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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer JOIV : International Journal on Informatics Visualization
Avisyah, Gisnaya Faridatul
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Open Artificial Intelligence Analysis using ChatGPT Integrated with Telegram Bot Avisyah, Gisnaya Faridatul; Putra, Ivandi Julatha; Hidayat, Sidiq Syamsul
Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Vol. 7 No. 1 (2023)
Publisher : P3M Politeknik Negeri Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31961/eltikom.v7i1.724

Abstract

Chatbot technology uses natural language processing with artificial intelligence that can interact quickly in answering a question and producing relevant answer. ChatGPT is the latest chatbot platform developed by Open AI which allows users to interact with text-based engines. This platform uses the GPT-3 (Generative Pre-trained Transformer) algorithm to help understand the response humans want and generate relevant responses. Using the platform, users can find answers to their questions quickly and relevantly. The method used for OpenAI's research on ChatGPT integrated through Telegram chatbot is using a waterfall method which utilizes open API tokens from Telegram. In this research we develop OpenAI application connected with telegram bot. This application can help provide a wide range of information, especially information related to the Semarang State Polytechnic. By using Telegram chatbot in the program, users can find it easy to ask because it is integrated with OpenAI using the API. Telegram chatbot, which has a chat feature, allows easy communication between users and chatbots. Thus, it may reduce system errors on the bot.
Advanced Instance Segmentation of Aeroponics Tissue Culture-Based Seeds Potatoes Based on Improved YOLOv8l-small Avisyah, Gisnaya Faridatul; Kurnianingsih, Kurnianingsih; Hidayat, Sidiq Syamsul
JOIV : International Journal on Informatics Visualization Vol 9, No 4 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.4.3085

Abstract

To improve agricultural production, this study develops an advanced instance segmentation system for aeroponic tissue culture-based potato seedlings. We present an IoT system that integrates multiple sensors for humidity, temperature, pH, and turbidity to enable real-time monitoring. Additionally, we adapt the YOLOv8l-small computer vision model, an optimized version of YOLOv8, designed explicitly for efficient potato leaf disease detection and segmentation, even in resource-constrained IoT environments. YOLOv8 is a significant advancement in the YOLO series, for instance, segmentation, combining better accuracy, efficiency, and flexibility. YOLOv8 outperforms previous methods in generating precise segmentation masks while maintaining real-time performance. These innovations make YOLOv8 a robust choice for a variety of computer vision tasks, including instance segmentation, in both research and practical applications. When tested on a custom dataset of potato leaf pictures, the suggested model produced mask mAP50 of 0.842 and mAP50-95 of 0.566, with a model size of 36.1 MB and an inference duration of 9.3 ms. These outcomes are similar to those of the original YOLOv8l model, which had a slower inference time of 11.0 ms and a much larger model size of 92.3 MB, albeit at the expense of a somewhat higher mAP50 of 0.843. The study concludes that the proposed model provides similar accuracy with greater computational efficiency, making it ideal for IoT-based agricultural systems. Future research will explore additional aspects, while practical experiments aim to reduce labor costs.
Co-Authors Kurnianingsih Kurnianingsih, Kurnianingsih Putra, Ivandi Julatha Sidiq Syamsul Hidayat, Sidiq Syamsul