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All Journal EMITTER International Journal of Engineering Technology International Journal of New Media Technology Angkasa: Jurnal Ilmiah Bidang Teknologi Jurnal ULTIMATICS Jurnal Teknokes
Kurniasari, Arvita Agus
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Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Environmental Science

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NutriTalk: Nutrition Intervention by Experts to Reduce the Impact of Stunting Through Mobile Based Applications Using Agile Method Kurniasari, Arvita Agus; Olivia, Zora; Suryana, Arinda Lironika; Widiyawati, Agatha; Maria Rosiana, Nita
Jurnal Teknokes Vol. 16 No. 3 (2023): September
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The prevalence of childhood stunting, a pervasive global health concern primarily attributed to persistent malnutrition, underscores an urgent need for intervention. In Indonesia, where stunting rates are alarmingly high, with approximately 27.6% of children under five affected, innovative solutions are imperative. This study introduces "Nutri Talk," a mobile application developed using Agile Methodology to revolutionize nutritional consulting services. The application facilitates seamless communication with nutrition specialists, offering evidence-based information and personalized consultations to empower parents in making informed dietary decisions for their children. The application demonstrates robust functionality and user satisfaction through rigorous testing, including Boundary Value Analysis (BVA) and User Acceptance Testing (UAT). "Nutri Talk" stands poised to mitigate the long-term impacts of stunting, leveraging technology to enhance nutritional outcomes. This research advocates for a comprehensive approach to combat stunting, combining mobile technology advancements with targeted interventions, ultimately contributing to improved childhood nutrition and development.
Approach Convolutional Neural Network LeNet-5 for Interactive Learning of Korean Syllables (Hangul) Al Fitra Yudha, Vasyilla Kautsar; Kurniasari, Arvita Agus; Arifianto, Aji Seto; Afriansyah, Faisal Lutfi
IJNMT (International Journal of New Media Technology) Vol 11 No 2 (2024): Vol 11 No 2 (2024): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ijnmt.v11i2.3705

Abstract

The increasing popularity of South Korean culture among Indonesian society has led to a growing interest in gaining a deeper understanding of the country, including a desire to master the Korean language. However, learning the Korean alphabet (hangul) often presents challenges due to its characters being unfamiliar to the Indonesian people. Therefore, engaging and interactive learning media are needed to assist in the learning process. Within this endeavor, a learning website called Learn Hangul was developed, focusing on two main features: learning hangul characters and their arrangement, as well as practicing writing syllables using Korean letters. This website was developed using the Convolutional Neural Network (CNN) LeNet-5 to facilitate learning, with black box testing results indicating good functionality. Model performance evaluation yielded satisfactory values, with model accuracy at 89.2%, precision at 89.7%, recall at 88.8%, and an F1-score of 89.2%. Direct testing with users also showed a high success rate, with 80% of respondents experiencing an increase in their knowledge of Korean characters (Hangul) after trying to learn them on the Learn Hangul website. Thus, the Learn Hangul website serves as a useful learning tool for those interested in studying the Korean alphabet (hangul).
Face Recognition untuk Smart Door Lock menggunakan Metode Haar-Cascades Classifier dan LBPH Kurniasari, Arvita Agus; Sudirman, Muhammad Farizul Imami; Ramadan, Asif Mahardhika; Firmansyah, Firdaus; Damayanti, Nur Hakiki
Angkasa: Jurnal Ilmiah Bidang Teknologi Vol 15, No 2 (2023): November
Publisher : Institut Teknologi Dirgantara Adisutjipto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28989/angkasa.v15i2.1662

Abstract

A home security system is one of the features that every homeowner must own and contemplate if they want their residence to be secure from theft and other unwanted security disturbances. Therefore, we require a support system that can enhance domestic security. In this research, the constructed system uses faces as security information. This system captures facial images using an ESP-CAM 32 board integrated with an Arduino UNO. As the system's output, this system will employ the Selenoid DoorLock and Relay features. This system detects faces using the Haar-Cascade Classifier and recognizes faces using the Local Binary Pattern Histogram (LBPH). Implementation of the method for obtaining results, namely Smart Door Lock, can autonomously unlock the door with a presentation of greater than 85%. However, if the face detected to open the door is not the same as the registrant's face and has less than 85% of the required data, the door will not open.
APPLICATION OF DEEP LEARNING TECHNIQUES FOR ENHANCING ARABIC VOCABULARY ACQUISITION IN STUDENTS AT MTS DARUN-NAJAH Isnaini, Misbachur Rohmatul; kurniasari, arvita agus; Arifianto, Aji Seto; Dewi Puspitasari, Pramuditha Shinta
ULTIMATICS Vol 16 No 2 (2024): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v16i2.3701

Abstract

Arabic vocabulary recognition is an important aspect of learning at MTs Darun - Najah, a school that emphasizes on Islamic religious education. This research proposes the application of Convolutional Neural Network (CNN) and EfficientNet B7 to create learning media for Arabic vocabulary recognition for students. This method is implemented in the form of a web-based application. The built application offers an innovative approach in learning by utilizing deep learning. The results of several trials conducted showed that the application of Convolutional Neural Network (CNN) and EfficientNet B7 achieved 90% accuracy with an average precision of 94.6%, recall 94.6%, and f1-score 94.6%. Tests using User Acceptence Testing (UAT) have a success accuracy rate of 87.2% which proves that users can accept quite well.
Content-Dependent Image Search System for Aggregation of Color, Shape and Texture Features Kurniasari, Arvita Agus; Barakbah, Ali Ridho; Basuki, Achmad
EMITTER International Journal of Engineering Technology Vol 7 No 1 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (717.8 KB) | DOI: 10.24003/emitter.v7i1.361

Abstract

The existing image search system often faces difficulty to find a appropriate retrieved image corresponding to an image query. The difficulty is commonly caused by that the users’ intention for searching image is different with dominant information of the image collected from feature extraction. In this paper we present a new approach for content-dependent image search system. The system utilizes information of color distribution inside an image and detects a cloud of clustered colors as something - supposed as an object. We applies segmentation of image as content-dependent process before feature extraction in order to identify is there any object or not inside an image. The system extracts 3 features, which are color, shape, and texture features and aggregates these features for similarity measurement between an image query and image database. HSV histogram color is used to extract color feature of image. While the shape feature extraction used Connected Component Labeling (CCL) which is calculated the area value, equivalent diameter, extent, convex hull, solidity, eccentricity, and perimeter of each object. The texture feature extraction used Leung Malik (LM)’s approach with 15 kernels.  For applicability of our proposed system, we applied the system with benchmark 1000 image SIMPLIcity dataset consisting of 10 categories namely Africans, beaches, buildings historians, buses, dinosaurs, elephants, roses, horses, mountains, and food. The experimental results performed 62% accuracy rate to detect objects by color feature, 71% by texture feature, 60% by shape feature, 72% by combined color-texture feature, 67% by combined color-shape feature, 72 % combined texture-shape features and 73% combined all features.
Co-Authors Achmad Basuki Afriansyah, Faisal Lutfi Agatha Widiyawati, Agatha Aji Seto Arifianto Al Fitra Yudha, Vasyilla Kautsar Ali Ridho Barakbah Damayanti, Nur Hakiki Dewi Puspitasari, Pramuditha Shinta Firdaus Firmansyah Isnaini, Misbachur Rohmatul Maria Rosiana, Nita Olivia, Zora Ramadan, Asif Mahardhika Sudirman, Muhammad Farizul Imami Suryana, Arinda Lironika