Prediction of Service Level Agreement Time of Delivery of Goods and Documents at PT Pos Indonesia Using the Random Forest Method

Authors

  • Muhammad Isa Ansori Faculty of Science and Technology, Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • Ririen Kusumawati Faculty of Science and Technology, Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia
  • M. Amin Hariyadi Faculty of Science and Technology, Program Specification for Master Study in Computer Science, Universitas Islam Negeri Maulana Malik Ibrahim, Indonesia

DOI:

https://doi.org/10.25008/ijadis.v4i2.1281

Keywords:

Service Level Agreement, SLA, Time of Delivery, PT Pos Indonesia (Persero), Random Forest Method, Prediction, Forecasting

Abstract

This study aimed to predict the service level agreement travel time for goods and document shipments at PT Pos Indonesia (Persero) from the island of Java to the islands of Kalimantan, Sulawesi, Maluku and Papua. This is very important because of the high competition between the logistics industry which is getting faster and faster. The random forest method was chosen because this method is easy to use and flexible for various kinds of data. The prediction results with Random Forest in this study have a good level of accuracy, namely 83.86% of the average 4 trials. This shows that the Random Forest method is the right choice for managing the existing data model at PT Pos Indonesia.

Downloads

Download data is not yet available.

Plum Analytics

   

Dimensions

            

References

A. Pamuji, M. Muzaki, and H. S. Setiawan, "Design of Web-Based Hajj Manasik Supervision Certification Information System," Int. J. Adv. Data Inf. Syst., vol. 3, no. 2, pp. 58–65, 2022, doi: 10.25008/ijadis.v3i2.1235.

D. Saputra, W. S. Dharmawan, and W. Irmayani, "Performance Comparison of the SVM and SVM-PSO Algorithms for Heart Disease Prediction," Int. J. Adv. Data Inf. Syst., vol. 3, no. 2, pp. 74–86, 2022, doi: 10.25008/ijadis.v3i2.1243.

E. M. M. van der Heide, R. F. Veerkamp, M. L. van Pelt, C. Kamphuis, I. Athanasiadis, and B. J. Ducro, "Comparing regression, naive Bayes, and random forest methods in the prediction of individual survival to second lactation in Holstein cattle," J. Dairy Sci., vol. 102, no. 10, pp. 9409–9421, 2019, doi: 10.3168/jds.2019-16295.

J. Maroco, D. Silva, A. Rodrigues, M. Guerreiro, I. Santana, and A. de Mendonca, "Data Mining Methods in The Prediction of Dementia," Bmc Res Notes, vol. 4, pp. 1–14, 2011.

N. Zimmerman et al., "A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring," Atmos. Meas. Tech., vol. 11, no. 1, pp. 291–313, 2018, doi: 10.5194/amt-11-291-2018.

Y. Ensafi, S. H. Amin, G. Zhang, and B. Shah, "Time-series forecasting of seasonal items sales using machine learning – A comparative analysis," Int. J. Inf. Manag. Data Insights, vol. 2, no. 1, p. 100058, 2022, doi: 10.1016/j.jjimei.2022.100058.

G. Louppe, "Understanding Random Forests: From Theory to Practice," no. July, 2014, [Online]. Available: http://arxiv.org/abs/1407.7502

A. Edinat, R. Al-Sayyed, and A. Hudaib, "A Survey on Improving QoS in Service Level Agreement for Cloud Computing Environment," Int. J. Interact. Mob. Technol., vol. 15, no. 21, pp. 119–143, 2021, doi: 10.3991/ijim.v15i21.26379.

L. Jin, V. Machiraju, and A. Sahai, "Analysis on Service Level Agreement of Web Services Analysis on Service Level Agreement of Web Services," HP Tech Rep. 2002, 105AD.

E. Marilly, O. Martinot, S. Betgé-Brezetz, and G. Delègue, "Requirements for service level agreement management," 2002 IEEE Work. IP Oper. Manag. IPOM 2002, vol. 00, no. C, pp. 57–62, 2002, doi: 10.1109/IPOM.2002.1045756.

Z. Liu, M. S. Squillante, and J. L. Wolf, "On maximizing service-level-agreement profits," Proc. ACM Conf. Electron. Commer., pp. 213–223, 2001, doi: 10.1145/501158.501185.

P. Bianco, G. A. Lewis, and P. Merson, "Software Engineering Institute Service Level Agreements in Service-Oriented Architecture Environments Software Architecture Technology Initiative Integration of Software-Intensive Systems Initiative," 2008, [Online]. Available: http://wvwv.sei.crtuj.edu

I. Z. Yakubu, Z. A. Musa, L. Muhammed, B. Ja’afaru, F. Shittu, and Z. I. Matinja, "Service Level Agreement Violation Preventive Task Scheduling for Quality of Service Delivery in Cloud Computing Environment," Procedia Comput. Sci., vol. 178, pp. 375–385, 2020, doi: 10.1016/j.procs.2020.11.039.

Y. Diao, L. Lam, L. Shwartz, and D. Northcutt, "Predicting service delivery cost for non-standard service level agreements," IEEE/IFIP NOMS 2014 - IEEE/IFIP Netw. Oper. Manag. Symp. Manag. a Softw. Defin. World, 2014, doi: 10.1109/NOMS.2014.6838265.

N. Ghosh and S. K. Ghosh, "An approach to identify and monitor SLA parameters for storage-as-a-service cloud delivery model," 2012 IEEE Globecom Work. GC Wkshps 2012, no. vi, pp. 724–729, 2012, doi: 10.1109/GLOCOMW.2012.6477664.

N. Nendi and A. Wibowo, "Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines," J. Teknol. Inf. dan Ilmu Komput., vol. 7, no. 6, p. 1169, 2020, doi: 10.25126/jtiik.2020722441.

M. L. Suliztia, "Penerapan Analisis Random Forest pada Prototype Sistem Prediksi Harga Kamera Bekas Menggunakan Flask," Fak. Mat. Dan Ilmu Pengetah. Alam, pp. 1–107, 2020.

A. Primajaya and B. N. Sari, "Random Forest Algorithm for Prediction of Precipitation," Indones. J. Artif. Intell. Data Min., vol. 1, no. 1, p. 27, 2018, doi: 10.24014/ijaidm.v1i1.4903.

M. R. Adrian, M. P. Putra, M. H. Rafialdy, and N. A. Rakhmawati, "Perbandingan Metode Klasifikasi Random Forest dan SVM Pada Analisis Sentimen PSBB," J. Inform. Upgris, vol. 7, no. 1, pp. 36–40, 2021, doi: 10.26877/jiu.v7i1.7099.

A. Ziegler and I. R. König, "Mining data with random forests: Current options for real-world applications," Wiley Interdiscip. Rev. Data Min. Knowl. Discov., vol. 4, no. 1, pp. 55–63, 2014, doi: 10.1002/widm.1114.

M. Utari, B. Warsito, and R. Kusumaningrum, "Implementation of Data Mining for Drop-Out Prediction using Random Forest Method," 2020 8th Int. Conf. Inf. Commun. Technol. ICoICT 2020, 2020, doi: 10.1109/ICoICT49345.2020.9166276.

Downloads

Published

2023-04-29

How to Cite

Ansori, M. I., Kusumawati, R., & Hariyadi, M. A. (2023). Prediction of Service Level Agreement Time of Delivery of Goods and Documents at PT Pos Indonesia Using the Random Forest Method. International Journal of Advances in Data and Information Systems, 4(1), 41-50. https://doi.org/10.25008/ijadis.v4i2.1281
Abstract views : 436 times