Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Preterm birth is one of the major contributors to perinatal and neonatal mortality. This issue became important in health research area especially human reproduction both in developed and developing country. In 2015 Indonesia rank fifth as the country with the highest number of premature babies in the world. The ability to reduce the number of preterm birth is to reduce risk factors associated with it. This research will be made the prediction model of preterm birth using hybrid multivariate adaptive regression splines (MARS) and Support Vector Machine (SVM). MARS used to select the attributes which suspected to affect premature babies. The result of this research is prediction model based on hybrid MARS-SVM obtains better performance than the other models

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Prediksi Jumlah Pengiriman Barang Menggunakan Kombinasi Metode Support Vector Regression, Algoritma Genetika dan Multivariate Adaptive Regression Splines

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2020722441 ◽

2020 ◽

Vol 7 (6) ◽

pp. 1169

Author(s):

Nendi Nendi ◽

Arief Wibowo

Keyword(s):

Support Vector Regression ◽

Mean Absolute Percentage Error ◽

Multivariate Adaptive Regression Splines ◽

Percentage Error ◽

Support Vector ◽

Regression Splines ◽

Absolute Percentage Error ◽

Logistics Company ◽

Adaptive Regression ◽

Adaptive Regression Splines

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 Support Vector Regression (SVR), algoritma genetika dan Multivariate Adaptive Regression Splines (MARS) dapat menghasilkan prediksi jumlah pengiriman barang harian dengan nilai Mean Absolute Percentage Error (MAPE) yaitu 0.0969% dengan parameter epsilon(𝜀) 1.92172577675873E-20, complexitas(𝑐) 62 dan gamma(γ) 1.0. AbstractThe 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.

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