Optimalisasi K-Means Dalam Pengelompokan Ancaman Insiden Aplikasi Yang Dilaporkan Melalui Service Desk TIK

Rimba Prasasti, Rifki Sadikin, Eni Heni Hermallani

Abstract


Layanan click, call, counter (3C) merupakan bentuk transformasi layanan digital Perpajakan. Insiden layanan 3C yang terjadi ini dilaporkan melalui Service Desk TIK. Banyaknya laporan insiden membuat kendala dalam penanganan penyelesaian permasalahan. Dengan menggunakan K-Means secara unsupervised learning untuk pengelompokan ancaman insiden diharapkan dapat membantu penyelesaian lebih efektif. Optimalisasi untuk meningkatkan nilai akurasi yang lebih baik dicari menggunakan word embedded dengan algortima Elkan dan algortima Lloyd padaK-Means. Hasil optimal didapatkan pada jumlah kluster 4 yang dievaluasi   menggunakan Silhouette Score, Calinski Harabasz dan Davies-Bouldin Index. Hasil optimal dari penerapan model pada algoritma K-Means dan parameter algoritma Elkan dengan word embedding CountVectorizer didapatkan sebesar 71,94% pengelompokan yang sesuai.

Full Text:

PDF

References


Y. I. Santoso, “Ditjen Pajak optimalisasi layanan digital, demi kejar pendapatan di tengah pandemi,” 2020. https://newssetup.kontan.co.id/news/ditjen-pajak-optimalisasi-layanan-digital-demi-kejar-pendapatan-di-tengah-pandemi (accessed Nov. 02, 2021).

K. P. Kinanti and D. Pratomo, “Pengaruh Penerapan Pendaftaran Npwp Secara Online ( E- Registration ), E-Billing Dan E-Filing Terhadap Kepatuhan Wajib ( Survei pada Wajib Pajak Orang Pribadi Non Karyawan di KPP Pratama Depok Cimanggis Tahun 2019 ),” e-Proceeding Manag., vol. 8, no. 6, pp. 1–8, 2021.

M. Zuraeva and N. Rulandari, “Analisis Kualitas Pelayanan Perpajakan dalam Rangka Meningkatkan Kepatuhan Wajib Pajak,” J. Pajak Vokasi, vol. 2, no. 1, pp. 37–44, 2020.

Direktorat Jenderal Pajak, “SURAT EDARAN DIREKTUR JENDERAL PAJAK NOMOR SE - 37/PJ/2013,” no. Agustus, 2013.

D. Safitri and S. P. Silalahi, “Pengaruh Kualitas Pelayanan Fiskus, Pemahaman Peraturan Perpajakan Dan Penerapan Sistem E-Filling Terhadap Kepatuhan Wajib Pajak: Sosialisasi Perpajakan Sebagai Pemoderasi,” J. Akunt. dan Pajak, vol. 20, no. 2, 2020, doi: 10.29040/jap.v20i2.688.

M. A. Prihandono, R. Harwahyu, and R. F. Sari, “Performance of machine learning algorithms for IT incident management,” 2020 11th Int. Conf. Aware. Sci. Technol. iCAST 2020, pp. 2–7, 2020, doi: 10.1109/iCAST51195.2020.9319487.

R. R. COSTA, Jorge, Rubén PEREIRA, “ITSM Automation - Using Machine Learning to Predict Incident Resolution Category,” no. 351, 2021.

F. Alamri and A. Widyatama, “TAM Sebagai Solusi Atas Minat Penggunaan Layanan E-Registration Wajib Pajak,” vol. 10, no. 2, pp. 89–99, 2019.

J. Luengo, D. García-Gil, S. Ramírez-Gallego, S. García, and F. Herrera, Big Data Preprocessing. 2020. doi: 10.1007/978-3-030-39105-8.

K. R. Chowdhary, Fundamentals of Artificial Intelligence. 2020. [Online]. Available: https://doi.org/10.1007/978-81-322-3972-7_19

Gong, Nan, Chunxiao Fan, Yuexin Wu, Yue Ming, “A Web Content Extraction Method Base on Punctuation Distribution and HTML Tag Similarity,” Proc. 3rd Int. Conf. Logist. Informatics Serv. Sci., 2013.

