CT-GAN UNTUK MENGATASI IMBALANCE DATA PADA PEMODELAN PREDIKSI EMPLOYEE ATTRITION

• YESSICA FARA DESVIA
• 14207118

---

ABSTRAK

 

ABSTRAK Nama : Yessica Fara Desvia NIM : 14207118 Program Studi : Ilmu Komputer Fakultas : Teknologi Informasi Jenjang : Strata Dua (S2) Konsentrasi : Data Mining Judul Tesis : “CT-GAN Untuk Mengatasi Imbalance Data Pada Pemodelan Prediksi Employee Attrition” Dengan ketersediaan big data di era Industri 4.0, pemodelan prediktif merupakan salah satu peluang utama untuk peningkatan bisnis. Manajemen sumber daya manusia merupakan bidang penting yang akan mendapatkan manfaat dari model prediktif, dan salah satu fungsinya yang krusial adalah mempertahankan kompetensi karyawan untuk tetap bekerja di perusahaan. Karyawan yang produktif dapat memutuskan untuk meninggalkan organisasi karena berbagai alasan. Dengan menggunakan model prediktif, faktor-faktor penyebab atrisi karyawan dapat dianalisis sehingga dapat diketahui faktor mana yang dominan. Tantangan utama untuk pemodelannya adalah data yang tidak seimbang yang menghasilkan akurasi yang rendah. Dalam penelitian ini, mengusulkan Generative Adversarial Network (GAN) untuk menghasilkan data sintetik yang tepat mirip dengan data nyata untuk pemodelan menggunakan dataset Retensi Karyawan dari 10 fitur dan 1 4.999 data. Setelah langkah pra-pemrosesan menggunakan GAN, kami mengusulkan peningkatan gradien untuk algoritma klasifikasi. Hasil menunjukkan bahwa metode yang diusulkan mengungguli metode yang ada dengan Accuracy 98.53%,
Precision 97.94%, Sensitivity 92.58%, AUC 99.78%, FP-Rate 40.90%, Specificity 99.64%. Model prediktif dapat langsung digunakan untuk organisasi mana pun dan juga dapat diadaptasi menggunakan data internalnya sendiri. Kata Kunci: Atrisi Karyawan, GAN
 

KATA KUNCI

CT-GAN Mengatasi Imbalance,Prediksi,Employee Attrition

---

DAFTAR PUSTAKA

 

