Pembobotan Kelas Berbasis Long Short Term Memory Untuk Mengatasi Ketidakseimbangan Data Pada Analisis Sentimen

• HARIANTO
• 14210160

---

ABSTRAK

ABSTRAK

Nama              : Harianto

NIM                 : 1420160

Program Studi : Ilmu Komputer

Fakultas           : Teknologi Informasi

Jenjang            : Strata Dua (S2)

Konsentrasi      : Data Mining

Judul            : Pembobotan Kelas Berbasis Long Short Term Memory Untuk Mengatasi Ketidakseimbangan Data Pada Analisis Sentimen

Seiring dengan perkembangan teknologi aplikasi mobile telah menjadi bagian penting dari kehidupan sehari-hari. Tak terkecuali dengan aplikasi investasi yang saat ini tersedia pada platform mobile seperti Bibit, Bareksa, Tanamduit, dan Ipot. Hal ini dapat mempermudah pengguna memantau portofolio investasinya dimanapun. Dalam situasi seperti ini analis sentimen ulasan pada aplikasi dapat memberikan masukan bagi pengguna agar dapat memilih aplikasi yang sesuai dengan kebutuhan mereka. Permasalahan yang sering ditemui pada penelitian analisa sentimen adalah data kelas yang tidak seimbang. untuk itu diperlukan penanganan yang baik terhadap masalah tersebut. Untuk itu pada penelitian ini akan dilakukan pengujian dan perbandingan teknik Pembobotan Kelas dengan metode konvensional seperti SMOTE, SMOTE-TOMEK, SMOTE-ENN dan Random Undersampling. Perbandingan juga dilakukan dengan model ensemble yaitu Bagging, Balanced Bagging dan Easy Ensemble. Model Hybrid CNN-LSTM dengan Class Weighting memberikan hasil tertinggi dibandingkan dengan LSTM dan CNN. Penerapan pembobotan kelas memberikan perbaikan pada hasil prediksi pada kelas minoritas dengan model terbaik adalah Hybrid CNN-LSTM dengan accurcy 86.30%, precision 64.28%, recall 62.19% dan f1-score 62.97%.

KATA KUNCI

Analisa Sentimen,Text Mining,Class Weighting,Class Imbalance

---

DAFTAR PUSTAKA

DAFTAR PUSTAKA

[1] H. Utami, “Analisis Sentimen dari Aplikasi Shopee Indonesia Menggunakan Metode Recurrent Neural Network,” Indonesian Journal of Applied Statistics, vol. 5, no. 1, pp. 31–38, May 2022, doi: 10.13057/IJAS.V5I1.56825.

[2] S. Shaikh, S. M. Daudpota, A. S. Imran, and Z. Kastrati, “Towards Improved Classification Accuracy on Highly Imbalanced Text Dataset Using Deep Neural Language Models,” Applied Sciences 2021, Vol. 11, Page 869, vol. 11, no. 2, p. 869, Jan. 2021, doi: 10.3390/APP11020869.

[3] K. Ciptady, M. Harahap, J. Jonvin, Y. Ndruru, and I. Ibadurrahman, “Prediksi Kualitas Kopi Dengan Algoritma Random Forest Melalui Pendekatan Data Science,” Data Sciences Indonesia (DSI), vol. 2, no. 1, pp. 20–29, Sep. 2022, doi: 10.47709/DSI.V2I1.1708.

[4] A. S. Suweleh, D. Susilowaty, H. Hairani, and K. Marzuki, “Penanganan Ketidak Seimbangan Kelas Menggunakan Pendekatan Level Data,” MATRIK?: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 20, no. 1, pp. 109– 116, Sep. 2020, doi: 10.30812/MATRIK.V20I1.846.

[5] M. Ihsan et al., “LSTM (Long Short Term Memory) for Sentiment COVID-19 Vaccine Classification on Twitter,” Digital Zone: Jurnal Teknologi Informasi dan Komunikasi, vol. 13, no. 1, pp. 79–89, May 2022, doi: 10.31849/DIGITALZONE.V13I1.9950.

[6] R. Arisandi, “PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG,” Jurnal Gaussian, vol. 12, no. 1, pp. 136–145, May 2023, doi: 10.14710/J.GAUSS.12.1.136-145.

