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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Buletin Udayana Mengabdi Krisna: Kumpulan Riset Akuntansi International Journal of Artificial Intelligence Research Jurnal Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal
Desy Purnami Singgih Putri
Program Studi Teknologi Informasi Universitas Udayana Bukit Jimbaran, Bali, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering

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Implementation of Text Mining for Evaluating the Relevance Between News Headlines and Content on a Web-Based Platform Purnawati, Desak Gede Inten; Singgih Putri, Desy Purnami; Piarsa , I Nyoman
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9732

Abstract

Technological advancements in the era of the Industrial Revolution 4.0 have significantly transformed how society accesses and consumes information, particularly through online news portals. This study aims to analyze the relevance between news headlines and article content on Indonesian online news platforms by employing text mining techniques and similarity checking methods. To enhance the accuracy of relevance assessment, this research utilizes two deep learning-based modeling algorithms: Long Short-Term Memory (LSTM) and IndoBERT. The data was collected from three leading Indonesian news portals detik.com, kompas.com, and suara.com with a total of 52,242 articles from the entertainment and national news categories, gathered between July 1 and September 30, 2024. The dataset includes attributes such as headline, category, publication date, author, article URL, and news content. The research process consists of several stages, including data collection through web scraping, data pre-processing (which involves cleaning the category, author, and content columns), content summarization, text similarity calculation, and data labeling into three classes (relevan, berlebihan, and nonrelevan). Evaluation results show that the IndoBERT model outperforms LSTM, achieving the best performance with a training accuracy of 0.9048 and a training loss of 0.2514, as well as a validation accuracy of 0.8604 and a validation loss of 0.4039. These findings demonstrate that IndoBERT is effective in assessing the coherence between news headlines and content in today’s digital age.
Scientific Paper Recommendation System: Application of Sentence Transformers and Cosine Similarity Using arXiv Data Putra, Ananda Pannadhika; Singgih Putri, Desy Purnami; Wiranatha, AA.Kt.Agung Cahyawan
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.9766

Abstract

Searching for relevant scientific literature faces complex challenges due to the proliferation of academic publications. This research develops a semantic similarity-based scientific paper recommendation system by utilizing Sentence Transformer (all-MiniLM-L6-v2 model) and cosine similarity algorithm on arXiv dataset (15,504 papers in Computer Science). The system is implemented as a Streamlit-based interactive web application that accepts user queries and recommends related papers based on semantic similarity. Performance evaluation using Precision, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), and Normalized Discounted Cumulative Gain (NDCG) metrics showed that embedding text from the Introduction section without pre-processing yielded the best performance (NDCG: 0.7590; MAP: 0.6960; MRR: 0.7254), outperforming Abstract-based or text combination approaches. A user test of 45 respondents confirmed the effectiveness of the system: 95.5% expressed satisfaction with the relevance of the recommendations, and 93.3% confirmed a significant reduction in manual search time. The findings prove that retaining the raw text structure in the Introduction is optimal for semantic representation. Development suggestions include multidomain dataset expansion and transformer model optimization for accuracy improvement.
Performance Evaluation of LSTM and GRU Models for Movie Genre Classification Based on Subtitle Dialogs Using Augmented Data and Cross-Validation Yonita Putri Utami, Ni Luh Putu; Singgih Putri, Desy Purnami; Dwi Rusjayanthi, Ni Kadek
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.25897

