Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)

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Credit Risk Management Prediction Using the Support Vector Machine (SVM) Algorithm

Authors
Iwan Setiawan1, Evi Martaseli2, Tugiman3, Nizirwan Anwar4, Mirfan5, Panji Kuncoro Hadi6, *, Imam Suhrawardi7, Hendry Gunawan4
1Nusa Putra University, Sukabumi, 43152, Indonesia
2Muhammadiyah University, Sukabumi, 43113, Indonesia
3Buddhi Dharma University, Banten, 15115, Indonesia
4Esa Unggul University, Jakarta, 11510, Indonesia
5Handayani University, Makassar, 90231, Indonesia
6PGRI University, Madiun, 63118, Indonesia
7Politeknik Negeri Lampung, Lampung, 35141, Indonesia
*Corresponding author. Email: panjikuncorohadi210971@gmail.com
Corresponding Author
Panji Kuncoro Hadi
Available Online 26 December 2022.
DOI
10.2991/978-94-6463-084-8_18How to use a DOI?
Keywords
Social-Media; Data Twitter; Random Forest Algorithm; Sentiment Classification; Polarity Analysis
Abstract

Information sharing throughout the globe or universe has become a characteristic of social media. There has been a lot of research into the classification of sentiments. In this study, Twitter has been mined for unstructured Gofood Reviews data. It has been preprocessed to analyze the reviews’ sentiment with polarity analysis, feature extraction with TF-IDF, and supervised learning with random forest. From June 1, 2022 to June 30, 2022, a total of 28763 tweets with the keyword “Gofood” were retrieved from Twitter. The data is processed by the Python programming language utilizing NLTK, Sastrawi for the Indonesian language, Textblob, TF-IDF, Random Forest Classification, and other algorithms. Twitter is a nearly limitless source for classifying text. This algorithm takes roughly five minutes to computer.

Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
Series
Advances in Computer Science Research
Publication Date
26 December 2022
ISBN
978-94-6463-084-8
ISSN
2352-538X
DOI
10.2991/978-94-6463-084-8_18How to use a DOI?
Copyright
© 2022 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Iwan Setiawan
AU  - Evi Martaseli
AU  - Tugiman
AU  - Nizirwan Anwar
AU  - Mirfan
AU  - Panji Kuncoro Hadi
AU  - Imam Suhrawardi
AU  - Hendry Gunawan
PY  - 2022
DA  - 2022/12/26
TI  - Credit Risk Management Prediction Using the Support Vector Machine (SVM) Algorithm
BT  - Proceedings of the First Mandalika International Multi-Conference on Science and Engineering 2022, MIMSE 2022 (Informatics and Computer Science) (MIMSE-I-C-2022)
PB  - Atlantis Press
SP  - 195
EP  - 206
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-084-8_18
DO  - 10.2991/978-94-6463-084-8_18
ID  - Setiawan2022
ER  -