Klasifikasi Tiket IT Helpdesk Dengan Menggunakan IndoBERT

• VIRDA MEGA AYU
• 14210170

---

ABSTRAK

ABSTRAK

Nama              : Virda Mega Ayu

NIM                 : 14210170

Program Studi : Ilmu Komputer

Fakultas          : Teknologi Informasi

Jenjang           : Strata Dua (S2)

Konsentrasi     : Data Mining

Judul      : “Klasifikasi Tiket IT Helpdesk Dengan Menggunakan IndoBERT” IT Helpdesk mempunyai peranan penting dalam perusahaan modern, sebagai jembatan yang menghubungkan Staf IT dan pengguna terkait layanan Teknologi Informasi dan masalah yang dihadapi. Namun proses pengkategorian manual di helpdesk memakan waktu dan sumber daya manusia. Dimana teknologi Machine Learning dan Deep Learning dengan Natural Language Processing (NLP) dapat diterapkan dalam sistem helpdesk untuk melakukan klasifikasi teks secara otomatis. Banyak penelitian terdahulu yang memperhatikan penggunaan teknik-teknik text preprocessing dengan berfokus pada penggunaan IndoBERT. Penelitian ini dibangun dengan model model IndoBERT embedding dan juga Word embedding dengan menggunakan layer CNN (Convolution Neural Network) dan GRU (Gated Recurrent Unit). Dimana model klasifikasi text mining dengan algoritma Deep Neural Network ini dilakukan pada masing-masing pemrosesan teknik text preprocessing (Text normalization, Stopwords removal, Stemming, dan Lemmatization) pada dataset IT helpdesk berbahasa Indonesia untuk melihat pengaruh dan hasil model terbaik dari masing-masing setiap tahapan text preprocessing tersebut terhadap feature extraction dan algoritma model klasifikasi yang digunakan. Hasil eksperimen menunjukkan bahwa nilai accuracy terbaik dihasilkan yaitu oleh proses data teks pada teknik Lemmatization dengan arsitektur CNN pada learning rate 5x10-5 sebesar 90,60%.

KATA KUNCI

Helpdesk,Klasifikasi,NLP,Text preprocessing,INDOBERT

---

DAFTAR PUSTAKA

DAFTAR REFERENSI

[1] et all Ridwan, Mohammad, “Penerapan Helpdesk Ticketing System Dalam Penanganan Keluhan Penggunaan Sistem Informasi Berbasis Web,” J. Tek. Inform., vol. 7, no. 1, pp. 71–82, 2019.

[2] F. Al-Hawari and H. Barham, “A Machine Learning based help desk system for IT service management,” J. King Saud Univ. - Comput. Inf. Sci., vol. 33, no. 6, pp. 702–718, 2021, doi: 10.1016/j.jksuci.2019.04.001.

[3] J. A. Putra and A. Budi, “Penerapan Natural Language Processing dalam Aplikasi Chatbot Sebagai Media Pencarian Informasi Dengan Menggunakan React (Studi Kasus: Institut Bisnis dan Informatika Kwik Kian Gie),” J. Inform. dan Bisnis, vol. 9, no. 2, pp. 1–12, 2020.

[4] F. P. Rachman, “Perbandingan Model Deep Learning untuk Klasifikasi Sentiment Analysis dengan Teknik Natural Languange Processing,” J. Teknol. dan Manaj. Inform., vol. 7, no. 2, pp. 113–121, 2021, doi: 10.26905/jtmi.v7i2.6506.

[5] S. Ahmed et al., “An Empirical Analysis of State-of-Art Classification Models in an IT Incident Severity Prediction Framework,” Appl. Sci., vol. 13, no. 6, p. 3843, 2023, doi: 10.3390/app13063843.

[6] D. F. Oliveira, A. S. Nogueira, and M. A. Brito, “Performance Comparison of Machine Learning Algorithms in Classifying Information Technologies Incident Tickets,” Ai, vol. 3, no. 3, pp. 601–622, 2022, doi: 10.3390/ai3030035.

[7] S. P. Paramesh, C. Ramya, and K. S. Shreedhara, “Classifying the Unstructured IT Service Desk Tickets Using Ensemble of Classifiers,” Proc. 2018 3rd Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS 2018, pp. 221–227, 2018, doi: 10.1109/CSITSS.2018.8768734.

