Angin sebagai salah satu fenomena alam yang mempengaruhi berbagai aspek dalam kehidupan manusia baik pengaruh positif maupun negatif. Aspek ini berperan besar dalam ekonomi, pariwisata, pembangunan, transportasi maupun perdagangan masyarakat. Data angin dalam hal ini kecepatan angin belum dapat diketahui secara pasti nilainya oleh karena itu perlu adanya prediksi. Adaptive Neuro Fuzzy Inference System (ANFIS) dan Radial Basis Function Neural Networkc(RBFNN) adalah algoritma yang dapat digunakan untuk prediksi data. Penelitian ini menggunakan ANFIS dan RBFNN untuk memprediksi kecepatan angin. Data prediksi yang digunakan dalam penelitian ini adalah data time series. Data kecepatan angin diperoleh dari BMKG (Badan Meteorologi Klimatogi dan Geofisika) Tanjungpinang, Kepualuan Riau. Hasil prediksi dengan kedua metode ini dibandingan dengan data asli untuk mengetahui metode mana yang lebih akurat dalam prediksi data. Hasil pengujian menggunakan kedua algoritma memperlihatkan akurasi terbaik (paling mendekati data asli/target) diperoleh oleh RBFNN yaitu dengan nilai RMSE adalah 0,1766 dan hasil RMSE ANFIS adalah 1,1456.
Persaingan global yang dihadapi saat ini, menuntut adanya perubahan di dalam pembelajaran agar kecakapan dan keterampilan anak didik semakin berkembang. Kemampuan literasi matematika menjadi salah satu yang harus dimiliki para siswa dalam menghadapi tantangan global tersebut. Kegiatan pelatihan dan pendampingan Computational Thinking dengan menerapkan High Order Thinking Skill (HOTS) yang dilakukan diharapkan dapat menambah wawasan siswa terhadap pemahaman dalam melakukan problem solving. Serta, menumbuhkan kreativitas siswa, budaya informasi, algoritma dan berpikir komputasional dalam menyelesaikan suatu permasalahan dalam bentuk tantangan yang dikenal dengan nama Bebras Challenge. Dalam tahapan pelaksanaannya dilakukan tahapan-tahapan yakni pre-test, pelatihan & pendampingan, serta post-test. Pre-test terhadap 15 siswa menunjukkan rerata siswa dalam menjawab soal secara benar adalah sebanyak 60%. Pelatihan-dan pendampingan dilakukan melalui aplikasi daring. Pertemuan dilaksanakan sebanyak 5 kali pertemuan. Sedangkan hasil dari post-test mengalami peningkatan yakni menjadi 78%. Hal ini menunjukkan tingkat keberhasilan siswa dalam memecahkan persoalan mengalami peningkatan yang baik.
Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.
Tanjungpinang is one of the fish producing cities. fish with a good level of freshness are needed to produce quality fish products. In this case, a system is needed that can recognize fresh and non-fresh fish. In this study using the HSV and GLCM methods as a feature then image recognition is carried out using the Radial Basis Function (RBF). In the RBF recognition method it is necessary to have a central point that becomes the data center. Data center retrieval uses the K-Means method, where this method greatly determines the success of the RBF's introduction. By determining the best number of data centers in the best data center, it is at number 7 with MAD of 0.98. At the time of image acquisition did not pay attention to lighting so as to produce training data with low quality. How in the introduction process using this RBF gets a low level of accuracy, which is equal to 50%
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