Alfayed, Abyan Bismar and Prasetyo, Muhammad Mirza Ralfie (2025) Pengembangan Model Prediksi Kunjungan Lokasi Operasional Menggunakan Pendekatan Supervised Learning untuk Meningkatkan Efisiensi Operasional pada PT Aplikanusa Lintasarta. Project Report. [s.n.], [s.l.]. (Unpublished)
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Abstract
PT Aplikanusa Lintasarta, sebagai salah satu perusahaan penyedia layanan Teknologi Informasi dan Komunikasi (TIK), menghadapi tantangan dalam optimalisasi kunjungan lokasi operasional. Tingginya frekuensi kunjungan yang tidak efisien dapat merugikan perusahaan dari segi biaya operasional, waktu, dan tenaga kerja. Tujuan dari Kerja Praktik ini adalah untuk mengembangkan Model Prediksi Sitevisit dengan menggunakan pendekatan supervised machine learning guna meningkatkan efisiensi operasional perusahaan.
Metodologi pengembangan model mencakup beberapa tahapan utama, dimulai dari analisis kebutuhan, Exploratory Data Analysis (EDA), data preprocessing untuk menangani missing value dan outlier, serta feature engineering untuk menciptakan fitur-fitur yang relevan. Proses ini diimplementasikan menggunakan KNIME Analytics Platform untuk membangun alur kerja (workflow) dan Python untuk analisis data. Beberapa algoritma klasifikasi dievaluasi, meliputi Decision Tree, Random Forest, Gradient Boosting, dan XGBoost.
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PT Aplikanusa Lintasarta, as one of the Information and Communication Technology (ICT) service providers, faces challenges in optimizing operational site visits. The high frequency of inefficient visits can be detrimental to the company in terms of operational costs, time, and labor. The objective of this internship is to develop a Site Visit Prediction Model using a supervised machine learning approach to improve the company's operational efficiency.
The model development methodology includes several key stages, starting with needs analysis, Exploratory Data Analysis (EDA), data preprocessing to handle missing values and outliers, and feature engineering to create relevant features. This process is implemented using the KNIME Analytics Platform to build workflows and Python for data analysis. Several classification algorithms are evaluated, including Decision Tree, Random Forest, Gradient Boosting, and XGBoost.
| Item Type: | Monograph (Project Report) |
|---|---|
| Uncontrolled Keywords: | Sitevisit Prediction, Machine Learning, Supervised Learning, XGBoost, KNIME, Streamlit, Tableau, Prediksi Kunjungan Lokasi, Pembelajaran Mesin, Pembelajaran Terawasi, XGBoost, KNIME, Streamlit, Tableau |
| Subjects: | Q Science > Q Science (General) > Q325.5 Machine learning. Support vector machines. Q Science > QA Mathematics > QA76.9.D343 Data mining. Querying (Computer science) T Technology > T Technology (General) > T385 Visualization--Technique T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Intelligent Electrical and Informatics Technology (ELECTICS) > Informatics Engineering > 55201-(S1) Undergraduate Thesis |
| Depositing User: | Abyan Bismar Alfayed |
| Date Deposited: | 18 Nov 2025 00:56 |
| Last Modified: | 18 Nov 2025 00:56 |
| URI: | http://repository.its.ac.id/id/eprint/128805 |
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