[8] S. Khairunnisa, A. Adiwijaya, and S. Al Faraby, “Pengaruh Text Preprocessing terhadap Analisis Sentimen Komentar Masyarakat pada Media Sosial Twitter (Studi Kasus Pandemi COVID-19),” J. MEDIA Inform. BUDIDARMA, vol. 5, no. 2, p. 406, Apr. 2021, doi: 10.30865/mib.v5i2.2835.

[9] A. Kurniasih and L. P. Manik, “On the Role of Text Preprocessing in BERT 119 Program Studi Komputer (S2) Universitas Nusa Mandiri Embedding-based DNNs for Classifying Informal Texts,” Int. J. Adv. Comput. Sci. Appl., vol. 13, no. 6, pp. 927–934, 2022, doi: 10.14569/IJACSA.2022.01306109.

[10] I. R. Hidayat and W. Maharani, “General Depression Detection Analysis Using IndoBERT Method,” Int. J. Inf. Commun. Technol., vol. 8, no. 1, pp. 41–51, 2022, doi: 10.21108/ijoict.v8i1.634.

[11] S. Saadah, Kaenova Mahendra Auditama, Ananda Affan Fattahila, Fendi Irfan Amorokhman, Annisa Aditsania, and Aniq Atiqi Rohmawati, “Implementation of BERT, IndoBERT, and CNN-LSTM in Classifying Public Opinion about COVID-19 Vaccine in Indonesia,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 4, pp. 648–655, 2022, doi: 10.29207/resti.v6i4.4215.

[12] M. R. Yanuar and S. Shiramatsu, “Aspect Extraction for Tourist Spot Review in Indonesian Language using BERT,” 2020 Int. Conf. Artif. Intell. Inf. Commun. ICAIIC 2020, pp. 298–302, 2020, doi: 10.1109/ICAIIC48513.2020.9065263.

[13] S. M. Isa, G. Nico, and M. Permana, “Indobert for Indonesian Fake News Detection,” ICIC Express Lett., vol. 16, no. 3, pp. 289–297, 2022, doi: 10.24507/icicel.16.03.289.

[14] B. Juarto, “Indonesian News Classification Using IndoBert,” pp. 0–2, 2023.

[15] M. Ikram, K. Sinapoy, Y. Sibaroni, and S. S. Prasetyowati, “Comparison of LSTM and IndoBERT Method,” vol. 5, no. 158, pp. 2–6, 2023.

[16] E. Alzahrani and L. Jololian, “How Different Text-Preprocessing Techniques using the Bert Model Affect the Gender Profiling of Authors,” pp. 01–08, 2021, doi: 10.5121/csit.2021.111501.

[17] F. Wicaksono and O. S. Baswara, “Design and Implementation of WebBased Helpdesk Information Systems Using Extreme Programming Methods,” ITEJ (Information Technol. Eng. Journals), vol. 5, no. 2, pp. 88– 96, 2020, doi: 10.24235/itej.v5i2.44.

[18] S. Syofian and A. Winandar, “Aplikasi Helpdesk Mendukung Sistem Ticketing,” J. Teknol. Inf., vol. 4, no. 1, pp. 1–7, 2017, [Online]. Available: 120 Program Studi Komputer (S2) Universitas Nusa Mandiri http://ejournal.urindo.ac.id/index.php/JTI/article/view/264/239

[19] M. M. Manika Lamba, Text Mining for Infoermation Professionals An Uncharted Territory. Delhi, India: Springer, 2022. doi: https://doi.org/10/1007/978-3-030-85085-2.

[20] Syukriyanto Latif, “TEXT MINING UNTUK KLASIFIKASI KONTEN ABSTRAK JURNAL BAHASA INGGRIS MENGGUNAKAN METODE REDUKSI DIMENSI DAN NAIVE BAYES,” Universitas Hasanuddin Makassar, 2018. [Online]. Available: http://dx.doi.org/10.1186/s13662-017- 1121-6%0Ahttps://doi.org/10.1007/s41980-018-0101- 2%0Ahttps://doi.org/10.1016/j.cnsns.2018.04.019%0Ahttps://doi.org/10.10 16/j.cam.2017.10.014%0Ahttp://dx.doi.org/10.1016/j.apm.2011.07.041%0 Ahttp://arxiv.org/abs/1502.020

[21] Chengqing Zong. Rui Xia. Jiajun Zhang, Text Data Mining, TSingkua U. beijing, China: Springer, 2021. doi: https://doi.org/10/\.1007/978-981-16- 0100-2.