Abstract

This study aims to evaluate and compare the performance of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in classifying movie genres based on subtitle dialogs. To address data imbalance across genres, data augmentation was applied to create balanced datasets with 500 and 700 samples per genre, in addition to the original dataset. The classification models were built using Word2Vec for word embedding, followed by LSTM and GRU architectures with a single hidden layer and dropout regularization. Model performance was assessed using accuracy and further validated through 5-fold cross-validation. The best test accuracy was achieved with the dataset containing 700 samples per genre, reaching 91% for LSTM and 92% for GRU. Cross-validation showed stable performance with average accuracies of 0.68 for LSTM and 0.67 for GRU. A paired t-test analysis yielded a p-value of 0.341, indicating no statistically significant difference between the two models. These findings suggest that both LSTM and GRU are effective for genre classification based on subtitle dialogs. The use of data augmentation is a key contribution of this study, enabling improved model performance on underrepresented genres. This research supports the development of automated movie recommendation systems that utilize subtitle-based genre prediction.
Automated Generation of Folklore Short Stories Using T5 Transformer Model Pirade, Evangelika; Darma Putra, I Ketut Gede; Singgih Putri, Desy Purnami
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10582

Abstract

High reading interest plays an important role in increasing knowledge and fostering a stronger literacy culture. With the growing access to information and technology, reading interest is also expected to improve through innovative and interactive platforms. However, traditional reading materials often fail to attract younger generations who are more engaged with digital content. To address this challenge, one of the efforts undertaken is the development of a modern platform that provides a collection of short stories enriched with cultural and educational values, tailored to appeal to contemporary readers. This study aims to design and implement a short story generation system using a Transformer-based language model, specifically T5 (Text-to-Text Transfer Transformer). The model is fine-tuned using a curated dataset of folktales from various regions, with the goal of producing relevant, engaging, and coherent narrative texts. The generation process is supported by pre-processing techniques to structure the data into narrative components such as introduction, conflict, climax, and resolution. The generated stories are then evaluated through human evaluation methods, including questionnaires and User Acceptance Testing (UAT), to assess their quality, coherence, engagement, and cultural relevance. This ensures that the system not only produces technically valid texts but also delivers narratives that are meaningful and enjoyable for readers. Ultimately, this study contributes to the promotion of literacy by presenting local wisdom and traditional values from diverse cultures through stories in a more modern, engaging, and accessible format for the younger generation.
Extractive Text Summarization of Student Essay Assignment Using Sentence Weight Features and Fuzzy C-Means Suwija Putra, I Made; Adiwinata, Yonatan; Singgih Putri, Desy Purnami; Sutramiani, Ni Putu
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (803.514 KB) | DOI: 10.29099/ijair.v5i1.187

Abstract

One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.One of the main tasks of a lecturer is to give students an academic assessment in the learning process. The assessment process begins with reading or checking the answers of student assignments that contain a combination of very long sentences such as essay or report assignments. This certainly takes a lot of time to get the primary information contained therein. It is necessary to summarize the answers so that the lecturer does not need to read the whole document but is still able to take the essence of the response to the task. This study proposes the application of summarizing text documents of student essay assignments automatically using the Fuzzy C-Means method with the sentence weighting feature. The sentence weighting feature is used by selecting the sentence with the highest weight in one cluster, helping the system to get the primary information from a document quickly. The results of this study indicate that the system succeeds in summarizing text with an average evaluation of the values of precision, recall, accuracy, and F-measure of 0.52, 0.54, 0.70, and 0.52, respectively.
Co-Authors Adiwinata, Yonatan Arsa, Dewa Made Sri Dwi Putra Githa Dwi Rusjayanthi, Dwi I Gusti Ayu Asri Pramesti I Ketut Adi Purnawan I ketut Gede Darma Putra I Made Suwija Putra, I Made I Putu Agung Bayupati I Putu Arya Dharmaadi I Putu Arya Putra Wibawa I Wayan Andis Indrawan Ni Kadek Dwi Rusjayanthi, Ni Kadek Ni Putu Sri Mariyatni Ni Putu Sutramiani Piarsa , I Nyoman Pirade, Evangelika Purnawati, Desak Gede Inten Putra, Ananda Pannadhika Putu Ananta Dama Putra Putu Githa Pratiwi Putu Irvan Arya Purwadana Sulya Arya Wasika Wiranatha, AA.Kt.Agung Cahyawan Yonita Putri Utami, Ni Luh Putu