Article Per Year (5 Year)
p-Index From 2020 - 2025
0.751
P-Index
This Author published in this journals
All Journal Indonesian Journal of Data and Science Prosiding Seminar Nasional Official Statistics
Dewi Widyawati
Unknown Affiliation
Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Mathematics Social Sciences

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
PENERAPAN METODE ELL-COUNTERFACTUAL UNTUK PEMETAAN KEMISKINAN LEVEL KECAMATAN DAN DESA/KELURAHAN Dewi Widyawati; Siti Muchlisoh
Seminar Nasional Official Statistics Vol 2020 No 1 (2020): Seminar Nasional Official Statistics 2020
Publisher : Politeknik Statistika STIS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (569.757 KB) | DOI: 10.34123/semnasoffstat.v2020i1.522

Abstract

Development program has been implemented by government has given great attention to eraditen poverty in order to improve social welfare. Poverty alleviation programs require accurate data and reaching up to the smallest areas. Poverty indicators are obtained from the National Socio-Economic Survey (SUSENAS) held by BPS. SUSENAS is designed to get the indicator to estimated until regency/city level, so to get the estimate until the smaller level has not requirements of the sample adequacy. Small Area Estimation can be used to get poverty indicators by optimizing the available data or without the addition the number of samples.This study discusses the application of Elbers, Lanjouw, and Lanjouw (ELL) methods combined with Counterfactual methods toobtain estimates of poverty indicators at the sub-district and village levels in Yogyakarta and to visualize them with poverty map. The data used are Population Census 2010, SUSENAS (2010 and 2018), PODES (2011 and 2018), as well as other BPS publications.The results showed that the estimation of poverty indicators with the ELL method had a relative error value (RSE) compared to the immediate estimation. By obtaining the indicators of poverty at lower levels of aggregation are expected to increase the credibility of government decision-making in poverty alleviation.
Spatial Prediction of Stunting Incidents Prevalence Using Support Vector Regression Method Gaffar, Andi Widya Mufila; Sugiarti; Dewi Widyawati; Andi Muhammad Kemai Arief Hidayat Paharuddin; Andi Vania Anastasia
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.68

Abstract

Stunting in toddlers is a major nutritional problem faced by Indonesia, with a high incidence rate occurring in several provinces across the country. This nutritional issue can occur at any age, starting from the prenatal stage, infancy, childhood, adolescence, adulthood, and even in the elderly. To reduce the prevalence of stunting in affected provinces, prevention efforts are essential, including predicting the spread of stunting incidents in each region. Therefore, this research conducted spatial prediction of the prevalence rate of stunting incidents using Machine Learning, specifically Support Vector Machine based Regression. The results of this study produced a prediction model with an RMSE (Root Mean Square Error) value of 0.008689303 and a multiple correlation coefficient of 0.65912721. Based on these findings, the predictive model utilized demonstrated satisfactory performance in predicting the prevalence rate of stunting incidents in each area
Comparison Analysis of Random Forest Classifier, Support Vector Machine, and Artificial Neural Network Performance in Multiclass Brain Tumor Classification Amaliah Faradibah; Dewi Widyawati; A Ulfah Tenripada Syahar; Sitti Rahmah Jabir; Lokapitasari Belluano, Poetri Lestari
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.73

Abstract

This study aims to analyze and compare the performance of three main classification models, namely Random Forest Classifier, Support Vector Machine, and Artificial Neural Network, in classifying Multiclass brain tumors based on MRI images. The research method includes exploratory data analysis (EDA), dataset preprocessing with image segmentation using the Canny method, and feature extraction using the Humoment method. The performance of the classification models is evaluated based on accuracy, precision, recall, and F1 score. The analysis results show variations in the performance of the three classification models, with Random Forest Classifier having an accuracy of 0.7, weighted precision of 0.55, weighted recall of 0.7, and weighted F1 score of 0.59; Support Vector Machine having an accuracy of 0.71, weighted precision of 0.5, weighted recall of 0.71, and weighted F1 score of 0.59; and Artificial Neural Network having an accuracy of 0.62, weighted precision of 0.6, weighted recall of 0.62, and weighted F1 score of 0.61. Visualization using box plots also reveals outliers in the performance of the three models. These findings indicate variations and outliers in the performance of the classification models for Multiclass brain tumor classification. Further analysis is needed to understand the factors that influence performance differences and identify ways to improve the classification model performance for brain tumor diagnosis based on MRI images
Comparison Analysis of Classification Model Performance in Lung Cancer Prediction Using Decision Tree, Naive Bayes, and Support Vector Machine Dewi Widyawati; Amaliah Faradibah; Lestari Lokapitasari Belluano, Poetri
Indonesian Journal of Data and Science Vol. 4 No. 2 (2023): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v4i2.76

Abstract

This research aims to analyze the performance of three classification models, namely Decision Tree Classifier, Support Vector Machine, and Naive Bayes Classifier, in predicting lung cancer using the "Lung Cancer Prediction" dataset. The performance evaluation metrics used include accuracy, precision weighted, recall weighted, and F1 weighted. As a preliminary step, exploratory data analysis (EDA) and dataset preprocessing, including feature selection, data cleaning, and data transformation, were conducted. The test data results showed that the Decision Tree Classifier and Naive Bayes Classifier had similar performances with high accuracy, precision, recall, and F1 values. Meanwhile, the Support Vector Machine also exhibited competitive performance, although its precision weighted value was slightly lower. Additionally, an outlier analysis was conducted using box plots, revealing that the Decision Tree Classifier had 2 outlier values, while the Support Vector Machine had 4 outlier values, and Naive Bayes had no outlier values. In conclusion, all three classification models demonstrated good potential in lung cancer prediction. However, selecting the best model requires consideration of relevant evaluation metrics for the application and accommodating the limitations of each model. Further evaluation and in-depth analysis are needed to ensure the reliability of the models in predicting lung cancer cases more accurately and consistently.
Co-Authors A Ulfah Tenripada Syahar Amaliah Faradibah Andi Muhammad Kemai Arief Hidayat Paharuddin Andi Vania Anastasia Gaffar, Andi Widya Mufila Lokapitasari Belluano, Poetri Lestari Siti Muchlisoh Sitti Rahmah Jabir Sugiarti