[22] M. L. Khan, M. Ittefaq, Y. I. M. Pantoja, M. M. Raziq, and A. Malik, “Public Engagement Model to Analyze Digital Diplomacy on Twitter: A Social Media Analytics Framework,” Int. J. Commun., vol. 15, pp. 1741–1769, 2021.

[23] A. Pambudi, “Penerapan Crisp-Dm Menggunakan Mlr K-Fold Pada Data Saham Pt. Telkom Indonesia (Persero) Tbk (Tlkm) (Studi Kasus: Bursa Efek Indonesia Tahun 2015-2022),” J. Data Min. dan Sist. Inf., vol. 4, no. 1, p. 1, 2023, doi: 10.33365/jdmsi.v4i1.2462.

[24] M. Rizki, D. Devrika, I. H. Umam, and F. S. Lubis, “Aplikasi Data Mining dalam Penentuan Layout Swalayan dengan Menggunakan Metode MBA,” J. Tek. Ind. J. Has. Penelit. dan Karya Ilm. dalam Bid. Tek. Ind., vol. 5, no. 2, p. 130, 2020, doi: 10.24014/jti.v5i2.8958.

[25] S. Shevira, I. Made, A. D. Suarjaya, and P. Wira Buana, “Pengaruh Kombinasi dan Urutan Pre-Processing pada Tweets Bahasa Indonesia,” JITTER-Jurnal Ilm. Teknol. dan Komput., vol. 3, no. 2, 2022.

[26] A. Setiawan, L. W. Santoso, and R. Adipranata, “Klasifikasi Artikel Berita 121 Program Studi Komputer (S2) Universitas Nusa Mandiri Bahasa Indonesia Dengan Naive Bayes Classifier,” J. Infra, vol. 8, no. 1, pp. 146–151, 2020.

[27] P. M. Rahate and M. B. Chandak, “Text normalization and its role in speech synthesis,” Int. J. Eng. Adv. Technol., vol. 8, no. 5 Special Issue 3, pp. 115– 122, 2019, doi: 10.35940/ijeat.E1029.0785S319.

[28] D. J. Ladani and N. P. Desai, “Stopword Identification and Removal Techniques on TC and IR applications: A Survey,” 2020 6th Int. Conf. Adv. Comput. Commun. Syst. ICACCS 2020, no. April 2021, pp. 466–472, 2020, doi: 10.1109/ICACCS48705.2020.9074166.

[29] C. D. Paice, “Stemming,” in Encyclopedia of Database Systems, L. Liu and M. T. Özsu, Eds., New York, NY: Springer New York, 2016, pp. 1–5. doi: 10.1007/978-1-4899-7993-3_942-2.

[30] M. Sharipov and O. Sobirov, “Development of a Rule-Based Lemmatization Algorithm Through Finite State Machine for Uzbek Language,” CEUR Workshop Proc., vol. 3315, pp. 154–159, 2022.

[31] A. Revina, K. Buza, and V. G. Meister, “IT Ticket Classification: The Simpler, the Better,” IEEE Access, vol. 8, no. January, pp. 193380–193395, 2020, doi: 10.1109/ACCESS.2020.3032840.

[32] R. Yendra, L. Marifni, and I. Suryani, “Klasifikasi Data Mining Untuk Seleksi Penerimaan Calon Pegawai Negeri Sipil Tahun 2017 Menggunakan Metode Naïve Bayes,” J. Sains Mat. dan Stat., vol. 6, no. 1, p. 65, 2020, doi: 10.24014/jsms.v6i1.9254.

[33] Y. Religia, “Feature Extraction Untuk Klasifikasi Pengenalan Wajah Menggunakan Support Vector Machine Dan K-Nearest Neighbor,” Pelita Teknol. J. Ilm. Inform. Arsit. dan Lingkung., vol. 14, no. 2, pp. 85–92, 2019.

[34] D. S. Asudani, N. K. Nagwani, and P. Singh, Impact of word embedding models on text analytics in Deep Learning environment: a review, no. 0123456789. Springer Netherlands, 2023. doi: 10.1007/s10462-023-10419- 1.

