JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Journal of Applied Informatics and Computing (JAIC) Volume 2, Nomor 1, Juli 2018. Berisi tulisan yang diangkat dari hasil penelitian di bidang Teknologi Informatika dan Komputer Terapan dengan e-ISSN: 2548-9828. Terdapat 3 artikel yang telah ditelaah secara substansial oleh tim editorial dan reviewer.
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<![CDATA[Game-Based Learning for Mathematics Lesson on 3rd Grade Elementary School ]]>
<![CDATA[Tanudidjaja, Miquel Jan]]>;
<![CDATA[Pranata, Caraka Aji]]>;
<![CDATA[., Bernadhed]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8661
<![CDATA[Integrating cutting-edge strategies to improve students' learning experiences has become increasingly important in the ever-changing world of education. This study investigates how third-grade students at SD Pius Purbalingga can benefit from using game-based learning as an instructional strategy to improve their mathematical education. The study focuses on how to hold children' attention and improve their knowledge of mathematics. The main subject of this study is the effectiveness of educational games in enhancing elementary school student's understanding of mathematics. A mathematics game was created to solve this problem by actively involving pupils and reiterating key mathematical ideas. This game-based strategy aimed to create an engaged and enjoyable learning experience for third-grade pupils with acceptable cognitive capacities. The findings suggest that students who played the math game significantly increased their involvement, comprehension, and memorization of mathematical ideas. This study adds to the growing evidence supporting using educational games as useful tools in mathematics instruction. The study's findings revealed increased academic performance among students, with male students experiencing a rise of 2.4% in their overall scores. In contrast, female students demonstrated a significantly higher increase of 8.5%, indicating a more pronounced advancement in their academic performance.]]>
<![CDATA[Evaluation of Telecommunication Customer Churn Classification with SMOTE Using Random Forest and XGBoost Algorithms ]]>
<![CDATA[Wakhidah, Lisa Nusrotul]]>;
<![CDATA[Zyen, Akhmad Khanif]]>;
<![CDATA[Wahono, Buang Budi]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8740
<![CDATA[Competition in the telecommunications industry, particularly among Internet Service Providers (ISPs), significantly influences customer churn, which negatively impacts revenue, profitability, and business sustainability. An effective approach to mitigate churn involves identifying potential churners early, enabling companies to implement strategic retention measures. However, predicting churn can be challenging due to the limited data available on churned customers. This study aims to predict customers likely to terminate or discontinue their subscriptions, focusing on addressing data imbalance using the Synthetic Minority Over-Sampling Technique (SMOTE). The dataset, sourced from Kaggle, comprises 21 attributes and 7,034 entries. The pre-processing phase includes data cleaning, feature encoding, and the implementation of Random Forest and XGBoost algorithms after data balancing with SMOTE. The findings reveal that the XGBoost algorithm achieves a prediction accuracy of 82%, outperforming Random Forest with 81%. Key factors influencing churn include Contract, TotalCharges, and tenure. The study concludes by emphasizing the significance of contract flexibility and the need to prioritize customers with high total costs or extended subscription periods to reduce churn rates. Future research is encouraged to investigate alternative methods for handling data imbalance and to explore advanced machine learning algorithms to further enhance prediction accuracy and the effectiveness of customer retention strategies.]]>
<![CDATA[User Experience Evaluation of YouTube Website Using Eye Tracking Method ]]>
<![CDATA[Larasati, Salsabila]]>;
<![CDATA[Putra, Pacu]]>;
<![CDATA[Oktadini, Nabila Rizky]]>;
<![CDATA[Meiriza, Allsela]]>;
<![CDATA[Sevtiyuni, Putri Eka]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8743