Daftar Pustaka [1] A. Szymkowiak, B. Melovi?, M. Dabi?, K. Jeganathan, and G. S. Kundi, “Information technology and Gen Z: The role of teachers, the internet, and technology in the education of young people,” Technol. Soc., vol. 65, no. March, 2021, doi: 10.1016/j.techsoc.2021.101565. [2] K. Venkataramanan and H. R. Rajamohan, “Emotion Recognition from Speech,” arxiv, Dec. 2019, doi: 10.1007/978-3-319-02732-6_7. [3] I. Livieris, E. Pintelas, and P. Pintelas, “Gender Recognition by Voice using an Improved Self-Labeled Algorithm,” Mach. Learn. Knowl. Extr. , vol. 1, no. 1, pp. 492–503, 2019, doi: 10.3390/make1010030. [4] A. B. Nassif, I. Shahin, S. Hamsa, N. Nemmour, and K. Hirose, “CASA- based speaker identification using cascaded GMM-CNN classifier in noisy and emotional talking conditions,” Appl. Soft Comput. , vol. 103, p. 107141, 2021, doi: 10.1016/j.asoc.2021.107141. [5] A. Yadav and Di. K. Vishwakarma, “A Multilingual Framework of CNN and Bi-LSTM for Emotion Classification,” 2020 11th Int. Conf. Comput.
Commun. Netw. Technol. ICCCNT 2020, 2020, doi: 10.1109/ICCCNT49239.2020.9225614. [6] H. Pérez-Espinosa, R. Zatarain-Cabada, and M. L. Barrón-Estrada, “Chapter 15 - Emotion recognition: from speech and facial expressions,” in
Biosignal Processing and Classification Using Computational Learning
and Intelligence, A. A. Torres-García, C. A. Reyes-García, L. VillaseñorPineda, and O. Mendoza-Montoya, Eds. Academic Press, 2022, pp. 307– 326. doi: https://doi.org/10.1016/B978-0-12-820125-1.00028-2. [7] C. Luna-Jiménez, D. Griol, Z. Callejas, R. Kleinlein, J. M. Montero, and F. Fernández-Martínez, “Multimodal emotion recognition on RAVDESS dataset using transfer learning,” Sensors, vol. 21, no. 22, pp. 1 –29, 2021, doi: 10.3390/s21227665. [8] M. Cohn, A. Pycha, and G. Zellou, “Intelligibility of face-masked speech depends on speaking style: Comparing casual, clear, and emotional speech,” Cognition, vol. 210, p. 104570, 2021, doi: https://doi.org/10.1016/j.cognition.2020.104570. [9] T. Gupta, D.-T. Truong, T. T. Anh, and C. E. Siong, “Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model,” 2022, [Online]. Available: http://arxiv.org/abs/2203.11774 [10] M. F. Kacamarga, T. W. Cenggoro, A. Budiarto, R. Rahutomo, and B. Pardamean, “Analysis of acoustic features in gender identification model for English and Bahasa Indonesia telephone speeches,” Procedia Comput.
Sci., vol. 157, pp. 199–204, 2019, doi: 10.1016/j.procs.2019.08.158. [11] F. Ertam, “An effective gender recognition approach using voice data via 50 deeper LSTM networks,” Appl. Acoust., vol. 156, pp. 351 –358, 2019, doi: 10.1016/j.apacoust.2019.07.033. [12] E. Yucesoy and V. V. Nabiyev, “Gender identification of a speaker using MFCC and GMM,” ELECO 2013 - 8th Int. Conf. Electr. Electron. Eng. , no. November, pp. 626–629, 2013, doi: 10.1109/eleco.2013.6713922. [13] P. Sunitha and K. S. Prasad, “Multi Band Spectral Subtraction for Speech Enhancement with Different Frequency Spacing Methods and their Effect on Objective Quality Measures,” Int. J. Image, Graph. Signal Process., vol. 10, no. 5, p. 54, 2019. [14] D. Doukhan, J. Carrive, F. Vallet, A. Larcher, and S. Meignier, “An OpenSource Speaker Gender Detection Framework for Monitoring Gender Equality,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2018-April, pp. 5214–5218, 2018, doi: 10.1109/ICASSP.2018.8461471. [15] S. Kanwal, S. Asghar, A. Hussain, and A. Rafique, “Identifying the evidence of speech emotional dialects using artificial intelligence: A crosscultural study,” PLoS One, vol. 17, no. 3, pp. 1 –15, 2022, doi: 10.1371/journal.pone.0265199. [16] T. Mittal, U. Bhattacharya, R. Chandra, A. Bera, and D. Manocha, “M3er: Multiplicative multimodal emotion recognition using facial, textual, and speech cues,” in Proceedings ofthe AAAI conference on artificial
