Tingkat usability dapat mempengaruhi kemudahan penggunaan suatu aplikasi termasuk aplikasi mobile, sehingga penting untuk dilakukanya evaluasi. Aplikasi Simalu merupakan sebuah aplikasi lokal Bali yang bergerak dalam bidang kebersihan lingkungan. Simalu tergolong aplikasi baru karena dirilis pada awal bulan Januari tahun 2018 dan belum pernah dilakukan evaluasi usability sebelumnya. Evaluasi usability dilakukan untuk meningkatkan User Experience pengguna, sehingga aplikasi dapat diterima dan digunakan lebih mudah oleh pengguna. Metode yang digunakan untuk melakukan evaluasi yaitu usability testing dengan teknik Retrospective Think Aloud dan Performance Measurement. Hasil yang didapatkan adalah aplikasi Simalu memiliki kualitas yang belum dapat dikatakan efektif, efisien dan memenuhi kepuasan pengguna, sehingga perbaikan desain juga dilakukan agar aplikasi lebih baik lagi untuk memenuhi harapan pengguna. Kata kunci: Aplikasi Simalu, Evaluasi Usability, User Experience, Usability Testing, Retrospective Think Aloud, Performance Measurement.

Face identification has been widely applied this time, such as security on gadgets, smart home security, and others. Face dominates as a biometric which is most increase in the next few years. Face is used for biometric identification which is considered successful among several other types of biometrics and accurate results. Face recognition utilizes facial features for security purposes. The classification method in this paper is K-nearest Neighbor (KNN). The K-Nearest Neighbor algorithm uses neighborhood classification as the predictive value of a good instance value. K-NN includes an instance-based learning group. This paper developed face identification using Principal Component Analysis (PCA) or eigenface extraction methods. The stages of face identification research using the KNN method are pre-processing in the input image. Preprocessing used in this research are contrass stretching, grayscale, and segmentation used haar cascade. This research is registered 30 people, each person had 3 images used for training and 2 images used for testing. The results obtained from several trials of k values are as follows. Experiments with a value of k=1 get the best accuracy, namely 81%, k=2 get 53% accuracy, and k=3 get 45% accuracy.

Herbs are used in traditional medicine. There are so many herbs are spread across the world, it is difficult to memorize it all. This paper describes an android application to recognize herbs by their leaf characteristics (shape, veins, and keypoints). Shape and veins of leaves are recognized by Invariant Moment Method as the feature extraction. City Block Distance used to calculate the distance between the features. Whereas for detection and keypoints extraction using Oriented FAST and Rotated BRIEF on OpenCV library. This keypoints distance calculation using Brute-Force Hamming. Matching is done by calculating the shortest distance between test image and reference image. If the result is less than or equal to threshold then image is match. Experiment result show this application can achieve 79% of success rate by using keypoints. This result is influenced by glossy leaf surface, so there is many reflected light that become noise.

Abstract

Medicinal plants are plants that have benefit in order to supply the needs of families traditionally medicine. Medicinal plants have diverse types that causing modern society have difficulty in recognizing these crops. Medicinal plants generally can be identified by the leaves, stems and fruit. One of the leaves characteristics can be distinguished based on vein structure and shape of its. Based on these problem, plant recognition based on vein and shape are made by using Localized Arc Pattern Method. There are two important processes in Plant Recognition Applications. First process is Enrollment and the second is Recognition process. In the Enrolment process, the leaves image filed as many as 6 images for each leaves type. This image then calculated based on the 42 special model pattern obtained and the feature is stored as a reference image. Leaves images that used as test image are 200 images. On the Recognition process, the test image will be process which as same as at Enrollment process, however feature from the test image will be comparing with reference image in database, then it calculate the difference value. This process uses a threshold value to determine whether the test images leaves are recognized or not. When dissimilarity value is smaller than the threshold is known as the same leaves, when instead then it known as a different leaves or not known at all. Experiment result shows in this application can recognize 77% of total leaves and False Accepted Ratio (FAR) equal to 4.5% and False Rejection Ratio (FRR) equal to 18.5%. This result was influenced by the shiny surface of leaf and shape of the leaves are small.