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All Journal Jurnal Ilmu Komputer dan Informasi IPTEK Journal of Proceedings Series TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Riau Jurnal Teknik Informatika
Tita Karlita
Politeknik Elektronika Negeri Surabaya
Computer Science & IT Dentistry Education Library & Information Science

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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.
Deep Metric Learning with Different Distance Metrics for Enhanced Classification Model in Typing Style Hendri Darmawan; Zulfa Muflihah; Tita Karlita
JUTI: Jurnal Ilmiah Teknologi Informasi Vol.23, No.2, July 2025
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v23i2.a1292

Abstract

Writing can be a powerful and unique medium of self-expression for every individual. Therefore, we propound a deep metric learning technique to acquire the vector representation of text, aiming to enhance the performance of deep learning classification models in typing style classification. The study also compared the effect of text pre-processing and distance metrics on model performance using tweet data from six different Twitter users. The outcomes of the study showed that the model without text pre-processing and with deep metric learning using the Cosine distance metric had the optimal result with an accuracy of 0.79, compared to the deep learning model with a categorical cross-entropy loss function which only had an accuracy of 0.76. Additionally, the model with text pre-processing also produced a good performance, with an accuracy of 0.63 using the deep metric learning approach and Cosine distance metric, and an accuracy of 0.64 using deep learning classification with a categorical cross-entropy loss function.
Convolutional neural network enhancement for mobile application of offline handwritten signature verification Wifda Muna Fatihia; Arna Fariza; Tita Karlita
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25849

Abstract

The increase in signature forgery cases can be attributed to the escape of forged signatures from manual signature verification systems. Researchers have developed various machine learning and deep learning methods to verify the authenticity of signatures, one of which uses convolutional neural networks (CNNs). This research aims to develop a mobile application for handwritten signature verification using CNN architecture by adding a batch normalization technique to its layer. The performance of our proposed method achieved a verification accuracy of 86.36%, with a 0.061 false acceptance rate (FAR), 0.303 false rejection rate (FRR), and 0.182 equal error rate (EER), which is compatible to be embedded in smartphones. However, there is still a need for further development of the CNN model and its integration with mobile applications.
Sistem Informasi Manajemen Budidaya Tanaman Melon Modul Pengelolaan Keuangan di CV Agro Utama Mandiri Lestari Fitrah Maharani Humaira; Valentia Nikma Prastita; Tita Karlita; Rengga Asmara
Riau Jurnal Teknik Informatika Vol. 4 No. 1 (2025): Maret 2025
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v4i1.3246

Abstract

The advancement of technology is progressing rapidly and will continue in line with the development of science. This progress is inevitable in all aspects of life, including in the corporate environment. CV Agro Utama Mandiri Lestari is a company engaged in agriculture as well as a training and self-sufficient rural development center. A company can grow quickly if its financial management data is accurate and precise. Financial management involves organizing the resources owned by the company to achieve its goals and objectives. In financial management, there are cash inflows and outflows, which are activities that always exist in every business entity. In this company, melon cultivation is still managed using a manual and simple financial system, making administrative activities prone to document loss, inaccuracy, and difficulties in analyzing fertilizer expenses. Therefore, a financial management system is highly necessary for CV Agro Utama Mandiri Lestari. This system features data management for income, expenses, human resources, harvests, capital, and profit and loss per harvest. Additionally, it can generate financial reports based on predefined filters. The system also provides a data visualization dashboard displaying financial information for CV Agro Utama Mandiri Lestari.
Co-Authors Ahmad Fauzi Makarim Ani Dwi Astuti Arna Fariza Arya Brahmanta Bima Sena Bayu Dewantara Fitrah Maharani Humaira Hendri Darmawan Muchammad Arfian Rengga Asmara Riyanto Sigit Umi Sa'adah Valentia Nikma Prastita Wifda Muna Fatihia Zulfa Muflihah