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All Journal Society: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS
Amili, Fadel
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Agriculture, Biological Sciences & Forestry Computer Science & IT Economics, Econometrics & Finance Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences

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WEBINARS & WORKSHOPS INTRODUCTION TO COMPUTER VISION: UNRAVELING THE WONDERS OF COMPUTER VISION Dwi Duta Mahardewantoro; Amili, Fadel; Ilyasa, Fahri; Ikhsan Adi Prayogo; Ikhsan Fitriansyah; Muhammad Arif; Rifqi Baihaqi Sabhan; Syuja Ardyansyach; Muhammad Adri Farqan; Nurvelly Rosanti; Poppy Meilina; Nurbaya Ambo, Sitti
Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Vol. 2 No. 1 (2024): Februari
Publisher : CV. Alina

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59407/jpki2.v2i1.494

Abstract

The computer vision workshop is a crucial platform addressing the escalating demands of this rapidly evolving field within computer science. In today's era, visual data processing is integral across diverse sectors such as healthcare, security, and industry, making the workshop a guiding beacon for participants navigating the complex terrain of computer vision. Central to its agenda is the dissemination of practical insights into training data for object recognition, a cornerstone for unleashing the full potential of computer vision applications. Participants delve into fundamental concepts, encompassing nuances of deep learning alongside the intricacies of data training and object detection. Employing Google Colab and Python programming language, the workshop emphasizes practical application, equipping participants with cutting-edge tools. Expected outcomes encompass participants fostering a profound grasp of underlying principles, refining proficiency in data training for nuanced object detection, and bolstering confidence through hands-on exercises. Designed holistically, the workshop integrates elements such as deep learning, computer vision, interactive Q&A sessions, and practical exercises, ensuring participants acquire a robust skill set ready for real-world challenges in the dynamic realm of computer vision.
Utilization of Machine Learning for Property Price Segmentation and Prediction Andriansyah, Akbar; Dzulkarnain, Mulki Djenfik; Afkarinah, Afni Izzah; Amili, Fadel; Ramadhika, Gilang; rosanti, Nurvelly; Ambo, Siti Nurbaya; Andharani, Yana; Sutrisno, Mirza
Society : Jurnal Pengabdian Masyarakat Vol 4, No 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.537

Abstract

Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39 participants from various backgrounds participated in this activity. The evaluation showed that 38.7% of participants were satisfied, while 51.6% were very satisfied with the program. In addition, the post-test results showed a significant increase in understanding compared to the pre-test results. Based on these results, this activity proved to be successful in providing new insights into the application of machine learning in property price prediction and equipping participants with practical skills that can be applied in the real world.
Utilization of Machine Learning for Property Price Segmentation and Prediction Andriansyah, Akbar; Dzulkarnain, Mulki Djenfik; Afkarinah, Afni Izzah; Amili, Fadel; Ramadhika, Gilang; rosanti, Nurvelly; Ambo, Siti Nurbaya; Andharani, Yana; Sutrisno, Mirza
Society : Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2025): Maret
Publisher : Edumedia Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55824/jpm.v4i2.537

Abstract

Advances in digital technology have encouraged the utilization of artificial intelligence, especially machine learning, in various sectors, including property price analysis. However, there are still many people who do not understand the basic concepts of this technology, so structured and applicable education is needed. To answer this challenge, an activity entitled “Utilization of Machine Learning for Property Price Segmentation and Prediction” was held which aimed to introduce and train participants in the application of machine learning to predict property prices. This activity consists of two main parts, namely webinars and workshops. The webinar focused on introducing the concepts of artificial intelligence, machine learning, and AI Project Cycle as the main method in analyzing house prices. Meanwhile, the workshop provided hands-on training to participants in building prediction models using Google Colab. This activity was carried out through a series of stages, starting from socialization, preparation of materials, pre-test to measure initial understanding, educational and practical sessions, to evaluation through post-test and filling in participant feedback. A total of 39 participants from various backgrounds participated in this activity. The evaluation showed that 38.7% of participants were satisfied, while 51.6% were very satisfied with the program. In addition, the post-test results showed a significant increase in understanding compared to the pre-test results. Based on these results, this activity proved to be successful in providing new insights into the application of machine learning in property price prediction and equipping participants with practical skills that can be applied in the real world.
Co-Authors Afkarinah, Afni Izzah Ambo, Siti Nurbaya Andharani, Yana Andriansyah, Akbar Dwi Duta Mahardewantoro Dzulkarnain, Mulki Djenfik Ikhsan Adi Prayogo Ikhsan Fitriansyah Ilyasa, Fahri Muhammad Adri Farqan Muhammad Arif Nurbaya Ambo, Sitti Nurvelly Rosanti Nurvelly Rosanti, Nurvelly Poppy Meilina Ramadhika, Gilang Rifqi Baihaqi Sabhan Sutrisno, Mirza Syuja Ardyansyach