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All Journal Brilliance: Research of Artificial Intelligence Journal of Computer Science Contributions (Jucosco)
Malik, Raihan
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

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Literasi Digital Bagi Pelaku UMKM Sebagai Sarana Meningkatkan Pembangunan Kelurahan Jatirahayu Hiswara, Abrar; Hidayat, Aldi Sandi; Prasetya, Rayhan Daffa; Malik, Raihan; Tua, Situmorang Ondo Palito; Damayanti, Risma; Alfarizi , Rafi Azhar; Afif, Aban Abyan; Akbar, Catur Rizky Nur; Alkahfi, Arfi; Wardhana, Erlangga Respaty
Journal Of Computer Science Contributions (JUCOSCO) Vol. 5 No. 1 (2025): Januari 2025
Publisher : Lembaga Penelitian, Pengabdian kepada Masyarakat dan Publikasi Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/tb3hrc71

Abstract

The advancement of information technology in the digital era has significantly impacted various aspects of life, including the economic and social sectors. However, the digital literacy level of the Jatirahayu community remains moderate, as reflected in their limited understanding of safe internet use and SME digitalization. The lack of awareness regarding digital security and the minimal utilization of digital platforms by SME entrepreneurs are the main challenges in this area. This Community Service Program (PkM) aims to enhance digital literacy among the community, particularly in internet security and ethics, while assisting SME digitalization by registering business locations on Google Maps. The implementation methods include initial surveys, coordination with local authorities, material preparation, invitation distribution, and conducting socialization sessions along with technical assistance for SMEs. The results indicate that the internet literacy workshop successfully raised awareness about information validation, personal data protection, and online fraud detection. Additionally, the SME digitalization program helped business owners understand the benefits of digital platforms and successfully register several businesses on Google Maps. The evaluation of program effectiveness revealed that most participants gained a better understanding of digital literacy, although further follow-up is needed for sustainable implementation. In conclusion, this program effectively increased community awareness of safe internet usage and positively impacted SME digitalization. Moving forward, further collaboration with local government and additional training will be essential to ensure optimal technology utilization for the Jatirahayu community.
Analysis Of Tokopedia Product Clustering Using The K-Means And K-Medoids Algorithms Malik, Raihan; Utomo, Pradita Eko Prasetyo; Hutabarat, Benedika Ferdian
Brilliance: Research of Artificial Intelligence Vol. 5 No. 2 (2025): Brilliance: Research of Artificial Intelligence, Article Research November 2025
Publisher : Yayasan Cita Cendekiawan Al Khwarizmi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/brilliance.v5i2.6992

Abstract

The Indonesian e-commerce market has experienced extraordinary growth, driven by increasing internet penetration and smartphone adoption, which necessitates advanced data analysis for competitive advantage. Clustering is a crucial data mining technique used to group products based on similar characteristics, providing in-depth insights into product performance. Previous studies often focused on single performance metrics, overlooking the nuances of combining multiple variables. This study aims to address this gap by implementing and comparing the K-Means and K-Medoids clustering algorithms on Tokopedia product data using a combination of numerical attributes: Price, Customer Rating, Number Sold, and Total Review. The methodology involved data preprocessing, Min-Max Scaling for normalization, and using the Elbow Method to determine the optimal number of clusters, which was found to be K=2. The clustering quality was rigorously evaluated using the Davies-Bouldin Index (DBI) and Silhouette Score. The results demonstrate that K-Means exhibits superior performance, achieving a lower DBI of 0.5717 and a higher Silhouette Score of 0.6012, compared to K-Medoids (DBI: 0.5870; Silhouette Score: 0.5857). Furthermore, K-Means proved significantly more efficient computationally, with an execution time of 0.0947 seconds versus 0.1622 seconds for K-Medoids. The main conclusion is that K-Means is more effective in creating compact and clearly separated clusters. This research contributes a valuable analytical framework for e-commerce managers to comprehensively understand product profiles, guiding more effective marketing and recommendation strategies.
Co-Authors Abrar Hiswara Afif, Aban Abyan Akbar, Catur Rizky Nur Alfarizi , Rafi Azhar Alkahfi, Arfi Benedika Ferdian Hutabarat Damayanti, Risma Hidayat, Aldi Sandi Prasetya, Rayhan Daffa Tua, Situmorang Ondo Palito Utomo, Pradita Eko Prasetyo Wardhana, Erlangga Respaty