Implementation of Data Mining Prediction Delivery Time Using Linear Regression Algorithm


  • Tri Wahyudi STIKOM Cipta Karya Informatika
  • Dava Septya Arroufu STIKOM Cipta Karya Informatika



Prediction, Data Mining, Linear Regression, Delivery


In the current era of modernization, online shopping has become a habit of the people, and is closely related to freight forwarding services in charge of delivering online shopping items from the seller to the buyer. So that buyers need a fast and safe delivery service to ensure the goods sent on time to their destination. Customer satisfaction is one of the most important factors in the shipping business. However, there are several obstacles that occur in the field that cause delays in the delivery of goods. Therefore, one solution that can be used to overcome this problem is to use data mining technology to predict delivery times. Using 1,000 datasets consisting of 4 Attributes, data processing will be carried out with prediction techniques using the Linear Regression algorithm. By utilizing data when the goods are taken, when the goods are on the way, until they reach the buyer, they can produce forecasts or predictions and produce several analyzes so that in the future there will be no delivery delays. Based on the RMSE (Root Mean Square Error) value which serves to generate the level value the error of the prediction results using this method and in an RMSE value of 0.370 %. It can be concluded that using the Linear Regression algorithm is proven to be accurate in predicting delivery times.


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Ahmad, R., & Alsmadi, I. (2021). Machine learning approaches to IoT security: A systematic literature review. Internet of Things, 14, 100365.

Artin, J., Valizadeh, A., Ahmadi, M., Kumar, S. A., & Sharifi, A. (2021). Presentation of a novel method for prediction of traffic with climate condition based on ensemble learning of neural architecture search (NAS) and linear regression. Complexity, 2021.

Baihaqi, D. I., Handayani, A. N., Pujianto, U., Rahman, A. A., Kurniawan, Y. I., Sulaksono, J., Irawan, R. H., Fahmi, I. N., Iman, Q., Wahyu, A., Agustina, W., Furqon, M. T., Rahayudi, B., Tungadi, E., Thalib, I., Nur, M., Utomo, Y., Firasari, E., Khultsum, U., … Sosial, B. (2021). Klasterisasi Dana Bantuan Pada Program Keluarga Harapan (PKH) Menggunakan Metode K-Means. Jurnal Teknologi Informasi Dan Ilmu Komputer, 3(1), 1231–1236.

Bengnga, A., & Ishak, R. (2018). Prediksi Jumlah Mahasiswa Registrasi Per Semester Menggunakan Linier Regresi Pada Universitas Ichsan Gorontalo. ILKOM Jurnal Ilmiah, 10(2), 136–143.

Camargo, C., Gonçalves, J., Conde, M. Á., Rodríguez-Sedano, F. J., Costa, P., & García-Peñalvo, F. J. (2021). Systematic Literature Review of Realistic Simulators Applied in Educational Robotics Context. Sensors, 21(12), 4031.

Cosenza, D. N., Korhonen, L., Maltamo, M., Packalen, P., Strunk, J. L., Næsset, E., ... & Tomé, M. (2021). Comparison of linear regression, k-nearest neighbour and random forest methods in airborne laser-scanning-based prediction of growing stock. Forestry: An International Journal of Forest Research, 94(2), 311-323.

Emioma, C. C., & Edeki, S. O. (2021). Stock price prediction using machine learning on least-squares linear regression basis. In Journal of Physics: Conference Series (Vol. 1734, No. 1, p. 012058). IOP Publishing.

Hafizah, Tugiono, & Maya, W. R. (2019). Penerapan Data Mining Dalam Memprediksi Jumlah Penumpang Pada CV . Surya Mandiri Sukses Dengan Menggunakan Metode Regresi Linier. Jurnal Teknologi Informasi Dan Sistem Komputer TGD, 2(1), 54–61.

Hasanah, M. A., Soim, S., & Handayani, A. S. (2021). Implementasi CRISP-DM Model Menggunakan Metode Decision Tree dengan Algoritma CART untuk Prediksi Curah Hujan Berpotensi Banjir. Journal of Applied Informatics and Computing, 5(2), 103–108.

Hertina, H., Nurwahid, M., Haswir, H., Sayuti, H., Darwis, A., Rahman, M., ... & Hamzah, M. L. (2021). Data mining applied about polygamy using sentiment analysis on Twitters in Indonesian perception. Bulletin of Electrical Engineering and Informatics, 10(4), 2231-2236.

Gilyén, A., Song, Z., & Tang, E. (2022). An improved quantum-inspired algorithm for linear regression. Quantum, 6, 754.

Kohli, S., Godwin, G. T., & Urolagin, S. (2021). Sales prediction using linear and KNN regression. In Advances in machine learning and computational intelligence (pp. 321-329). Springer, Singapore.

Kumar, S., Kar, A. K., & Ilavarasan, P. V. (2021). Applications of text mining in services management: A systematic literature review. International Journal of Information Management Data Insights, 1(1), 100008.

Laia, D., Buulolo, E., & Sirait, M. J. F. (2018). Implementasi Data Mining Untuk Memprediksi Pemesanan Driver Go-Jek Online Dengan Menggunakan Metode Naive Bayes (Studi Kasus: Pt. Go-Jek Indonesia). KOMIK (Konferensi Nasional Teknologi Informasi Dan Komputer), 2(1), 434–439.

Liantoni, F. (2016). Klasifikasi Daun Dengan Perbaikan Fitur Citra Menggunakan Metode K-Nearest Neighbor. Jurnal ULTIMATICS, 7(2), 98–104.

Nofitri, R., & Irawati, N. (2019). Integrasi Metode Neive Bayes Dan Software Rapidminer Dalam Analisis Hasil Usaha Perusahaan Dagang. JURTEKSI (Jurnal Teknologi Dan Sistem Informasi), 6(1), 35–42.

Öztürk, O. B., & Ba?ar, E. (2022). Multiple linear regression analysis and artificial neural networks based decision support system for energy efficiency in shipping. Ocean Engineering, 243, 110209.

Pink, M., & Djohan, N. (2021). Effect of ecommerce post-purchase activities on customer retention in Shopee Indonesia. Enrichment: Journal of Management, 12(1), 519-526.

Saltz, J. S. (2021, December). CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 2337-2344). IEEE.

Schröer, C., Kruse, F., & Gómez, J. M. (2021). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526-534.

Widiyanto, P., Endri, E., Sakti, R. F. J., Setiawan, E. B., Manfaluthy, M., Suryaningsih, L., ... & Limakrisna, N. (2021). The relationship between service quality, timeliness of arrival, departure flip ship logistics and people and customer satisfaction: A case in Indonesia. Academy of Entrepreneurship Journal, 27(6), 1-12.

Zhang, Y. (2021). Sales Forecasting of Promotion Activities Based on the Cross-Industry Standard Process for Data Mining of E-commerce Promotional Information and Support Vector Regression. J. Comput, 32, 212-225.




How to Cite

Wahyudi, T., & Arroufu, D. S. (2022). Implementation of Data Mining Prediction Delivery Time Using Linear Regression Algorithm. Journal of Applied Engineering and Technological Science (JAETS), 4(1), 84–92.