G. N. R Prasad Sr Asst professor, “Identification of Bloom’s Taxonomy level for the given Question paper using NLP Tokenization technique,” Turkish J. Comput. Math. Educ., vol. 12, no. 13, pp. 1872–1875, 2021.

D. Na and C. Xu, “Automatically generation and evaluation of stop words list for Chinese patents,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 13, no. 4, pp. 1414–1421, 2015, doi: 10.12928/TELKOMNIKA.v13i4.2389.

A. Jabbar, S. Iqbal, M. I. Tamimy, S. Hussain, and A. Akhunzada, “Empirical evaluation and study of text stemming algorithms,” Artif. Intell. Rev., vol. 53, no. 8, pp. 5559–5588, 2020, doi: 10.1007/s10462-020-09828-3.

K. K. Purnamasari and I. S. Suwardi, “Rule-based Part of Speech Tagger for Indonesian Language,” IOP Conf. Ser. Mater. Sci. Eng., vol. 407, no. 1, 2018, doi: 10.1088/1757-899X/407/1/012151.

W. bin, angela wang, fenxiao chen, yuncheng wang and c.-c. jay kuo, “Evaluating word embedding models : methods and experimental results,” vol. 8, 2019, doi: 10.1017/ATSIP.2019.12.

M. Hamisu and A. Mansour, “Detecting Advance Fee Fraud Using NLP Bag of Word Model,” pp. 94–97, 2020.

A. Addiga and S. Bagui, “Sentiment Analysis on Twitter Data Using Term Frequency-Inverse Document Frequency,” pp. 117–128, 2022, doi: 10.4236/jcc.2022.108008.

D. K. Hashim and L. A. N. Muhammed, “Performance of K-means algorithm based an ensemble learning,” Bull. Electr. Eng. Informatics, vol. 11, no. 1, pp. 575–580, 2022, doi: 10.11591/eei.v11i1.3550.

K. Aoyama, K. Saito, and T. Ikeda, “Accelerating a Lloyd-Type k-Means Clustering Algorithm with Summable Lower Bounds in a Lower-Dimensional Space,” no. 11, pp. 2773–2783, 2018.

C. Elkan, “Using the Triangle Inequality to Accelerate k-Means,” Proceedings, Twent. Int. Conf. Mach. Learn., vol. 1, pp. 147–153, 2003.

K. R. Shahapure and C. Nicholas, “Cluster Quality Analysis Using Silhouette Score,” pp. 2020–2021, 2020, doi: 10.1109/DSAA49011.2020.00096.

A. K. Singh, S. Mittal, P. Malhotra, and Y. V. Srivastava, “Clustering Evaluation by Davies-Bouldin Index(DBI) in Cereal data using K-Means,” Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, pp. 306–310, 2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00057.

X. Wang and Y. Xu, “An improved index for clustering validation based on Silhouette index and Calinski-Harabasz index,” IOP Conf. Ser. Mater. Sci. Eng., vol. 569, no. 5, 2019, doi: 10.1088/1757-899X/569/5/052024.




DOI: http://dx.doi.org/10.24014/sitekin.v20i1.19454

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Rimba Prasasti, Rifki Sadikin, Eni Heni Hermallani



Editorial Address:
FAKULTAS SAINS DAN TEKNOLOGI
UIN SULTAN SYARIF KASIM RIAU
Kampus Raja Ali Haji
Gedung Fakultas Sains & Teknologi UIN Suska Riau
Jl.H.R.Soebrantas No.155 KM 18 Simpang Baru Panam, Pekanbaru 28293
Email: sitekin@uin-suska.ac.id
© 2023 SITEKIN, ISSN 2407-0939

SITEKIN Journal Indexing:

Google Scholar | Garuda | Moraref | IndexCopernicus | SINTA


Creative Commons License
SITEKIN by http://ejournal.uin-suska.ac.id/index.php