[35] A. Nurdin, B. Anggo Seno Aji, A. Bustamin, and Z. Abidin, “Perbandingan Kinerja Word Embedding Word2Vec, Glove, Dan Fasttext Pada Klasifikasi 122 Program Studi Komputer (S2) Universitas Nusa Mandiri Teks,” J. Tekno Kompak, vol. 14, no. 2, p. 74, 2020, doi: 10.33365/jtk.v14i2.732.

[36] A. Vaswani et al., “Attention is all you need,” Adv. Neural Inf. Process. Syst., vol. 2017-Decem, no. Nips, pp. 5999–6009, 2017.

[37] A. D. Rendragraha, M. A. Bijaksana, and A. Romadhony, “Pendekatan Metode Transformers untuk Deteksi Bahasa Kasar dalam Komentar Berita Online Indonesia,” e-Proceeding Eng., vol. 8, no. 2, pp. 3385–3395, 2021.

[38] authors Anonymous, “Under review as a conference paper at ICLR 2018 WEIGHTED TRANSFORMER NETWORK FOR MACHINE TRANSLATION,” pp. 1–11, 2018.

[39] A. Kazemnejad, I. Padhi, K. N. Ramamurthy, P. Das, and S. Reddy, “The Impact of Positional Encoding on Length Generalization in Transformers,” pp. 1–32, 2023, [Online]. Available: http://arxiv.org/abs/2305.19466

[40] Y. J. Park, M. A. Lee, G. J. Yang, S. J. Park, and C. B. Sohn, “Biomedical Text NER Tagging Tool with Web Interface for Generating BERT-Based Fine-Tuning Dataset,” Appl. Sci., vol. 12, no. 23, 2022, doi: 10.3390/app122312012.

[41] A. Subakti, H. Murfi, and N. Hariadi, “The performance of BERT as data representation of text clustering,” J. Big Data, vol. 9, no. 1, 2022, doi: 10.1186/s40537-022-00564-9.

[42] M. Munikar, S. Shakya, and A. Shrestha, “Fine-grained Sentiment Classification using BERT,” Int. Conf. Artif. Intell. Transform. Bus. Soc. AITB 2019, pp. 2–5, 2019, doi: 10.1109/AITB48515.2019.8947435.

[43] K. N. Elmadani, M. Elgezouli, and A. Showk, “BERT Fine-tuning For Arabic Text Summarization,” no. March, pp. 0–4, 2020, [Online]. Available: http://arxiv.org/abs/2004.14135

[44] F. Koto, A. Rahimi, J. H. Lau, and T. Baldwin, “IndoLEM and IndoBERT: A Benchmark Dataset and Pre-trained Language Model for Indonesian NLP,” COLING 2020 - 28th Int. Conf. Comput. Linguist. Proc. Conf., pp. 757–770, 2020, doi: 10.18653/v1/2020.coling-main.66.

[45] Simon and Schuster, Deep Learning with Python, Special Sa. Shelter Island 123 Program Studi Komputer (S2) Universitas Nusa Mandiri NewYork: Manning, 2021.

[46] M. R. Yogaswara, “Klasifikasi Keluhan Pelanggan Menggunakan Metode Convolutional Neural Network (Cnn) Studi Kasus Pt Telkom Akses Witel Surabaya Selatan,” 2021.

[47] Wahyudi Setiawan, Deep Learning Menggunakan Convolutional Neural Network Teori dan Aplikasi. Malang: Tim MNC Publishing, 2020.

[48] Stephan Raaujmakers, Deep Learning for Natural Language Processing, 7th ed. Manning. 2020.

[49] N. A. Shafirra and I. Irhamah, “Klasifikasi Sentimen Ulasan Film Indonesia dengan Konversi Speech-to-Text (STT) Menggunakan Metode Convolutional Neural Network (CNN),” J. Sains dan Seni ITS, vol. 9, no. 1, 2020, doi: 10.12962/j23373520.v9i1.51825.

[50] 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 Review Analysis Sentiment Customer Product Indihome And First Media Using Convolutional Neural Network,” eProceedings …, vol. 8, no. 5, pp. 9049–9061, 2021, [Online]. Available: https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineerin g/article/view/15877

[51] L. Qing, W. Linhong, and D. Xuehai, “A novel neural network-based method for medical text classification,” Futur. Internet, vol. 11, no. 12, 2019, doi: 10.3390/FI11120255.

