Pembangunan Aplikasi Mobile Pengenalan Objek Untuk Pendidikan Anak Usia Dini

Abstract

<p class="Abstrak">Penggunaan ponsel sudah sangat erat dengan kehidupaan anak usia dini sehingga menimbulkan beberapa dampak negatif bagi anak usia dini terutama berkurangnya interaksi dengan dunia sekitarnya. Salah satu teknologi yang dapat dikembangkan pada ponsel adalah <em>computer vision. </em>Salah satu penggunaan <em>computer vision </em>adalah <em>object recognition</em> yang memberikan solusi untuk membantu mengenali objek.<em> </em>Pada penelitian ini dibangun sistem pengenalan objek benda di dalam rumah yang diaplikasikan pada ponsel yang diharapkan membantu anak usia dini mengenali benda disekitarnya. <em>MobileNet </em>merupakan salah satu <em>feature extraction</em> yang memiliki kinerja yang baik dan ringan digunakan pada perangkat ponsel. Arsitektur <em>MobileNet </em>terdiri dari <em>layer depthwise convolution </em>dan <em>layer pointwise </em><em>convolution </em>dalam mengekstraksi fitur<em>. </em>Percobaan ini juga menggunakan arsitektur <em>Single Shot Multibox Detector (SSD) </em>sebagai metode dalam mendeteksi objek<em>. Pre-trained model </em>dari dataset <em>COCO </em>digunakan pada eksperimen<em>,</em> untuk mengenali 20 jenis objek benda di dalam rumah. Dari hasil eksperimen, <em>MobileNetV2 </em>menghasilkan nilai <em>mean Average Precision (mAP)</em> yang lebih baik dibandingkan dengan <em>MobileNetV1 </em>dan<em> InceptionV2, </em>yaitu<em> </em>sebesar 99,34%.</p><p class="Abstrak"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"> <em>Mobile phone usage has been very close to early childhood life, so giving rise to some negative impact on early childhood, especially reduced interaction with the surrounding world. One of the technologies that can be developed on the cellphone is computer vision. One of the uses of computer vision is object recognition that provides solutions to help to recognize objects. This research builds a system for recognition objects inside in house that is developed on a cellphone that is expected to help early childhood recognize objects in the surrounding. MobileNet is one of feature extraction that has good performance and efficient use on a cellphone. MobileNet architecture consists of a depthwise convolution layer and pointwise convolution layer in extracting features. The experiment also uses the architecture of Single Shot Multibox Detector (SSD) as a method of detecting objects. We used MobileNet architecture as a pre-trained model that had previously been trained on COCO datasets, and implement transfer learning for 20 types of objects commonly found inside the house. The experimental result indicates that the mean Average Precision (mAP) of MobileNetV2 could exceed MobileNetV1 and InceptionV2 of 99.34%.</em></p>

Mobile Learning with Edmodo : Digital Virtual Class

“…Using LMS wisely can add to the repertoire of learning models. The use of online media as a tool in the learning process will provide variations in the learning process (Agushinta R. & Satria, 2018;Sahudra et al, 2021;Supriadi et al, 2021). Educators and students are expected to increase creativity and learning innovation in the midst of the Covid-19 pandemic (Le et al, 2022;Saravanan et al, 2021).…”

Object Detection to Identify Shapes of Swallow Nests Using a Deep Learning Algorithm

“…In measuring the performance of the object detection model, mAP can be used which is indicated by the formula (8) [26]. Based on the mAP formula shown in formula (8), it is necessary to find the value of the Average Precision (AP) for each class first with formula (9).…”

“…SSD itself is an object detection architecture that has high accuracy and is fast [6], while MobileNet is a lightweight feature extractor [7]. In the research of M. F. Supriadi, E. Rachmawati, and A. Arifianto, the mean Average Precision (mAP) value of the SSD MobileNet V2 architecture is better than the SSD MobileNet V1 in detecting 20 types of objects in the house [8]. In addition, some studies develop the MobileNet SSD architecture using the Feature Pyramid Network (FPN) module to improve detection accuracy [9].…”

Perancangan Ui/Ux Design Laman PPM Roudlotul Jannah Surakarta Versi Mobile Menggunakan Metode Design Thinking

“…Penggunaan internet kian masif seiring perkembangan teknologi informasi dan komunikasi yang begitu pesat, salah satunya perangkat smartphone [1]. Pada gambar 1 memperkuat hal tersebut, dilaporkan oleh agensi pemasaran, [2] dalam "Digital 2023 Indonesia" pada Januari 2023 pengguna internet di Indonesia mencapai angka 212,9 juta pengguna dengan smartphone menjadi gawai paling populer yang digunakan oleh masyarakat Indonesia dengan data sebesar 353,8 juta koneksi mobile atau 128% dari jumlah penduduk Indonesia.…”