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Iskandar (Scopus ID: 55316114000), Iwan
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Computer Science & IT

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The Implementation of Data Mining to Determine the Level of Students' Understanding in Utilizing E-Learning Using the K-Nearest Neighbor Method Iskandar (Scopus ID: 55316114000), Iwan; Candra, Reski Mai
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 10, No 2 (2024): December 2024
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v10i2.33728

Abstract

The implementation of Information Technology is increasingly developing due to the growing demand. According to data obtained from the Indonesian Internet Service Providers Association (APJII) 2022 report, the number of internet users in Indonesia is 210.02 million, an increase of 27.9 million from the previous year. The application of E-Learning in various schools, campuses, and educational courses has been carried out. The utilization of e-learning media undoubtedly facilitates educators in transferring their knowledge to students. This research evaluates the level of understanding of each student who has used E-Learning during Covid-19 as a learning medium. In obtaining this level of understanding, the K-Nearest Neighbor (K-NN) method is applied. The data analyzed are based on assignment scores, quizzes, mid-term exams, and final exams from various related courses, namely Science and Mathematics Course Group, Programming Course Group, and Basic Informatics Course Group. A total of 1,627 data points were collected from the period between 2020 and 2021 when online learning was conducted using E-Learning. The data was processed using the KNN method with an 80:20 split between training and testing data. The analyzed K values were 3, 5, 7, 9, 11, 13, 15, 17, 19, and 21. The calculation results showed an accuracy of 75.69% at K=17 for the Basic Informatics Course Group, 77.61% at K=15 for the Science and Mathematics Course Group, and 96.20% at K=3 for the Programming Course Group.
Network Routing Optimization Using Tabu Search Algorithm in Dynamic Routing Iskandar (Scopus ID: 55316114000), Iwan
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.26655

Abstract

Internet penetration is increasing along with the need for data packages for communication such as social media, chatting, video conferencing and others. On large-scale networks such as the Internet, dynamic routing is used to build routing protocol information in the routing table automatically. Currently, Djikstra's algorithm is used to solve the shortest path problem in dynamic routing. In this research, the optimization of the algorithm is carried out in determining the best path or trajectory. One of the optimization algorithms is the Tabu Search Algorithm which can guide heuristic local search procedures to explore the solution area outside the local optimum point. This optimization is assessed from the test parameters measured from the smallest cost. The data analyzed is in the form of bandwidth and topological flow. From the results of tracing the path of data packets sent through 9 routers using the Tabu Search algorithm with the parameters namely number of Neighbor Solutions = 50, Length of tabu list = 10, Maximum Number of Iterations = 100, the result of the path matrix value is 180.9676. The path taken is router 0-2-4-8-9
Data Mining for Analyzing Consumer Segmentation: Identifying Consumer Preference Patterns Using the Fuzzy C-Means Clustering on Halal Products Iskandar (Scopus ID: 55316114000), Iwan; Nazir, Alwis
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i2.38608

Abstract

Halal products are increasingly popular worldwide, not only in Muslim-majority countries but also in non-Muslim nations.  The global halal market exceeds USD 650 million annually, emphasizing the importance of halal certification, particularly in Indonesia as the world’s largest Muslim-majority country. This research aims to cluster consumers of halal meat products by analyzing factors influencing consumer behavior in purchasing certified halal beef and chicken. The study employs the Fuzzy C-Means (FCM) clustering algorithm on 176 respondents’ questionnaire data consisting of 36 parameters. The experiment was performed using Google Colab with a maximum of 1000 iterations, error tolerance of 0.0001, and fuzziness coefficient m = 2.4. Results show that two optimal clusters were formed, with a Partition Coefficient Index (PCI) value of 0.9993, indicating excellent clustering quality. The first cluster consists primarily of young consumers aged 15–24 with lower spending, while the second cluster includes adults aged 35–54 with higher income. Both groups prioritize halal certification and logo visibility when choosing meat products. The findings provide insights for halal product retailers and policymakers to enhance halal product distribution, certification support, and marketing strategies in Indonesia.
Co-Authors Nazir, Alwis Reski Mai Candra