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COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi
Published by Universitas Nurul Jadid
ISSN : 27750124     EISSN : 27747875     DOI : https://doi.org/10.33650/coreai
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering Transportation
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi sebagai bagian dari semangat menyebarluaskan ilmu pengetahuan beberapa hasil dari penelitian dan pemikiran untuk pengabdian kepada masyarakat luas. Situs Jurnal COREAI ini menyediakan artikel-artikel jurnal untuk dibaca maupun diunduh secara gratis. Jurnal kami adalah jurnal ilmiah nasional yang merupakan sumber referensi akademisi di bidang Teknologi dan Informasi. Jurnal COREAI menerima artikel ilmiah dengan lingkup penelitian pada: Technology Management. Business Intelligence and Knowledge Management. Teknik Komputer Pengolahan Citra. Sistem Pendukung Keputusan. Data Mining. Robotik. Algoritma Genetika. Sistem Kecerdasan Buatan. Jaringan Komputer. Big Data. Enterprise Computing. Internet of Things. Sistem Database. Energy Management. Sistem Pakar. Sistem Penunjang Keputusan.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 146 Documents
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Performance Evaluation of CPU and GPU Architectures in Google Colab-Based Parallel Computing Using Execution Time and Speedup Didik Haryanto; Muhamad Nur Hoiri; Ahmad Sidiq; Amelia Azzahrah; Amarudin Amarudin
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15758

Abstract

Modern computing demands have driven the use of processing devices capable of handling large workloads quickly and efficiently. The CPU plays an important role as the central controller for general-purpose instructions, whereas the GPU provides parallel-processing capabilities that are better suited to large-scale numerical operations. This study aims to evaluate performance differences between CPU and GPU architectures in Google Colab-based parallel computing. A quantitative experimental method was employed using a matrix multiplication test scenario implemented with the PyTorch library. Tests were performed on five matrix sizes: 500 x 500, 1000 x 1000, 2000 x 2000, 3000 x 3000, and 4000 x 4000. The measured parameters included CPU execution time, GPU execution time, and speedup. The results show that the GPU was not optimal for the small 500 x 500 matrix, with a speedup of 0.47 times. However, for larger matrices, the GPU delivered substantial performance gains, achieving speedups of 23.25 to 27.30 times over the CPU. These findings indicate that GPUs are more effective for large-scale parallel processing.
Web-Based School Information System Using the Laravel Framework as a Platform for Information and Promotion at High Schools Vita Ulvita Sari
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.15829

Abstract

This study aims to design and implement a web-based school information system application as a medium for disseminating information and a promotional tool at PGRI Purwoharjo High School. The problem faced is the limitation of information dissemination, which remains conventional and is therefore ineffective and inefficient in reaching the wider community. The research method employed the System Development Life Cycle (SDLC) using the Waterfall model, which includes the stages of requirements analysis, system design, implementation, testing, and maintenance. The system was developed using the Laravel framework, which supports structured and dynamic data management. The results of this study show that the developed system is capable of effectively providing information services, including school profiles, news, announcements, and online registration services for new students. In addition, this system also serves as a promotional medium capable of enhancing the school’s visibility and image within the community. Thus, the implementation of this web-based information system is expected to improve the quality of information services and support digital transformation within the school environment.
Development of a Web-Based Library Information System Using the Waterfall Method to Improve the Efficiency of Book Lending Services Andika Chozin Asyrori; Dina Cahya Pratiwi
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.13702

Abstract

Libraries play a vital role as information centers, but manual management often leads to challenges such as recording errors, delays in the borrowing process, and low service efficiency. This study aims to develop a web-based library information system in an educational library environment to improve the speed and accuracy of services. The waterfall method was used as the development approach, comprising the stages of requirements analysis, design, implementation, and testing. Implementation was carried out using PHP with the CodeIgniter framework and a MySQL database. The results of the study show that the system is capable of providing key features such as book search, online borrowing and returns, book and member data management, and transaction verification by the administrator. Black-box testing showed that all system functions operate in accordance with user needs. Thus, the developed system has been proven to improve operational efficiency, speed up transaction processes, and minimize recording errors in library services.
Comparison of MobileNetV2, EfficientNet-B0, and ResNet50 for Fruit Freshness Classification Based on Accuracy and F1-Score Nadiyah Nadiyah; Fathur Rizal; Andi Wijaya; Zainal Arifin
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.16300

