Pemodelan Kondisi Perkerasan Jalan dengan Metode Probabilistik Rantai Markov (Studi Kasus Ruas Jalan Provinsi di Kota Medan)

Abstract

To obtain optimal maintenance program planning on the road network, road managers need information on estimates of changes in road pavement conditions in the future. The purpose of this study was to obtain an overview of changes in the condition of provincial road pavements in Medan City in the next 5 years (2021 – 2025) using the Markov Chain probabilistic modeling method, and to determine the proper pavement preservation in accordance with the description of changes in provincial road pavement conditions in Medan City. in the next 5 years (2021 – 2025). The study was conducted on 8 roads in the working area of the Road and Bridge Technical Implementation Unit (UPT JJ) Medan, the Department of Highways and Construction. The preparation of the Transition Probability Matrix (MPT) is for road pavement conditions in 2019 – 2020, with state conditions in 2020. Prediction of road pavement conditions in 2021 obtained from the Markov Chain model is validated with road pavement conditions from the 2021 SDI survey. The results show that the Markov Chain prediction provides decent (good enough) prediction results with a MAPE value of 24.70% with quite significant results with the results of the 2021 SDI survey. For example, in Section 1, the Markov Model is in good condition 30.555% while SDI is in good condition 27.888% , then in the Markov Model Moderate conditions 69.445% while SDI Moderate conditions 72.112%. From this modeling, recommendations for the necessary countermeasures are routine maintenance on sections 1, 2, 3, 4 and 8 as well as rehabilitation/reconstruction on sections 5, 6 and 7.