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All Journal GMPI Conference Series Journal of Educational Technology and Instruction
Amartiwi, Utih
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Physics Social Sciences

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Implementation of Computer Vision for Efficient Attendance and School Uniform Checking System Maulana, Faris; Sinaga, M. Ali Akbar; Rizal, Hairul; Mahendra, Bella Nideni; Anggraini, Lita; Amartiwi, Utih
Journal of Educational Technology and Instruction Vol. 2 No. 2 (2023): Journal of Educational Technology and Instruction
Publisher : Tauladan Fathimah Azzahra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70290/jeti.v2i2.94

Abstract

Managing school administrative tasks can consume substantial time and effort. Every semester, teachers find themselves occupied in repetitive manual tasks such as attendance tracking, disciplinary documentation, and assignment grading. These sometimes could take longer time than preparation for teaching. In this research, we proposed an Artificial Intelligence (AI) approach to handle this problem. In the era of Industry 4.0, AI has been managed to be personal assistance and do human repetitive tasks efficiently. However, the implementation of AI in Indonesia especially for educational institutions is still rare. Therefore, we have developed an innovative AI-driven attendance and uniform detection system by implementing computer vision models. Computer vision is a field of AI that equips machines with the ability to interpret and understand visual information from images and videos, enabling them to classify images and detect objects. The results show that computer vision has successfully facilitated swift and accurate detections for this task. We have also incorporated a timestamp to provide information about the time when students arrive at school. Subsequently, all the recorded data will be saved and organized within the school’s database. As a result, teachers are liberated from the tedium of manual data entry and can redirect their efforts toward pedagogical materials and instructional strategies.
DjunkGo: A Mobile Application for Trash Classification with VGG16 Algorithm Wulandari, Sekar Ayu; Ma’ruf, Muhammad; Priyatno, Aditya Rachman; Halimun, Naomi; Abdulah, Zeni Malik; Amartiwi, Utih
GMPI Conference Series Vol 2 (2023): 4th International Conference of Integrated Intellectual Community (ICONIC)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (173.372 KB) | DOI: 10.53889/gmpics.v2.175

Abstract

Garbage is one of the big problems in many countries including Indonesia. A bad waste management and low awareness of people participating in sorting the trash are 2 obstacles that we face in daily life. However, if we can ask them to sort the trash properly, they will not only help the waste collector, but also improve the waste management in the country. That encourages us to develop a mobile application that helps people to identify the type of the trash they have so that they can sort it by themselves. This application applies image processing and VGG16 algorithm to identify the trash with accuracy 90%. Furthermore, this application also links them to an appropriate agency that can recycle their trash based on its type. Therefore, the waste sorting process will be easier and recycling is also faster.
Smart Plant: A Mobile Application for Plant Disease Detection Suhaman, Jali; Sari, Tia; Kamandanu, Kamandanu; Aulianti, Dwy; Adhi, Muhammad; Amartiwi, Utih
GMPI Conference Series Vol 2 (2023): 4th International Conference of Integrated Intellectual Community (ICONIC)
Publisher : Gemilang Maju Publikasi Ilmiah (GMPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (184.526 KB) | DOI: 10.53889/gmpics.v2.173

Abstract

Indonesia is one of the big producers of agricultural products in the world. Agriculture sector plays an important role in the national economic development structure. However, the proportion of young farmers (ages 20 to 30 years old) is only 8% of the farmer population (BPS, 2019). Majority proportion comes from old people with age interval from 50 to 60 years old. (Taufik Leoni, 2020). Based on our case study in Purwokerto, the problem that is often found by old age farmers is the reduced ability to see and recognize plant diseases. Furthermore, they also face the difficulty to follow the development of agricultural science so that some of their knowledge is outdated. That encourages us to make a mobile application to identify plant disease and connect them with scientists. Since the majority of farmers in Purwokerto plant tomatoes, we limit this research for tomato disease only. After studying some related previous research, we found most of them used a deep structure of Convolutional Neural Network (CNN) to reach a high accuracy. However, since our aim is to make daily use technology for old people, a high complexity model does not fit for this case. Therefore, we proposed our own CNN model with less complexity but got 89% accuracy. For future works, we plan to develop it for the other plants and hope it will help all farmers to do quality control, especially for the old age farmers.
Co-Authors Abdulah, Zeni Malik Adhi, Muhammad Anggraini, Lita Aulianti, Dwy Halimun, Naomi Kamandanu, Kamandanu Mahendra, Bella Nideni Maulana, Faris Ma’ruf, Muhammad Priyatno, Aditya Rachman rizal, Hairul sari, tia Sekar Ayu Wulandari Sinaga, M. Ali Akbar Suhaman, Jali