Abstract

Freshness is a key determinant of fruit quality and safety, while manual assessment is subjective and slow. This study aims to compare three convolutional neural network architectures, namely MobileNetV2, EfficientNet-B0, and ResNet50, for the freshness classification of apples, bananas, and oranges in fresh and rotten conditions. The Food Freshness Dataset from Kaggle with 29,502 images was used and divided with a ratio of 70:20:10 into six classes. All three models were built using a transfer learning scheme with feature extraction on ImageNet pre-trained weights, an input size of 224×224, and an identical training configuration for 10 epochs. The main difference between the models lies in the specific preprocessing functions of each architecture to ensure a fair comparison. Evaluation was carried out on test data using accuracy and F1-score macro and weighted. The results show that ResNet50 achieved the highest performance with an accuracy of 0.9780 and a macro F1-score of 0.9787, followed by EfficientNet-B0 (0.9759; 0.9774) and MobileNetV2 (0.9726; 0.9732). Class-by-class analysis revealed that the Rotten Orange class was the most difficult for all models. EfficientNet-B0 and MobileNetV2 performed comparable to ResNet50 but with a much smaller number of parameters, making them more efficient for resource-constrained applications. This study emphasizes the importance of reporting F1-scores alongside accuracy on class-imbalanced data.
The Digital Transformation of Generation Z’s Da’wah Through Information Technology and Artificial Intelligence on TikTok Dede Ahmad Fauzan; Ilham Maulana; Muhammad Khoirul; Abin Maulana Aksa
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 7, No 1 (2026): Sustainable Information Technology Innovation Supports a Digital-Based Smart Eco
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v7i1.12096

Abstract

This study aims to evaluate the effect of exposure to viral artificial intelligence (AI)-based da’wah content on TikTok on Generation Z’s interest in digital da’wah. This quantitative study involved 30 active TikTok users aged 18–25 through an online questionnaire survey supplemented with qualitative data from open-ended questions. Data were analyzed using simple linear regression with the Microsoft Excel Data Analysis ToolPak. The results of the analysis showed a positive but weak correlation between exposure to AI-based da’wah content and interest in digital da’wah (R = 0.233; R² = 0.054; p = 0.215). This effect was not statistically significant and explained only 5.4% of the variation in interest in digital da’wah. Thematic analysis revealed that while AI-generated da’wah content has the potential to enhance accessibility and the appeal of the message, respondents expressed concerns about the credibility of the source, the loss of personal nuance, and the risk of misinformation. These findings suggest that the virality of AI-generated da’wah content has not yet become a primary driver of Gen Z’s interest in digital da’wah. Other factors, such as personal motivation, social support, and digital literacy, are believed to play a greater role and should therefore be investigated further
Komparasi Algoritma K-Nearest Neighbor dengan Linear Regression untuk Memprediksi Total Kasus Positif Covid-19 di Pamekasan Luluk Suhartini; Moh. Wasil
COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi Vol 4, No 1 (2023): Metaverse dan Masa Depan Interaksi Digital: Perspektif Teknologi dan Sosial
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/coreai.v4i1.16557

Abstract

COVID-19 telah ditetapkan sebagai pandemic sejak 30 Januari 2020 oleh WHO. Hal ini mengacu pada tingkat penyeba-ran virus yang sudah mewabah ke belahan dunia, tak terkecuali Indonesia. Jumlah kasus baru tersebar di 34 provinsi dan hampir di semua kabupaten di Indonesia, termasuk Kabupaten Pamekasan. Dengan jumlah kasus COVID-19 yang selalu berubah, maka diperlukan kajian untuk memprediksi jumlah kasus COVID-19 di masa yang akan datang. Pada penelitian ini dilakukan perbandingan algoritma k-Nearest Neighbor dengan Linear Regression dalam memprediksi total kasus COVID-19. Dataset yang digunakan adalah dataset pasien kasus positif COVId-19 di Kabupaten Pamekasan. Pada penelitian ini digunakan model validasi 10 Fold Cross Validation untuk mengukur performa algoritma kNN dan Linear Re-gression sedangkan model evaluasi menggunakan Root Mean Square Error (RMSE) dengan menggunakan tools RapidMiner. Berdasarkan hasil penelitian, algoritma kNN memperoleh nilai RMSE lebih kecil dari pada Linear Regression dan dapat diterapkan untuk memprediksi kasus Covid-19 di Pamekasan dengan nilai RMSE sebesar 0.057.

Page 15 of 15 | Total Record : 146

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