PENGARUH FAKTOR OPERASIONAL PELABUHAN TERHADAP KETERLAMBATAN BONGKAR MUAT DI PT KRAKATAU BANDAR SAMUDERA MENGGUNAKAN DECISION TREE

    Putri Ivana Anggraeni Mumtaz, - (2025) PENGARUH FAKTOR OPERASIONAL PELABUHAN TERHADAP KETERLAMBATAN BONGKAR MUAT DI PT KRAKATAU BANDAR SAMUDERA MENGGUNAKAN DECISION TREE. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Kegiatan bongkar muat merupakan salah satu aktivitas utama dalam operasional pelabuhan yang berperan penting dalam mendukung kelancaran arus logistik serta distribusi barang. Namun demikian, keterlambatan dalam proses bongkar muat masih sering terjadi dan berdampak negatif terhadap efisiensi operasional pelabuhan. Penelitian ini bertujuan untuk membangun model Decision Tree C4.5 untuk mengklasifikasikan faktor operasional terhadap keterlambatan bongkar muat di pelabuhan serta menentukan faktor operasional yang paling berpengaruh terhadap keterlambatan bongkar muat di PT Krakatau Bandar Samudera dari hasil klasifikasi model Decision Tree C4.5. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan kuantitatif dengan menerapkan teknik data mining. Data yang digunakan berupa raw data yang diperoleh langsung dari perusahaan. Proses analisis data dilakukan dengan memanfaatkan platform Google Collaboratory. Berdasarkan hasil penelitian, tonnage diketahui sebagai faktor paling berpengaruh terhadap terjadinya keterlambatan. Hasil model akurasi sebesar 80% pada data tahun 2023 dan 78% pada tahun 2024, yang menunjukkan performa prediktif yang cukup baik. Penelitian ini diharapkan dapat memberikan kontribusi nyata bagi perusahaan dalam menghadapi tantangan keterlambatan bongkar muat. Loading and unloading activities are one of the main activities in port operations that play a vital role in supporting the smooth flow of logistics and distribution of goods. However, delays in the loading and unloading process still frequently occur and negatively impact port operational efficiency. This study aims to build a Decision Tree C4.5 model to classify operational factors that contribute to loading and unloading delays at the port and determine the operational factors that most influence loading and unloading delays at PT Krakatau Bandar Samudera from the results of the Decision Tree C4.5 model classification. The approach used in this study is a quantitative approach by applying data mining techniques. The data used are raw data obtained directly from the company. The data analysis process was carried out using the Google Collaboratory platform. Based on the results of the study, tonnage is known to be the most influential factor in the occurrence of delays. The model accuracy results were 80% in 2023 data and 78% in 2024, which indicates quite good predictive performance. This research is expected to provide a real contribution to companies in facing the challenges of loading and unloading delays.

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    Official URL: https://repository.upi.edu/
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?hl=en&user=gbX8i0oAAAAJ&view_op=list_works&gmla=AH8HC4y0jXQnBZqQRD_Uzgbv-C7Q1kkp3fJtqlgHjg3c_v6_wsuU2sujWWwtXn_pidXhfkuwmULrt_jEZbYjkwNQ66xTDnUeNPoHoKJpEgPLkJFsxwqRZUUpHWr25A ID SINTA Dosen Pembimbing Luthfi Anzani: 6721957 Willdan Aprizal Arifin: 6745746
    Uncontrolled Keywords: Bongkar muat, Decision Tree C4.5, Faktor operasional, Keterlambatan,PT Krakatau Bandar Samudera Decision Tree C4.5, Delay, Loading and unloading, Operational factors, PT Krakatau Bandar Samudera
    Subjects: L Education > L Education (General)
    Divisions: UPI Kampus Serang > S1 Sistem Informasi Kelautan
    Depositing User: Putri Ivana Anggraeni Mumtaz
    Date Deposited: 24 Sep 2025 06:53
    Last Modified: 24 Sep 2025 06:53
    URI: http://repository.upi.edu/id/eprint/138010

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