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INDONESIA
JOURNAL OF APPLIED INFORMATICS AND COMPUTING
Published by Politeknik Negeri Batam
ISSN : -     EISSN : 25486861     DOI : 10.3087
Core Subject : Science,
Computer Science & IT
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.
Arjuna Subject : -
Articles 695 Documents
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Evaluation of Telecommunication Customer Churn Classification with SMOTE Using Random Forest and XGBoost Algorithms Wakhidah, Lisa Nusrotul; Zyen, Akhmad Khanif; Wahono, Buang Budi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8740

Abstract

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.
User Experience Evaluation of YouTube Website Using Eye Tracking Method Larasati, Salsabila; Putra, Pacu; Oktadini, Nabila Rizky; Meiriza, Allsela; Sevtiyuni, Putri Eka
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8743

Abstract

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.
Comparison of Machine Learning Models for Heart Disease Classification with Web-Based Implementation Ramadhan, Angga Ramda; Saefulloh, Nandang; Utami, Nisa; Diana, Muji; Utomo, Abiyyu Aji Prasetyo; Wicaksana, Yusuf Eka
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8744

Abstract

Heart disease has become one of the most concerning diseases in Indonesia according to research published in 2018 by the Health Ministry of Indonesia. Based on said research, 15 out of 1000 Indonesians suffer from heart disease. Furthermore, according to data published by the Health Ministry of Indonesia, 3 million premature deaths (under 60 years old) occurred in 2013 due to heart disease. Therefore, this research aims to develop a web-based system designed to aid health workers in screening for heart diseases and producing early diagnosis. In developing this system, 5 models were evaluated based on performance and the model with the best metrics was selected to be used in the final system. These models were: Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, and K-Nearest Neighbours. SMOTE and ADASYN was also used to deal with imbalanced data, and the resulting balanced data was used as additional training scenarios in order to compare the result with algorithms trained using imbalanced data. Cross validation, accuracy, precision, recall, f1-score, and ROC with AUC were set as evaluation metrics. Results show that Random Forest trained with data balanced using ADASYN achieved the highest AUC score of 0.920. Meanwhile, Logistic Regression scored lowest with an AUC score of 0.500. These results indicate that Random Forest is the most suitable for this system Therefore, Random Forest was selected as the algorithm to be used in the final system. Furthermore, this system has been tested successfully using the black-box method and is ready to be implemented.
Innovation in Digitalization of UI/UX Design with User Centered Design to Increase Customer Satisfaction Felicia, Yohana; Ibrahim, Ali; Indah, Dwi Rosa; Seprina, Iin
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8749

Abstract

In the era of rapid digitalization, the economic sector, especially small and medium enterprises, is required to adapt to the development of digitalization. SDS YPMM Cooperative is one example of a small business that still relies on traditional systems in its business processes. To overcome this problem, a website-based User Interface (UI) and User Experience (UX) design was carried out to digitize the SDS YPMM cooperative business process. In the design, the User Centered Design method is used which focuses on user needs. This method is done by Specify the context of use, specify user and organizational requirements, produce design solutions, and evaluate design against user requirements. At the evaluation stage, the System Usability Scale (SUS) was used to measure the level of usability of the resulting design. The evaluation results obtained 179 respondents out of 533 population for the user interface showed a score of 84.97, which falls into the "acceptable" category and a rating of B or "Excellent". Meanwhile, the admin interface received 5 respondents from 5 populations and scored 93, which falls into the "acceptable" category and an A or "Best Imaginable" rating. These findings indicate that the UI/UX design is highly accepted by both categories of users, thereby improving operational efficiency and shopping experience, which creates satisfaction with SDS YPMM cooperative services.
Development of Virtual Lab on Collision Dynamics Learning Object with Collision Algorithm Integration Yusupa, Ade; Tarigan, Victor; Sengkey, Daniel F.
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8765

Abstract

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.
Implementation of MobileNet Architecture for Skin Cancer Disease Classification Faudyta, Haniifa Aliila; Sinaga, Jesica Trivena; Subhiyakto, Egia Rosi
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8771

Abstract

As the number of occurrences of skin cancer increases year, it becomes more and more crucial to identify the disease accurately and effectively. This study aims to implement and evaluate the MobileNet architecture for classifying nine types of skin lesions using the ISIC 2020 dataset and to compare MobileNet's performance with other CNN architectures, such as VGG-16 and LeNet, in terms of accuracy and computational efficiency. The study also investigates the impact of image preprocessing on model accuracy. The methodology comprises data collection, preprocessing, and model development, leveraging transfer learning from MobileNet pre-trained on ImageNet. Data preprocessing involves resizing images to 224 x 224 pixels and normalizing pixel values. To augment the dataset, techniques such as rotation, zooming, horizontal flipping, and brightness and contrast adjustment are applied. To address class imbalance, oversampling is used to obtain 500 images per class. The model architecture includes Global Average Pooling, a Dense layer with 1024 units and ReLU activation, and a Dropout layer with a 0.2 probability. Various training scenarios with batch sizes (8, 16, 32, 64) and learning rates (0.001, 0.0001) are evaluated, incorporating dropout and ReLU activations. The optimal performance was achieved with oversampling, dropout, and a learning rate of 0.0001, yielding a training accuracy of 99.64% and a validation accuracy of 86.89% after oversampling, resulting in 3,600 training and 900 validation images with an 80:20 data split. The results suggest overfitting due to dataset limitations. Future work should focus on fine-tuning and ensemble methods to improve validation performance.
Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method Pariyasto, Sofyan; ., Suryani; Warongan, Vicky Arfeni; Sari, Arini Vika; Widiyanto, Wahyu Wijaya
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8776

Abstract

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.
Knowledge Discovery Melalui Pemodelan Topik pada Ulasan Pengguna Aplikasi GoPartner Menggunakan BERTopic, LDA, dan NMF Pratiwi, Metti Detricia; Tania, Ken Ditha
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8782

Abstract

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.
UX Analysis of the Virtual Tour 360 Application at Universitas Dr. Soetomo Campus Choiron, Achmad; Hamidan, Rusdi
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8813

Abstract

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.
Analysis of the Use of MTCNN and Landmark Technology to Improve the Accuracy of Facial Recognition on Official Documents Chandra, Ferri Rama; Ngemba, Hajra Rasmita; Hamid, Odai Amer; Lapatta, Nouval Trezandy; Hendra, Syaiful; Nugraha, Deny Wiria; Syahrullah, Syahrullah
Journal of Applied Informatics and Computing Vol. 9 No. 1 (2025): February 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i1.8814

Abstract

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.

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