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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Ardhi: Jurnal Pengabdian dalam Negri
Irfan Rasyid
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Engineering

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Electronic Product Recommendation System Using the Cosine Similarity Algorithm and VGG-16 Irfan Rasyid; Yudianto, Muhammad Resa Arif; Maimunah; Tuessi Ari Purnomo
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12936

Abstract

The recommendation system is a mechanism for filtering a batch of data into numerous data sets based on what the user wants. Cosine similarity is one of the algorithms used in creating recommendation model. This algorithm employs a calculation approach between two things by measuring the cosine between the two objects to be compared. Image-based recommendation systems were recently introduced since word processing to generate recommendations had the issue of duplicating product descriptions for different types of items. Before processing with cosine similarity, image feature extraction requires the use of a deep learning algorithm, VGG16. The purpose of this research is to make it easier for customers to select the desired electronic goods by providing product recommendations based on product visual similarity. This model is able to recommend 10 products that are similar to the selected product. The presented product has a cosine value near one, and the discrepancy with the selected product's cosine value is modest. The mAP technique was used for model testing, and the smartwatch category received the greatest mAP value of 94.38%, while the headphone category had the lowest value of 70.84%. The average mAP attained is 81.50%. These findings show that mAP accuracy varies by category. This disparity is due to the unequal dataset in each category.
Peran Mahasiswa KKN UNP dalam Meningkatkan Semangat Kepemudaan melalui Lomba di Jorong Sungai Talang, Nagari Salimpek Shaiki Roma; Zulsi Febriani; Noriatul Usni; Galuh Atalia; Irfan Rasyid
ARDHI : Jurnal Pengabdian Dalam Negri Vol. 4 No. 3 (2026): Juni: ARDHI : Jurnal Pengabdian Dalam Negri
Publisher : Asosiasi Riset Pendidikan Agama dan Filsafat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ardhi.v4i3.2020

Abstract

The Kuliah Kerja Nyata (KKN) program is a tangible manifestation of higher education's community service. One of the main challenges in rural areas today is the decline in social interaction and the spirit of mutual cooperation among youth due to modernization. This study aims to describe the role of Padang State University KKN students in increasing the enthusiasm and participation of youth in Jorong Sungai Talang, Nagari Salimpek, Lembah Gumanti District. The social intervention carried out was the organization of folk competitions specifically consisting of cracker-eating, eel-catching, chili-grinding, and coconut-grating contests. These competitions were designed to be inclusive for all levels of society, including children, teenagers, mothers, and fathers. This study uses a qualitative approach with the Participatory Action Research (PAR) method. Data collection was carried out through participatory observation, in-depth interviews, and documentation. The results showed that the selection of competition types closely related to the daily life and local wisdom of Minangkabau (such as grinding chilies and grating coconuts) and traditional games (catching eels and eating crackers) was highly effective in breaking social ice. Youth acting as the organizing committee showed a significant increase in enthusiasm and responsibility. Cross-generational participation (from children to parents) created strong social cohesion, revived oral traditions, and fostered communal solidarity in Jorong Sungai Talang.
Co-Authors Akhmad Sofyan Arif Rahutama Bambang Yudho Suranta Galuh Atalia Maimunah Muhammad Resa Arif Yudianto Noriatul Usni Shaiki Roma Tuessi Ari Purnomo Zulsi Febriani