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All Journal Dinamik System Information and Computer Technology (SYNCTECH)
Agung Setyadi
Unknown Affiliation
Computer Science & IT

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Remote Sensing (Penginderaan Jauh) Dewi Handayani Untari Ningsih; Agung Setyadi
Dinamik Vol 8 No 2 (2003)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v8i2.516

Abstract

Remote sensing is the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigations Remote sensing data is of such nature and volume as to require it to be compatible with processing and outputing by computers. They are the easiest, fastest, and most efficient way to produce images, extract data sets, and assist in decision making. One special function is to assist in manipulating other kinds of data about the spatial or locational aspects of areas in the world that are the subjects of interpretation and decision making. The bulk of the data in such systems have in common a geographical significance, that is, they are tied to definite locations on the Earth. In this sense, they are similar to or actually make up what has become a powerful tool in decision making and management. The Image-Based Information System (IBIS) was developed in 1975 at the Jet Propulsion Laboratory, and is designed to be a comprehensive geographic information system that performs operations on raster image, tabular, and graphics format data, using the Video Image Communication And Retrieval (VICAR) image processing system. This was accomplished by the creation of a new VICAR-based file format for tabulating raster format geographic information over multiple data planes.
Implementasi AI Object Recognition Real-Time Menggunakan TensorFlow.js dan Integrasi WhatsApp Setyadi, Agung; Rizaldy, Adhy; Reski, Atika; Fauzan Bobihu , Muhammad Al
System Information and Computer Technology (SYNCTECH) Vol. 2 No. 1 (2026): February
Publisher : Subaltren Inti Media

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Abstract

Real-time monitoring system development frequently encounters accessibility challenges when deployed on mid-to-low-end hardware. This study documents the experimental process of developing a web-based AI object recognition system utilizing TensorFlow.js and the COCO-SSD model. Prior research employing COCO-SSD has demonstrated suboptimal performance, with response times exceeding 33 ms. During the development phase, custom logic incorporating overlap mechanisms and cooldown features was implemented to address limitations inherent in basic object detection when recognizing human-object interactions. To optimize real-time performance, this logic was applied at the application level rather than within the AI model itself, leveraging the latest deep learning methodologies proven to outperform YOLO. Using a private training dataset comprising limited facial and indoor object images, the system successfully visualizes bounding boxes and sends instant WhatsApp alerts via whatsapp-web.js. The methodology adheres to an integrated web-based object detection workflow. Experimental results demonstrate a responsive system with latency below 30 ms, meeting real-time performance standards. This paper concludes that the JavaScript-based AI stack, combined with spatial logic, effectively provides a functional solution for automatic activity recognition.
Co-Authors Dewi Handayani Untari Ningsih Fauzan Bobihu , Muhammad Al Reski, Atika Rizaldy, Adhy