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
60 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.]]>
<![CDATA[The Effect of Fast Food Consumption on Obesity in Teenagers : A Literature Review and Secondary Data Analysis ]]>
<![CDATA[Indriyani]]>;
<![CDATA[Geraldine, Keysia Feliani]]>;
<![CDATA[Putri, Alesyia Tharfaira]]>;
<![CDATA[Sukmariwati, Anisa]]>;
<![CDATA[Azzahra, Siti Alya]]>;
<![CDATA[Azzahra, Alika Fatihah]]>;
<![CDATA[Azzahra, Nailah]]>;
<![CDATA[Praceka, Java Ahmad Forouzan]]>;
<![CDATA[Zaina, Ratu Faiza]]>;
<![CDATA[Audhyanto, Claudya Valerinne]]>;
<![CDATA[Putri, Alifa Kaliyana]]>;
<![CDATA[Kareema, Najwa Haniah Nur]]>;
<![CDATA[Hidayat, Yuyun]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.229
<![CDATA[Obesity among teenagers is a growing public health concern and is often linked to the frequent consumption of fast food, which is typically high in calories, fat, and sodium, but low in dietary fiber. This study aims to explore the impact of fast food consumption on the prevalence of obesity in teenagers through a literature review and analysis of secondary data from two prior studies. A Chi-Square test was conducted to examine the relationship between the frequency of fast food intake and obesity status. The combined analysis indicates that teenagers who consume fast food more than three times per week tend to have a higher prevalence of obesity. However, statistical testing revealed that this association is not significant (Chi-Square value = 0.0145; p-value = 0.9042). These results suggest that teenage obesity is not solely influenced by fast food intake but is also affected by other factors such as physical activity, overall dietary habits, and genetic predisposition.]]>
<![CDATA[Accuracy of the Jian-Yang-Jiang-Liu-Liu Spectral Conjugate Gradient Method in Estimating Extended Exponential Weibull Parameters ]]>
<![CDATA[Tjayadi, Steven]]>;
<![CDATA[Malik, Maulana]]>;
<![CDATA[Novkaniza, Fevi]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.244
<![CDATA[Life is filled with uncertainty and risk. The analysis of lifetime is needed to be a tool that can manage uncertainty. Lifetime is defined as data that contains the time until the occurrence of an event. Based on its definition, lifetime data is like Hazard rate data or mortality data because mortality data can be defined as data that contains the probability of an object surviving until that moment per unit time interval. The analysis of mortality data aims to model the distribution of time to event and/or the determinants of time to event. One of the distribution models that can be used to analyze mortality data is Weibull distribution. However, the Weibull distribution is not very suitable for modeling the more complex versions of data. Therefore, an extension of the Weibull distribution that is more flexible in modeling data is used, namely the Extended Exponential Weibull (ExEW) distribution. The ExEW distribution has four parameters whose estimation can be calculated using the maximum likelihood estimation (MLE) method. However, parameters estimated with MLE are often too difficult to calculate analytically, hence the use of optimization methods. One of the optimization methods that can be used to determine the estimated parameters of the ExEW distribution is the conjugate gradient method. To date, many conjugate gradient methods have been developed, including the Liu-Feng-Zou (LFZ) spectral conjugate gradient method and the Jian-Yang-Jiang-Liu-Liu (JYJLL) spectral conjugate gradient method. Previous research suggests that the JYJLL spectral conjugate gradient method has more efficient computational performance than the LFZ spectral conjugate gradient method. Through data simulation, this study provides results that the JYJLL spectral conjugate gradient conjugate method has better accuracy than the LFZ spectral conjugate gradient method in parameter estimation of the ExEW distribution. In addition, the ExEW distribution is the most suitable distribution in modeling various forms of Hazard rate data compared to the Weibull and exponential distributions.]]>
<![CDATA[Clustering of Regencies and Cities in West Java Province Based on Horticultural Indicators Using the K-Means Method ]]>
<![CDATA[Lakui, Rivani]]>;
<![CDATA[Sastro, Gerry]]>;
<![CDATA[Setiawan, Tabah Heri]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.249
<![CDATA[National food security largely depends on the capacity of domestic production. However, Indonesia continues to rely on food imports, including horticultural products. West Java Province, as one of the country’s main food-producing regions, possesses diverse geographical conditions that support the cultivation of various commodities and therefore becomes the focus of this study. This research aims to classify the 27 districts and cities of West Java Province based on horticultural indicators in order to identify spatial patterns and development potential. The study employed secondary data from the Central Bureau of Statistics (BPS) of West Java and World Climate for 2023, including horticultural production (ornamental plants, bio-pharmaca, vegetables, and fruits), annual average rainfall, and temperature. The analysis used the K-Means clustering method, with the Silhouette Index as an evaluation measure to determine the optimal number of clusters. Results indicate that three clusters provided the best accuracy. Cluster 1 (e.g., Cianjur and Bandung Regencies) consists of areas with high horticultural production, low temperatures, and moderate rainfall. Cluster 2 (e.g., Bogor and Sukabumi Regencies) represents regions with low production, moderate temperature, and high rainfall. Cluster 3 (e.g., Cirebon and Indramayu Regencies) includes areas with moderate production, high temperature, and low rainfall. The findings provide a foundation for local and national governments to design targeted horticultural development strategies that enhance productivity, improve farmers’ welfare, and support sustainable food security.]]>
