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Felix, Michael
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Humanities Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Social Sciences

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Potensi Implementasi Fair Trade dan Studi Kasus di Indonesia Felix, Michael
PERSPEKTIF Vol. 13 No. 3 (2024): PERSPEKTIF July
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/perspektif.v13i3.11744

Abstract

Fairtrade, a trade system, seeks to facilitate improved and enduring trading circumstances for producers in developing nations. This article examines the implementation of Fair Trade in Indonesia, including the encountered hurdles, and explores the influence of economic and social elements on the process. This article aims to evaluate the execution and obstacles of Fair Trade in Indonesia, focusing on the economic and social aspects that may impede the progress. The primary concerns identified include the uneven implementation of fairtrade principles and practices in different countries, as well as the challenges in increasing public knowledge and approval of the concept. The analysis of these issues employs Alexander Wendt's constructivist theory to comprehend the social dynamics and social construction associated with fair trade. The data were gathered via literature research and document analysis on fair trade in Indonesia. The results indicate that while fair trade can be a positive catalyst for change, its execution is hindered by corrupt practices, intricate certification procedures, and a dearth of public knowledge. To achieve the intended efficacy, it is necessary to implement policy reforms and enhance compliance with fair trade principles.
Hybrid Feature Combination of TF-IDF and BERT for Enhanced Information Retrieval Accuracy Aprilio, Pajri; Felix, Michael; Nugraha, Putu Surya; Fahmi, Hasanul
JISA(Jurnal Informatika dan Sains) Vol 8, No 1 (2025): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v8i1.2179

Abstract

Text representation is a critical component in Natural Language Processing tasks such as information retrieval and text classification. Traditional approaches like Term Frequency-Inverse Document Frequency (TF-IDF) provide a simple and efficient way to represent term importance but lack the ability to capture semantic meaning. On the other hand, deep learning models such as Bidirectional Encoder Representations from Transformers (BERT) produce context-aware embeddings that enhance semantic understanding but may overlook exact term relevance. This study proposes a hybrid approach that combines TF-IDF and BERT through a weighted feature-level fusion strategy. The TF-IDF vectors are reduced in dimension using Truncated Singular Value Decomposition and aligned with BERT vectors. The combined representation is used to train a fully connected neural network for binary classification of document relevance. The model was evaluated using the CISI benchmark dataset and compared with standalone TF-IDF and BERT models. Experimental results show that the hybrid model achieved a training accuracy of 97.43 percent and the highest test accuracy of 80.02 percent, outperforming individual methods. These findings confirm that combining lexical and contextual features can enhance classification accuracy and generalization. This approach provides a more robust solution for improving real-world information retrieval systems where both term specificity and contextual relevance are important.
Co-Authors Aprilio, Pajri Fahmi, Hasanul Nugraha, Putu Surya