In this study, we present the improved artificial neural network based on cosine similarity in facial emotion recognition. We apply a shifting window that employs a neural network for two concurrent processes consisting of face detection and emotional recognition. To prevent the slow and futile computations, non-face areas need to be filtered from neurons on each network layer, thus we propose the improved artificial neural network based on cosine similarity. Cosine similarity is employed to bypass the process of non-face areas in the neural network. The accuracy of the proposed method reaches 0.84, while the accuracy of the original neural network method reaches 0.74. It can be concluded that our methods work accurately. It can be concluded that our method works accurately. The proposed method is superior to the state-of-theart algorithms
ABSTRAK ABSTRACTThe radar systems are categorized into two types: civilian and military radar systems. The two types of radar systems have similarity that has been used for monitoring air traffic. The aircraft that is monitored in the air is currently experiencing a large amount improvement. To facilitate the aircraft monitoring, it required a system called multitarget aircraft tracking. This research proposed a hybrid method of multitarget tracking algorithm (MTT) with time window clustering for data pre processing. This research started with the preparation of the radar data recorded directly. Data recording was simulated with algorithms that have been designed. Finally, performance of the algorithm was compared with multiple hypothesis tracking without clustering time window. The test is done using data recording lasted approximately eighteen minutes. The test result shows that the proposed method is better than the MHT without CTW method. It can be seen from the correct target 87.66%, the undetected target 12.81%, the maintain target 80.5% and the inexisting target 23.65%.
The Co-19 pandemic affects worldwide classical education. It has led to the optimization of E-Learning. Learning Management Systems integrate the learning materials and human resources systems, but cannot provide human interaction in real-time. Teleconferencing platforms allowed communication in real-time, but cannot integrate the learning materials and human resources systems. In order to optimize real-time interaction in the learning management system, a teleconference-oriented learning management system is proposed. The configuration of database and user interface are required to combine the two systems. The Teleconference Platform was also hired as LMS course. In order to determine the experience of the teleconference-oriented LMS, the User Experience Questionnaire (UEQ) of the proposed LMS and the original LMS is tested using T-Test. UEQ is filled out by 85 students. The result show that attractiveness, perspicacity, performance, dependability, and stimulation reached respectively 0
Kecepatan pengiriman menjadi faktor penting kepuasan pengguna jasa pengantar barang. Untuk itu, peneliti mengusulkan rekomendasi driver yang komersial yang memiliki lintasan/jalur tercepat pada sistem informasi pengantaran barang. Kebutuhan akses yang realtime menuntut algoritma mencari jalur terbaik menggunakan metode yang sederhana dan cepat. Metode certain factor merupakan salah satu metode yang dapat bekerja secara cepat dengan mempertimbangkan seberapa besar tingkat kepastian jalur. Pada sistem tersebut, peneliti mengekstrak solusi yang dihasilkan dari google map. Selanjutnya, metode certain factor menghitung probabilitas melalui : (1) pengambilan nilai jarak, (2) klasifikasi kelas jarak, dan (3) perhitungan Nilai CF. Metode evaluasi yang digunakan dalam penelitian ini berbasis recall dimana performa Certainty Factor (CF) akan diuji dan dibandingkan dengan data yang digunakan dari pemesanan Sitara dengan posisi jemput yang berbeda-beda berupa nilai lotitude dan langitude sebanyak 10 data. Hasil menunjukkan bahwa recall mencapai 100%. Hal ini mengindikasikan metode certain factor memiliki tingkat keberhasilan sistem yang tinggi dalam menemukan kembali sebuah informasi.
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