ANALISIS KOMPARATIF MODEL DATA MINING DALAM PREDIKSI KETEPATAN PENYELESAIAN SERVICE LEVEL AGREEMENT
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Abstract
Kepatuhan terhadap Service Level Agreement (SLA) sangat penting dalam manajemen layanan teknologi informasi untuk menjamin kualitas layanan dan mengatur ekspektasi penyelesaian insiden. Namun, banyak organisasi kesulitan memprediksi apakah tiket insiden akan memenuhi SLA, yang dapat menyebabkan ketidakpuasan pengguna, eskalasi masalah, dan beban kerja tinggi bagi tim IT. Tingginya volume tiket harian membuat identifikasi manual terhadap tiket berisiko tinggi menjadi tidak efektif. Penelitian ini bertujuan mengembangkan model klasifikasi berbasis machine learning untuk memprediksi kepatuhan tiket terhadap SLA. Empat algoritma dievaluasi: XGBoost, Random Forest, Decision Tree, dan Logistic Regression. Tahapan mencakup preprocessing, encoding fitur kategorikal, seleksi fitur berbasis Random Forest, penyeimbangan data menggunakan SMOTE, dan hyperparameter tuning dengan Optuna. Hasil menunjukkan XGBoost memiliki performa terbaik dengan akurasi 99,98%, precision 0,9437, recall 0,9710, dan F1-score 0,9571. Selain akurat dan efisien, model ini unggul secara interpretatif melalui SHAP, yang menjelaskan kontribusi tiap fitur. Kesimpulannya, XGBoost direkomendasikan sebagai model paling andal untuk menjadi alat bantu strategis bagi manajer layanan TI dalam mengidentifikasi insiden yang berisiko tinggi gagal memenuhi SLA.
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References
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Supono, S. (2020). Model Penilaian Kapabilitas Proses Layanan Service Level Agreement (SLA) Pada Cloud Computing. Jurnal Sains Dan Informatika, 6(1), 62–71. https://doi.org/10.34128/jsi.v6i1.209
Tai, T.-E., Haw, S.-C., Ng, K.-W., Naveen, P., & Al-Tarawneh, M. (2023). Performance Evaluation on Resolution Time Prediction Using Decision Tree, Random Forest and eXtreme Gradient Boosting. 2023 International Conference on Computer Applications Technology (CCAT), 74–79. https://doi.org/10.1109/CCAT59108.2023.00021
Tan, W., Hai, Z., Jinjing, T., Yao, Z., Li Da, X., & and Guo, K. (2022). A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0. Enterprise Information Systems, 16(12), 1939426. https://doi.org/10.1080/17517575.2021.1939426
Yudistira, N., & Putra, A. F. (2021). Algoritma Decision Tree Dan Smote Untuk Klasifikasi Serangan Jantung Miokarditis Yang Imbalance. Jurnal Litbang Edusaintech, 2(2), 112–122. https://doi.org/10.51402/jle.v2i2.48
Amaral, C., Fantinato, M., & Peres, S. (2018). Incident management process enriched event log. In UCI Machine Learning Repository. https://doi.org/10.24432/C57S4H
Amazonas, J. R., Akbari-Moghanjoughi, A., Santos-Boada, G., & Solé Pareta, J. (2019). Service Level Agreements for Communication Networks: A Survey. INFOCOMP Journal of Computer Science, 18(1 SE-), 32–56. https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/584
Ansori, M. A., Anshori, M. F., Pratama, A. W., & Gunawan, A. (2023). Prediksi Waktu Tempuh SLA (Service Level Agreement) Pengiriman PT. Pos Indonesia Menggunakan Random Forest. Jurnal Sistem Informasi, 6(1), 39–46. https://doi.org/10.34128/jsi.v6i1.209
Bhargavi, N., & Sekhar, R. R. (2021). An architectural approach to provide efficient service with the Service Level Agreement. International Journal of Computer Science, Engineering, and Information Technology, 7(1), 1–8. https://www.academia.edu/download/65870068/CSEIT217133.pdf
Dachi, J. M. A. S., & Sitompul, P. (2023). Analisis Perbandingan Algoritma XGBoost dan Algoritma Random Forest Ensemble Learning pada Klasifikasi Keputusan Kredit. Jurnal Riset Rumpun Matematika Dan Ilmu Pengetahuan Alam, 2(2), 87–103. https://doi.org/10.55606/jurrimipa.v2i2.1470
Dewi, S. P., Nurwati, N., & Rahayu, E. (2022). Penerapan Data Mining Untuk Prediksi Penjualan Produk Terlaris Menggunakan Metode K-Nearest Neighbor. Building of Informatics, Technology and Science (BITS), 3(4), 639–648. https://doi.org/10.47065/bits.v3i4.1408
Didavi, A. B. K., Agbokpanzo, R. G., & Agbomahena, M. (2021). Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system. Proceedings of the 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), 1–5. https://doi.org/10.1109/BioSMART54244.2021.9677566
Ergün, S. (2023). Explaining xgboost predictions with shap value: A comprehensive guide to interpreting decision tree-based models. New Trends in Computer Sciences, 1(1), 19–31. https://doi.org/10.3846/ntcs.2023.17901
Gonçalves, J. P. de B., Gomes, R. L., Villaca, R. da S., Municio, E., & Marquez-Barja, J. (2020). A Service Level Agreement Verification System using Blockchains. 2020 IEEE 11th International Conference on Software Engineering and Service Science (ICSESS), 541–544. https://doi.org/10.1109/ICSESS49938.2020.9237735
Hou, C., Cao, B., & Fan, J. (2022). A data-driven method to predict service level for call centers. IET Communications, 16(10), 1241–1252. https://doi.org/https://doi.org/10.1049/cmu2.12192
Jamalpur, B. (2020). Data Exploration As a Process of Knowledge Finding and the Role of Mining Data Towards Information Security. Journal of Mechanics of Continua and Mathematical Sciences, 15(6), 213–223. https://doi.org/10.26782/jmcms.2020.06.00017
Jessfry, V., & Siddik, M. (2024). Penerapan Data Mining Menggunakan Algortima Apriori Dalam Membangun Sistem Persediaan Barang. Journal Of Information Systems And Informatics Engineering, 8(1), 187–199.
