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All Journal JOIV : International Journal on Informatics Visualization IJAIT (International Journal of Applied Information Technology)
Kannan, Rathimala
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Computer Science & IT

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Poverty Level Prediction Based on E-Commerce Data Using Naïve Bayes Algorithm and Similarity-Based Feature Selection Aji, Pramuko; Wijaya, Dedy Rahman; Hernawati, Elis; Yualinda, Sherla; Yualinda, Sherli; Frasanta, Muhammad Akbar Haikal; Kannan, Rathimala
IJAIT (International Journal of Applied Information Technology) Vol 07 No 02 (November 2023)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v7i02.5374

Abstract

The poverty rate is an important measure of any country because it indicates how well the economy develops and how well the economic prosperity distributes among citizens. The Central Statistics Agency, or BPS, measures the poverty rates in Indonesia using the concept of the ability to meet demands (basic needs approach). Using this approach, spending becomes a measure of poverty, defined as an economic incapacity to satisfy food and non-food requirements. Thus, the poor are individuals whose monthly per capita spending is less than the poverty threshold. In this study, the machine learning method using Naive Bayes with similarity-based feature selection and e-commerce data has been proposed to predict the poverty level in Indonesia. We proposed the method to be used as a complement to the results of the costly surveys and censuses conducted by BPS. Our experiments show that the classifier shows little relevance between the predicted and the original values or actual poverty prediction based on BPS data. A limited number of features does not necessarily result in poor accuracy, however great accuracy is not always achieved if a lot of features are being used.
Predicting Student's Soft Skills Based on Socio-Economical Factors: An Educational Data Mining Approach Kannan, Rathimala; Jet, Chew Chin; Ramakrishnan, Kannan; Ramdass, Sujatha
JOIV : International Journal on Informatics Visualization Vol 7, No 3-2 (2023): Empowering the Future: The Role of Information Technology in Building Resilien
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3-2.2342

Abstract

Recent changes in the labor market and higher education sector have made graduates' employability a priority for researchers, governments, and employers in developed and emerging nations. There is, however, still a dearth of study about whether graduate students acquire the employability skills that businesses want of them because of their higher education. To determine a student's future employment and career path, it is critical to evaluate their soft skills. An emerging area called educational data mining (EDM) aims to gather enormous volumes of academic data produced and maintained by educational institutions and to derive explicit and specific information from it. This paper aims to predict students' soft skills such as professional, analytical, linguistic, communication, and ethical skills, based on their socio-economic, academic, and institutional data by leveraging data mining methods and machine learning techniques. All five soft skills were predicted using prediction models created using linear regression, probabilistic neural networks, and simple regression tree techniques. This study used a dataset from an open source that Universidad Technologica de Bolivar published. It covers academic, social, and economic data for 12,411 students. The experimental results demonstrated that the linear regression algorithm performed better than the others in predicting all five soft skills compared to machine learning methods. This finding can assist higher education institutions in making informed decisions, providing tailored support, enhancing student success and employability, and continuously modifying their programs to meet the needs of students.
Co-Authors Dedy Rahman Wijaya Elis Hernawati Frasanta, Muhammad Akbar Haikal Jet, Chew Chin Pramuko Aji Ramakrishnan, Kannan Ramdass, Sujatha Yualinda, Sherla Yualinda, Sherli