Analisis Sentimen Opini Publik terhadap Kasus Korupsi Timah di Youtube Menggunakan Metode Oversampling dan Algoritma Decision Tree

Authors

  • Relin Pramudiya Universitas Multi Data Palembang, Indonesia
  • Aldo Kadafi Universitas Multi Data Palembang, Indonesia
  • Daniel Udjulawa Universitas Multi Data Palembang, Indonesia

DOI:

https://doi.org/10.29240/arcitech.v4i1.10472

Keywords:

Corruption, Decision Tree, Public Opinion, Sentiment Analysis, SMOTE Oversampling

Abstract

This study analyzes public opinion regarding the corruption case of PT. Timah (Tbk), which caused state losses of up to IDR 271 trillion, through YouTube comments. The methods used are the Decision Tree algorithm and the SMOTE Oversampling method. A total of 2501 comments were collected and processed. The stages include data preprocessing, sentiment labeling, and model training. The results show that the use of SMOTE improves the accuracy and performance of the model. With SMOTE, the model achieves an accuracy of 56%, a precision of 0.55, a recall of 0.55, and an F1-score of 0.55, while without SMOTE, the model only achieves 54%, a precision of 0.52, a recall of 0.52, and an F1-score of 0.52. Precision, recall, and F1-score also increase when using SMOTE. This study highlights the importance of the Oversampling technique in dealing with class imbalance to improve the accuracy and sentiment analysis model. These results make a significant contribution to sentiment analysis, highlighting the role of SMOTE in overcoming class imbalance and creating a more accurate model.

Downloads

Download data is not yet available.

References

A. Rahim, A. M., Inggrid Yanuar Risca Pratiwi, & Muhammad Ainul Fikri. (2023). Klasifikasi Penyakit Jantung Menggunakan Metode Synthetic Minority Over-Sampling Technique Dan Random Forest Clasifier. Indonesian Journal of Computer Science, 12(5), 2995–3011. https://doi.org/10.33022/ijcs.v12i5.3413

Agus Trianto, G., Marzuki, M. F., Sihotang, T. Y., & Irsyad, H. (2023). 2 ND MDP STUDENT CONFERENCE (MSC) 2023 Universitas Multi Data Palembang | 1 KLASIFIKASI OPINI TERHADAP RESESI INDONESIA 2023 PADA TWITTER MENGGUNAKAN ALGORITMA DECESION TREE. 1–9.

Antrag, I. La, Situmaeng, Y. T., Arinda, S., & Rochim, A. A. (2024). Penegakan Hukum Pertambangan Timah Ilegal Pasca Kasus Korupsi Tata Niaga Timah Di Bangka Belitung. 3(02), 184–191.

Cahyaningtyas, C., Nataliani, Y., & Widiasari, I. R. (2021). Analisis Sentimen Pada Rating Aplikasi Shopee Menggunakan Metode Decision Tree Berbasis SMOTE. Aiti, 18(2), 173–184. https://doi.org/10.24246/aiti.v18i2.173-184

Fadli, M., Wijaya, V., Pribadi, M. R., & Widhiarso, W. (2023). Effect of TF-IDF Extraction and Application of SMOTE on Model Performance in Detecting Spam Email. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), November, 637–641. https://doi.org/10.1109/EECSI59885.2023.10295851

Hanyfah, Z., Oktapia, A., & Tirta, M. (2024). Analisis Perhitungan Kerugian Negara dari Hasil Dugaan Tindak Pidana Korupsi yang dilakukan Oleh PT Timah (Tbk). Journal of Law and Nation (JOLN), 3(Mei), 351–358.

Harun, A., & Putri Ananda, D. (2021). Analisa Sentimen Opini Publik Tentang Vaksinasi Covid-19 di Indonesia Menggunakan Naïve bayes dan Decission Tree. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 1(1), 58–64. https://doi.org/10.57152/malcom.v1i1.63

Kurniati, K., Kusmiati, H., & Rahmi, N. (2023). Analisis Hashtag UTBK-SNBT di Twitter Menggunakan Netlytic Tools. Journal Computer Science and Information Systems : J-Cosys, 3(1), 44–48. https://doi.org/10.53514/jco.v3i1.383

MZ, Y., Boboring, J. E., & Fuadiah, N. (2023). Penerapan Metode K-Nearest Neighbor Dan Decision Tree Untuk Analisis Sentimen (Studi Kasus Mario Dandi). Indonesian Journal Of Information Technology, 1–6.

Permada, D. N. R., & Sari, P. (2024). The effect of current ratio and debt to equity ratio on return on equity at PT. Timah Tbk. Journal of Economics and Business Letters, 4(1), 43–53. https://doi.org/10.55942/jebl.v4i1.272

Pradana, R. S., & Nooraeni, R. (2023). Penerapan SMOTE pada Data Tidak Seimbang dalam Pemodelan Status NEET Penduduk Usia Muda di Provinsi Banten Tahun 2022. Jurnal Kebijakan Pembangunan, 18(1), 91–104.

Pratiwi, S. A., Fauzi, A., Arum, S., Lestari, P., & Cahyana, Y. (2024). KLIK: Kajian Ilmiah Informatika dan Komputer Prediksi Persediaan Obat Pada Apotek Menggunakan Algoritma Decision Tree. Media Online, 4(4), 2381–2388. https://doi.org/10.30865/klik.v4i4.1681

Putri, D. (2021). Korupsi Dan Prilaku Koruptif. Jurnal Pendidikan, Agama Dan Sains, V, 49–54.

Ramadhanti, I. R., & Belitung, U. B. (2024). Jurnal Bevinding Vol 02 No 01 Tahun 2024 Fakultas Hukum Universitas Islam Batik Surakarta. 02(01), 28–43.

Singgalen, Y. A. (2023). Analisis Sentimen Top 10 Traveler Ranked Hotel di Kota Makassar Menggunakan Algoritma Decision Tree dan Support Vector Machine. Media Online), 4(1), 323–332. https://doi.org/10.30865/klik.v4i1.1153

Vanacore, A., Pellegrino, M. S., & Ciardiello, A. (2024). Fair evaluation of classifier predictive performance based on binary confusion matrix. Computational Statistics, 39(1), 363–383. https://doi.org/10.1007/s00180-022-01301-9

Yudistira, N., & Putra, A. F. (2021). Algoritma Decision Tree Dan Smote Untuk Klasifikasi Serangan Jantung Miokarditis Yang Imbalance. Jurnal Litbang Edusaintech, 2(2), 112–122. https://doi.org/10.51402/jle.v2i2.48

Downloads

Published

30-06-2024

How to Cite

Pramudiya, R., Kadafi, A., & Udjulawa, D. (2024). Analisis Sentimen Opini Publik terhadap Kasus Korupsi Timah di Youtube Menggunakan Metode Oversampling dan Algoritma Decision Tree. Arcitech: Journal of Computer Science and Artificial Intelligence, 4(1), 1–18. https://doi.org/10.29240/arcitech.v4i1.10472

Issue

Vol. 4 No. 1 (2024): June 2024

Section

Articles

Citation Check

License

Copyright (c) 2024 Relin Pramudiya, Aldo Kadafi, Daniel Udjulawa

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Authors who publish with Arcitech: Journal of Computer science and Artificial Intelligence agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).