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All Journal International Journal Of Science, Technology & Management (IJSTM) Scientica: Jurnal Ilmiah Sains dan Teknologi
Irmayani , Deci
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Journal : International Journal Of Science, Technology

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Analysis Of Student Satisfaction Level In The Faculty Of Science And Technology Using The Convolution Neural Network Method Martua , Rifki Agus; Irmayani , Deci; Yanris, Gomal Juni
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1178

Abstract

The Faculty of Science and Technology at Labuhanbatu University is one of the leading faculties that focuses on the development of science and technology. This faculty offers various study programs designed to prepare students to face the challenges of the digital era and industrial revolution 4.0. This research, using survey and interview methods, aims to collect accurate and objective data regarding student perceptions and experiences in various aspects, such as the quality of educational services, quality of teaching, and available supporting facilities such as extracurricular activities, seminars and research projects, ease of access. information and academic support from optimal staff and teaching staff.
Implementation of K-Nearest Neighbors Algorithm in Analyzing Public Interest in Shoping at Supermarkets Siti Kholijah, Siti Kholijah; Sihombing, Volvo; Irmayani , Deci
International Journal of Science, Technology & Management Vol. 5 No. 5 (2024): September 2024
Publisher : Publisher Cv. Inara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46729/ijstm.v5i5.1183

Abstract

People's shopping patterns and behaviors continue to develop along with technological advances and lifestyle changes, thus requiring retail business actors, especially supermarkets, to better understand their customers' interests and preferences. In this context, accurate analysis of customer shopping interests is very important to improve customer satisfaction and optimize marketing strategies. One solution that can be implemented to analyze people's shopping interests is the application of the K-Nearest Neighbors algorithm, a simple yet effective nearest neighbor-based classification method for recognizing patterns from existing data. This study aims to apply the K-Nearest Neighbors algorithm to classify people's interest in shopping at supermarkets. This study also evaluates the effectiveness and performance of the algorithm in the context of business decision-making in the retail sector. The research methodology includes collecting data on people's shopping interests, data pre-processing, implementing the K-Nearest Neighbors algorithm, and evaluating model performance using evaluation metrics such as accuracy, precision, recall, and F1-score. The results of this study indicate that the K-Nearest Neighbors algorithm is able to achieve an accuracy of 88%, with precision, recall, and F1-score all reaching 92.86%. These results indicate that the K-Nearest Neighbors model is very effective in classifying people's shopping interests, with a low error rate. The resulting confusion matrix also shows the model's ability to identify customers who are interested in shopping with little prediction error. This study concludes that we can rely on the K-Nearest Neighbors algorithm to analyze people's shopping interests in supermarkets. This model not only shows good performance in classification but also has great potential to be implemented in recommendation systems and customer segmentation in the real world. This study contributes to the development of consumer behavior analysis methods in the retail sector, as well as providing a basis for further research to explore other algorithms or combinations of techniques to improve the accuracy and effectiveness of classification models.
Co-Authors Hasibuan, Jasman Martua , Rifki Agus Rizal , Khairul Sihombing, Volvo Siti Kholijah, Siti Kholijah Yanris, Gomal Juni