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All Journal Brilliance: Research of Artificial Intelligence
Benedika Ferdian Hutabarat
Universitas Jambi, Indonesia
Decision Sciences, Operations Research & Management Mathematics Other

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Analysis Of Tokopedia Product Clustering Using The K-Means And K-Medoids Algorithms Raihan Malik; Pradita Eko Prasetyo Utomo; Benedika Ferdian Hutabarat
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.
Semantic FAQ Chatbot Using SBERT (Sentence-BERT) and Cosine Similarity for Academic Services Rahul Marcellino Holis; Pradita Eko Prasetyo Utomo; Benedika Ferdian Hutabarat
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.v5i2.7027

Abstract

Handling repetitive inquiries in academic environments requires significant time and human resources, potentially delaying service delivery. This study developed a semantic FAQ chatbot using Sentence-BERT (SBERT) and Cosine Similarity to improve efficiency and consistency of academic information services at Universitas Jambi. The system encodes user queries into dense vector embeddings and compares them with FAQ entries using cosine similarity. A dataset of 65 frequently asked questions was collected through interviews and direct observation with students, lecturers, staff, and helpdesk officers. To evaluate semantic understanding, these entries were expanded into 130 question variations using paraphrasing. Model performance was measured with a confusion matrix and standard metrics. At a similarity threshold of 0.5, the system achieved 79.2% accuracy, 81.7% precision, 96.3% recall, and an F1-score of 88.4%. The results show that SBERT effectively identifies semantically similar questions with different wordings, handling both formal and informal Indonesian queries. High recall demonstrates that most relevant questions were successfully retrieved, while precision remains sufficient to ensure reliable responses. This study demonstrates that SBERT-based semantic matching can successfully handle Indonesian academic FAQ with diverse linguistic variations, enabling 24/7 accessibility and consistent service delivery independent of staff availability. Future work should expand the dataset to include emerging queries and conduct pilot deployment to validate operational effectiveness and user satisfaction
Analysis and Design of E-Report Card System at SMAN 6 Muaro Jambi Using SSAD Shela Safitri; Benedika Ferdian Hutabarat; Dewi Lestari
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.v5i2.7029

Abstract

Manual management of report cards at SMAN 6 Muaro Jambi often leads to recording errors, data loss, and limited access to real-time academic information, which hinders effective communication between schools, teachers, students, and parents. This study aims to analyze and design a web-based E-Report Card system using the Structured System Analysis and Design (SSAD) method as a structured and reliable solution to these challenges. The SSAD approach was applied through several stages, including problem identification, requirement analysis, system design using Data Flow Diagrams (DFD), Entity Relationship Diagrams (ERD), and wireframes, followed by prototype evaluation through usability testing. The evaluation process utilized Maze as a usability testing tool to assess the system’s interface performance using the Mean Adjusted Usability Score (MAUS). The analysis results produced a system design that features structured data flow, an integrated database, and an interactive user interface prototype tailored to stakeholder needs. The usability testing results indicated the highest MAUS score for Students/Parents users (93), while Subject Teachers obtained the lowest score (67), suggesting the need for interface improvement for better task efficiency. Overall, the designed web-based E-Report Card system demonstrates the potential to overcome inefficiencies in manual report card management while enhancing accuracy, transparency, and accessibility of academic information in schools.
Co-Authors Dewi Lestari Pradita Eko Prasetyo Utomo Rahul Marcellino Holis Raihan Malik Shela Safitri