<![CDATA[YouTube is one of the most popular social media in Indonesia, with one of its features being the Clip Feature, which allows users to share 5-60 seconds video snippets, but many users still experience difficulty in accessing this feature. Based on a survey of more than 130 respondents, 60% were unaware of the Clip Feature, 85% had never used it, and 75% had difficulty finding its location in the YouTube interface. This research aims to evaluate the user experience in accessing the Clip Feature on the YouTube website using the Eye Tracking method, as well as analyzing user attention patterns. Through the RealEye.io tool, the results show that the quality of the test data is very good, with an average E-T data integrity value of 90.33% and gaze on screen of 89.73%. Heatmaps and gaze plot analysis show that respondents' attention patterns tend to show confusion, especially when looking for the Clip feature. This is supported by the results of the attention & emotion graphs analysis, which overall show that the average attention level of respondents is at 0.318, with an increase in the emotion of surprise experienced by respondents more than the emotion of happy. Although the Clip Feature offers significant benefits, users still experience difficulties in accessing it, which results in a decreased user experience. This research is expected to provide new recommendations in improving the user experience of YouTube website, specifically to make the Clip feature more accessible and effective to use.]]>
<![CDATA[Development of Virtual Lab on Collision Dynamics Learning Object with Collision Algorithm Integration ]]>
<![CDATA[Yusupa, Ade]]>;
<![CDATA[Tarigan, Victor]]>;
<![CDATA[Sengkey, Daniel F.]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8765
<![CDATA[The objective of this study is to evaluate the efficacy of a Virtual Lab employing a collision algorithm in enhancing students' conceptual comprehension of collision dynamics, in comparison to traditional pedagogical approaches, within the context of physics education.The methodology employed in this study is as follows: The study employed an experimental approach, comprising a comparison between two groups: an experimental class that used the Virtual Lab, and a control class that utilised traditional teaching methods. Both groups were subjected to pre-tests to ascertain their existing level of understanding, after which post-tests were conducted to evaluate their knowledge after the instruction period. An independent t-test was employed to analyse the differences in post-test outcomes between the two groups.The results are as follows: The findings indicated a significant improvement in the experimental class's understanding, with an average increase from the pre-test to the post-test of 33.89%, in comparison to a 30.74% improvement in the control class. The results of the t-test demonstrated a statistically significant difference (t = 4.32, p < 0.05), indicating that the Virtual Lab was more effective in enhancing conceptual comprehension. In conclusion, the Virtual Lab, based on the collision algorithm, has been demonstrated to be an effective tool for teaching collision dynamics, offering a more interactive and engaging experience than traditional methods. This study highlights the potential of technology-based learning tools to enhance physics education and recommends further development of Virtual Labs with interactive features to increase accessibility and understanding in diverse educational environments.]]>
<![CDATA[Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method ]]>
<![CDATA[Pariyasto, Sofyan]]>;
<![CDATA[., Suryani]]>;
<![CDATA[Warongan, Vicky Arfeni]]>;
<![CDATA[Sari, Arini Vika]]>;
<![CDATA[Widiyanto, Wahyu Wijaya]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8776
<![CDATA[The high death rate caused by pneumonia and Covid-19 is still quite high. Based on data released by WHO, 14% of deaths in children under 5 years old are caused by pneumonia. One of the processes carried out to help the diagnosis process is to look at lung images using X-Ray images. To obtain information about normal lung X-Ray images, Pneumonia and Covid-19, calculations are carried out using the color difference in each pixel of the X-ray image. The calculation process will provide output in the form of numbers in units of 0 to 100. This is done to facilitate the process of identifying the similarity of each X-Ray image being compared. The research stages are carried out with stages starting from adjusting the image size, then by breaking down the pixel values of the two images being compared and the process of calculating the difference in value from each pixel with the same coordinates. After calculating a combination of 30,000 combinations using 300 x-ray images, the results obtained in the form of the level of similarity between normal x-ray images and pneumonia x-ray images are the highest with a similarity percentage of 80.06%. The combination of normal images and pneumonia images is 10,000 combinations using 100 normal x-ray images and 100 pneumonia x-ray images. Normal x-ray images and covid x-ray images have a similarity of 79.18%. The combination of normal images and covid images is 10,000 combinations. The combination uses 100 normal x-ray images and 100 covid x-ray images. Pneumonia x-ray images and covid x-ray images have the lowest similarity level of 78.87%. The combination of pneumonia x-ray images and covid x-ray images is 10,000 combinations. The data used in the combination are 100 pneumonia images and 100 covid images. From the test results, the information obtained was that Accuracy was worth 0.54, Precision was worth 0.54, Recall was worth 0.59 and F1-score was worth 0.56.]]>
