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Indonesia merupakan negara maritim yang lebih dari 70 % wilayahnya adalah lautan. Lautan memiliki banyak fenomena alam yang mempengaruhi kehidupan sehari-hari masyarakat bahari atau masyarakat yang hidup tergantung pada laut. Salah satu fenomena alam dari laut adalah pasang surut. Pasang surut air laut dalam hal ini tinggi memegang peranan penting pada masyarakat diberbagai aspek seperti transportasi, pariwisata dan ekonomi. Prediksi tinggi pasang surut air dapat bermanfaat untuk memudahkan segala aktifitas masyarakat bahari. Penelitian ini menggunakan metote Particle Swarm Optimization (PSO) dan Jaringan Syaraf Tiruan (JST) untuk prediksi tinggi pasang surut air laut . Metode Particle Swarm Optimization dan Neural Network memiliki beberapa parameter inputan seperti, jumlah neuron input, learning rate , swarm , c1,c2 inertia min, inertia max. Data yang digunakan sebanyak 1000 yang terbagi menjadi  700 data training dan 300 data testing. Hasil pengujian menunjukkan akurasi prediksi adalah 91.56 %  dengan menggunakan 90 swarm, learning rate 0,9 dan iterasi sebanyak 20 kali. Abstract Indonesia is a maritime country where 70% of its territory is  the ocean. Oceans have many natural phenomena that affect the daily lives of maritime communities or people who live dependent on the sea. One of the natural phenomena of the sea is tide level. Tide level plays an important role in the community both directly and indirectly such as transportation, tourism and the economy. Predictions of tide level can be useful to facilitate all marine activities. This study uses Particle Swarm Optimization (PSO) and Artificial Neural Networks (ANN) to forecast tide level. PSO is used to optimize the minimum error value on the network in order to get the ideal ANN network. The Particle Swarm Optimization and Neural Network methods have several input parameters such as number of input neurons, learning rate, swarm, c1, c2 inertia min, inertia max. The number of data being used in this reseach is 1000 which divided into 700 training data and 300 testing data. The test results shows the prediction accuracy level is 0. 078373 using 90 swarms, learning rate is 0.9 and iteration is 20 times.

Optimasi Jaringan Syaraf Tiruan Backpropagation Dengan Particle Swarm Optimization Untuk Prediksi Pasang Surut Air Laut