<![CDATA[Enterprise Architecture Planning for the Transformation of Career Development Center (CDC) Business Processes at Ma’soem University ]]>
<![CDATA[Nugraha, Muhamad Fahmi]]>;
<![CDATA[Naasyiah, Rifdah]]>;
<![CDATA[Maulana, Doni Rizki]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.268
<![CDATA[The Career Development Center (CDC) at Ma’soem University supports students and alumni in preparing for the workforce through services such as counseling, psychological testing, tracer studies, and job vacancy information. However, the absence of an integrated system makes these services less efficient and difficult to manage. This study proposes a centralized information system using the Enterprise Architecture Planning (EAP) approach to support the CDC’s digital transformation. The design includes data, application, and technology architectures aimed at improving service delivery and operational performance. The system development process involves observation, interviews, business process modeling (BPMN), and data structure design (ERD). The result is a system design with key modules supported by secure technological infrastructure. The system is planned to be implemented in stages and is expected to help the CDC operate more effectively and align with the needs of the industrial sector.]]>
<![CDATA[Unpacking the Impact of Digital Touchpoint Excellence and Switching Resilience on Building Millennial Loyalty and Financial Decision-Making at Phintraco Sekuritas ]]>
<![CDATA[Hapsari, Ayuningtyas Yuli]]>;
<![CDATA[Anwar, Tezza Adriansyah]]>;
<![CDATA[Sugiana, Neng Susi Susilawati]]>;
<![CDATA[Widjatun, Vincentia Wahju]]>;
<![CDATA[Padmanegara, Oliver Hasan]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.269
<![CDATA[The purpose of this study is to investigate the influence of Digital Touchpoint Excellence and Switching Resilience on Millennial Loyalty and Financial Decision-Making in the context of digital investment services at Phintraco Sekuritas. In particular, the study seeks to understand how high quality digital interactions and user resilience contribute to sustained loyalty among millennials in Java and Bali. A quantitative research method was employed using a structured online questionnaire distributed to millennial respondents aged 20–40 who actively use digital investment platforms. A sample of 384 respondents was selected through proportional random sampling across major cities in Java and Bali. The data were analyzed using Structural Equation Modeling (SEM) with AMOS to test the relationships among variables. The results reveal that Digital Touchpoint Excellence significantly influences both Financial Decision-Making and Millennial Loyalty, with Financial Decision-Making acting as a partial mediator. Furthermore, Switching Resilience strengthens the impact of Financial Decision-Making on Millennial Loyalty, indicating its role as a moderating variable. The findings suggest that creating seamless, personalized, and reliable digital touchpoints is crucial in enhancing user confidence in financial decisions, which in turn drives loyalty. This study provides practical implications for fintech service providers, emphasizing the importance of investing in digital customer experiences and resilience-building strategies to retain millennial users. Uniqueness of this study lies in its integrated approach that combines psychological resilience and digital UX excellence within a financial decision-making context, specifically targeting millennial behavior in the Indonesian capital market a perspective that remains underexplored in previous research. ]]>
<![CDATA[The Influence of Service Quality on Customer Loyalty at Ibis Hotel Bandung ]]>
<![CDATA[Mukhlis, Ervie Nur Afifah]]>;
<![CDATA[Nurarifian, Muhammad Chairully]]>;
<![CDATA[Agustian, Deryl]]>;
<![CDATA[Syafrilyudin, Rafli]]>;
<![CDATA[Hidayatullah, Tubagus]]>;
<![CDATA[Sugiana, Neng Susi Susilawati]]>
<![CDATA[ International Journal of Mathematics, Statistics, and Computing Vol. 3 No. 4 (2025): International Journal of Mathematics, Statistics, and Computing ]]>
Publisher : <![CDATA[Communication In Research And Publications ]]>
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DOI: 10.46336/ijmsc.v3i4.270
<![CDATA[This study aims to analyze the influence of service quality on customer loyalty at Ibis Hotel Bandung. The hospitality industry is highly competitive, and maintaining customer loyalty is crucial for business sustainability. Service quality is considered a key factor influencing customer decisions to repurchase and recommend hotel services to others. This research uses a quantitative method with a survey approach, involving 100 customers who have stayed at Ibis Hotel Bandung. Data were collected through questionnaires measuring five dimensions of service quality: tangibles, reliability, responsiveness, assurance, and empathy. The data were analyzed using multiple linear regression to determine the significance of each dimension’s effect on customer loyalty.The results show that service quality significantly influences customer loyalty at Ibis Hotel Bandung. Among the dimensions, reliability and assurance have the highest positive impact, indicating that customers value consistent service performance and the confidence provided by hotel staff. Tangibles, such as cleanliness and room facilities, also contribute to loyalty, though to a lesser extent. Responsiveness and empathy were found to positively affect loyalty by creating comfort and trust in customers during their stay. These findings imply that Ibis Hotel Bandung must continuously maintain and improve its service quality, especially in ensuring reliable and assured services to increase customer retention and recommendations. This study contributes to the hotel management literature by reaffirming that excellent service quality is a strategic asset in building and sustaining customer loyalty in the hospitality industry.]]>
