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Moh. Diqi
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ASTEEC Headquarters: Jl. Tajem, Kregan, Maguwoharjo, Depok, Sleman Yogyakarta, 55281, Indonesia
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INDONESIA
International Journal of Informatics Engineering and Computing
Published by ASTEEC Publisher
ISSN : -     EISSN : 30909112     DOI : https://doi.org/10.70687/ijimatic
Core Subject : Science,
Computer Science & IT
International Journal of Informatics Engineering and Computing (IJIMATIC) is an international, peer-reviewed, open-access journal that publishes original theoretical and empirical work on the science of informatics and its application in multiple fields. Our concept of informatics encompasses technologies of information and communication, as well as the social, linguistic, and cultural changes that initiate, accompany, and complicate their development. IJIMATIC aims to be an international platform to exchange novel research results in simulation-based science across all computer science disciplines.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 22 Documents
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Modeling Automatic Waste Sorting Using Ultrasonic Sensors Akhmad Wakhid; Marselina Junia Sipit
International Journal of Informatics Engineering and Computing Vol. 2 No. 2 (2025): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/31bqbr09

Abstract

Waste management remains a critical challenge due to the increasing volume of solid waste and the inefficiency of manual sorting processes. This study develops and implements an Internet of Things (IoT)-based automatic waste sorting system using an ESP32 microcontroller. The proposed system integrates ultrasonic sensors, an inductive proximity sensor, and an MQ135 gas sensor to automatically detect and classify metal and non-metal waste. The system also connects to the Blynk platform to enable real-time monitoring and notification capabilities, allowing users to observe system conditions remotely. Experimental evaluation is conducted using 100 waste samples consisting of 50 metal objects and 50 non-metal objects. The results show that the system correctly classifies 48 metal samples and 42 non-metal samples. Meanwhile, 8 non-metal samples are misclassified as metal, and 2 metal samples are incorrectly detected. Based on these results, the system achieves an overall classification accuracy of 90%, indicating reliable performance in distinguishing between metal and non-metal waste materials. Further evaluation using precision, recall, and F1-score metrics confirms the effectiveness of the proposed system. The metal class achieves a precision of 85.71%, a recall of 96%, and an F1-score of 90.57%. For the non-metal class, the system records a precision of 95.45%, a recall of 84%, and an F1-score of 89.39%. These results demonstrate balanced classification performance for both categories. Therefore, the developed IoT-based automatic waste sorting system provides a practical and reliable approach for improving waste management efficiency and supporting intelligent waste processing based on material characteristics.
Automatic Detection of Cabbage Pest Attacks Based on Leaf Images with Machine Learning Approach Ni Wayan Surya Wardhani; Prayudi Lestantyo; Atiek Iriany; Nur Silviyah Rahmi
International Journal of Informatics Engineering and Computing Vol. 3 No. 1 (2026): International Journal of Informatics Engineering and Computing
Publisher : ASTEEC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70687/3szcd282

Abstract

Farmers in cabbage farming face many problems, one of which is pest attack. Plutella xylostella L. is a major pest on cabbage (known as cabbage leaf caterpillar) which can cause a decrease in production of up to 100 percent. Decision Support System (DSS) was developed to classify the attack rate of Plutella to reduce the negative effects of using various types of high doses of pesticides and short spraying intervals but causing residual effects and killing natural enemies. DSS has a role in helping farmers to make decisions regarding the time of pesticide treatment needed to minimize negative effects and increase productivity. In this study, DSS was developed to detect damage to cabbage (Brassica oleracea L) crops so that farmers can determine pesticide doses and spraying intervals based on a website. The results of the system is presented in the form of images and the percentage of damage to cabbage plants. Therefore, the CART method can be used to analyze the level of damage to plants that are attacked by pests.

Page 3 of 3 | Total Record : 22

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