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Gambier leaves are widely used in cosmetics, beverages, and medicine. Tarantang village in West Sumatera, Indonesia, is famous for its gambier commodity. Farmers usually classify gambier leaves by area and color. They inherit this ability through generations. This research creates a tool to imitate the skill of the farmers to classify gambier leaves. The tool is a box covered from outside light. Two LEDs are attached inside the box to get maintain light intensity. A camera is used to capture the leaf image and a raspberry Pi processes the leaf features. A mini monitor is provided to operate the system. Six hundred and twenty-five gambier leaves were classified into five grades. Leaves categorized into grades 1, 2, and 3 are forbidden to be picked. Grade 4 leaves are allowed to be picked and those in grade 5 are the recommended ones for picking. Leaf features are area, perimeter, and intensity of leaf image. Three artificial neural networks are developed based on each feature. One thousand leaf images were used for training and 500 leaf images were used for testing. The accuracies of the features are about 93%, 96% and 97% for area, perimeter and intensity, respectively. A combination of rules are introduced into the system based on the feature accuracy. Those rules can give 100% accuracy compared to the farmer’s recommendation. A real time application to classify the leaves could provide classification with the same decision result as the classifying performed by the farmers.

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.

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