Optimasi Prediksi Jumlah Kontainer Aktual di Kapal Menggunakan Random Forest dan XGBoost dengan Hyperparameter Tuning
Article Sidebar
Main Article Content
Abstract
The maritime logistics industry plays a crucial role in ensuring the smooth flow of trade; however, it often faces discrepancies between the number of containers booked and the number actually loaded onto ships. These discrepancies can lead to operational inefficiencies, shipment delays, and additional costs for companies. PT XYZ, as a maritime logistics service provider, encounters similar challenges. Therefore, this study aims to analyze the factors causing container discrepancies and to develop a predictive system for estimating the actual number of containers as a decision-support tool. This research adopts data mining, machine learning, and ensemble learning approaches, focusing on the Random Forest Regressor and Extreme Gradient Boosting (XGBoost) algorithms combined through a Voting Regressor. Hyperparameter tuning using GridSearchCV is applied to improve the model’s ability to capture complex data patterns. A quantitative approach following the CRISP-DM framework is employed, including data exploration, cleaning, feature selection, modeling, and evaluation. The study utilizes historical container booking data from PT XYZ in 2023, consisting of more than 138,000 records. The results show that the Voting Regressor achieves the best performance with an R² value of 0.7874 and an MSE of 1.6282, supported by consistent RMSE and MAE metrics. The model is implemented in a Flask-based web application that enables practical container count prediction through Microsoft Excel file uploads. The implementation of this predictive system has the potential to help PT XYZ reduce loading discrepancies, minimize additional costs, and optimize logistics planning, while also contributing academically to the application of machine learning in the maritime logistics sector.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
References
Alia, P. A., Prayogo, J. S., Kartiko, E. Y., Prasetyo, D., Khairunusi, Y., Na’am, J., Wijaya, A., Setyadi, A. T., Remawati, D., Mair, Z. R., Febriana, R. W., Setyadinsa, R., Maspupah, A., & Cahyono, W. A. (2023). Sistem Basis Data. PT Penamuda Media.
Banerjee, P., Kumar, B., Singh, A., Kumar, R., & Kumar, R. (2019). Comparative performance analysis of optimized round robin scheduling (ORR) using dynamic time quantum with round robin scheduling using static time quantum in Real Time System. International Journal of Engineering and Computer Science, *8*(12), 24890–24893. https://doi.org/10.18535/ijecs/v8i12.4399
Fauziyah, S., & Sugiarti, Y. (2022). Literature Review: Analisis Metode Perancangan Sistem Informasi Akademik Berbasis Web. Jurnal Ilmiah Ilmu Komputer, *8*(2), 87–93. https://doi.org/10.35329/jiik.v8i2.229
Ginting, D. (2021). Penanganan Pengangkutan Barang Melalui Container Pada Pt. Elang Sriwijaya Perkasa Palembang. Agriprimatech, *5*(1), 23–30. https://doi.org/10.34012/agriprimatech.v5i1.2074
Jatmiko, Y. A., Padmadisastra, S., & Chadidjah, A. (2019). Analisis Perbandingan Kinerja Cart Konvensional, Bagging Dan Random Forest Pada Klasifikasi Objek: Hasil Dari Dua Simulasi. Media Statistika, *12*(1), 1–12. https://doi.org/10.14710/medstat.12.1.1-12
Laudon, K. C., & Laudon, J. P. (2020). Management Information Systems: Managing the Digital Firm (16th ed.). Pearson Education Limited.
Mahamat, A. A., Boukar, M. M., Leklou, N., Celino, A., Obianyo, I. I., Bih, N. L., Stanislas, T. T., & Savastanos, H. (2024). Decision Tree Regression vs. Gradient Boosting Regressor Models for the Prediction of Hygroscopic Properties of Borassus Fruit Fiber. Applied Sciences (Switzerland), *14*(17). https://doi.org/10.3390/app14177540
Mallach, E. G. (2020). Information Systems: What Every Business Student Needs to Know (2nd ed.). Routledge.
Muslim, M. A., Prasetiyo, B., Mawarni, E. L. H., Herowati, A. J., Mirqotussa'adah, S., Rukmana, S. H., & Nurzahputra, A. (2019). Data Mining: Algoritma C4.5, Disertai Contoh Kasus dan Penerapannya dengan Program Computer.
Ningtyas, D. F., & Setiyawati, N. (2021). Implementasi Flask Framework pada Pembangunan Aplikasi Purchasing Approval Request. Jurnal Janitra Informatika Dan Sistem Informasi, *1*(1), 19–34. https://doi.org/10.25008/janitra.v1i1.120
Pradhan, M., & Kumar, U. D. (2019). Machine Learning Using Python. Wiley.