Meneses, C. J., & Grinstein, G. G. (1998). Transactions on Information and Communications Technologies vol 19 © 1998 WIT Press, www.witpress.com, ISSN 1743-3517. 19, 1–7.
Nugraha, Y., Martin, A., Nugraha, Y., Martin, A., Trustworthy, U., Level, S., & Open, A. (2018). Understanding Trustworthy Service Level Agreements : Open Problems and Existing Solutions To cite this version : HAL Id : hal-01684231 Understanding Trustworthy Service Level Agreements : Open Problems and Existing Solutions.
Nur, N., Wajidi, F., Sulfayanti, S., & Wildayani, W. (2023). Implementasi Algoritma Random Forest Regression untuk Memprediksi Hasil Panen Padi di Desa Minanga. Jurnal Komputer Terapan, 9(1 SE-), 58–64. https://doi.org/10.35143/jkt.v9i1.5917
Sahai, R., Al-Ataby, A., Assi, S., Jayabalan, M., Liatsis, P., Loy, C. K., Al-Hamid, A., Al-Sudani, S., Alamran, M., & Kolivand, H. (2023). Insurance Risk Prediction Using Machine Learning BT - Data Science and Emerging Technologies (Y. B. Wah, M. W. Berry, A. Mohamed, & D. Al-Jumeily (eds.); pp. 419–433). Springer Nature Singapore.
Suja, T. L., & Booba, B. (2020). A feasibility study of service level agreement compliance for start-ups. In Data Management, Analytics and Innovation. Proceedings of ICDMAI 2020. https://books.google.com/books?hl=en&lr=&id=lFj4DwAAQBAJ&oi=fnd&pg=PA407
Supono, S. (2020). Model Penilaian Kapabilitas Proses Layanan Service Level Agreement (SLA) Pada Cloud Computing. Jurnal Sains Dan Informatika, 6(1), 62–71. https://doi.org/10.34128/jsi.v6i1.209
Tai, T.-E., Haw, S.-C., Ng, K.-W., Naveen, P., & Al-Tarawneh, M. (2023). Performance Evaluation on Resolution Time Prediction Using Decision Tree, Random Forest and eXtreme Gradient Boosting. 2023 International Conference on Computer Applications Technology (CCAT), 74–79. https://doi.org/10.1109/CCAT59108.2023.00021
Tan, W., Hai, Z., Jinjing, T., Yao, Z., Li Da, X., & and Guo, K. (2022). A novel service level agreement model using blockchain and smart contract for cloud manufacturing in industry 4.0. Enterprise Information Systems, 16(12), 1939426. https://doi.org/10.1080/17517575.2021.1939426
Yudistira, N., & Putra, A. F. (2021). Algoritma Decision Tree Dan Smote Untuk Klasifikasi Serangan Jantung Miokarditis Yang Imbalance. Jurnal Litbang Edusaintech, 2(2), 112–122. https://doi.org/10.51402/jle.v2i2.48
Published
2025-06-30
How to Cite
FATIMAH, Aisyah; TANIA, Ken Ditha; MEIRIZA, Allsela.
ANALISIS KOMPARATIF MODEL DATA MINING DALAM PREDIKSI KETEPATAN PENYELESAIAN SERVICE LEVEL AGREEMENT.
JOISIE (Journal Of Information Systems And Informatics Engineering), [S.l.], v. 9, n. 1, p. 100-108, june 2025.
ISSN 2527-3116.
Available at: <https://ejournal.pelitaindonesia.ac.id/ojs32/index.php/JOISIE/article/view/4943>. Date accessed: 13 july 2025.
doi: https://doi.org/10.35145/joisie.v9i1.4943.
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