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[Comparison of Linear Regression and LSTM (Long Short-Term Memory) in Cryptocurrency Prediction ]]>
<![CDATA[Marisa Istaltofa]]>;
<![CDATA[Sarwido Sarwido]]>;
<![CDATA[Adi Sucipto]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1575
<![CDATA[Abstract Cryptocurrency, particularly Bitcoin, has become a major topic in the financial and digital trading sectors due to its ability to facilitate direct transactions without intermediaries and the transparency offered by blockchain technology. However, the high volatility of Bitcoin prices necessitates accurate prediction methods to support better investment decisions. This research aims to compare the accuracy of Linear Regression and Long Short-Term Memory (LSTM) methods in predicting Bitcoin prices using historical data from Yahoo Finance. The research process begins with the collection of historical Bitcoin price data from September 17, 2014, to July 15, 2024, followed by data processing that includes cleaning and splitting the dataset into training and test data. Linear Regression and LSTM models are applied to the training data and tested to evaluate their performance in price prediction. The research findings show that the LSTM model significantly outperforms the Linear Regression model in terms of prediction accuracy. The LSTM model records much lower Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), as well as perfect R² scores on both datasets, demonstrating its high precision in prediction. In contrast, the Linear Regression model shows higher errors and lower explanatory power of data variability. These findings indicate that LSTM is more effective in capturing temporal patterns and Bitcoin price fluctuations, offering better accuracy and potentially being more suitable for future cryptocurrency price analysis, providing better guidance for investors in this highly dynamic market.]]>
<![CDATA[Development of Sentiment Analysis System of Simple Pol Application on Google Play Store Using Naive Bayes Classifier Method and BERT Prediction ]]>
<![CDATA[Muhammad Dhito Maulidan]]>;
<![CDATA[Sri Sumarlinda]]>;
<![CDATA[Sopingi Sopingi]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1577
<![CDATA[Digitalization in public services raises various sentiments that are very dynamic, one example is the Simpel Pol Health Test application made by PT Cipta Sari Arsonia (CSA). The research objective is to obtain useful information from accurate community review sentiments for service improvement and feedback for service providers and application developers. The method used is Naïve Bayes Classifier with Tf-idf weighting, Multinomial Naïve Bayes with review value indicators and review sentences predicted by the BERT method as a determinant of sentiment value whether positive or negative. Sentiment towards the application shows quite encouraging results, from 3000 data analyzed with 1772 positive reviews and 263 negative reviews with 80% training data and 20% test data, the naïve bayes classification model is able to provide a high level of accuracy, which is 88.7% with a precision of 88.5%, recall of 100% and f1-score of 93.9%. The data showed that most people gave a positive response to this application, with the dominant word being 'easy'. This system was developed using the local-based streamlit framework and proved to be quite reliable in developing systems for data processing and web-based data analysis even though the scraping process is slightly longer than the google colab service. Future research is expected to be able to predict data that is positive or negative with several parameters and several sentiment analysis methods at once and their comparison.]]>
<![CDATA[Post-Election Sentiment Analysis 2024 via Twitter (X) Using the Naive Bayes Classifier Algorithm ]]>
<![CDATA[Yessi Mayasari]]>;
<![CDATA[Yusuf Ramadhan Nasution]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1582
<![CDATA[This study examines sentiment related to the topic of Twitter after the 2024 election, where the topic focused on the 2024 presidential election. Where there are a lot of public opinions and comments after the 2024 presidential election. One of them is the phenomenon when Anies-Muhaimin and Ganjar-Mahfud filed a lawsuit with the Constitutional Court (MK) to appeal over suspicions of fraud over the victory of the elected pair Prabowo-Gibran. By applying the Naïve Bayes Classifier algorithm to analyze public sentiment. Through data crawling, preprocessing, feature extraction, and sentiment classification, the study identified the dominant sentiment and its intensity among social media users. This methodology utilizes quantitative data analysis, using Twitter data linked to specific election-related hashtags. The findings reveal a mix of negative and positive sentiments, reflecting diverse public opinion about election results and related political developments. The accuracy of Naïve Bayes Classifier is highlighted, demonstrating its effectiveness in sentiment classification in the context of social media. This research contributes to understanding public sentiment in the political realm and improving methodological approaches in sentiment analysis using machine learning.]]>
<![CDATA[Comparison of Fisher-Yates Shuffle and Linear Congruent Algorithms for Question Randomization ]]>
<![CDATA[Nugroho Dwi Saputro]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1584
