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All Journal Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Teknologi Dan Sistem Informasi Bisnis
Ramadhani, M. Akbar Tri
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Computer Science & IT Electrical & Electronics Engineering

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Pemodelan Prediksi Nilai IQ Menggunakan Algoritma Machine Learning Ramadhani, M. Akbar Tri; Permata Sari, Dewi; Sabilah, Anisa Aulia; Tabitha, Aghnia Hafsa; Rochmah, Ainur; Saputra, Andika; Natasya, Erin; Pratamah, Destra Andika
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 2 (2025): April 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i2.1851

Abstract

IQ stands for Intelligence Quotient. It is a numerical score obtained from various psychometric tests designed to measure a person's general intelligence or cognitive ability. IQ is often used as an indicator of academic potential, success in the workplace, and adaptability to new environments. However, it is important to remember that IQ is only one aspect of human intelligence. IQ assessments are widely used in fields as diverse as education, psychology, and employment. Manually administered IQ tests are often time-consuming, require human intervention, and are prone to error. On the other hand, the development of data-driven technology allows for faster and more accurate information processing. Machine learning is a system that can learn to make its own decisions without being reprogrammed by humans, allowing computers to become smarter and learn from their experience with data. That's why the author conducted research to develop an IQ prediction model using machine learning algorithms.
Comparative Study: Performance Comparison of You Only Look Once and Convolutional Neural Networks Algorithms in Human Object Detection Sari, Dewi Permata; Ramadhani, M. Akbar Tri; Abdurrahman, Abdurrahman
Indonesian Journal of Artificial Intelligence and Data Mining Vol 8, No 3 (2025): November 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v8i3.37676

Abstract

The evolution of object identification technologies, particularly for person detection applications, has increasingly accelerated due to the merger of deep learning and artificial intelligence with computer vision. This study intends to test the efficacy of two object detection algorithms, YOLOv8n and CNN MobileNetSSD, in identifying human objects in digital photos. A dataset of 12,334 human-labeled photos from the Roboflow platform was utilized to train the YOLOv8n model, while performance results for the CNN MobileNetSSD model were acquired from a prior article. The precision, recall, and F1-score of each model were examined. Experimental results reveal that YOLOv8n attains 94% precision, 92% recall, and a 92.9% F1-score, representing a considerable enhancement over MobileNetSSD. Conversely, MobileNetSSD got an F1-score of 85.2%, with a precision of 86.5% and a recall of 84.1%. The findings show that CNN MobileNetSSD is more ideal for non-time-sensitive or resource-limited scenarios; however, YOLOv8n is preferable for real-time human identification tasks due to its greater accuracy and faster inference. This comparative analysis is important for differentiating object detection models matched to certain application needs.
Co-Authors Abdurahman Abdurrahman ANDIKA SAPUTRA Dewi Permata Sari Natasya, Erin Permata Sari, Dewi Pratamah, Destra Andika ROCHMAH, AINUR Sabilah, Anisa Aulia Tabitha, Aghnia Hafsa