[52] A. Kholik, “Klasifikasi Menggunakan Convolutional Neural Network (Cnn) Pada Tangkapan Layar Halaman Instagram,” J. Data Min. dan Sist. Inf., vol. 2, no. 2, p. 10, 2021, doi: 10.33365/jdmsi.v2i2.1345.

[53] N. Rochmawati, H. B. Hidayati, Y. Yamasari, H. P. A. Tjahyaningtijas, W. Yustanti, and A. Prihanto, “Analisa Learning Rate dan Batch Size pada Klasifikasi Covid Menggunakan Deep Learning dengan Optimizer Adam,” J. Inf. Eng. Educ. Technol., vol. 5, no. 2, pp. 44–48, 2021, doi: 10.26740/jieet.v5n2.p44-48. 124 Program Studi Komputer (S2) Universitas Nusa Mandiri

[54] Y. A. Suwitono and F. J. Kaunang, “Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Daun Dengan Metode Data Mining SEMMA Menggunakan Keras,” J. Komtika (Komputasi dan Inform., vol. 6, no. 2, pp. 109–121, 2022, doi: 10.31603/komtika.v6i2.8054.

[55] F. Masykur, M. B. Setyawan, and K. Winangun, “Epoch Optimization on Rice Leaf Image Classification Using Convolutional Neural Network (CNN) MobileNet,” CESS (Journal Comput. Eng. Syst. Sci., vol. 7, no. 2, p. 581, 2022, doi: 10.24114/cess.v7i2.37336.

[56] A. Arkadia, B. Hananto, D. S. Prasvita, and K. K. Lstm, “Optimasi Long Short Term Memory Dengan Adam Menggunakan Data Udara Kota DKI Jakarta,” pp. 559–568, 2022.

[57] S. Bock and M. Weis, “A Proof of Local Convergence for the Adam Optimizer,” Proc. Int. Jt. Conf. Neural Networks, vol. 2019-July, no. January, 2019, doi: 10.1109/IJCNN.2019.8852239.

[58] M. T. Razaq, D. Nurjanah, and H. Nurrahmi, “Analisis Sentimen Review Film menggunakan Naive Bayes Classifier dengan fitur Tf-Idf,” vol. 9, no. 5, pp. 6053–6071, 2022.

[59] M. M. Al Haromainy, D. A. Setyawan, O. K. Waluya, and A. Z. Arifin, “Ambiguitas machine translation pada cross language chatbot bea cukai,” Regist. J. Ilm. Teknol. Sist. Inf., vol. 5, no. 1, pp. 55–62, 2019, doi: 10.26594/register.v5i1.1387.

[60] T. Iskandar Zulkarnain Maulana Putra, A. Farhan Bukhori, dan Ilmu Pengetahuan Alam, and U. Gadjah Mada, “Model Klasifikasi Berbasis Multiclass Classification dengan Kombinasi Indobert Embedding dan Long Short-Term Memory untuk Tweet Berbahasa Indonesia (Classification Model Based on Multiclass Classification with a Combination of Indobert Embedding and Long ,” J. Ilmu Siber dan Teknol. Digit., vol. 1, no. 1, pp. 1– 28, 2022, [Online]. Available: https://doi.org/10.35912/jisted.v1i1.1509

[61] G. Z. Nabiilah, S. Y. Prasetyo, Z. N. Izdihar, and A. S. Girsang, “BERT base model for toxic comment analysis on Indonesian social media,” Procedia Comput. Sci., vol. 216, no. 2022, pp. 714–721, 2023, doi: 125 Program Studi Komputer (S2) Universitas Nusa Mandiri 10.1016/j.procs.2022.12.188.

[62] A. Candra, Wella, and A. Wicaksana, “Bidirectional encoder representations from transformers for cyberbullying text detection in indonesian social media,” Int. J. Innov. Comput. Inf. Control, vol. 17, no. 5, pp. 1599–1615, 2021, doi: 10.24507/ijicic.17.05.1599.

[63] T. A. S. Rohmah and W. Maharani, “Personality Detection on Twitter Social Media Using IndoBERT Method,” Build. Informatics, Technol. Sci., vol. 4, no. 2, pp. 448–453, 2022, doi: 10.47065/bits.v4i2.1895.

[64] B. Wilie et al., “IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language Understanding,” 2020, [Online]. Available: http://arxiv.org/abs/2009.05387