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"Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and veri?ed using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks."

"Dengan hanya melakukan perbaikan jalan ketika luas kerusakan sudah membesar, dana yang dikeluarkan akan menjadi boros dan tidak efektif. Penelitian ini bertujuan untuk mengetahui biaya pemeliharaan untuk jalan lingkar luar Universitas Indonesia dengan menggunakan metode Life Cycle Cost Analysis (LCCA) dan menentukan strategi pemeliharaan jalan yang paling cost-effective berdasarkan analisis biaya pemeliharaan jalan. Berdasarkan tiga skenario pemeliharaan jalan, didapatkan hasil bahwa strategi pemeliharaan jalan yang paling cost-effective adalah skenario pertama berupa pemeliharaan jalan setiap dua tahun sekali memiliki nilai Net Present Value (NPV) terendah yaitu sebesar Rp 23.490.602,00. Hasil perbandingan nilai NPV menunjukkan potensi penghematan biaya sebesar Rp 18.826.750,00 dari anggaran biaya pemeliharaan jalan dengan cara overlay di jalan lingkar luar Universitas Indonesia jika strategi skenario pertama diterapkan. ......By simply doing pavement repairs when the area of damage has been enlarged, the amount of cost expended would be wasteful and ineffective. This study aims to determine the pavement maintenance cost for outer ring road of University of Indonesia using Life Cycle Cost Analysis (LCCA) method and determine the most cost-effective maintenance strategies based on the analysis of pavement maintenance costs. Based on three three pavement maintenance scenarios, the result shows thatthe most cost-effective maintenance strategy is the first scenario, pavement maintenance every two years, with the lowest value of Net Present Value (NPV) Rp 23.490.602,00. The comparison result of NPV value shows potential reduced cost Rp 18.826.750,00 from University of Indonesia?s pavement maintenance budget using overlay work if the first scenario strategy is implemented."