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All Journal Journal of Applied Geospatial Information JURNAL MEDIA INFORMATIKA BUDIDARMA
Ganiswari, Syuhra Putri
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Engineering Environmental Science Physics

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Machine Learning-Enhanced Geographically Weighted Regression for Spatial Evaluation of Human Development Index across Western Indonesia Firmansyah, Gustian Angga; Zeniarja, Junta; Azies, Harun Al; winarno, Sri; Ganiswari, Syuhra Putri
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6755

Abstract

The HDI (Human Development Index) is one of the important components to measure the level of success in efforts to improve the quality of human life. The human development index is built with three dimensions, namely the longevity and health dimension, the knowledge dimension and the decent standard of living dimension. The longevity and health dimension is measured using Life expectancy at birth. The knowledge dimension is measured using expected years of schooling and average years of schooling. Meanwhile, the decent standard of living dimension is measured using Adjusted per capita expenditure. This study aims to find factors that influence HDI (Human Development Index) in Western Indonesia Region using machine learning models. The results obtained are that HDI is influenced by average years of schooling, expected years of schooling, Life expectancy at birth, and Adjusted per capita expenditure which are sorted from the most significantly influential. The model used in this study is GWR (Geographically Weighted Regression) with evaluation results including, AIC of 215.3162, AICc of 226.5107, and the accuracy level in the form of R-square of 99.38% which means this model is good to use.
Data-Driven Modeling of Human Development Index in Eastern Indonesia's Region Using Gaussian Techniques Empowered by Machine Learning Ganiswari, Syuhra Putri; Azies, Harun Al; Nugraha, Adhitya; Luthfiarta, Ardytha; Firmansyah, Gustian Angga
Journal of Applied Geospatial Information Vol 7 No 2 (2023): Journal of Applied Geospatial Information (JAGI)
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jagi.v7i2.6757

Abstract

The Human Development Index (HDI) is a statistical measure used to measure and evaluate the progress and quality of human life in a country. For the Government of Indonesia, HDI is important because it is used to create or develop effective policies and programs. In addition, HDI is also used as one of the allocators in determining the General Allocation Fund. The 2022 HDI data released by BPS shows that there has been an increase in the HDI in each district/city over the last 12 years, including in the regions of Eastern Indonesia. High and low HDI values are influenced by several factors, and there are indications that there is spatial diversity where surrounding areas tend to have HDI levels that are not far from the area. The Geographically Weighted Regression method is used in this study because it takes into account spatial aspects. However, the GWR model must be built repeatedly if there is regional expansion. Therefore, a GWR model that applies machine learning methods is needed where the model is built and tested using different datasets, namely training data and test data, so that the model can predict new data better. The results obtained are that the GWR model with test data has a better R-Square value when compared to the GWR model previously trained using training data, which is 0.9946702, based on the linear regression model shows the results that the most influential factor on HDI in Eastern Indonesia is expected years of schooling (X2).
Optimizing Sentiment Analysis of Working Hours Impact on Generation Z’s Mental Health Using Backpropagation Farsya, Nabila Zibriza; Luthfiarta, Ardytha; Maharani, Zahra Nabila; Ganiswari, Syuhra Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7827

Abstract

The topic of working hours' impact, Generation Z, and mental health are discussions that are often found on social media such as X (used to be Twitter). The sentiment analysis addressing these topics is needed to find out people’s opinions regarding these topics. It could also be helpful as a consideration for the decision-making process for related topics research. Therefore, this research aims to improve the accuracy of the model generated by the previous sentiment analysis research regarding the working hours’ impact on Gen Z’s mental health. The contribution of this research is by building a robust Backpropagation Neural Network model and utilizing SMOTETomek to achieve higher accuracy. This research compared two oversampling techniques for data balancing: SMOTE and SMOTETomek. The result shows that this research has successfully outperformed the baseline research with the best accuracy of 91% using SVM by generating the best accuracy of 93.01% with SMOTETomek. For comparison, SMOTETomek has outperformed SMOTE by generating the best accuracy of 93.01%, while the best accuracy generated with SMOTE is 92.26%. It indicates that in the case of Indonesian text sentiment analysis of this research, SMOTETomek has a better effect compared to SMOTE.
Optimizing Sentiment Analysis of Working Hours Impact on Generation Z’s Mental Health Using Backpropagation Farsya, Nabila Zibriza; Luthfiarta, Ardytha; Maharani, Zahra Nabila; Ganiswari, Syuhra Putri
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7827

Abstract

The topic of working hours' impact, Generation Z, and mental health are discussions that are often found on social media such as X (used to be Twitter). The sentiment analysis addressing these topics is needed to find out people’s opinions regarding these topics. It could also be helpful as a consideration for the decision-making process for related topics research. Therefore, this research aims to improve the accuracy of the model generated by the previous sentiment analysis research regarding the working hours’ impact on Gen Z’s mental health. The contribution of this research is by building a robust Backpropagation Neural Network model and utilizing SMOTETomek to achieve higher accuracy. This research compared two oversampling techniques for data balancing: SMOTE and SMOTETomek. The result shows that this research has successfully outperformed the baseline research with the best accuracy of 91% using SVM by generating the best accuracy of 93.01% with SMOTETomek. For comparison, SMOTETomek has outperformed SMOTE by generating the best accuracy of 93.01%, while the best accuracy generated with SMOTE is 92.26%. It indicates that in the case of Indonesian text sentiment analysis of this research, SMOTETomek has a better effect compared to SMOTE.
Co-Authors Adhitya Nugraha Ardytha Luthfiarta Farsya, Nabila Zibriza Firmansyah, Gustian Angga Harun Al Azies Junta Zeniarja Maharani, Zahra Nabila Sri Winarno