Temporal observation is a series of processes started by collecting the necessary data, which is then processed, so that valid information is obtained to support the right decision. To increase the ease of data collection, an automatic algorithm is needed to increase efficiency, shorten the time, and reduce the required resources. The automatic algorithm based on the geographic information system developed in this study was applied to monitoring mangrove forests in Gopek Beach, located on the north coast of Serang, Banten. Using the cloud computing process from an automatic algorithm, the results of vegetation monitoring showed increased efficiency in time and resources. Thus, this study can be used for Geographic Information Systems learning materials in schools or universities.
Accurate weather forecasts play an important role in today's world as various sectors such as marine, navigation, agriculture and industry are basically dependent on weather conditions. Weather forecasts are also used to predict the occurrence of natural disasters. Weather forecasting determines the exact value of weather parameters and then predicts future weather conditions. In this study the parameters used are. Different weather parameters were collected from the Serang Maritime Meteorological Station and then analyzed using a neural network-based algorithm, namely Long-short term memory (LSTM). In predicting future weather conditions using LSTM neural networks are trained using a combination of different weather parameters, the weather parameters used are temperature, humidity, rainfall, and wind speed. After training the LSTM model using these parameters, future weather predictions are performed. The prediction results are then evaluated using RMSE. Prediction results show that the model is more accurate when predicting temperature data with RMSE 0.37, then RMSE wind speed 0.72, RMSE sunlight 2.79, and RMSE humidity 5.05. This means that the model is very good at studying weather data, inversely proportional to humidity data.
Chlorophyll-a is an indicator of the abundance of phytoplankton in the waters that play a role in the photosynthesis process. Chlorophyll-a measurement can be done in two ways, namely conventional and the use of remote sensing technology. This research method utilizes remote sensing technology Landsat 8 imagery processed using ER Mapper 7.1 software. The purpose of this study is to inform the comparison of chlorophyll-a before and after the tsunami disaster in the waters of Palu Bay, Central Sulawesi Province. The results showed that these waters had increased the abundance of phytoplankton after the tsunami disaster
Produksi ikan di Muara Gading Mas menurun dengan adanya larangan penggunaan alat penangkapan ikan Pukat Hela (Trawls). Akan tetapi saat ini produksi ikan menunjukkan peningkatan seiring perkembangan alat tangkap modifikasi dari trawl yaitu jaring arad. Namun, informasi mengenai pola distribusi hasil tangkapan mulai dari ikan didaratkan di TPI sampai ke konsumen belum tersedia, selain itu belum pernah dilakukannya prediksi hasil tangkapan guna mendukung pasokan distribusi. Tujuan penelitian adalah mengetahui pola distribusi hasil tangkapan jaring arad dan menguji metode prediksi hasil tangkapan. Metode yang digunakan adalah studi kasus dengan pelaksanaan pada bulan November 2021 hingga Maret 2022. Guna mengetahui pola distribusi analisis yang diterapkan adalah Supply Chain sedangkan metode pengujian menggunakan analisis Conjugate Gradient pada Back Propagation Neural Network dalam memperoleh nilai terbaik. Didapatkan hasil dari analisis supply chain bahwa TPI Muara Gading Mas sudah mampu menyediakan pasokan untuk daerah sekitarnya sebesar 94% namun untuk tujuan luar kota sebesar 6%. Berdasarkan hasil pengujian BPNN, learning rate dengan 0.1, toleransi error 0.01 dan epoch 50 telah memperoleh akurasi terbaik dengan nilai mean square error (MSE) sebesar 1.2446 x 10-6. Hasil penelitian menunjukkan bahwa algoritma BPNN dapat diterapkan untuk metode prediksi alternatif.Kata kunci; Arad, Distribusi, Ikan, TPI Muara Gading Mas, Prediksi
This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling.
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