore.v11i1.859

Abstract

Freedom of speech on social media is sometimes inappropriate with the ethics of communicating and has led to cyberbullying. Instagram is the most commonly used social media in cyberbullying. Cyberbullying needs to be minimized because it has many adverse effects. One way that can be done is by identifying cyberbullying comments so those comments can be deleted automatically. The method used in this study is text classification using Support Vector Machine (SVM) algorithm with the application of Binary Particle Swarm Optimization (BPSO) optimization method as features selection. The study aims to build a cyberbullying comments classification model and compare the classification model performance with and without the application of features selection. The experimental results showed that modeling with SVM produces a reasonably accurate classification performance over 72% for all classification performance on each C. The application of BPSO for features selection can improve classification performance by increasing accuracy and specificity. However, the model without features selection on C = 0,1 is chosen in this study case because it has the highest sensitivity with good accuracy and specificity that can detect cyberbullying comments more accurately.
Co-Authors Anang Kurnia Dewi Fortuna Fitri Dewi Shyntia Itasia Dina Sulvianti