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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Annual Research Seminar Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Indonesian Journal of Electrical Engineering and Computer Science Jurnal Generic JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Dinda Lestarini
Fakultas Ilmu Komputer Universitas Sriwijaya
Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Using machine learning approach towards successful crowdfunding prediction Sarifah Putri Raflesia; Dinda Lestarini; Rizka Dhini Kurnia; Dinna Yunika Hardiyanti
Bulletin of Electrical Engineering and Informatics Vol 12, No 4: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i4.5238

Abstract

Crowdfunding is a concept that emerged due to difficulties in raising funds for community business projects, social activities, micro-enterprises, and start-ups conventionally. Crowdfunding uses internet technology as a bridge between the donor and the recipient of funds so that it can reach a wider range of donors. This study aims to compare the performance of machine learning approaches in predicting crowdfunding campaign success. Three machine learning algorithms were employed to predict crowdfunding campaign success, namely logistic regression, random forest, and extreme gradient boosting (XGBoost). The dataset used in this study contains data about all projects posted on Kickstarter from January 2020 to September 2022. To improve the prediction model's performance, experiments using principal component analysis (PCA) feature reduction and log transformation were conducted. The results show that the implementation of log transformation on the dataset can increase the prediction model's performance. Meanwhile, XGBoost algorithm performs better than linear regression and random forest.
Detection of Indonesian wildlife sales and promotion through social media using machine learning approach Lestarini, Dinda; Rusdy, Taufiqurrahman; Iriyani, Silfi; Raflesia, Sarifah Putri
Bulletin of Electrical Engineering and Informatics Vol 12, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i6.5418

Abstract

Social media is one of the communication media that is widely used in the digital era as it is today. The use of social media allows people who are far apart to communicate and exchange media, both voice, video, and images quickly and even in real-time. In the past, the sale of protected animals was mostly done on the black market, usually involving a supply chain between sellers that usually existed in traditional markets or certain communities. With the existence of social media, the trend in conducting transactions and promoting wild animals has shifted from traditional to modern thanks to the support of existing technology. Protected wild animals are of concern to the local government or the global world to protect their existence. Therefore, this research proposes a machine learning (ML) based approach to detect the promotion and sale of wild animals on social media. The implementation of Naïve Bayes classifier (NBC) has a high accuracy in detecting trade in wild animals on social media with an accuracy value of 86. The implementation of ML-based approach is expected to produce new technology that allows authorities to know and monitor social media in order to reduce the sale and promotion of protected wildlife.
Co-Authors Adelia Azahra Adelia Rizki Putri Ahmad Rifai Ahmad Syakir Muliana Ali Bardadi Allsela Meiriza, Allsela Andesta Angelina Darmawahyuni, Annisa Desty Rodiah Dila Okta Dwi Putri Dinna Yunika Hardiyanti Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Firdaus Fransiska Prihatini Sihotang, Fransiska Prihatini Hardini Novianti Iriyani, Silfi Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Pibriana, Desi Rahmadiana, Reva Nur Richa Pratiwi Rizka Dhini Kurnia Rusdy, Taufiqurrahman Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia Sarifah Putri Raflesia, Sarifah Putri Silfi Iriyani Siti Nurmaini Taufiqurrahman Rusdy Taufiqurrahman Taufiqurrahman Via Mariska, Inneke