Content Based Batik Image Retrieval (CBBIR) is an area of research that focuses on image processing based on characteristic motifs of batik. Basically the image has a unique batik motif compared with other images. Its uniqueness lies in the characteristics possessed texture and shape, which has a unique and distinct characteristics compared with other image characteristics. To study this batik image must start from a preprocessing stage, in which all its color images must be removed with a grayscale process. Proceed with the feature extraction process taking motifs characteristic of every kind of batik using the method of edge detection. After getting the characteristic motifs seen visually, it will be calculated by using 4 texture characteristic function is the mean, energy, entropy and stadard deviation. Characteristic function will be added as needed. The results of the calculation of characteristic functions will be made more specific using the method of wavelet transform Daubechies type 2 and invariant moment. The result will be the index value of every type of batik. Because each motif there are the same but have different sizes, so any kind of motive would be divided into three sizes: Small, medium and large. The perfomance of Batik Image similarity using this method about 90-92%.
Speech impaired is the inability of someone to speak, even though speaking ability is important in order to communicate with other people. Dealing with this as someone who has speech impairments has their own way of communicating, namely by using sign language, but not everyone understands the sign language. The MFCC and Backpropagation ANN methods are implemented on a Single Board Computer (SBC) designed to overcome speech impaired communication problems. The MFCC method is used to retrieve the features of speech impairment and the Backpropagation ANN is used for sound pattern recognition.The system was trained using 750 sound samples consisting of 5 speakers, each uttering as many as 30 repetitions of the pronunciation of words (makan, kamar, kerja, harga and lapar), then tested using 125 sound samples consisting of 5 speakers, each saying 5 repetitions of words. Training and testing of Backpropagation ANN using input coefficients generated from MFCC. The results showed that the MFCC and Backpropagation ANN methods were able to identify speech words with 60% accuracy, 40% precision and 40% sensitivity.
Luas perairan Indonesia sebesar 64.97 % dari luas wilayah Indonesia dengan jumlah nelayan sebanyak 2.17 juta nelayan di seluruh Indonesia. Kebanyakan dari nelayan Indonesia tersebut masih menggunakan cara tradisional dalam melaut. Dalam penelitian ini dilakukan untuk dapat membuat server sistem tracking perahu nelayan secara realtime. Dengan sistem ini, nelayan kecil dengan kegiatan nya hanya 1 – 2 hari melaut (one day fishing) dalam melakukan pelayarannya tingkat keselamatan nelayan dapat ditingkatkan dikarenakan dapat terpantau posisinya. Sistem yang dikembangkan menggunakan server berbasis mqtt protocol di sisi gateway. Server menggunakan mysql server menggunakan get JSON Parsing. Sebagai front end sistem tracking untuk menampilkan kordinat perahu berbasis web. Dari hasil implementasi didapat bahwa server dapat merespon dengan baik dari end device dengan melakukan pengiriman data secara periodik.
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