IMPLEMENTASI MACHINE LEARNING UNTUK MENINGKATKAN KUALITAS OPERASIONAL SERVICE KENDARAAN DENGAN METODE RANDOM FOREST DAN LOGISTIC REGRESSION
Abstract
Vehicle motor repairs (service) are an important aspect for motor vehicle owners to undertake. This activity is carried out by automotive workshops to ensure that the customer's vehicle is in prime condition. To boost sales, some automotive workshops offer various promotional packages to attract customer interest. However, in practice, this is done manually by workshop staff, resulting in suboptimal performance in offer presentations (customer calls). This research aims to build a recommendation system for package deals and offer dates to enhance the quality of customer calls in the operations of automotive workshops using Random Forest and Logistic Regression. The dataset used is operational data from customer calls at one automotive workshop in Bali. The Random Forest model achieves 91 percent accuracy, while Logistic Regression achieves 72 percent accuracy. The system developed can be used to recommend good package deals and offer dates to customers.
Perbaikan kendaraan bermotor (service) merupakan hal penting untuk dilakukan bagi pemilik kendaraan bermotor. Kegiatan ini dilakukan oleh bengkel otomotif untuk memastikan kondisi kendaraan customer dalam kondisi prima. Untuk meningkatkan penjualan, beberapa bengkel otomotif menawarkan berbagai paket promo untuk menarik minat customer. Namun dalam pelaksanaannya, hal ini dilakukan secara manual oleh staff bengkel yang mengakibatkan performa penawaran (customer call) kurang optimal. Penelitian ini bertujuan untuk membangun sistem rekomendasi paket dan tanggal penawaran untuk meningkatkan kualitas customer call pada operasional bengkel otomotif menggunakan Random Forest dan Logistic Regression. Dataset yang digunakan adalah data operasional customer call salah satu bengkel otomotif di Bali. Model Random Forest mencapai akurasi 91 persen dan Logistic Regression mencapai akurasi 72 persen. Sistem yang dibangun dapat digunakan untuk merekomendasikan paket dan tanggal penawaran yang baik untuk ditawarkan kepada customer.
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DOI: http://dx.doi.org/10.17977/um032v7i2p206-213
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