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
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<![CDATA[Hangout Places Recommendation System Using Content-Based Filtering and Cosine Similarity Methods ]]>
<![CDATA[Abdul Raihan]]>;
<![CDATA[Ahmad Ibrahim A.M]]>;
<![CDATA[Alfian Akbar Gozali]]>
<![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.v4i2.1464
<![CDATA[Coffee shops are becoming the new normal for friends and coworkers to hang out. Selecting the ideal location to hang out can be exceedingly difficult. There are too many choices, and it can be difficult to know where to begin. Based on this problem, a web application that responds to the growing need for an easy method of finding local hangouts is named Nongkies. With a focus on social interaction and exploration, this platform uses a recommender system to find cafes, restaurants, and entertainment venues easily. Key features include location-based search, category, and details places. Extensive testing has confirmed the reliability of Nongkies, offering user-friendly and accurate search results. This system is a website app that suggests places to users based on their preferences. This application was developed using the cosine similarity method, which is a systematic approach that uses a similar method based on cosine angles. Content that is less alike gets lower rankings, while more similar content gets the highest rankings in recommendations. Moreover, this app helps users find local hangouts and directions to those locations, especially university students, and the selection of places to socialize has a significant effect on students' learning experiences.]]>
<![CDATA[Mobile Assistant Application for Street Food Consumers in Bandung ]]>
<![CDATA[Julius Angger Satrio Wicaksono]]>;
<![CDATA[Kadek David Kurniawan]]>;
<![CDATA[Alfian Akbar Gozali]]>
<![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.v4i2.1470
<![CDATA[In the dynamic city of Bandung, the lively street food scene has captured the fascination of tourists, offering a diverse selection of tempting dishes. Nevertheless, a persistent challenge arises from the lack of comprehensive details about these street foods, presenting a hurdle for consumers in making well-informed and health-conscious choices. This predicament underscores the necessity for a solution, leading to the introduction of the Mobile Assistant Application for Street Food Consumers in Bandung. Harnessing cutting-edge computer vision technology, this application seeks to provide a solution by furnishing users with an intuitive and effective tool for accessing in-depth information regarding street foods. The outcomes of thorough experimentation highlight the application's success in precisely identifying a wide array of street foods in Bandung. Users benefit from accurate information on ingredients and nutritional values, empowering them to make informed dietary decisions and elevating the overall street food experience in Bandung. This inventive solution not only addresses the prevailing information gap but also contributes to the well-being of consumers, ushering in a healthier and more enlightened food culture in Bandung at the tip of one's finger.]]>
<![CDATA[Development of Palm Oil Production and Sales Monitoring System Based On Android ]]>
<![CDATA[Chikal Fachdiana]]>;
<![CDATA[Rafie Novianto Sudrajat]]>;
<![CDATA[Alfian Akbar Gozali]]>
<![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.v4i2.1473
<![CDATA[Palm oil is one of the most widely used vegetable oils in the world. It is used as a raw material for the economic area and contributes to foreign exchange earnings. The palm oil enterprise performs a critical position in Indonesia's economic development, lowering poverty and creating different businesses supporting the enterprise. This paper aims to assist in improving forecasting, essential factor identification, early caution structures, overall performance monitoring, and decision help for bunches of palm production. in this paper, a machine based totally on system learning is created and applied in order to estimate palm production using models with algorithm decision tree and timeseries.]]>
<![CDATA[CNN-LSTM for MFCC-based Speech Recognition on Smart Mirrors for Edge Computing Command ]]>
<![CDATA[Aji Gautama Putrada]]>;
<![CDATA[Ikke Dian Oktaviani]]>;
<![CDATA[Mohamad Nurkamal Fauzan]]>;
<![CDATA[Nur Alamsyah]]>
<![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.v4i2.1504