[7] A. Handayani et al., “Evaluasi Tiga Jenis Algoritme Berbasis Pembelajaran Mesin untuk Klasifikasi Jenis Tumor Payudara,” Jurnal Nasional Teknik Elektro dan Teknologi Informasi, vol. 6, no. 4, pp. 394–403, Nov. 2017, Accessed: Jul. 21, 2023. [Online]. Available: https://journal.ugm.ac.id/v3/JNTETI/article/view/2806

[8] K. Setyo Nugroho, I. Akbar, and A. Nizar Suksmawati, “DETEKSI DEPRESI DAN KECEMASAN PENGGUNA TWITTER MENGGUNAKAN BIDIRECTIONAL LSTM,” 2021.

[9] J. Nurvania, J. Jondri, and K. M. Lhaksamana, “Analisis Sentimen Pada Ulasan Di Tripadvisor Menggunakan Metode Long Short-term Memory (lstm),” eProceedings of Engineering, vol. 8, no. 4, Aug. 2021, Accessed: Oct. 27, 2023. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/ar ticle/view/15240/14963 63 Program Studi Komputer (S2) Universitas Nusa Mandiri

[10] W. I. Sabilla and C. Bella Vista, “Implementation of SMOTE and Under Sampling on Imbalanced Datasets for Predicting Company Bankruptcy,” Jurnal Komputer Terapan, vol. 7, no. 2, pp. 329–339, Dec. 2021, doi: 10.35143/jkt.v7i2.5027.

[11] F. Agung, J. Ayomi, and K. E. Dewi, “Analisis Emosi pada Media Sosial Twitter Menggunakan Metode Multinomial Naive Bayes dan Synthetic Minority Oversampling Technique,” Komputa?: Jurnal Ilmiah Komputer dan Informatika, vol. 12, no. 2, pp. 9–19, Sep. 2023, doi: 10.34010/KOMPUTA.V12I2.9454.

[12] R. Arisandi, “PERBANDINGAN MODEL KLASIFIKASI RANDOM FOREST DENGAN RESAMPLING DAN TANPA RESAMPLING PADA PASIEN PENDERITA GAGAL JANTUNG,” Jurnal Gaussian, vol. 12, no. 1, pp. 136–145, May 2023, doi: 10.14710/J.GAUSS.12.1.136-145.

[13] W. R. D. Jollyta and M. Zarlis, Konsep Data Mining dan Penerapan. Yogyakarta, 2020.

[14] S. Mohapatra, N. Shukla, S. Jain, and S. Chachra, “Nsmav-Bot: Intelligent Dual Language Tutor System,” in 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), 2018, pp. 1–5. doi: 10.1109/ICCUBEA.2018.8697582.

[15] D. Arteaga, J. Arenas, F. Paz, M. Tupia, and M. Bruzza, “Design of information system architecture for the recommendation of tourist sites in the city of Manta, Ecuador through a Chatbot,” in 2019 14th Iberian Conference on Information Systems and Technologies (CISTI), 2019, pp. 1–6. doi: 10.23919/CISTI.2019.8760669.

[16] F. D. Telaumbanua, P. Hulu, T. Z. Nadeak, R. R. Lumbantong, and A. Dharma, “Penggunaan Machine Learning Di Bidang Kesehatan,” JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP), vol. 2, no. 2, pp. 391–399, Jan. 2020, doi: 10.34012/jutikomp.v2i2.657.

[17] R. Rino Pratama and A. Korespondensi, “Analisis Model Machine Learning Terhadap Pengenalan Aktifitas Manusia,” MATRIK?: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 19, no. 2, pp. 302–311, May 2020, doi: 10.30812/MATRIK.V19I2.688.

[18] L. Breiman, “Random Forests,” vol. 45, pp. 5–32, 2001.

[19] T. Hasegawa and K. Kondo, “Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition,” IEEE Internet Things J, vol. 10, no. 6, pp. 5506–5518, Mar. 2022, doi: 10.1109/JIOT.2022.3222221.

[20] J. Wu, “Introduction to Convolutional Neural Networks,” 2017.