<![CDATA[This research aims to compare Fisher-Yates algorithm and Linear Congruent algorithm in generating random numbers or permutations. The test is conducted using the Chi-Square method to evaluate the quality of randomness generated by both algorithms. The Chi-Square value of the shuffling results is calculated and compared with the critical value of Chi-Square at a significance level of 0.05 with a degree of freedom (df) of 4, which is 9.488. The results show that the Chi-Square value for the Fisher-Yates algorithm is 3.8 and for the Linear Congruent algorithm is 4.3, both of which are below the critical value. This indicates that there is not enough evidence to reject the Null Hypothesis (H₀), implying that the difference in randomness quality between the two algorithms is not statistically significant. Therefore, both algorithms are considered to have equivalent performance. The decision to choose one of the algorithms can be based on other considerations such as complexity and efficiency. Further research is recommended to explore the performance of the algorithms under different conditions.]]>
<![CDATA[Document Similarity Using Term Frequency-Inverse Document Frequency Representation and Cosine Similarity ]]>
<![CDATA[Adi Widianto]]>;
<![CDATA[Eka Pebriyanto]]>;
<![CDATA[Fitriyanti Fitriyanti]]>;
<![CDATA[Marna Marna]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1589
<![CDATA[Document similarity is a fundamental task in natural language processing and information retrieval, with applications ranging from plagiarism detection to recommendation systems. In this study, we leverage the term frequency-inverse document frequency (TF-IDF) to represent documents in a high-dimensional vector space, capturing their unique content while mitigating the influence of common terms. Subsequently, we employ the cosine similarity metric to measure the similarity between pairs of documents, which assesses the angle between their respective TF-IDF vectors. To evaluate the effectiveness of our approach, we conducted experiments on the Document Similarity Triplets Dataset, a benchmark dataset specifically designed for assessing document similarity techniques. Our experimental results demonstrate a significant performance with an accuracy score of 93.6% using bigram-only representation. However, we observed instances where false predictions occurred due to paired documents having similar terms but differing semantics, revealing a weakness in the TF-IDF approach. To address this limitation, future research could focus on augmenting document representations with semantic features. Incorporating semantic information, such as word embeddings or contextual embeddings, could enhance the model's ability to capture nuanced semantic relationships between documents, thereby improving accuracy in scenarios where term overlap does not adequately signify similarity.]]>
<![CDATA[Implementation of Android-Based Flutter Framework and Waterfall Method in Building Marketplace Applications (MariUmroh) ]]>
<![CDATA[Mardiah Ramadhani]]>;
<![CDATA[Ilka Zufria]]>;
<![CDATA[Ali Ikhwan]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 4 No 2 (2024): August ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v4i2.1594
<![CDATA[The MariUmroh application is an Android-based Umrah and Hajj travel marketplace application designed specifically to bring together sellers and buyers in one digital platform. The MariUmroh marketplace application aims to make it easier for prospective pilgrims and Umrah and Hajj travel companies to interact, transact, and promote their products online. In its business processes, the MariUmroh application uses a B2C (Business to Customer) business model. Where business activities are carried out by travel admins to customers using electronic media directly. The development of the Android-based MariUmroh application is built with the Flutter Framework which uses the Dart programming language. In building the MariUmroh marketplace application, the development system uses the waterfall method, in its creation it begins with needs analysis, system design, program code writing, program testing, program implementation and maintenance. In the MariUmroh application which offers various Umrah and Hajj travel packages to the wider community easily and more efficiently, and sellers easily promote their products and don't have to worry about losing consumers, the features provided by the application are also very useful for pilgrims during their worship at holy land. In building this application, MySQL is used as a database for data storage. Keywords: Marketplace, Umrah and Hajj travel, flutter framework, Waterlfall]]>
<![CDATA[Integration of RFM Method and K-Means Clustering for Customer Segmentation Effectiveness ]]>
<![CDATA[Nafissatus Zahro]]>;
<![CDATA[Nadia Annisa Maori]]>;
<![CDATA[Gentur Wahyu Nyipto Wibowo]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1649
<![CDATA[Penelitian ini bertujuan untuk mengintegrasikan metode RFM dan K-Means Clustering untuk segmentasi pelanggan. Rumusan masalah yang diajukan adalah bagaimana mengintegrasikan kedua metode ini agar segmentasi pelanggan lebih efektif. Data transaksi pelanggan ADAPTA.Id tahun 2022 yang meliputi 2.252 transaksi pelanggan dianalisis untuk menghasilkan nilai RFM, dinormalisasi, dan diklaster menggunakan K-Means. Dua klaster optimal diidentifikasi dengan skor silhouette sebesar 0,8511. Dari total 2.252 transaksi pelanggan, terdapat dua klaster utama: klaster pertama berisi 10 pelanggan dengan frekuensi pembelian tinggi dan nilai transaksi signifikan, sedangkan klaster kedua terdiri dari 918 pelanggan dengan frekuensi dan nilai transaksi lebih rendah. Mayoritas pelanggan berada di klaster kedua. Segmentasi ini memungkinkan perusahaan untuk merancang strategi pemasaran yang lebih efektif dengan memfokuskan sumber daya untuk mempertahankan pelanggan bernilai tinggi dan meningkatkan aktivitas pembelian di klaster bernilai rendah. Pendekatan ini menawarkan wawasan mendalam untuk strategi bisnis yang lebih efisien, serta meningkatkan kepuasan dan loyalitas pelanggan. Skor silhouette yang tinggi menegaskan validitas klaster.]]>
