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International Journal of Artificial Intelligence and Science
Published by Asosiasi Doktor Sistem Informasi Indonesia
ISSN : -     EISSN : 30642728     DOI : https://doi.org/10.63158/IJAIS
Core Subject : Science,
Computer Science & IT
The International Journal of Artificial Intelligence and Science (IJAIS) is independently organized and managed by the Asosiasi Doktor Sistem Informasi Indonesia (ADSII). IJAIS is an open-access journal designed for researchers, lecturers, and students to publish their findings in the fields of Artificial Intelligence and Science. IJAIS serves as a platform for sharing innovative and original research, showcasing the latest advancements and technological developments in Artificial Intelligence and Science.
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 1 (2024): September" : 5 Documents clear
Optimizing Arduino-Based Laser Cut Machine Settings for Home Industry Subagyo, Ibnu Rivansyah; Prasetyaningrum, Putri Taqwa
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.3

Abstract

The rapid development of laser technology has significantly impacted various industrial sectors, particularly through the use of CNC laser cutting machines. These machines offer distinct advantages and limitations, making them suitable for processing a wide range of materials. This study aims to identify the most effective and efficient settings for a diode-based CNC laser cutting machine, specifically for cutting plywood. An experimental approach was employed, involving the design, creation, and testing of the machine. The research focused on optimizing the focus point and operational settings to achieve precise cuts. The results indicate that the optimal focus point is 12.6 mm, with the best cutting performance achieved at a speed of 500 mm/min, 30% laser power, and 7 passes. The findings suggest that this CNC laser machine is highly efficient for small-scale industries, offering affordability, ease of production, and reduced labor costs by automating multiple machines with a single computer. However, its application is limited in large-scale manufacturing due to constraints related to the Arduino-based control system and the maximum work area size.
Vehicle Detection on The Traffic Using Detection Transformer (DETR) Algorithm Khoiriyah, Rofiatul; Hendrawan, Aria
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.4

Abstract

Object detection is a computer vision technique aimed at detecting and identifying objects in images or videos. In recent years, with advancements in Machine Learning and Deep Learning, object detection has made significant progress in various fields such as healthcare, security, and transportation. The DETR algorithm is a novel approach in object detection that combines transformer architecture with attention techniques to address object detection challenges. This research applies the DETR algorithm with ResNet backbone for vehicle detection on the roads, involving 6 object classes: Car, Truck, Bus, Motorcycle, Pickup Car, and Truck Box. Four training experiments were conducted: DETR-ResNet50, DETR-ResNet101, DETR-DC5-ResNet50, and DETR-DC5-ResNet101. The implementation results show that DETR-DC5 improves the accuracy of vehicle detection. DETR-DC5 with ResNet-101 achieved the highest score for AP50, which is 0.957. However, it should be noted that DETR-DC5 with ResNet-50 managed to maintain overall AP stability, with a lower parameter of 35.5. The model's outcomes in this study can be effectively applied for vehicle detection on the roads.
Traffic Vehicle Detection Using Faster R-CNN: A Comparative Analysis of Backbone Architectures Hakim, Luqman; Hendrawan, Aria; Khoiriyah, Rofiatul
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.5

Abstract

Object detection is a crucial task in computer vision, where advanced deep learning models have shown significant improvements over traditional methods. In this study, the Faster R-CNN algorithm is applied to a traffic dataset containing six vehicle categories: Bus, Car, Motorcycle, Pick Up Car, Truck, and Truck Box. The novelty of the research lies in the comparison of four backbone architectures ResNet50, ResNet50V2, MobileNetV3 Large, and MobileNetV3 Large 320 evaluated for their performance in vehicle detection at IoU thresholds of 0.5 and 0.75. The results reveal that ResNet50 provided the best overall performance, achieving mAP scores of 0.966 at IoU 0.5 and 0.887 at IoU 0.75, offering a balanced trade-off between precision and recall. ResNet50V2 and MobileNetV3 Large also performed well, with mAP scores of 0.945 and 0.870 for ResNet50V2, and 0.969 and 0.843 for MobileNetV3 Large, respectively. However, MobileNetV3 Large 320 showed the lowest detection performance, with mAP scores of 0.857 at IoU 0.5 and 0.551 at IoU 0.75. These findings provide useful insights into the suitability of different architectures for vehicle detection tasks, particularly in traffic surveillance applications.
BBCA Stock Price Prediction Using Linear Regression Method Saputra, Shannon Dominique; Widiantoro, Albertus Dwiyoga
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.7

Abstract

This study focuses on predicting the stock price of Bank Central Asia (BBCA) using linear regression techniques, a widely utilized statistical method in financial forecasting. Stock price prediction is critical for investors, particularly in volatile markets like Indonesia. This research analyzes the relationship between key variables, such as adjusted closing prices and trading volume, based on historical data. The methodology includes data collection, preprocessing, model construction, and evaluation using metrics like Root Mean Square Error (RMSE) to assess the model's accuracy. The results indicate that linear regression can effectively predict BBCA stock prices with reasonable accuracy, providing a practical and interpretable tool for investors. These findings contribute to financial forecasting by demonstrating the utility of linear regression in stock price prediction, particularly in emerging markets.
A Comparative Study of Logistic Regression and Support Vector Machine for COVID-19 Symptom Prediction Ferdiansyah, Ferdiansyah; Putra, Briandy Tri; Yulianingsih, Evi; Fatmasari, Fatmasari; Idham, Muhammad
International Journal of Artificial Intelligence and Science Vol. 1 No. 1 (2024): September
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/IJAIS.v1.i1.8

Abstract

The rapid spread of COVID-19 has created a critical need for accurate and efficient tools to predict symptoms and aid in early diagnosis. This study aims to compare the effectiveness of two machine learning models, Logistic Regression and Support Vector Machine (SVM), in predicting COVID-19 symptoms based on patient data. The dataset used contains key COVID-19 symptoms, which were processed and modeled using both techniques. Logistic Regression was evaluated alongside SVM using three different kernels: Linear, Sigmoid, and Radial Basis Function (RBF). The models' performance was measured using the Confusion Matrix to assess accuracy. Logistic Regression achieved an accuracy of 96.78%, while the SVM with the RBF Kernel slightly outperformed it with an accuracy of 96.85%. The SVM with the Sigmoid Kernel performed the least effectively, with an accuracy of 95.19%. These findings suggest that both models are highly effective for symptom prediction, with the RBF Kernel showing the best overall performance in handling complex, non-linear data patterns.

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