intelligence, 2020, vol. 34, no. 02, pp. 1359–1367. [17] A. Muppidi and M. Radfar, “Speech emotion recognition using quaternion convolutional neural networks,” ICASSP, IEEE Int. Conf. Acoust. Speech
Signal Process. - Proc., vol. 2021 -June, pp. 6309–6313, 2021, doi: 10.1109/ICASSP39728.2021.9414248. [18] F. A. Shaqra, R. Duwairi, and M. Al-Ayyoub, “Recognizing emotion from speech based on age and gender using hierarchical models,” Procedia
Comput. Sci., vol. 151, no. 2018, pp. 37–44, 2019, doi: 10.1016/j.procs.2019.04.009. [19] A. Eronen, A. K"ah"ari, P. Kallio, and T. Virtanen, “Improving speech recognition accuracy using mel scale features in noisy and reverberant environments,” J. Signal Process. Syst. , vol. 91, no. 1, pp. 61–73, 2019. [20] J.-H. Chen, X.-J. Li, and Q.-F. Lin, “Deep learning based speech recognition with mel-scale feature,” in 2021 IEEE International
Conference on Artificial Intelligence and Computer Applications (ICAICA), 2021, pp. 63–67. [21] Y. Gao, Z. Li, X. Lin, and Y. Wang, “Emotion recognition from speech using mel frequency cepstral coefficient and convolutional neural network,” in 2021 International Conference on Electronics, Information,
and Communication (ICEIC), 2021, pp. 1–4. [22] G. Xie, S. Liu, Y. Zhang, Y. Peng, and X. Zhou, “A hybrid feature 51 extraction approach for speech recognition based on Mel-frequency cepstral coefficients and prosodic features,” Neurocomputing, vol. 275, pp. 1076–1087, 2018. [23] K. W. Cheuk, H. Anderson, K. Agres, and D. Herremans, “NnAudio: An on-the-Fly GPU Audio to Spectrogram Conversion Toolbox Using 1D Convolutional Neural Networks,” IEEE Access, vol. 8, pp. 161981 – 162003, 2020, doi: 10.1109/ACCESS.2020.3019084. [24] T. Zhang, G. Feng, J. Liang, and T. An, “Acoustic scene classification based on Mel spectrogram decomposition and model merging,” Appl.
Acoust., vol. 182, p. 108258, 2021, doi: 10.1016/j.apacoust.2021.108258. [25] A. Goni, “How to Detect COVID-19 Cough From Mel Spectrogram Using Convolutional Neural Network,”
https://www.analyticsvidhya.com/blog/2021/06/how-to-detect-covid19-
cough-from-mel-spectrogram-using-convolutional-neuralnetwork/#:~:text=Mel%20spectrogram%20is%20a%20spectrogram,is%20
contained%20by%20the%20signal. , 2021. [26] I. T. Handoko and S. Suyanto, “Klasifikasi Gender dan Usia berdasarkan Suara Pembicara Menggunakan Hidden Markov Model,” Indones. J.
Comput., vol. 4, no. 3, pp. 99–106, 2019, doi: 10.21108/indojc.2019.4.3.375. [27] A. Winursito, R. Hidayat, and A. Bejo, “Improvement of MFCC feature extraction accuracy using PCA in Indonesian speech recognition,” 2018 Int.
Conf. Inf. Commun. Technol. ICOIACT 2018, vol. 2018-Janua, pp. 379– 383, 2018, doi: 10.1109/ICOIACT.2018.8350748. [28] R. B. Handoko and S. Suyanto, “Klasifikasi Gender Berdasarkan Suara Menggunakan Support Vector Machine,” Indones. J. Comput. , vol. 4, no. 1, p. 9, 2019, doi: 10.21108/indojc.2019.4.1.244. [29] L. Rabiner and B.-H. Juang, Fundamentals of speech recognition. PrenticeHall, Inc., 1993. [30] S. Young et al., The HTK Book (for HTK Version 3.4.1). University of Cambridge, Engineering Department, 2019. [Online]. Available: http://htk.eng.cam.ac.uk/docs/docs.shtml [31] X. Huang, A. Acero, and H.-W. Hon, Spoken Language Processing: A