<![CDATA[Smart mirrors are conventional mirrors that are augmented with embedded system capabilities to provide comfort and sophistication for users, including introducing the speech command function. However, existing research still applies the Google Speech API, which utilizes the cloud and provides sub-optimal processing time. Our research aim is to design speech recognition using Mel-frequency cepstral coefficients (MFCC) and convolutional neural network–long short-term memory (CNN-LSTM) to be applied to smart mirror edge devices for optimum processing time. Our first step was to download a synthetic speech recognition dataset consisting of waveform audio files (WAVs) from Kaggle, which included the utterances “left,” “right,” “yes,” “no,” “on,” and “off. ” We then designed speech recognition by involving Fourier transformation and low-pass filtering. We benchmark MFCC with linear predictive coding (LPC) because both are feature extraction methods on speech datasets. Then, we benchmarked CNN-LSTM with LSTM, simple recurrent neural network (RNN), and gated recurrent unit (GRU). Finally, we designed a smart mirror system complete with GUI and functions. The test results show that CNN-LSTM performs better than the three other methods with accuracy, precision, recall, and an f1-score of 0.92. The speech command with the best precision is "no," with a value of 0.940. Meanwhile, the command with the best recall is "off," with a value of 0.963. On the other hand, the speech command with the worst precision and recall is "other," with a value of 0.839. The contribution of this research is a smart mirror whose speech commands are carried out on the edge device with CNN-LSTM.]]>
<![CDATA[Sentiment Analysis of Handling "Klitih" in Yogyakarta Using Naïve Bayes ]]>
<![CDATA[Windha Mega P Dhuhita]]>;
<![CDATA[Ilham Ferry Pratama]]>;
<![CDATA[Bayu Setiaji]]>
<![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.v4i2.1527
<![CDATA[Activities that lead to crimes called "klitih" often occur and disturb the community. The community's response to the handling carried out by the regional government also varied. The public expressed this response using various types of social media, one of which was Twitter. This research analyzes sentiment or responses given by the public by utilizing the social media Twitter to collect data. Data in the form of tweets that have been taken will go through text processing. After that, the text will be weighted using two methods as a comparison, namely tf-idf and count vector. Then the data will be divided into training data and test data to proceed to the classification stage. Classification is carried out using the Naïve Bayes algorithm. To evaluate the results of Naïve Bayes classification, researchers used the Confusion Matrix, by comparing weighting methods and dividing the training data and test data into several different ratios, to find out the scenario that produces the best level of accuracy. The sentiment obtained was dominated by negative sentiment at 75.8%, while positive sentiment was 24.2%. By using existing data, it was found that weighting with count vector had an accuracy rate of 82%. Meanwhile, weighting using TF-IDF obtained an accuracy of 80%.]]>
<![CDATA[Application of Artificial Intelligence in the Design of 2D Escape From Pirates Game with A Star Algorithm Search Method ]]>
<![CDATA[Rahmat Kurniawan]]>;
<![CDATA[Armansyah Armansyah]]>;
<![CDATA[Muhammad Idris]]>
<![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.v4i2.1558
<![CDATA[This research is designed to provide a challenging gaming experience by integrating strategy and problem-solving elements. The A* algorithm was chosen due to its efficient ability to find the shortest path in a complex search space. The implementation of this algorithm allows the main character to dynamically avoid obstacles and pirate threats and reach the destination in an optimal way. The test results show that the A* algorithm not only improves game performance but also provides a more realistic and challenging experience for the player. For testing this application, using obstacles and measured based on the value of nodes on the game map. Based on the test results, the A Star algorithm was successfully applied when comparing the computations in the game and manual calculations in the Escape from Pirates game in the test. Thus, this research contributes to the development of artificial intelligence-based games and opens opportunities for further innovation in interactive game design.]]>
<![CDATA[Sentiment Classification of User Reviews for KAI Access Application Using Naive Bayes Method ]]>
<![CDATA[Rafi Andi Hidayah]]>;
<![CDATA[Rokhmatul Insani]]>;
<![CDATA[Berlian Rahmy Lidiawaty]]>
<![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.v4i2.1565