Putra, A. I., & Santika, R. R. (2020). Implementasi Machine Learning dalam Penentuan Rekomendasi Musik dengan Metode Content-Based Filtering. Edumatic : Jurnal Pendidikan Informatika, *4*(1), 121–130. https://doi.org/10.29408/edumatic.v4i1.2162
Putri Primawanti, E., & Ali, H. (2022). Pengaruh Teknologi Informasi, Sistem Informasi Berbasis Web Dan Knowledge Management Terhadap Kinerja Karyawan (Literature Review Executive Support Sistem (Ess) for Business). Jurnal Ekonomi Manajemen Sistem Informasi, *3*(3), 267–285. https://doi.org/10.31933/jemsi.v3i3.818
Putri, M. P., Barovih, G., Azdy, R. A., Yuniansyah, Saputra, A., Sriyeni, Y., Rini, A., & Admojo, F. T. (2022). Algoritma dan Struktur Data. Widina Bhakti Persada.
Putri, R. A. (2022). Buku Ajar Basis Data (Edisi Kedua). Media Sains Indonesia.
Rayadin, M. A., Musaruddin, M., Saputra, R. A., & Isnawaty, I. (2024). Implementasi Ensemble Learning Metode XGBoost dan Random Forest untuk Prediksi Waktu Penggantian Baterai Aki. BIOS: Jurnal Teknologi Informasi Dan Rekayasa Komputer, *5*(2), 111–119.
Raharjo, B. (2021). Sistem Manajemen Database. Yayasan Prima Agus Teknik.
Saadah, S., & Salsabila, H. (2021). Prediksi Harga Bitcoin Menggunakan Metode Random Forest. Jurnal Komputer Terapan, *7*(1), 24–32. https://doi.org/10.35143/jkt.v7i1.4618
Salsabil, M., Lutvi, N., & Eviyanti, A. (2024). Implementasi Data Mining Dalam Melakukan Prediksi Penyakit Diabetes Menggunakan Metode Random Forest Dan Xgboost. Jurnal Ilmiah Komputasi, *23*(1), 51–58. https://doi.org/10.32409/jikstik.23.1.3507
Saputra, D. B., Atina, V., & Nastiti, F. E. (2024). Penerapan Model Crisp-Dm Pada Prediksi Nasabah Kredit. Idealis: Indonesia Journal Information System, *7*, 240–247.
Siji George, C. G., & Sumathi, B. (2020). Grid search tuning of hyperparameters in random forest classifier for customer feedback sentiment prediction. International Journal of Advanced Computer Science and Applications, *11*(9), 173–178. https://doi.org/10.14569/IJACSA.2020.0110920
Sinambela, D. P., Naparin, H., Zulfadhilah, M., & Hidayah, N. (2023). Implementasi Algoritma Decision Tree dan Random Forest dalam Prediksi Perdarahan Pascasalin. Jurnal Informasi Dan Teknologi, *5*(3), 58–64. https://doi.org/10.60083/jidt.v5i3.393
Stair, R. M., Reynolds, G. W., Bryant, J., Frydenberg, M., Greenberg, H., & Schell, G. (2021). Principles of Information Systems. Cengage Learning.
Sunarko, B., Hasanah, U., Hidayat, S., Muhammad, N., Ardiansyah, M. I., Ananda, B. P., Hakiki, M. K., & Baroroh, L. T. (2023). Penerapan Stacking Ensemble Learning untuk Klasifikasi Efek Kesehatan Akibat Pencemaran Udara. Edu Komputika Journal, *10*(1), 55–63. https://doi.org/10.15294/edukomputika.v10i1.72080
Syahputri, K., Irwan, M., & Nasution, P. (2023). Peran Database Dalam Sistem Informasi Manajemen. Jurnal Akuntansi Keuangan Dan Bisnis, *1*(2), 54–58. https://jurnal.ittc.web.id/index.php/jakbs/article/view/36
Ucha Putri, S., Irawan, E., Rizky, F., Tunas Bangsa, S., -Indonesia Jln Sudirman Blok No, P. A., & Utara, S. (2021). Implementasi Data Mining Untuk Prediksi Penyakit Diabetes Dengan Algoritma C4.5. Jurnal, *2*(1), 39–46.
Umam, K. (2021). Algoritma dan Pemrograman Komputer dengan Python. Duta Media Publishing.
Umar, R., Hadi, A., Widiandana, P., & Anwar, F. (2019). Perancangan Database Point of Sales Apotek Dengan Menerapkan Model Data Relasional. Query: Journal of Information Systems, 33–41.
Zahra, R. A. (2016). Penerapan Algoritma Random Forest Dengan Hyperparameter Tuning Untuk Memprediksi Harga Sewa Kost Di Kota Bandar Lampung. Skripsi.