The growth of transportation sector is inevitable things that appear as public transportation user needs, commonly they need safe, reliable and punctual transportation. Medium speed train on Java's north line with maximum speed 160 km/h on existing track emerge as a solution that issued by local Indonesian government. Increasing speed on existing track must be considered by increasing TQI value. TQI is track quality index that consists of : track gauge, cant, longitudinal level, and lateral level. The recent value of TQI on existing track along Java's north line is 20, and Surabaya-Cepu line was taken as object of this examination. By using simple linear regression and multi linear regression that involved 4 parameters and speed as an approach, the value of TQI that obtained as recommendation on maximum speed 160 km/h.
ABSTRAK Dalam analisis regresi, salah satu asumsi yang harus dipenuhi adalah tidak adanya hubungan antar variabel independen. Hubungan yang kuat antar variabel independen disebut dengan multikolinieritas. Berbagai metode dapat menanggulangi kasus multikolinieritas, semua itu bergantung pada tujuan dari penelitian. Beberapa metode tersebut adalah ridge regression, principal component regression, regresi robust dan pemilihan model terbaik. Pada penelitian ini, metode pemilihan model terbaik dipilih untuk digunakan karena bertujuan untuk menentukan variabel independen yang signifikan dengan mempertimbangkan korelasi parsial pada data track quality index (TQI) kereta api Indonesia. Untuk mengukur besarnya TQI diperlukan empat indikator yang kemudian menjadi variabel dalam penelitian ini, yaitu lebar jalur, angkatan, listringan dan pertinggian. Hasil analisis menunjukkan variabel pertinggian, angkatan dan listringan berpengaruh besarnya nilai TQI dengan variasi data yang dapat dijelaskan model sebesar 99,7%.Kata kunci: multikolinieritas, stepwise, track quality index. ABSTRACTIn regression analysis, one of the assumptions is the absence of relationships between independent variables. Relationship between independent variables is called multicollinearity.Various methods can overcome multicollinearity cases, all of which depend on the purpose of the study. Some of these methods are ridge regression, principal component regression, robust regression and selection of the best models. In this study, the best model selection method was chosen because it aims to determine significant independent variables taking into account the partial correlation of track quality index (TQI) data. To measure the magnitude of TQI, four indicators are needed which then become the variables in this study, namely the width of the track, force, lightning and elevation. The results of the analysis show that the height, force and list
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