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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Media Infotama MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Syntax Jurnal Informatika Indonesian Journal of Artificial Intelligence and Data Mining JMM (Jurnal Masyarakat Mandiri) SINTECH (Science and Information Technology) Journal Jurnal Nasional Komputasi dan Teknologi Informasi Jurnal Sistem Informasi Kaputama (JSIK) Majalah Ilmiah Kaputama Journal of Information Systems and Informatics Jurnal Sistem Informasi dan Informatika (SIMIKA) JISA (Jurnal Informatika dan Sains) International Journal of Advances in Data and Information Systems Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pengabdian Masyarakat Nusantara Journal of Artificial Intelligence and Engineering Applications (JAIEA) IT For Society : Journal of Information Technology Journal of Comprehensive Science Eduvest - Journal of Universal Studies Jurnal Hasil Penelitian dan Pengembangan (JHPP) Journal of Data Science, Technology, and Artificial Intelligence
Fahmi, Hasanul
Unknown Affiliation
Aerospace Engineering Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Library & Information Science Mathematics Neuroscience Social Sciences Transportation Other

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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.
PREDICTING REVENUE OF SHARIA BANKING TRANSACTIONS USING RNN, LSTM, GRU, DECISION TREE, AND QSPM (CASE STUDY: PT BANK TBV SYARIAH) Septian Fakhrudin Arianto; Hasanul Fahmi
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 7 No. 2 (2024): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v7i2.3467

Abstract

The banking business will continue to grow significantly along with the increase in the number of transactions carried out by customers through the channels provided by the bank. The variety of products and features offered by PT Bank TBV Syariah to customers means that resources are not optimal. Hence, the bank's revenue growth target still needs to be achieved. This research aims to predict transactions that can affect bank revenues by using transaction data sources for the period January 2022 to February 2024 and which products and features need to be optimized so that it is hoped that banks can run their business appropriately and according to targets. The methods in this research are the RNN, LSTM, GRU, and Decision Tree methods. To enrich information, this research adds QSPM-based strategy analysis using SWOT that the company previously defined. The expected results are to prove the effectiveness of the model used in predicting PT Bank TBV Syariah transaction data to produce MAE, MSE, and RMSE with the lowest values​​, as well as recommendations that PT Bank TBV Syariah must carry out to increase revenue. This research is expected to provide accurate and effective predictions for projecting PT Bank TBV Syariah transaction data, support strategic decision-making, and produce recommendations for significantly increasing bank income.
Co-Authors Ahmad Zaki Fadilla Rangkuti Alifah, Nurli Aprilio, Pajri Arifiandy, Rony Aulia, Afina Nisa Ayu, Desita Puspita BROTOKUNCORO, AGUNG Cexarian, Hafizh ChungKiat, Ong Daniel Happy Putra Felix, Michael FUYU, FUYU Gerhard T, Jonathan Girsang, Rame R GUFANG, GUFANG harianja, turiami Hariyanto, Dian Tri Hastuti, Rani Puji Herry Utomo, Wiranto Lianzhai, Duan Linli, Peng murdani, toynbe Naswir, Ahmad Fadhil natanael, anju mastaurat niar, iinvera Nugraha, Putu Surya Nurul Putri Utami Prabu Wardono Pratama, Andreyanto Pratama, Jaka Yudhi Rahman, Fajri Fathur rendiarno, rendiarno Ricky Setiawan Rila Mandala Rimanti, Dewinda Rosalina Rosalina Rusdianto Roestam Rustam, Michiavelly Sahuri, Genta Sahuri Saptona, Albert Septian Fakhrudin Arianto Siahaan, R. Fanry Sihombing, Darvin Markus Simangunsong, Agustina Simanjorang, R. Mahdalena Sinaga, Anita Sindar RM Sitohang, Rirido Apriyanto sofyan, mhd heri Sulindawaty Tengfei, Li Tjong Wan Sen turnip, hendra nicodemus Wahyuni, Elda Wika Wahyuni Williem Williem, Williem Wiranto Herry Utomo Yunita, Yustina Zhang Jie