[21] N. Husin, “Komparasi Algoritma Random Forest, Naïve Bayes, dan Bert Untuk Multi-Class Classification Pada Artikel Cable News Network (CNN),” Jurnal Esensi Infokom?: Jurnal Esensi Sistem Informasi dan Sistem Komputer, vol. 7, no. 1, pp. 75–84, May 2023, doi: 10.55886/infokom.v7i1.608. 64 Program Studi Komputer (S2) Universitas Nusa Mandiri

[22] P. Liu, X. Qiu, and X. Huang, “Recurrent Neural Network for Text Classification with Multi-Task Learning,” May 2016.

[23] A. A. Rizal and S. Soraya, “Multi Time Steps Prediction dengan Recurrent Neural Network Long Short Term Memory,” MATRIK?: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, vol. 18, no. 1, pp. 115–124, Nov. 2018, doi: 10.30812/MATRIK.V18I1.344.

[24] E. D. Tarkus, S. R. U. A. Sompie, and A. Jacobus, “Implementasi Metode Recurrent Neural Network pada Pengklasifikasian Kualitas Telur Puyuh,” Jurnal Teknik Informatika, vol. 15, no. 2, pp. 137–144, Jun. 2020, doi: 10.35793/JTI.15.2.2020.29552.

[25] Navin Kumar Manaswi, Deep Learning with Applications Using Python, vol. 1. Apress Berkeley, CA, 2018.

[26] A. Saxena, Predicting bitcoin price using lstm And Compare its predictability with arima model 1. 2018. doi: 10.13140/RG.2.2.15847.57766.

[27] Z. Zhao, W. Chen, X. Wu, P. C. Y. Chen, and J. Liu, “LSTM network: a deep learning approach for short-term traffic forecast,” IET Intelligent Transport Systems, vol. 11, no. 2, pp. 68–75, Mar. 2017, doi: 10.1049/IET-ITS.2016.0208.

[28] A. Bhuvaneswari, J. T. Jones Thomas, and P. Kesavan, “Embedded Bi-directional GRU and LSTMLearning Models to Predict Disasterson Twitter Data,” Procedia Comput Sci, vol. 165, pp. 511–516, 2019, doi: https://doi.org/10.1016/j.procs.2020.01.020.

[29] L. Zeng, W. Ren, and L. Shan, “Attention-based bidirectional gated recurrent unit neural networks for well logs prediction and lithology identification,” Neurocomputing, vol. 414, pp. 153–171, 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.026.

[30] H. M. Lynn, S. B. Pan, and P. Kim, “A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks,” IEEE Access, vol. 7, pp. 145395–145405, 2019, doi: 10.1109/ACCESS.2019.2939947.

[31] Moh. Rohman, Moh. A. Rohman, - Suhartono, and T. Chamidy, “Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile,” Techno.Com, vol. 22, no. 2, pp. 358–372, May 2023, doi: 10.33633/tc.v22i2.7876.

[32] T. Li, M. Hua, and X. Wu, “A Hybrid CNN-LSTM Model for Forecasting Particulate Matter (PM2.5),” IEEE Access, vol. 8, pp. 26933–26940, 2020, doi: 10.1109/ACCESS.2020.2971348.

[33] R. Barzegar, M. T. Aalami, and J. Adamowski, “Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model,” Stochastic Environmental Research and Risk Assessment, vol. 34, no. 2, pp. 415–433, Feb. 2020, doi: 10.1007/S00477-020-01776-2/METRICS. 65 Program Studi Komputer (S2) Universitas Nusa Mandiri

[34] M. Alhussein, K. Aurangzeb, and S. I. Haider, “Hybrid CNN-LSTM Model for ShortTerm Individual Household Load Forecasting,” IEEE Access, vol. 8, pp. 180544– 180557, 2020, doi: 10.1109/ACCESS.2020.3028281.

[35] C. Alifia Putri and S. Al Faraby, “Analisis Sentimen Review Film Berbahasa Inggris Dengan Pendekatan Bidirectional Encoder Representations from Transformers,” JATISI (Jurnal Teknik Informatika dan Sistem Informasi), vol. 6, no. 2, pp. 181–193, Jan. 2020, doi: 10.35957/JATISI.V6I2.206.

