International Journal of Mathematics, Statistics, and Computing
International Journal of Mathematics, Statistics, and Computing (IJMSC) is an official journal of the Communication in Research and Publications (CRP) and publishes original research papers that cover the theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). IJMSC will act as a platform to encourage further research in Mathematics, Statistics, and Computing, theory and applications. The rapid development of science and technology has had a significant impact on various aspects of human life, including in the fields of economy, education, culture and government. The positive impacts of science and technology include facilitating access to information and communication, accelerating production and service processes, as well as providing new business and investment opportunities. Mathematics, statistics, and computer science have a very important role for the advancement of science and technology. Among them are as a basis for computer programming, basic calculations in the development of modern tools, can solve a problem even with big data. The mission of the International Journal of Mathematics, Statistics, and Computing (IJMSC) is to enhance the dissemination of knowledge across all disciplines in theory, practice, history, methodology or models of Mathematics, Statistics, and Computing (MSC). The above discipline is not exhaustive, and papers representing any other social science field will be considered. The IJMSC particularly encourage manuscripts that discuss the latest research findings or contemporary research that can be used directly or indirectly in addressing critical issues and sharing of advanced knowledge and best practices in Mathematics, Statistics, and Computing (MSC). The essential but not exclusive, audiences are academicians, graduate students, researchers, policy-makers, regulators, practitioners, and others interested in business, management, economics, and social development studies. For ensuring a wide range of audiences, this journal accepts only the articles in English. The scope of mathematics are: Algebra, Applied Mathematics, Financial Mathematics, Approximation Theory, Combinatorics, Computing in Mathematics, Operations Research Methodology, Discrete Mathematics, Mathematical Physics, Geometry and Topology, Logic and Foundations of Mathematics, Number Theory, Numerical Analysis, and other relevant matters. The scope of statistics are: Probability Theory, Central Limit Theorem Computation, Sample Survey, Statistical Modeling, Statistical Theory, Computational Statistics, Data Sciences, Actuarial Sciences, Regression Models, Time Series Models, and other relevant matters. The scope of computing are: Algorithms and Data Structures, Computer Architecture, Software Engineering, Artificial Intelligence and Robotics, Human and Computer Interaction, Informatics Organizations, Programming Languages, Operating Systems and Networks, Databases, Computer Graphics, Computing Science, BioInformatics, Information Technology, and other relevant matters.
Articles
54 Documents
<![CDATA[Sentiment Analysis of Public Comments on the YouTube Video “Trump Unveils Sweeping Global Tariffs in Watershed Moment for World Trade” by BBC News Using the Long Short-Term Memory Method ]]>
<![CDATA[Riswandi, Calvin]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.210
<![CDATA[This study aims to analyze public sentiment towards the announcement of global tariffs by the President of the United States, Donald Trump, using the Long Short-Term Memory (LSTM) method. The analysis focused on user comments from one video uploaded by BBC News on its official YouTube channel, titled “Trump Unveils Sweeping Global Tariffs in Watershed Moment for World Trade.”. Sentiment analysis is performed by classifying public comments into positive or negative sentiment categories, through preprocessing stages such as case folding, cleansing, normalization, stop words, stemming and tokenization. The processed data is then used to train and evaluate the LSTM model, which is known to capture temporal relationships and contextual meaning in text data. The results showed that the sentiment was negative, with 64.6% of the comments showing negative sentiment and only 34.4% showing positive sentiment. The performance of this LSTM method gives a performance of 76% Accuracy with 77% precision, 84% recall, and 81% f1-score on negative sentiment and 74% precision, 64% recall, and 69% f1-score on positive sentiment. These findings demonstrate the public's critical view of Donald Trump's global tariff policy and confirm the effectiveness of the LSTM method in extracting sentiment trends from online discussions. This research contributes to the analysis of public opinion in the context of international economic policy.]]>
<![CDATA[Optimization of Machine Learning Models for Sentiment Analysis of TikTok Comment Data on the Progress of the Ibu Kota Nusantara as New Capital City of Indonesia ]]>
<![CDATA[Saputra, Renda Sandi]]>;
<![CDATA[Dwiputra, Muhammad Bintang Eighista]]>;
<![CDATA[Saputra, Moch Panji Agung]]>;
<![CDATA[Ismail, Muhammad Iqbal Al-Banna]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.232
