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Stevedoring Time Estimation on Smart Port Services Using K-NN Algorithm

机译:使用K-NN算法的智能端口服务的StevedAing时间估计

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Smart Port Service serves the process of ship queuing automatically using a configured system. Inside is an estimated ship docking time (Stevedoring Time). The ship docking time estimation is done to predict the loading and unloading time of the ship at the port. This will later support smart port to create a queue on each dock. To create a stevedoring time estimation system, KNN (K-Nearest Neighbor) is used to classify ships based on specifications from the ship. This ship classification is based on Length of All (LOA) or length of ship, Grosston or tonnage of ships and commodities from ships. Ship specifications will be provided by the Long Range (LoRa) device after LoRa has previously detected the ship to be docking. KNN will make the class based on data from the port of Tanjung Perak. This class is divided into 5 which is the estimated time of docking from the ship. The results after the system was tested resulted in an accuracy of 94.3% in providing estimated docking time from ships. And the most influential parameter in this research is ship commodity. The efficiency of stevedoring process in port could minimize the budget of ship expenses.
机译:智能端口服务使用已配置的系统自动提供船舶排队的过程。里面是估计的船舶对接时间(Stevedoring Time)。船舶对接时间估计是为了预测船舶在端口的装载和卸载时间。稍后将支持智能端口在每个码头上创建队列。为了创建StevedAing时间估计系统,knn(k-collect邻居)用于基于来自船舶的规格对船舶进行分类。该船舶分类基于所有(LOA)或船舶长度的长度,格罗斯顿或船舶的船舶和商品。船舶规格将由Lora先前检测到船舶进行对接后的长距离(LORA)设备提供。 KNN将根据Tanjung Perak港口的数据制作课程。该类分为5,这是从船舶对接的估计时间。测试后的结果测试后,在从船舶提供估计的对接时间时,精度为94.3%。本研究中最具影响力的参数是船舶商品。港口陷入困境过程的效率可以最大限度地减少船舶费用的预算。

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