guide to theory, algorithm, and system development. Prentice Hall PTR, 2001. [32] L. Deng and D. Yu, Deep Learning: Methods and Applications. Now Publishers Inc, 2014. [Online]. Available: https://doi.org/10.1561/2200000040 [33] M. A. Uddin, M. S. Hossain, R. K. Pathan, and M. Biswas, “Gender Recognition from Human Voice using Multi-Layer Architecture,” INISTA
2020 - 2020 Int. Conf. Innov. Intell. Syst. Appl. Proc. , pp. 0–6, 2020, doi: 10.1109/INISTA49547.2020.9194654. 52 [34] J. Li, X. Zhang, L. Huang, F. Li, S. Duan, and Y. Sun, “Speech Emotion Recognition Using a Dual-Channel Complementary Spectrogram and the CNN-SSAE Neutral Network,” Appl. Sci., vol. 12, no. 19, p. 9518, 2022, doi: 10.3390/app12199518. [35] Y. Bengio, “Deep learning for computer vision,” Annu. Rev. Comput. Sci. , vol. 6, pp. 21–36, 2018, doi: 10.1146/annurev-cs-050317-035749. [36] M. Xu, F. Zhang, and W. Zhang, “Head Fusion: Improving the Accuracy and Robustness of Speech Emotion Recognition on the IEMOCAP and RAVDESS Dataset,” IEEE Access, vol. 9, pp. 74539–74549, 2021, doi: 10.1109/ACCESS.2021.3067460. [37] S. Han, F. Leng, and Z. Jin, “Speech Emotion Recognition with a ResNetCNN-Transformer Parallel Neural Network,” in IEEE 3rd International
Conference on Comunication, Information System and Computer
Enginering, 2021, pp. 6–10. [38] B. Salian, O. Narvade, R. Tambewagh, and S. Bharne, “Speech Emotion Recognition using Time Distributed CNN and LSTM,” ITM Web Conf., vol. 40, p. 03006, 2021, doi: 10.1051/itmconf/20214003006. [39] I. Shahin, A. B. Nassif, and N. Hindawi, “Speaker identification in stressful talking environments based on convolutional neural network,” Int. J.
Speech Technol. , vol. 24, no. 4, pp. 1055–1066, 2021, doi: 10.1007/s10772- 021-09869-1. [40] H. Dolka, M. V. Arul Xavier, and S. Juliet, “Speech emotion recognition using ANN on MFCC features,” 2021 3rd Int. Conf. Signal Process.
Commun. ICPSC 2021, no. May, pp. 431–435, 2021, doi: 10.1109/ICSPC51351.2021.9451810. [41] J. Zhao, X. Mao, and L. Chen, “Speech emotion recognition using deep 1D & 2D CNN LSTM networks,” Biomed. Signal Process. Control, vol. 47, pp. 312–323, 2019, doi: 10.1016/j.bspc.2018.08.035. [42] O. U. Kumala and A. Zahra, “Indonesian Speech Emotion Recognition using Cross-Corpus Method with the Combination of MFCC and Teager Energy Features,” Int. J. Adv. Comput. Sci. Appl. , vol. 12, no. 4, pp. 163– 168, 2021, doi: 10.14569/IJACSA.2021.0120422. [43] Z. T. Liu, A. Rehman, M. Wu, W. H. Cao, and M. Hao, “Speech emotion recognition based on formant characteristics feature extraction and phoneme type convergence,” Inf. Sci. (Ny)., vol. 563, pp. 309–325, 2021, doi: 10.1016/j.ins.2021.02.016. [44] M. S. Fahad, A. Deepak, G. Pradhan, and J. Yadav, “DNN-HMM-Based Speaker-Adaptive Emotion Recognition Using MFCC and Epoch-Based Features,” Circuits, Syst. Signal Process. , vol. 40, no. 1, pp. 466–489, 2021, doi: 10.1007/s00034-020-01486-8. [45] F. F. WATI, “Analisis Sentimen Review Aplikasi Tik-Tok Dengan Algoritma K-Nearest Neighbor, Naive Bayes Dan Support Vector Machine 53 Tesis,” STMIK Nusa Mandiri Jakarta, pp. 6–31, 2020, [Online]. Available: https://repository.nusamandiri.ac.id/index.php/repo/viewitem/11534