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INDONESIA
MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer
Published by Universitas Bumigora
ISSN : 18584144     EISSN : 24769843     DOI : 10.30812/matrik
Core Subject : Science,
Computer Science & IT
MATRIK adalah salah satu Jurnal Ilmiah yang terdapat di Universitas Bumigora Mataram (eks STMIK Bumigora Mataram) yang dikelola dibawah Lembaga Penelitian dan Pengabadian kepada Masyarakat (LPPM). Jurnal ini bertujuan untuk memberikan wadah atau sarana publikasi bagi para dosen, peneliti dan praktisi baik di lingkungan internal maupun eksternal Universitas Bumigora Mataram. Jurnal MATRIK terbit 2 (dua) kali dalam 1 tahun pada periode Genap (Mei) dan Ganjil (Nopember).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 15 Documents
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Search results for , issue "Vol. 24 No. 2 (2025)" : 15 Documents clear
The Mitigating Overfitting in Sentiment Analysis Insights from CNN-LSTM Hybrid Models Susandri Susandri; Ahmad Zamsuri; Nurliana Nasution; Yoyon Efendi; Hiba Basim Alwan
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4742

Abstract

This study aims to improve sentiment analysis accuracy and address overfitting challenges in deep learning models by developing a hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks. The research methodology involved multiple stages, starting with preprocessing a dataset of 5,456 rows. This process included removing duplicate data, empty entries, and neutral sentiments, resulting in 2,685 usable rows. To overcome data quantity limitations, data augmentation expanded the training dataset from 2,148 to 10,740 samples. Data transformation was carried out using tokenization, padding, and embedding techniques, leveraging Word2Vec and GloVe to produce numerical representations of textual data. The hybrid model demonstrated strong performance, achieving a training accuracy of 99.51%, validation accuracy of 99.25%, and testing accuracy of 87.34%, with a loss value of 0.56. Evaluation metrics showed precision, recall, and F1-Score values of 86%, 87%, and 86%, respectively. The hybrid model outperformed individual models, including Convolutional Neural Networks (70% accuracy) and Long Short-Term Memory Networks (81% accuracy). It also surpassed other hybrid models, such as the multiscale Convolutional Neural Network-Long Short-Term Memory Network, which achieved a maximum accuracy of 89.25%. The implications of this study demonstrate that the hybrid model based on Convolutional Neural Networks and Long Short-Term Memory Networks effectively improves sentiment analysis accuracy while reducing the risk of overfitting, particularly in small or imbalanced datasets. Future research is recommended to enhance data quality, adopt more advanced embedding techniques, and optimize model configurations to achieve better performance.
Optimization of Content Recommendation System Based on User Preferences Using Neural Collaborative Filtering Lusiana Efrizoni; Junadhi Junadhi; Agustin Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4775

Abstract

Recommender systems play a crucial role in enhancing user experience across various digital platforms by delivering relevant and personalized content. However, many recommender systems still face challenges in providing accurate recommendations, especially in cold-start situations and when user data is limited. This study aims to address these issues by optimizing content recommendation systems using Neural Collaborative Filtering (NCF), a deep learning-based approach capable of capturing non-linear relationships between users and items. We compare the performance of NCF with traditional methods such as Matrix Factorization (MF) and Content-Based Filtering (CBF) using the MovieLens-1M dataset. The research method employed is a quantitative approach that encompasses several stages, including preprocessing, model training, and evaluation using metrics such as Root Mean Squared Error (RMSE) and Precision@K. The results of this research are significant, demonstrating that NCF achieves the lowest RMSE of 0.870, outperforming MF with an RMSE of 0.950 and CBF with an RMSE of 1.020. Additionally, the Precision@K achieved by NCF is 0.73, indicating the model’s superior ability to provide more relevant recommendations compared to baseline methods. Hyperparameter tuning reveals that the optimal combination includes an embedding size of 16, three hidden layers, and a learning rate of 0.005. Despite its excellent performance, NCF still faces challenges in handling cold-start cases and requires significant computational resources. To address these challenges, integrating additional metadata and exploring regularization techniques such as dropout are recommended to enhance generalization. The implications of the findings from this study suggest that NCF can significantly improve prediction accuracy and recommendation relevance, thus having the potential for widespread application across various domains, such as e-commerce, streaming services, and education, to enhance user experience and the efficiency of recommendation systems. Further research is needed to explore innovative solutions to address cold-start challenges and reduce computational demands.
Sentiment Study of ChatGPT on Twitter Data with Hybrid K-Means and LSTM: Analisis Sentimen Berdasarkan Hasil Klasterisasi K-Means pada Data Pengguna ChatGPT Menggunakan LSTM Dimas Afryzal Hanan; Ario Yudo Husodo; Regania Pasca Rassy
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4791

