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All Journal Jurnal Ilmu Komputer dan Informasi IPTEK Journal of Proceedings Series EMITTER International Journal of Engineering Technology
Tita Karlita
Politeknik Elektronika Negeri Surabaya
Computer Science & IT

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Mobile Application to Identify Indonesian Flowers on Android Platform Tita Karlita; Achmad Basuki; Lakmi Makarti
EMITTER International Journal of Engineering Technology Vol 1 No 1 (2013)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v1i1.10

Abstract

Although many people love flowers, they do not know their name. Especially, many people do not recognize local flowers. To find the flower image, we can use search engine such as Google, but it does not give much help to find the name of local flower. Sometimes, Google cannotshow the correct name of local flowers. This study proposes an application to identify Indonesian flowers that runs on the Android platform for easy use anywhere. Flower recognition is based on the color features using the Hue-Index, shape feature using Centroid Contour Distance (CCD), and the similarity measurement using Entropy calculations. The outputs of this application are information about inputted flower image including Latinname, local name, description, distribution and ecology. Based on tests performed on 44 types of flowers with 181 images in the database, the best similarity percentage is 97.72%. With this application, people will be expected to know more about Indonesia flowers.Keywords: Indonesian flowers, android, hue-index, CCD, entropy
Android App for Information of Food Additives Contained in Packaged Foods Umi Sa'adah; Tita Karlita; Muchammad Arfian
IPTEK Journal of Proceedings Series Vol 1, No 1 (2014): International Seminar on Applied Technology, Science, and Arts (APTECS) 2013
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23546026.y2014i1.224

Abstract

Today, the usage of food additives can be found in nearly every food, especially in packaged foods. Meanwhile, the majority of people are unaware of the impact of the use of food additives in the human body when consumed freely. One of the easiest ways to determine the ingredients of a packaged food,  is to read the label of product composition. If it is doubtful, then the product was not consumed. However, public knowledge about food ingredients listed in the product composition is generally relatively low. So, even if they could determine the ingredients of the packaged food products, but still do not know the impact of their use. In this research, built an Android app of Food Additives Glossary that presents information about each type of food additives. This app has a barcode scanner and decoder using ZXing library to facilitate the acquisition of data from the food additives listed in packaged foods. User reads the barcode of packaged food products using the barcode scanner. This applications will display of food additives contained therein. Furthermore, the user can select one of the food additives and the application will display some information, such as: INS code, category (safety, unhealthy, dangerous), ADI (Acceptable Daily Intake) limits, side effects and further description of the food additives.
Yoga Pose Rating using Pose Estimation and Cosine Similarity Ani Dwi Astuti; Tita Karlita; Rengga Asmara
Jurnal Ilmu Komputer dan Informasi Vol. 16 No. 2 (2023): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Informatio
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v16i2.1151

Abstract

One type of exercise that many people do today is yoga. However, doing yoga yourself without an instructor carries a risk of injury if not done correctly. This research proposes an application in the form of a website that can assess the accuracy of a person's yoga position, by using ResNet for pose estimation and cosine similarity for calculating the similarity of positions. The application will recognize a person's body pose and then compare it with the poses of professionals so that the accuracy of their position can be assessed. There are three types of datasets used, the first is the COCO dataset to train a pose estimation model so that it can recognize someone's pose, the second is a reference dataset that contains yoga poses performed by professionals, and the third is a dataset that contains pictures of yoga poses that are considered correct. There are 9 yoga poses used, namely Child's Pose, Swimmers, Downdog, Chair Pose, Crescent Lunge, Planks, Side Plank, Low Cobra, Namaste. The optimal pose estimation model has a precision value of 87% and a recall of 88.2%. The model was obtained using the Adam optimizer, 30 epochs, and a learning rate of 0.0001.
Co-Authors Achmad Basuki Ahmad Fauzi Makarim Ani Dwi Astuti Arya Brahmanta Bima Sena Bayu Dewantara Lakmi Makarti Muchammad Arfian Rengga Asmara Riyanto Sigit Umi Sa'adah