<![CDATA[Knowledge Discovery Melalui Pemodelan Topik pada Ulasan Pengguna Aplikasi GoPartner Menggunakan BERTopic, LDA, dan NMF ]]>
<![CDATA[Pratiwi, Metti Detricia]]>;
<![CDATA[Tania, Ken Ditha]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8782
<![CDATA[Transportation and food delivery services are one of the driving sectors of the digital economy in Indonesia. The e-Conomy SEA 2023 report shows that the transportation and food delivery services sector experienced a decrease in GMV in 2023 by 8% from the previous year. The decline in GMV indicates a decrease in transaction value in the transportation and food delivery service sector. GoPartner is an application developed by GoTo to assist driver partners in carrying out various services in the gojek application which is one of the applications engaged in the transportation sector and food delivery services. Drivers as people who provide services directly to consumers are certainly one of the factors that influence customer behavior in using services. To find out the problems faced by drivers, this research conducts knowledge discovery through topic modeling on GoPartner application reviews using BERTopic, LDA, and NMF, each of these methods has a different approach. Based on the research results and the quality of the topics generated, BERTopic and LDA have better quality in analyzing GoPartner user reviews.]]>
<![CDATA[UX Analysis of the Virtual Tour 360 Application at Universitas Dr. Soetomo Campus ]]>
<![CDATA[Choiron, Achmad]]>;
<![CDATA[Hamidan, Rusdi]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8813
<![CDATA[This research investigates the effectiveness of the Virtual Tour 360 application implemented at Universitas Dr. Soetomo Campus, Surabaya, as a tool for enhancing prospective students' understanding and familiarity with campus facilities. Focusing on user experience (UX), this study evaluates key aspects such as the flow of the virtual tour, camera height for indoor and outdoor captures, image resolution and file size, and overall application size for online accessibility. User feedback highlights a high level of satisfaction, with 85.1% finding the application beneficial, especially on mobile devices, the preferred access method. The immersive 360-degree campus visualizations and user-friendly navigation have received positive responses, effectively providing a favorable first impression of the university. To further enrich user experience, optimizing mobile display quality and enhancing navigation features are recommended to offer a more comprehensive and interactive campus introduction.]]>
<![CDATA[Analysis of the Use of MTCNN and Landmark Technology to Improve the Accuracy of Facial Recognition on Official Documents ]]>
<![CDATA[Chandra, Ferri Rama]]>;
<![CDATA[Ngemba, Hajra Rasmita]]>;
<![CDATA[Hamid, Odai Amer]]>;
<![CDATA[Lapatta, Nouval Trezandy]]>;
<![CDATA[Hendra, Syaiful]]>;
<![CDATA[Nugraha, Deny Wiria]]>;
<![CDATA[Syahrullah, Syahrullah]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8814
<![CDATA[A face recognition system consists of two stages: face detection and face recognition. Detection of features such as eyes and mouth is important in facial image processing, especially for official documents such as identity cards. To ensure identification accuracy, this research applies facial landmark extraction technology and MTCNN (Multi-Task Cascaded Convolutional Neural Network). The purpose of this research is to evaluate the accuracy of MTCNN in detecting facial features at the Department of Population and Civil Registration (dukcapil) Palu City, using facial landmarks and waterfall methods as an application development methodology. The evaluation results show that MTCNN has high face recognition accuracy and good positioning ability regardless of what GPU in use as long have right CPU and System Operation. In comparison, the Viola-Jones algorithm is effective for high-speed applications, while SSD offers balanced performance with GPU