[36] M. A. Rosid, A. S. Fitrani, I. R. I. Astutik, N. I. Mulloh, and H. A. Gozali, “Improving Text Preprocessing For Student Complaint Document Classification Using Sastrawi,” IOP Conf Ser Mater Sci Eng, vol. 874, no. 1, p. 012017, Jun. 2020, doi: 10.1088/1757-899X/874/1/012017.

[37] S. H. Badjrie, O. N. Pratiwi, and H. D. Anggana, “Analisis Sentimen Review Customer Terhadap Produk Indihome Dan First Media Menggunakan Algoritma Convolutional Neural Network,” eProceedings of Engineering, vol. 8, no. 5, Oct. 2021, Accessed: Oct. 30, 2022. [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/ar ticle/view/15877/15590

[38] I. H. Hidayat, R. E. Parwanto, and Rudy, “Sentiment Analysis on the Perception and Mindset of the People of Indonesia on the Use of Vaccines to Deal with the Covid-19 Pandemic using the Text Mining Method,” in 2022 International Conference on Information Management and Technology (ICIMTech), 2022, pp. 57–61. doi: 10.1109/ICIMTech55957.2022.9915072.

[39] S. Sucipto, A. G. Tammam, and R. Indriati, “Hoax Detection at Social Media With Text Mining Clarification System-Based,” JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika), vol. 3, no. 2, Dec. 2018, doi: 10.29100/JIPI.V3I2.837.

[40] J. Pennington, R. Socher, and C. D. Manning, “Glove: Global Vectors for Word Representation.,” in EMNLP, 2014, pp. 1532–1543.

[41] “Google Developers. (n.d.). Google Play,” https://developer.android.com/distribute/play/.

[42] H. He and E. A. Garcia, “Learning from Imbalanced Data,” IEEE Trans Knowl Data Eng, vol. 21, no. 9, pp. 1263–1284, 2009, doi: 10.1109/TKDE.2008.239.

[43] E. M. O. N. Haryanto, A. K. A. Estetikha, and R. A. Setiawan, “IMPLEMENTASI SMOTE UNTUK MENGATASI IMBALANCED DATA PADA SENTIMEN ANALISIS SENTIMEN HOTEL DI NUSA TENGGARA BARAT DENGAN MENGGUNAKAN ALGORITMA SVM,” Informasi Interaktif, vol. 7, no. 1, pp. 16–20, Jan. 2022, Accessed: Jul. 17, 2023. [Online]. Available: http://ejournal.janabadra.ac.id/index.php/informasiinteraktif/article/view/1615

[44] I. M. Nur and Ismatullah, “Implementasi Adaptive Synthetic Bernoulli Naïve Bayes untuk Mengatasi Imbalance Class Data pada Kasus Mental Health di Indonesia,” Journal Of Data Insights, vol. 1, no. 1, pp. 10–18, May 2023, Accessed: Jul. 17, 66 Program Studi Komputer (S2) Universitas Nusa Mandiri 2023. [Online]. Available: https://jurnalnew.unimus.ac.id/index.php/jodi/article/view/134

[45] M. I. Amal, E. S. Rahmasita, E. Suryaputra, and N. A. Rakhmawati, “Sentiment Classification Analysis On Phone Identity Card Data Leaks Issues On Twitter,” Jurnal Teknik Informatika dan Sistem Informasi, vol. 8, no. 3, pp. 645 – 660–645 – 660, Dec. 2022, doi: 10.28932/JUTISI.V8I3.5483.

[46] I. D. Nugraha and Y. Azhar, “Deteksi Depresi Pengguna Twitter Indonesia Menggunakan LSTM-RNN,” Jurnal Nasional Pendidikan Teknik Informatika?: JANAPATI, vol. 11, no. 3, pp. 320–329, Dec. 2022, doi: 10.23887/JANAPATI.V11I3.50674.

[47] Moh. Rohman, Moh. A. Rohman, - Suhartono, and T. Chamidy, “Bidirectional GRU dengan Attention Mechanism pada Analisis Sentimen PLN Mobile,” Techno.Com, vol. 22, no. 2, pp. 358–372, May 2023, doi: 10.33633/tc.v22i2.7876.