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"Panjang jalan tol yang beroperasi di Indonesia masih jauh tertinggal dibandingkan dengan negara lain. Diperlukan percepatan yang signifikan agar dapat mengimbangi kebutuhan lalu lintas yang ada. Percepatan pembangunan harus didukung oleh kemampuan penyelenggara jalan dalam proses pembangunan, salah satu nya pekerjaan pemindahan tanah mekanis. Data histori pemindahan tanah mekanis dapat dimanfaatkan sebagai basis data pengetahuan yang dapat diolah oleh pendekatan data mining untuk dilakukan intepretasi dan prediksi produktivitas. Penelitian ini bertujuan mengembangan decision support system pemindahan tanah mekanis pada pekerjaan konstruksi jalan tol dengan implementasi pendekatan menggunakan data historis dari 7 ruas jalan tol utama di Pulau Jawa. Implementasi model terhadap beberapa kasus hipotekal menggunakan pendekatan data mining (DM) dengan tools R yang digunakan dalam penelitian ini memperlihatkan bahwa pendekatan artificial neural network (ANN) memiliki nilai fit tertinggi. Hasil uji menunjukan R2 0,89 ± 0,02, MAD 0,48 ± 0,02dan RMSE 0,62 ± 0,01, dengan compaction productivity yang merupakan variable importance tertinggi. Selain itu, konsep DSS yang dikembangkan dengan pendekatan geographical information system (GIS) mampu memberikan pendekatan yang lebih sederhana bagi pengambil keputusan untuk menjalankan manajemen pekerjaan pemindahan tanah mekanis pada proyek jalan tol.

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The length of toll roads operating in Indonesia is still lower than in other countries. Significant acceleration is needed to keep up with the traffic needs. Acceleration of development should be supported by the ability of road operators in the development process, one of his earthwork movement. The data of earthwork movement can be utilized as a knowledge base. The knowledge that can be processed by a data mining approach to interpretation and productivity predictions. The aim of this study is development decision support system earthwork movement of toll construction project using historical data from 7 major toll roads in Java. Implementation of models on some hipotecal cases using a data mining approach (DM) with R tools used in this study shows that artificial neural network (ANN) approach has the highest fit value. The test results showed R2 0.89 ± 0.02, MAD 0.48 ± 0.02 and RMSE 0.62 ± 0.01, with compaction productivity which is the highest importance variable. In addition, the DSS concept developed with the geographical information system (GIS) approach is able to provide a simpler approach for decision makers to run the earthwork management on toll road projects."

"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."

"Pada 28 September 2018, gempa berkekuatan 7,4 melanda pulau Sulawesi dengan episentrumnya terletak sekitar 80 km dari Kota Palu. Gempa tersebut diikuti dengan tsunamii, tanah longsor dan liiquefaksi yang menyebabkan kerusakan besar pada berbagai jenis infrastruktur. Saat melaksanakan penyelesaian rehabilitasi dan rekonstruksi dalam berbagai bidang, terjadi tantangan lainnya yaitu pandemic covid-19 yang menurunkan produktivitas kinerja pelaksanaan. Kondisi tersebut menuntut para pihak terkait segera melakukan pengendalian proyek, agar tujuan dari pelaksanaan rehabiltasi dan rekonstruksi dapat tercapai dengan baik. Pelaksanaan praktik keinsinyuran ini merinci proses perencanaan, pelaksanaan dan pengendalian pekerjaan Rehabilitasi dan Rekonstruksi Jalan Palupi-Simoro, Kalukubula-Kalawara, Biromaru-Palolo, Akses Huntap Pombewe (RR-02) terdampak gempa bumi dan liquefaksi. Pelaksanaan praktik difokuskan pada perencanaan dan pelaksanaan penanganan area liquefaksi serta pengendalian terhadap dampak pandemic covid-19. Praktik keinsinyuran dilaksanakan dalam rentang waktu 4 bulan yaitu dari Bulan Februari sampai dengan Mei 2021. Dalam praktik keinsinyuran ini didapatkan proses perencanaan rehabilitasi dan rekonstruksi dilakukan secara lengkap dengan memperhatikan proses mitigasi bencana dan trauma healing. Perbaikan kondisi tanah di area liquefaksi dilaksanakan dengan cara vibrator coulomb stone. Sedangkan pengendalian kinerja dilakukan dengan melakukan optimasi produktivitas menggunakan data mining dengan tetap memperhatikan protokol kesehatan yang ketat.

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On 28 September 2018, an earthquake measuring 7.4 struck the island of Sulawesi, with its epicenter located about 80 km from Palu City. The earthquake was followed by tsunamis, landslides, and liquefaction causing massive damage to various types of infrastructure. When carrying out the completion of rehabilitation and reconstruction in various fields, another challenge occurred, namely the COVID-19 pandemic, which reduced the productivity of implementation performance. This condition requires the relevant parties to immediately take control of the project so that the rehabilitation and reconstruction objectives can be adequately achieved. The implementation of this engineering practice details planning, implementing, and controlling the Rehabilitation and Reconstruction work of Palupi-Simoro, Kalukubula-Kalawara, Biromaru-Palolo, Huntap Access Pombewe (RR-02) roads affected by the earthquake and liquefaction. Thus, implementing the practice of planning and implementing the handling of the liquefaction area and controlling the impact of the COVID-19 pandemic. The engineering practice is carried out in a span of 4 months, from February to May 2021. In this engineering practice, it is found that the rehabilitation and reconstruction planning process is carried out in full by taking into account the process of disaster mitigation and trauma healing. Improvement of soil conditions in the liquefaction area was carried out utilizing a coulomb stone vibrator. Meanwhile, performance control is carried out by optimizing productivity using data mining while still paying attention to strict health protocols."