Abstract—Endemic fish species are key components in seafood culinary excursions. Despite the increasing interest in leveraging technology to enhance various seafood culinary activities, there is a shortage of comprehensive datasets containing images of seafood used in artificial intelligence research, mainly those showcasing endemic fish. This research endeavors to bridge this gap by increasing the accuracy of fish recognition and introducing a new dataset comprising images of native fish for application in various machine-learning investigations.
The recognition of woven fabrics through image processing has a high value in preserving cultural heritage and assisting in identifying and classifying traditional textile products. To this end, this study proposes an approach that utilizes a Decision Tree (DT) to recognize images of woven fabrics typical of East Nusa Tenggara. DT's effectiveness in high-dimensional data classification makes it an ideal tool for modeling unique patterns in woven fabric drawings.
Lantana flower, originally known as a parasitic and poisonous plant, is expansive to fill many livestock fields. Lantana data sets are open source and can be used by many researchers to create models with higher accuracy. currently the accuracy using this dataset has reached 99.8% using k-NN and preceded by feature extraction using VGG-16 Lantana flower, originally known as a parasitic and poisonous plant, is expansive to fill many livestock fields. Lantana data sets are open source and can be used by many researchers to create models with higher accuracy.
This data contains EMG records of forearm movements. this data can be used for the learning process for students and lecturers or researchers. The sensor used to record data is "Myo Arm-Band". The data is equipped with eight features and ends with the arm movement label still using the Indonesian language term.
