<p>Sektor usaha logistik telah berkembang sangat pesat di Indonesia saat ini. PT. XYZ adalah sebuah perusahaan logistik yang menyediakan jasa pengiriman barang dari satu tempat menuju ke tempat yang lain. Sebagai perusahaan logistik dengan jumlah kendaraan 2.100 unit armada truk dan akan terus bertambah seiring dengan target yang dicanangkan perusahaan, dimana pada 2020 jumlah armada truk harus mencapai 6.000 unit truk. Saat ini strategi operasional logistik dihasilkan berdasarkan pengalaman dari steakholder. Hal ini tentu tidak bisa dipertanggung jawabkan secara ilmiah. Prediksi jumlah pengiriman barang harian dapat menjadi solusi dalam membantu perusahaan dalam merencanakan, memonitoring dan mengevaluasi strategi operasional logistik. Hasil pengujian menunjukkan penggabungan metode <em>Support Vector Regression</em> (SVR), algoritma genetika dan <em>Multivariate Adaptive Regression Splines </em>(MARS) dapat menghasilkan prediksi jumlah pengiriman barang harian dengan nilai <em>Mean Absolute Percentage Error</em> (MAPE) yaitu 0.0969% dengan parameter <em>epsilon</em>(𝜀) 1.92172577675873E-20, <em>complexitas</em>(𝑐) 62 dan <em>gamma</em>(γ) 1.0.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Abstract"><em>The logistics business sector has developed very rapidly in Indonesia today. PT XYZ is a national logistics company that provides freight forwarding services from one place to another. As a national-scale logistics company, the company is supported by a fleet of 2,100 trucks. The number of fleets will continue to grow in line with the target set by the company, namely in 2020 the number of truck fleets must reach 6,000 trucks. Currently the logistics operational strategy is produced based on stakeholder experience, this certainly causes problems in the company's overall operations. Prediction of the number of daily goods shipments can be a solution in helping companies in planning, monitoring and evaluating logistical operational strategies, based on the company's ability in the availability of a fleet of vehicles for shipping. This study proposes a combination of Support Vector Regression (SVR) methods, genetic algorithms and Multivariate Adaptive Regression Splines (MARS) for problem solving in the prediction process, including in the selection of appropriate training data. The test results show that the combination of the three methods can produce predictions of the number of daily shipments with values of Mean Absolute Percentage Error (MAPE) 0.0969%, epsilon (𝜀) 1.92172577675873E- 20, complexity (𝑐) 62, and gamma (γ) 1.0.</em></p><p class="Judul2"><strong><em><br /></em></strong></p>
Financial Forecasting With Multivariate Adaptive Regression Splines and Queen Genetic Algorithm-Support Vector Regression
