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All Journal UG Journal Prosiding KOMMIT Jurnal Teknik Informatika UNIKA Santo Thomas Aptisi Transactions on Technopreneurship (ATT) Communicology: Jurnal Ilmu Komunikasi Journal of Computer Science and Engineering (JCSE) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST)
Detty Purnamasari
Jurusan Sistem Informasi, Universitas Gunadarma
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Health Professions Social Sciences

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Journal : Journal of Computer Science and Engineering (JCSE)

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Algorithms for Question Answering to Factoid Question Fadhila, Raihan Pambagyo; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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The development of transformer-based natural language processing (NLP) has brought significant progress in question answering (QA) systems. This study compares three main models, namely BERT, Sequence-to-Sequence (S2S), and Generative Pretrained Transformer (GPT), in understanding and answering context-based questions using the SQuAD 2.0 dataset that has been translated into Indonesian. This research uses the SEMMA (Sample, Explore, Modify, Model, Assess) method to ensure the analysis process runs systematically and efficiently. The model was tested with exact match (EM), F1-score, and ROUGE evaluation metrics. Results show that BERT excels with an Exact Match score of 99.57%, an F1-score of 99.57%, ROUGE-1 of 97%, ROUGE-2 of 30%, and ROUGE-L of 97%, outperforming S2S and GPT models. This study proves that BERT is more effective in understanding and capturing Indonesian context in QA tasks. This research offers explanations for the implementation of Indonesian-based QA and can be a reference in the development of more accurate and efficient NLP systems.
Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization Oktiana, Milda Safrila; Sulistyo, Satria Harya; Zahwa, Refina Nur; Chair, Luthfi Muhammad; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 1: February (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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The Plasmodium parasite, which spreads through the bite of the Anopheles mosquito, causes malaria, a significant global health concern. Notwithstanding attempts to curtail its proliferation, malaria continues to be a predominant cause of mortality in tropical nations, especially in Sub-Saharan Africa and certain regions of Southeast Asia. Timely identification and precise diagnosis are essential for effective treatment. This research seeks to create a malaria classification model using deep learning based on the EfficientNetV2B0 architecture. The model is engineered to identify malaria parasite infections in microscopic images of erythrocytes. The dataset used is an open-source collection of photographs depicting red blood cells categorised as either infected or uninfected with malaria. The development method encompasses multiple critical stages, beginning with data collection, followed by preprocessing, data augmentation, and modelling using transfer learning with the EfficientNetV2B0 model. Bayesian optimisation is used to improve the model's accuracy by adjusting its hyperparameters. Assessment metrics, including accuracy, precision, recall, and F1-score, are used to evaluate the trained model's performance. The results show that the model has an accuracy of 96%, with equivalent precision, recall, and F1-scores for both the infected (under the heading "Parasitised") and uninfected (under the heading "Uninfected") groups. The model is extremely effective in diagnosing malaria, making it a valuable diagnostic tool for malaria control and prevention, especially in resource-constrained locations.Malaria Parasite Classification from Microscopic Images using EfficientNetV2B0 with Bayesian Optimization
Comparative Analysis of Parameter-Efficient-Fine-Tuning and Full Fine-Tuning Approaches for Indonesian Dialogue Summarization using mBART Aji, Ananda Bayu; Purnamasari, Detty
Journal of Computer Science and Engineering (JCSE) Vol 6, No 2: August (2025)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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This study addresses the urgent need for efficient Indonesian dialogue summarization systems in remote working contexts by adapting the multilingual mBART-large-50 model. The DialogSum dataset was translated into Indonesian using Opus-MT, and two fine-tuning approaches—full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) with LoRA—were evaluated. Experiments on 1,500 test samples revealed that full fine-tuning achieved superior performance (ROUGE-1: 0.3726), while PEFT reduced energy consumption by 68.7% with a moderate accuracy trade-off (ROUGE-1: 0.2899). A Gradio-based interface demonstrated practical utility, enabling direct comparison of baseline, fine-tuned, and PEFT models. Critical findings include translation-induced terminology inconsistencies (e.g., "Hebes" vs. "Hebei") and context retention challenges in long dialogues. This work contributes a scalable framework for low-resource language NLP and provides actionable insights for optimizing computational efficiency in real-world applications.This study addresses the urgent need for efficient Indonesian dialogue summarization systems in remote working contexts by adapting the multilingual mBART-large-50 model. The DialogSum dataset was translated into Indonesian using Opus-MT, and two fine-tuning approaches, full fine-tuning and Parameter-Efficient Fine-Tuning (PEFT) with LoRA, were evaluated. Experiments on 1,500 test samples revealed that full fine-tuning achieved superior performance (ROUGE-1: 0.3726), while PEFT reduced energy consumption by 68.7% with a moderate accuracy trade-off (ROUGE-1: 0.2899). A Gradio-based interface demonstrated practical utility, enabling direct comparison of baseline, fine-tuned, and PEFT models. Critical findings include translation-induced terminology inconsistencies (e.g., "Hebes" vs. "Hebei") and context retention challenges in long dialogues. This work contributes a scalable framework for low-resource language NLP and provides actionable insights for optimizing computational efficiency in real-world applications.
Co-Authors A. Priyono, Albertus Bayu Achmad Benny Mutiara Aji, Ananda Bayu Anacostia Kowanda Anneke Annassia P.S. Annisa Lintang R. Chair, Luthfi Muhammad Dian Faradilla, Norma Fadhila, Raihan Pambagyo Hendry Gustin I Made Wiryana, I Made I Wayan Simri Wicaksana Ika Pretty Siregar Junior Lie Lintang Yuniar Banowosari M. Khoirul Anam Nur Farida Irmawati Nuryani Nuryani Oktiana, Milda Safrila Putra, Souza Nurafrianto Windiartono Rogayah, Rogayah Sulistyo, Satria Harya Yanda, Nafa Zahwa, Refina Nur