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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Nuansa Informatika
Hartono, Sherren
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Computer Science & IT

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Cinema e-Ticket Application Design and Usability Evaluation Using SUS Haryanti, Marta Lenah; Fraderic, Fedelis; Hartono, Sherren; Salim, Jonathan
NUANSA INFORMATIKA Vol. 19 No. 1 (2025): Nuansa Informatika 19.1 Januari 2025
Publisher : FKOM UNIKU

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25134/ilkom.v19i1.296

Abstract

The internet has become an essential need in the digital era, facilitating various activities, including e-commerce, supported by advances in Information and Communication Technology (ICT). One form of its implementation is an online cinema ticket booking application, which offers convenience in choosing showtimes, seats, and payments. This study aims to analyze user needs in online cinema ticket booking and design an interface mobile application design that is evaluated using the System Usability Scale (SUS) method. The approach used is User-Centered Design (UCD), which focuses on user experience. The results of the study showed that the evaluation of usability testing with SUS from 30 respondents produced an average score of 81.583 (grade A), which indicates a very good level of application usability. This study recommends the application of the SUS method and the development of a UCD-based design to improve the user experience of the cinema e-ticket application, with the potential for further optimization through variations in evaluation methods and increasing the number of respondents
Breast Cancer Detection using Decision Tree and Random Forest Kaunang, Fergie Joanda; Hakim, Bhustomy; Fraderic, Fedelis; Hartono, Sherren; Mulyanto, Andrew Kristanto
Journal of Applied Informatics and Computing Vol. 9 No. 2 (2025): April 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i2.9073

Abstract

Cancer is one of the most challenging diseases to cure and is a chronic condition that contributes significantly to global mortality. With advancements in artificial intelligence (AI) technology, AI-integrated systems can provide quick and accurate diagnoses based on collected medical data. By leveraging machine learning techniques, this study aims to compare the performance of two models using the Decision Tree (DT) and Random Forest (RF) algorithms on routine blood test data. The research process involves data preprocessing techniques such as handling missing values, detecting outliers, and feature selection, followed by applying the bootstrap aggregating technique to enhance model performance. Feature selection is used to identify the most significant features in the data that contribute to cancer detection. Using the KBest feature selection technique, the study found that the features age, BMI, leptin, adiponectin, and MCP-1 had the highest correlation with the target variable. The resulting models were evaluated to compare the performance of each algorithm. The evaluation results showed that the RF algorithm outperformed DT, achieving an accuracy of 89.65% on the processed dataset using the bootstrap technique, compared to DT's accuracy of 80.17%. Additionally, the RF algorithm demonstrated superior metric values, including a precision of 91.66% and an F1-score of 87.12%. This study concludes that the RF algorithm is more effective than DT for detecting cancer in limited datasets, especially when used with the bootstrap technique. The findings are expected to support the development of decision support systems in healthcare services for more accurate early cancer detection.
Co-Authors Bhustomy Hakim Fraderic, Fedelis Kaunang, Fergie Joanda Marta Lenah Haryanti Mulyanto, Andrew Kristanto Salim, Jonathan