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INDONESIA
Jurnal CoreIT
Published by Universitas Islam Negeri Sultan Syarif Kasim Riau
ISSN : 2460738X     EISSN : 25993321     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi published by Informatics Engineering Department – Universitas Islam Negeri Sultan Syarif Kasim Riau with Registration Number: Print ISSN 2460-738X | Online ISSN 2599-3321. This journal is published 2 (two) times a year (June and December) containing the results of research on Computer Science and Information Technology.
Arjuna Subject : -
Articles 173 Documents
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Implementation of Digital Invitation by Utilizing Grapes.js for Invitation Design and QR Scanner for Attendance Tracking Ristian Aditya; Eko Budi Setiawan
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

This research aims to help people manage and organize event invitations by using digital invitations. Many digital invitation platforms still need to improve, especially in design customization and guest attendance recording. Based on interviews with 17 respondents aged 20-30, 70% have used digital invitations before. Still, they feel the existing system could be more optimal in processing attendance and customizing designs according to preferences. Meanwhile, 30% of respondents who have never used digital invitations stated that they are still more comfortable with printed invitations due to their lack of understanding of digital invitation technology and the features it offers. To overcome this problem, this research proposes the development of a digital invitation application that not only supports the creation and distribution of invitations but also features online payment through Midtrans and QR scanners to record attendance more efficiently and use the application. The test results show that 95% of respondents feel that the application speeds up the creation and distribution of invitations, 87% find it helpful with the design customization feature, 96% strongly agree that the attendance confirmation feature makes it easier to manage guests, and 97% state that the payment process through the application is easy to do.
Clustering of Halal MSME Aid Recipients: Uncovering Patterns and Characteristics Using the K-Medoids Method yelfi Vitriani; Siska Kurnia Gusti
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 11 No. 2 (2025): December 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

The rapid growth of the halal industry has strengthened the strategic role of Micro, Small, and Medium Enterprises (MSMEs) in meeting market expansion. However, the absence of structured insights regarding the characteristics and patterns of halal MSME aid recipients has hindered the formulation of effective and targeted support programs. This study aims to identify the clustering patterns of halal MSME beneficiaries in Indonesia using the K-Medoids algorithm optimized with Principal Component Analysis (PCA). A total of 129 MSME datasets were collected through validated questionnaires consisting of demographic variables, aid history, business performance, and operational challenges. Preprocessing included data cleaning, transformation, and dimensionality reduction using PCA. The optimal PCA dimension was determined as two components based on the Davies-Bouldin Index (0.1737). K-Medoids clustering produced three optimal clusters validated using Silhouette (0.4602), Davies-Bouldin Index (0.7861), and Elbow Method (K=3). Each cluster shows distinctive characteristics in income range, business legality, type of aid received, challenges, and performance outcomes. The novelty of this research lies in the application of PCA-optimized K-Medoids for halal MSME segmentation, providing insightful foundations for evidence-based policymaking.
Analysis of Combined Local and Global Pooling Layers in CNNs for CIFAR-10 Classification Fitra Salam S. Nagalay; Sriyanto Sriyanto
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol. 12 No. 1 (2026): June 2026
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

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Abstract

In the development of artificial intelligence technology, particularly deep learning, convolutional neural networks (CNNs) have become one of the most popular architectures for image classification tasks. The ability of CNN to extract features from image data without manual processing makes it superior, especially in handling complex image data. One of the important components in CNN is pooling. Which serves to reduce the dimensions of the data while preserving important information. This study analyzes the impact of the combination of local and global pooling on the performance of CNN in classifying the CIFAR-10. This approach was carried out by training two CNN models, namely a model with a combination of pooling and a model with local pooling only. The training process uses the k-fold cross validation method. Performance evaluation was conducted using accuracy, precision, recall, and F1-score metrics. The research results show that the model with the pooling combination achieved an average accuracy of 83.75%, slightly higher than the local pooling, which resulted in an accuracy of 83.61%. Additionally, the model with the pooling combination demonstrated stability during training and good generalization capability. This research contributes to the optimization of CNN architecture by demonstrating that the combination of local and global pooling has the potential to improve model performance, especially on datasets with high feature diversity.

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