Abstract

The rapid evolution of artificial intelligence (AI) has transformed the way people interact with technology, with ChatGPT emerging as a standout innovation in natural language processing (NLP). While it offers immense benefits, such as improving productivity and accessibility, it has also sparked debates about trust, transparency, and user experience. This makes understanding public sentiment about ChatGPT both timely and essential.This study explores user sentiments by combining K-Means clustering and Long Short-Term Memory (LSTM) models for analysis. The research utilized a dataset from Kaggle, which underwent extensive preprocessing, including text cleaning, tokenization, and lemmatization. Key features were extracted using TF-IDF and Word2Vec techniques, while clustering was refined with the Elbow Method and Silhouette Score. The data was grouped into three clusters focusing on ChatGPT’s functions, its developers, and user activities. Sentiment analysis using LSTM achieved an impressive accuracy of 98% after five training cycles. The findings highlight that negative sentiments, particularly around technical challenges and transparency, dominate user feedback, signaling areas for improvement. While positive sentiments exist, they remain overshadowed by critical perspectives. This study underscores the importance of enhancing user trust and experience while ensuring ethical and transparent AI development. The insights provided aim to guide developers and policymakers in creating AI technologies that are more user-focused and socially responsible. Future research should include multilingual and cross-platform data to paint a more comprehensive picture.
Novel Application of K-Means Algorithm for Unique Sentiment Clustering in 2024 Korean Movie Reviews on TikTok Platform Baiq Rima Mozarita Erdiani; Aryo Yudo Husodo; Ida Bagus Ketut Widiartha
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4794

Abstract

In recent years, social media has become one of the main factors influencing public perception of films. As a rapidly growing video-sharing platform, TikTok plays a crucial role in shaping audience opinions through comments, short reviews, and user discussions. This phenomenon is increasingly relevant in the Korean film industry, attracting global attention with its diverse genres and engaging narratives. However, a deep understanding of how audiences respond to films based on genre remains limited, especially in the dynamic context of social media. Therefore, this study aims to analyze audience sentiment toward Korean films released in 2024 on TikTok, focusing on sentiment distribution across four main genres: comedy, romance, action, and fun stories. The research methodology includes data collection through web crawling on TikTok, followed by text preprocessing and feature extraction using IndoBERT. Sentiment classification uses SentimentIntensityAnalyzer to categorize comments into positive, negative, or neutral. Since the dataset consists of unlabeled text, K-Means clustering is employed to identify sentiment groupings, with validation using principal component analysis to ensure cluster quality. The findings indicate that the romance and comedy genres are predominantly associated with neutral sentiment, reaching 89.6% and 87.4%, respectively. In contrast, the action genre exhibits higher sentiment polarization, with 14.9% positive and 24.7% negative sentiment. The fun story genre shows a more evenly distributed sentiment pattern. The main challenges include determining the optimal number of clusters and addressing imbalanced sentiment distribution across genres. This study provides valuable insights for filmmakers and marketers to understand audience reactions on social media better, enabling more targeted promotional strategies. Additionally, it contributes to the literature on sentiment analysis in the film industry, emphasizing the importance of genre-specific audience reception patterns for future research.
Evaluation Analysis of the Necessity of Stemming and Lemmatization in Text Classification Ni Wayan Sumartini Saraswati; Christina Purnama Yanti; I Dewa Made Krishna Muku; Dewa Ayu Putu Rasmika Dewi
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4833

Abstract

Stemming and lemmatization are text preprocessing methods that aim to convert words into their root and to the canonical or dictionary form. Some previous studies state that using stemming and lemmatization worsens the performance of text classification models. However, some other studies report the positive impact of using stemming and lemmatization in supporting the performance of text classification models. This study aims to analyze the impact of stemming and lemmatization in text classification work using the support vector machine method, in this case, devoted to English text datasets and Indonesian text datasets, and analyze when this method should be used. The analysis of the experimental results shows that the use of stemming will generally degrade the performance of the text classification model, especially on large and unbalanced datasets. The research process consisted of several stages: text preprocessing using stemming and lemmatization, feature extraction with Term Frequency-Inverse Document Frequency (TF-IDF), classification using SVM, and model evaluation with 4 experiment scenarios. Stemming performed the best computation time, completing in 4 hours, 51 minutes, and 41.3 seconds on the largest dataset. While lemmatization positively impacts classification performance on small datasets, achieving 91.075% accuracy results in the worst computation time, especially for large datasets, which take 5 hours, 10 minutes, and 25.2 seconds. The Experimental results also show that stemming from the Indonesian balanced dataset yields a better text classification model performance, reaching 82.080% accuracy.

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