<![CDATA[Comparison of Sentiment for Midi Kriing and Alfagift Apps Using SVM with TF-IDF Weighting ]]>
<![CDATA[Diva Ananda Putra]]>;
<![CDATA[Elma Regina Nababan]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1661
<![CDATA[The advancement of information and communication technology has impacted various aspects of life, including shopping. With increasing internet access, online shopping apps have become a primary tool for consumers. Alfa Group, a major player in the retail industry, has launched two online shopping apps, Midi Kriing and Alfagift. This study aims to compare user sentiment for these two apps based on data from Google Play Store.Using the Support Vector Machine method with TF-IDF weighting, this research analyzes 2,000 reviews from each app. The data, collected from Google Play Store, was divided into 80% for training the model and 20% for testing it. The results indicate that Midi Kriing has an overall accuracy of 87%, while Alfagift has an overall accuracy of 85%. Both apps demonstrate strong performance in sentiment detection, but Midi Kriing is slightly superior in overall accuracy. These findings provide insights into user satisfaction with the apps and can help consumers determine the best online shopping app from Alfa Group. Additionally, the results can be used by Alfa Group to enhance the services of both apps in the future.]]>
<![CDATA[Climate Change Sentiment Analysis using LSTM ]]>
<![CDATA[Marchel Yusuf Rumlawang Arpipi]]>;
<![CDATA[Teny Handhayani]]>;
<![CDATA[Janson Hendryli]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1719
<![CDATA[This research aims to observe the sentiment of Indonesian people towards climate change using the Long Short-Term Memory (LSTM) methods. The data samples used in this study are primary data that have been collecting by using the Twitter Application Programming Interface (API) that provides by a platform known as RapidAPI. This data sample is text data with 2425 total samples obtained during the time period from 01 January 2020 to 25 August 2024. The sentiment is classified as positive, negative, and neutral. The performance of the LSTM model is evaluate using accuracy, precision, recall, F1-score, and confusion matrix and then compare with other models such as Ensemble Model, Naive Bayes, and Linear SVC. By conducting Exploratory Data Analysis (EDA), it is reveals that the distribution of public sentiment towards climate change in Indonesia from the collected data is mostly positive. However, there are not many individuals that are still ignorant and skeptical about the issue, resulting in a negative sentiment that can be fatal to the environment and its surroundings. When comparing the Ensemble Model, Naive Bayes, and Linear SVC, the LSTM model successfully identifies the perception patterns between sentences according to their sentiments. LSTM obtains an accuracy of 60% and outperforms Ensemble Model, Naive Bayes, and Linear SVC. This research also highlights the technical challenges in processing and analyzing dynamic and diverse data so that the results obtained are better, especially in terms of data quality before further processing.]]>
<![CDATA[Analysis of Student Academic Performance to Identify New Patterns Using Linear Regression Algorithm ]]>
<![CDATA[Adelia Putri Septiani]]>;
<![CDATA[Akhmad Khanif Zyen]]>;
<![CDATA[Buang Budi Wahono]]>
<![CDATA[ Indonesian Journal of Data Science, IoT, Machine Learning and Informatics Vol 5 No 1 (2025): February ]]>
Publisher : <![CDATA[Research Group of Data Engineering, Faculty of Informatics ]]>
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DOI: 10.20895/dinda.v5i1.1723
<![CDATA[Abstract This research aims to analyze and identify new patterns in student academic performance using linear regression algorithms. Using data from 1001 respondents, this study analyzes the relationship between various variables such as study hours, previous scores, extracurricular activities, sleep hours, and learning practices on academic performance index. The research methodology employs a quantitative approach with linear regression analysis to identify relationships between variables. The results show significant correlations with an R-squared value of 0.783, indicating that 78.3% of the variation in performance index can be explained by the studied variables. Key findings reveal a synergistic effect between study hours and active learning practices, with performance improvements of up to 23%. The research also identifies a threshold effect on study hours above 6 hours which no longer provides significant impact. Optimal sleep patterns of 7-8 hours show positive correlation with highest academic performance. This study provides important contributions to understanding the factors influencing academic performance and can be used as a basis for developing more effective learning strategies. Keywords: academic performance, linear regression, learning patterns, educational data analysis, performance index.]]>