<![CDATA[KAI Access is a train ticket booking application that offers convenience and various features for its users. However, the app has received a low rating of 2.4 out of 5 stars on the Play Store, indicating user dissatisfaction. This study conducts a quantitative sentiment analysis of the KAI Access application based on user sentiments expressed on Twitter. Using the CRISP-DM method, data were collected from Twitter with the Tweepy tool, amassing around 4,000 tweets from June to August 2023. The data underwent a preprocessing stage to ensure the quality and accuracy of the analysis. This stage involved removing duplicate tweets, eliminating retweets, and filtering out emoticons and other non-text elements. In the modeling stage, the Multinomial Naive Bayes Classifier algorithm was employed, achieving an accuracy rate of 84.6%. The model performed better at identifying negative reviews, with a precision of 0.96, recall of 0.86, and an F1-score of 0.91. In contrast, the identification of positive reviews was less effective, with a precision of 0.41, recall of 0.75, and an F1-score of 0.53. These findings shed light on the low ratings for KAI Access, particularly in the context of user reviews. The results of this study provide further understanding regarding the low rating given to KAI Access, particularly in the context of user reviews. By using this classification system, it is hoped that developers can design more specific improvements to enhance the user experience, especially in handling positive reviews which have the potential for performance improvement.]]>
<![CDATA[Comparative Analysis of Linear Regression, Decision Tree and Gradient Boosting for Predicting Stock Price of Bank Rakyat Indonesia ]]>
<![CDATA[Rahma Dwi Ningsih]]>;
<![CDATA[Sarwido Sarwido]]>;
<![CDATA[Gentur Wahyu Nyipto Wibowo]]>
<![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.v4i2.1566
<![CDATA[An investment is the placement of a current amount of funds in the hope of generating a profit in the future. There are several types of investments, including stocks, which are attractive options as they can bring a huge return to investors. However, rapidly fluctuating stock prices are influenced by various factors, such as company performance, interest rates, economic conditions, and government policies. In Indonesia, PT Bank Rakyat Indonesia Tbk (BBRI) had the largest profit among the 10 largest banks by the end of March 2024, with a profit of IDR 13.8 trillion. The higher the bank's return, the greater the investor's interest in purchasing the stock, influencing the stock price. The goal of stock price prediction is to forecast the stock's future price in order to increase investors' potential profits. Various methods, such as Linear Regression, Decision Tree, and Gradient Boosting, have been developed for stock price prediction. Comparative analysis is needed to determine the most accurate method in predicting the stock price of People's Bank of Indonesia, using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared metrics (R2). The analysis showed that Linear Regression achieved the highest accuracy with R2 of 0.96, MAE of 65.72 and RMSE of 86.74 compared to the Decision Tree and Gradient Boosting models.]]>
<![CDATA[Determining Air Quality Influential Parameters Using Machine Learning Techniques ]]>
<![CDATA[Evita Fitri]]>;
<![CDATA[Andi Saryoko]]>
<![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.v4i2.1567
<![CDATA[Air quality is an important issue in public health and the environment. This research aims to develop an air quality prediction model based on PM10 and PM2.5 parameters using various regression and machine learning approaches. The dataset used includes air pollutant standard index (ISPU) data from a number of stations in the Jakarta area with an observation period from January to April 2024. The research method includes collecting datasets, reviewing literature and testing several models of machine learning techniques. Furthermore, the handling of outliers was carried out using the numeric outliers node and data normalization to prepare the data before dividing the training and testing data. The models evaluated include Linear Regression, Random Forest Regression, Gradient Boosted Trees, and Multilayer Perceptron (MLP), with validation using 10 times cross-validation. The results showed that the Random Forest Regression and Gradient Boosted Trees models provided good prediction performance for both PM10 and PM2.5 parameters. Random Forest Regression showed the lowest RMSE value on testing data for PM10 (0.048) and PM2.5 (0.037), while Gradient Boosted Trees showed the lowest RMSE value on training data for PM2.5 (0.032). The process of handling outliers and normalizing the data successfully improved the prediction accuracy of the model. Suggestions for future research include the exploration of new models, the addition of meteorological and socio-economic variables, and the application of models in real-time air quality monitoring systems.]]>
<![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.]]>
