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All Journal Konstelasi: Konvergensi Teknologi dan Sistem Informasi Prosiding Seminar Nasional Indonesia
Nala Widyadhana
Universitas Pembangunan Nasional “Veteran” Jawa Timur
Humanities Computer Science & IT Social Sciences Other

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Optimizing Semantic Clustering of Cultural Heritage Question-Answering Corpora Using Sentence-BERT Embeddings and PCA-Enhanced K-Means Nala Widyadhana; Nur Cahyo Wibowo; Tri Lathif Mardi Suryanto
KONSTELASI: Konvergensi Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): Juni 2026
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/konstelasi.v6i1.15153

Abstract

This study examines semantic text clustering using all-MiniLM-L6-v2 sentence embeddings and K-Means on a Dewi Durga question-answering corpus from Indian, Javanese, and Balinese cultural contexts. The dataset contains 1,620 Context-Question-Answer entries extracted from Chapters 1-22. Text preprocessing included structural checking, missing-value inspection, duplicate detection, case folding, non-alphanumeric character removal, and whitespace normalization. Each context was transformed into a 384-dimensional dense embedding vector. The optimal cluster number was evaluated using Auto K across K values from 2 to 10 with Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, while Manual K = 5 was used as a comparative setting for more detailed thematic interpretation. Six embedding transformation scenarios were tested in both modes. The results show that Auto_K_S5, combining normalization and PCA with 50 components, achieved the strongest internal validation performance with a Silhouette Score of 0.098899, Davies-Bouldin Index of 2.912914, and Calinski-Harabasz Index of 186.476974. Manual_K5_S3 produced more granular themes related to ritual, mythology, history, archaeology, and religious narrative.
KLASIFIKASI RISIKO OBESITAS BERBASIS GRADIENT BOOSTING PADA DATA MEDIS Wildan Hafiz Firmansyah; Nala Widyadhana
Prosiding Seminar Nasional Indonesia Vol. 4 No. 1 (2026): Prosiding Seminar Nasional Indonesia
Publisher : CV. Adiba Aisha Amira

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20309818

Abstract

Obesity is a growing health concern that can lead to various chronic diseases, making accurate risk identification an important preventive effort. The development of machine learning techniques enables the utilization of medical data to support intelligent decision-making in the healthcare domain. This study aims to apply the Gradient Boosting algorithm as a classification method to predict obesity risk based on medical data. The dataset used contains information related to eating habits, physical activities, and individual characteristics. The research process includes data preprocessing, data transformation and normalization, class mapping, and data partitioning into training and testing sets with a ratio of 70:30. The Gradient Boosting model is constructed using multiple decision trees with specific parameter settings to classify obesity risk into two categories, namely obese and non-obese. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics. The experimental results show that the proposed model achieves good classification performance with an accuracy exceeding 90%, while the performance gap between training and testing data remains relatively small. This indicates that the model has strong generalization capability and does not suffer from overfitting. Therefore, the application of Gradient Boosting on medical data proves to be an effective approach for obesity risk classification and has the potential to support intelligent health information systems in assisting medical practitioners with more precise obesity prevention and management strategies.
Co-Authors Wibowo, Nur Cahyo Wildan Hafiz Firmansyah