<![CDATA[Sentiment analysis plays a crucial role in understanding public opinion on social media platforms, especially in discussions related to government policies such as the relocation of Indonesia’s new capital city, known as Ibu Kota Nusantara (IKN). While machine learning algorithms like Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression (LR) are widely used for sentiment classification tasks, previous studies often focus on performance comparisons without addressing the impact of data imbalance or regularly optimizing model parameters. These issues can lead to suboptimal classification performance, especially in real-world social media data. This study aims to improve the accuracy and robustness of sentiment classification by applying two enhancement strategies: text data augmentation and hyperparameter tuning. Three models Naïve Bayes, SVM, and Logistic Regression were trained and evaluated in three experimental stages: (1) using original data, (2) after applying augmentation, and (3) after augmentation combined with hyperparameter tuning via GridSearchCV. The evaluation results show progressive improvements across the three stages. In the first stage (original data), Logistic Regression achieved the highest accuracy of 80.41%, while Naïve Bayes and SVM reached 79.73% and 76.98%, respectively. However, all models struggled to classify the minority class (positive sentiment), as reflected in their lower recall and F1-scores. After applying augmentation, performance improved significantly across all models. SVM, in particular, reached an accuracy of 92.77%, followed by Logistic Regression (86.57%) and Naïve Bayes (86.22%), with better balance between precision and recall for both sentiment classes. hyperparameter tuning further optimized model performance. Logistic Regression became the best-performing model, achieving an accuracy of 93.80%, along with high precision, recall, and F1-scores for both classes. SVM and Naïve Bayes also showed stable improvements, with accuracies of 92.88% and 87.72%, respectively.]]>
<![CDATA[Comparison of Activation Functions in Recurrent Neural Network for Litecoin Cryptocurrency Price Prediction ]]>
<![CDATA[Saputra, Moch Panji Agung]]>;
<![CDATA[Azahra, Astrid Sulistya]]>;
<![CDATA[Pirdaus, Dede Irman]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.233
<![CDATA[The rapid advancement of information technology and digitalization has significantly transformed the financial sector, particularly with the emergence of cryptocurrencies characterized by high price volatility and complex movement patterns. Accurate price prediction of these crypto assets is essential to support investment decision-making and risk management. This study aims to compare the performance of six activation functions ReLU, Tanh, Sigmoid, Softplus, Swish, and Mish in a Simple Recurrent Neural Network (RNN) model for predicting the price of Litecoin, a widely traded cryptocurrency. Using historical daily closing price data from May 2020 to April 2025, the data were preprocessed through Min-Max normalization and sliding window sequence formation to fit the RNN input requirements. Each activation function was applied in the RNN model under consistent training conditions, and model performance was evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Results indicate that the Swish activation function outperforms others by achieving the lowest RMSE of 4.58 and the highest R² score of 0.9578, demonstrating superior prediction accuracy and stable convergence. Tanh also showed competitive results, while Sigmoid and Softplus performed less effectively. In conclusion, Swish is recommended as the most suitable activation function for RNN-based cryptocurrency price forecasting due to its balance of accuracy and computational efficiency.]]>
<![CDATA[Trend Analysis of Stunting Prevalence in West Java (2019-2024) Using WHO Thresholds ]]>
<![CDATA[Beatrice, Florentia]]>;
<![CDATA[Tatsbita, Ghaisha Izzati]]>;
<![CDATA[Indrayana, Ayesha Qabila Ramazani]]>;
<![CDATA[Yanuar, Alfarizi Haunan]]>;
<![CDATA[Nurshiyam, Dini]]>;
<![CDATA[Maulana, Rafeyfa Ashyla Putri]]>;
<![CDATA[Kamila, Mutiara Shofa]]>;
<![CDATA[Aprilia, Lia]]>;
<![CDATA[Anggraeni, Salwa]]>;
<![CDATA[Ilham, Nur Indah Khairunnisa Kurniawaty]]>;
<![CDATA[Arafah, Safila Siti]]>;
<![CDATA[Purwandi, Avicenna Ihyal Faza]]>;
<![CDATA[Hidayat, Yuyun]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 3 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i3.236
<![CDATA[This study aims to analyze the trend of stunting prevalence among children under five across various cities and regencies in West Java Province during the 2019–2024 period using a simple linear regression approach. The data utilized in this study are secondary data obtained from official local government sources, along with the stunting classification standards established by the World Health Organization (WHO). The results indicate that 40% of regions showed statistically significant declines, such as Indramayu Regency, Bekasi City, and Karawang Regency. Conversely, 60% of the regions showed a decrease in stunting rates that was not statistically significant, including Cirebon City and Garut Regency. The variation in the coefficient of determination (R²) highlights differences in model strength across regions, while the p-value suggests that not all downward trends can be considered statistically significant. These findings are expected to serve as a basis for formulating more targeted and effective interventions to reduce stunting prevalence in West Java Province.]]>
