Sentimen Masyarakat Terkait Perpindahan Ibukota Via Model Random Forest dan Logistic Regression

  • Martaliana Putri Agustina Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana
  • Hendry Hendry Fakultas Teknologi Informasi, Universitas Kristen Satya Wacana

Abstract

This study aims to determine public opinion regarding the relocation of the capital of Indonesia. The pros and cons conveyed by the community are important because they can be constructive input for the government. The applications used to support the research are Orange and Twitter. The data obtained must go through several processes such as preprocess text, sentiment analysis, and testing algorithms to ensure data accuracy before making a decision tree. This research uses Random Forest and Logistic Regression as the research models. As for the results, Random Forest obtains higher accuracy value than Logistic Regression and it is considered that time did not affect the classification. The result obtained from the decision tree is that more people choose to have a neutral opinion on this program. People prefer to entrust everything to the government, because the government must have thought about the positive or negative impacts in the long term.

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References

[1] I. Priyadi, J. Santony, and J. Na’am, “Data Mining Predictive Modeling for Prediction of Gold Pri=ces Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices,” Indones. J. Artif. Intell. Data Min., vol. 2, no. 2, p. 93, 2019, doi: 10.24014/ijaidm.v2i2.6864.
[2] I. A. Safra et al., “Analisa sentimen persepsi masyarakat terhadap pemindahan ibukota baru di kalimantan timur pada media sosial twitter 1,2,” pp. 978–979, 2020.
[3] P. Arsi and R. Waluyo, “ANALISIS SENTIMEN WACANA PEMINDAHAN IBU KOTA INDONESIA MENGGUNAKAN ALGORITMA SUPPORT VECTOR MACHINE ( SVM ) SENTIMENT ANALYSIS ON THE DISCUSSION OF RELOCATING I NDONESIA ’ S CAPITAL CITY USING THE SUPPORT VECTOR MACHINE ( SVM ),” vol. 8, no. 1, pp. 147–156, 2021, doi: 10.25126/jtiik.202183944.
[4] P. Teknologi, J. Ilmiah, A. Lingkungan, and A. Taufiq, “MEDIA SOSIAL TWITTER MENGGUNAKAN ALGORITMA NAIVE Text Mining,” vol. 14, no. 1, 2020.
[5] M. N. Rizaldi and S. Al Faraby, “Klasifikasi Argument Pada Teks dengan Menggunakan Metode Multinomial Logistic Regression Terhadap Kasus Pemindahan Ibu Kota Indonesia di Twitter,” vol. 4, no. L, pp. 904–913, 2020, doi: 10.30865/mib.v4i4.2348.
[6] E. Mas, E. D. W. S. Kom, M. Kom, A. A. S. Kom, and M. Kom, “ANALISIS SENTIMEN : PEMINDAHAN IBU KOTA INDONESIA PADA TWITTER,” vol. 1, no. 2, pp. 397–401, 2020.
[7] S. Akhmad, P. P. Adikara, and R. C. Wihandika, “Analisis Sentimen Kebijakan Pemindahan Ibukota Republik Indonesia dengan Menggunakan Algoritme Term-Based Random Sampling dan Metode Klasifikasi Naïve Bayes,” J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 3, no. 10, pp. 10086–10094, 2019.
[8] D. Statistika and U. Diponegoro, “3 1,2,3,” vol. 9, pp. 237–246, 2020.
[9] A. Primajaya et al., “Random Forest Algorithm for Prediction of Precipitation,” vol. 1, no. 1, pp. 27–31, 2018.
[10] D. Derisma, “Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining,” J. Appl. Informatics Comput., vol. 4, no. 1, pp. 84–88, 2020, doi: 10.30871/jaic.v4i1.2152.
[11] V. I. Santoso, G. Virginia, Y. Lukito, U. Kristen, and D. Wacana, “PENERAPAN SENTIMENT ANALYSIS PADA HASIL EVALUASI DOSEN DENGAN METODE SUPPORT VECTOR MACHINE,” vol. 14, no. 1, pp. 72–76, 2017.
[12] P. Bidang, K. Sains, Y. Mardi, J. Gajah, M. No, and S. Barat, “Jurnal Edik Informatika Data Mining : Klasifikasi Menggunakan Algoritma C4 . 5 Data mining merupakan bagian dari tahapan proses Knowledge Discovery in Database ( KDD ) . Jurnal Edik Informatika.”
[13] A. R. Febie Elfaladonna, “Analisa Metode Classification-Decission Tree Dan Algoritma,” Sci. Inf. Technol., vol. 2, no. 1, pp. 10–17, 2019.
[14] Orange Data Mining. 2016, 4 April. Getting Started with Orange 07: Model Evaluation and Scroling [video]. https://youtu.be/pYXOF0jziGM
[15] Orange Data Mining. 2020, 4 Agustus. Text Mining: Twitter Data Analysis [video]. https://youtu.be/HDkI6G4slzQ
Published
2021-11-30
How to Cite
Agustina, M., & Hendry, H. (2021). Sentimen Masyarakat Terkait Perpindahan Ibukota Via Model Random Forest dan Logistic Regression. AITI, 18(2), 111-124. https://doi.org/10.24246/aiti.v18i2.111-124
Section
Articles