Journal of Dinda : Data Science, Information Technology, and Data Analytics
Journal of Dinda : Data Science, Information Technology, and Data Analytics as a publication media for research results in the fields of Data Science, Information Technology, and Data Analytics, but not implicitly limited. Published 2 times a year in February and August. The journal is managed by the Data Engineering Research Group, Faculty of Informatics, Telkom Purwokerto Institute of Technology. Journal of Dinda is a medium for scientific studies resulting from research, thinking, and critical-analytic studies regarding Data Science, Informatics, and Information Technology. This journal is expected to be a place to foster enthusiasm in education, research, and community service which continues to develop into supporting references for academics. FOCUS AND SCOPE Journal of Dinda : Data Science, Information Technology, and Data Analytics receive scientific articles with the scope of research on: Machine Learning, Deep Learning, Artificial Intelligence, Databases, Statistics, Optimization, Natural Language Processing, Big Data and Cloud Computing, Bioinformatics, Computer Vision, Speech Processing, Information Theory and Models, Data Mining, Mathematical, Probabilistic and Statical Theories, Machine Learning Theories, Models and Systems, Social Science, Information Technology
Articles
87 Documents
<![CDATA[The Utilizing GPT-4o Mini in Designing a WhatsApp Chatbot to Support the New Student Admission Process at Telkom University ]]>
<![CDATA[Ruhallah, Muhammad Lutfi]]>;
<![CDATA[Pratami, Rahmat]]>;
<![CDATA[Gozali, Alfian Akbar]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.1963
<![CDATA[The rapid adoption of Artificial Intelligence (AI) in higher education has revolutionized student support services, yet delivering scalable, real-time assistance through familiar platforms remains a challenge. This study presents the design, implementation, and evaluation of a WhatsApp-based chatbot powered by a fine-tuned GPT-4o Mini model to streamline the new student admission process at Telkom University. A specialized dataset comprising frequently asked questions and admission-related dialogues was curated and preprocessed for model fine-tuning over 288 epochs. The chatbot system integrates the WhatsApp Business API, a Webhook interface, and the n8n automation platform, all deployed on a Virtual Private Server (VPS) to ensure reliability and low-latency communication. Functional and performance testing involved manual scenario-based assessments and quantitative measurements of response accuracy and latency. Results indicate that the chatbot consistently delivers contextually relevant answers—achieving an average accuracy above 85%—and reduces average response time to under 3 seconds. User interaction studies with prospective and current students revealed high satisfaction levels, highlighting improvements in accessibility and reduction of administrative workload. Challenges identified include occasional misinterpretation of complex queries and the need for enhanced scalability under peak loads. Future work will focus on periodic dataset updates, advanced prompt engineering, scalability stress testing, and the integration of multimodal features such as voice and image recognition. By aligning AI-driven conversational interfaces with users’ existing digital habits, this chatbot demonstrates a viable approach for enhancing admission services and operational efficiency in Indonesian higher education institutions.]]>
<![CDATA[Heart Failure Classification Using a Hybrid Model Based on SVM and Random Forest ]]>
<![CDATA[Abdilllah, Muh Sajid]]>;
<![CDATA[Mulyo, Harminto]]>;
<![CDATA[Wibowo, Gentur Wahyu Nyipto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2001
<![CDATA[This study discusses the development of a model to classify heart failure disease by combining two algorithms in the field of data mining: Support Vector Machine (SVM) and Random Forest (RF). The dataset used is the Heart Failure Prediction Dataset, consisting of 918 patient records containing medical information such as blood pressure, cholesterol levels, and heart rate. The research process began with data cleaning, normalization using MinMaxScaler, and data balancing with the SMOTE technique to equalize the number of cases between heart failure patients and non-patients. The data was then split into training and testing sets. Each model (SVM and RF) was tested individually and also combined into a hybrid model. Validation was performed using 5-Fold Cross Validation to ensure consistent results. The results show that SVM performed better in terms of precision for detecting heart failure after applying SMOTE, while RF remained stable even without data balancing. The hybrid model combining both algorithms achieved the best performance, with an accuracy of 91.20%, precision of 90.85%, recall of 92.44%, and an AUC score of 0.961. These results indicate that the hybrid model can detect heart failure more accurately and in a more balanced manner. With its high and consistent performance, this model is suitable for use as a decision support system in the medical field, particularly for early detection of heart failure.]]>
<![CDATA[Classification of Indonesian Disasters with Decision Trees Based on Spatial and Text Data ]]>
<![CDATA[Ramadhan, Ridwan]]>;
<![CDATA[Saputra, Ragil Raditya]]>;
<![CDATA[Innova, Zacky]]>;
<![CDATA[Prabowo, Ary]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2006
<![CDATA[Indonesia is one of the countries with a very high level of natural disaster vulnerability. The types of disasters that frequently occur include earthquakes, floods, landslides, volcanic eruptions, and others. This is because Indonesia is located at a geographical position where three world tectonic plates meet and has tropical climate conditions that make it prone to disasters. Therefore, Indonesia needs a system that can classify disaster types automatically and accurately to help the decision-making process quickly and accurately. This research aims to develop a natural disaster classification model based on information such as location (regency and province), time of occurrence (date), and causes that lead to disasters. The method used for classification in this research is the Decision Tree algorithm, because this algorithm can handle both numerical and categorical data and has high interpretability. Classification processing is also performed using textual cause data using Term Frequency-Inverse Document Frequency (TF-IDF) technique to convert text format into numerical form that can be processed by machine learning algorithms. The dataset obtained from the National Disaster Management Agency (BNPB) is open source. Test results show that the trained Decision Tree model can classify disaster types with an accuracy of 87%. This model also shows good precision, recall, and f1-score values in each disaster category. It is hoped that the results of this research can help in developing historical data-based disaster detection systems and assist government and society in responding to disasters more effectively and efficiently.]]>