device requirements for optimal performance. While MTCNN proved to be effective, challenges still exist, such as false positives and false negatives, especially in poor lighting conditions and extreme poses. Image and camera quality, including resolution and facial expression, also affects detection accuracy. These findings suggest that the application of MTCNN can improve face recognition accuracy for official documents, although it requires addressing existing challenges. With this technology, it is expected that errors in facial recognition can be minimized, resulting in more reliable data that meets the standards for issuing identity documents. This research contributes to the development of a more accurate and efficient face recognition system for personal identification applications.]]>
<![CDATA[Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction ]]>
<![CDATA[Wijaya, Ekaprana]]>;
<![CDATA[Soeleman, Moch. Arief]]>;
<![CDATA[Andono, Pulung Nurtantio]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8820
<![CDATA[This study aims to enhance price prediction accuracy using Artificial Neural Networks (ANN) by comparing three optimization methods: Stochastic Gradient Descent (SGD), Adam, and RMSprop. The research employs a systematic approach involving the design, training, and validation of ANN models optimized by these techniques. Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R Square are utilized to evaluate the effectiveness of each method. The results indicate that the Adam optimization method outperforms the others, achieving the lowest MSE of 0.0000503 and the lowest MAE of 0.0046, resulting in an impressive R Square value of 0.9989. Adam's superior performance can be attributed to its adaptive learning rate mechanism, which effectively adjusts to the high volatility and noise characteristic of stock price data, enabling the model to converge faster and more accurately. In comparison, SGD produced a higher MSE of 0.0001208 and MAE of 0.0075, while RMSprop yielded an MSE of 0.0000726 and MAE of 0.0059. These findings highlight Adam's ability to significantly enhance the predictive capabilities of ANN, particularly in dynamic and complex datasets, making it a preferred choice for this application. The novelty of this research lies not only in its comparative analysis of various optimization methods within the ANN framework but also in the exploration of unique ANN features and their application to a specific stock price prediction case study, providing deeper insights into the practical implications of optimization strategies. This study lays the groundwork for future research by suggesting the exploration of additional optimization algorithms and more complex neural network architectures to further improve prediction accuracy.]]>
<![CDATA[Application of Gated Recurrent Unit in Electroencephalogram (EEG)-Based Mental State Classification ]]>
<![CDATA[Giri, Gst. Ayu Vida Mastrika]]>;
<![CDATA[Sanjaya ER, Ngurah Agus]]>;
<![CDATA[Suhartana, I Ketut Gede]]>
<![CDATA[ Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025 ]]>
Publisher : <![CDATA[Politeknik Negeri Batam ]]>
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DOI: 10.30871/jaic.v9i1.8825
<![CDATA[The classification of mental states based on electroencephalogram (EEG) recordings has recently gained significant interest in cognitive monitoring and human-computer interaction fields. Due to high signal variability and sensitivity to noise, correct classification is still tricky, even with advances in the analysis of EEG signals. Among deep learning models, Gated Recurrent Unit (GRU) models have established great potential for sequential EEG data analysis. The applications of the GRUs are less reviewed in tasks concerning classification cases of mental states compared to hybrid and convolutional models. Based on this paper, we will propose a method for developing a model based on the GRU network trained with raw EEG data in the classification tasks of mental states of concentration and relaxed conditions. We analyzed 400 EEG recordings taken from 10 subjects within a controlled environment and collected using the Muse EEG Headband. The mean, standard deviation, skewness, kurtosis, power spectral density, zero-crossing rate, and root mean square were extracted as statistical features from the raw EEG data. After parameter tuning, the GRU-based model achieved an excellent average accuracy value of 95.94% and also yielded precision, recall, and F1-scores within the range of 0.95 to 0.97 over 5-fold cross-validation. This shows that GRU works well in classifying mental states based on the EEG data.]]>