<![CDATA[Comparison of Accuracy of Linear Regression and Random Forest Models in Predicting Bitcoin Prices ]]>
<![CDATA[Awwaluddin, Ahmad Habib]]>;
<![CDATA[Tamrin, Teguh]]>;
<![CDATA[Widiastuti, Nur Aeni]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2014
<![CDATA[Abstract Bitcoin is a digital asset that has experienced significant growth in value since its launch in 2009. However, its high price volatility makes predicting Bitcoin's price movements a challenge for investors and financial analysts. Therefore, a data-driven approach capable of capturing patterns in historical Bitcoin price data is needed to support more accurate investment decision-making. This study aims to evaluate and compare the performance of two prediction algorithms, namely Linear Regression and Random Forest, in predicting Bitcoin prices based on daily historical data from 2018 to 2025. The dataset was obtained from the Kaggle platform and processed through pre-processing, predictive feature formation, and data normalization. Two validation schemes were used: a 70:30 data split and cross-validation using K-Fold Cross Validation (10-fold). Model performance evaluation was carried out using three main metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results show that the Linear Regression model produces better performance than Random Forest, both on split data and cross-validation, even though Random Forest has been optimized using GridSearchCV. The lowest RMSE value was obtained from Linear Regression in the K-Fold scheme, at 1314.47. These findings indicate that a simple model such as Linear Regression can still be effective in predicting Bitcoin prices if the data is properly processed. This research is expected to serve as a reference for developers of digital asset price prediction systems and stakeholders in data-driven decision-making.. Keywords: Bitcoin, Prediksi Harga, Regresi Linier, Random Forest, Evaluasi Model, Machine Learning, K-Fold Cross Validation]]>
<![CDATA[ROC and COPRAS Methods in New Student Admissions Application (PPDB) MAN HUMBANG HASUNDUTAN ]]>
<![CDATA[Tua, Anri Hafiz]]>;
<![CDATA[Putri, Raissa Amanda]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2015
<![CDATA[The development of information and communication technology, especially in the education sector, has opened up opportunities to increase efficiency and transparency in various processes, including New Student Admissions (PPDB). MAN Humbang Hasundutan faces challenges in manually screening hundreds of prospective students every year, which often introduces bias and inaccuracies in the selection process. Therefore, this research aims to develop a web-based PPDB application with the integration of the Rank Order Centroid (ROC) method for weighting criteria and Complex Proportional Assessment (COPRAS) for ranking. The ROC method assigns weights to criteria based on their level of importance, while the COPRAS method determines the ranking by taking into account the level of significance and utility of alternatives. The implementation of this application enables the processing of prospective student data quickly and objectively, as well as increasing the fairness and transparency of the selection process. Based on the results of previous research, the COPRAS method with ROC weighting has proven to be effective in assisting decision making in various fields. The proposed PPDB application is expected to simplify the selection process at MAN Humbang Hasundutan while increasing the credibility of the educational institution.]]>
<![CDATA[Analysis of Public Sentiment Toward the Increase in VAT Rates Using the SVM Algorithm ]]>
<![CDATA[Rahman, Elsa Azila]]>;
<![CDATA[Lubis, Aidil Halim]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2025
<![CDATA[The Policy Of Increasing the Value Added Tax (VAT), particularly on luxury goods as stipulated in Minister of Finance Regulation (PMK) Number 131 of 2024, has sparked various public responses, many of which are captured through social media. In today's digital era, social media has become a primary platform for the public to express their opinions openly, including on government policies. This study aims to analyze public sentiment toward the VAT policy in order to provide insights for more responsive policymaking. A total of 4,000 comments were collected from the X platform using web crawling techniques, followed by preprocessing, resulting in 3,553 clean comments. Sentiment labeling was conducted automatically using a lexicon-based approach, which revealed that the majority of comments expressed positive sentiment (73.3%), while the remainder were negative (26.7%). Sentiment classification was performed using the Support Vector Machine (SVM) algorithm with a polynomial kernel and an 80:20 training-testing data split. Evaluation results showed that the model achieved an accuracy of 76.65%. The SVM model demonstrated excellent performance in detecting positive sentiment (precision 76.18%, recall 100%, and F1-score 86.51%), but was less effective in identifying negative sentiment (precision 100%, recall 7.78%, and F1-score 14.44%). These findings indicate that while the model is effective in recognizing positive opinions, further optimization is needed to improve performance in detecting negative sentiments.]]>
<![CDATA[Academic Monitoring Information System Using Task Centered System Design Method Based On Web ]]>
<![CDATA[Haliza, Nur]]>;
<![CDATA[Suendri, Suendri]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 2 (2025): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i2.2030
<![CDATA[Manual academic monitoring systems at SMA Swasta Teladan Cinta Damai present several challenges, such as delayed information delivery, data entry errors, and lack of transparency in academic records. This study aims to design and develop a web-based Academic Monitoring Information System using the Task Centered System Design (TCSD) approach, which focuses on the actual needs and tasks of users such as teachers, students, and parents. The system is developed using PHP as the programming language and MySQL as the database, and follows the Waterfall development model, which includes stages such as requirements analysis, system design, implementation, and testing. The results show that the system can present academic information in real time, improve monitoring efficiency, and facilitate access to information for all stakeholders. With its intuitive interface and task-oriented features, this system provides a digital solution that enhances the quality of academic management in the school environment.]]>
