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Andre Rusli
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Ekstraksi Kebutuhan Aplikasi Berdasarkan Feedback Pengguna Menggunakan Naïve Bayes dan Gamifikasi Andre Rusli
Ultimatics : Jurnal Teknik Informatika Vol 10 No 1 (2018): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2251.751 KB) | DOI: 10.31937/ti.v10i1.778

Abstract

Requirements engineering is a series of activities which aims to elicit, analyze, evaluate, and document the requirements of a system that is being developed. The activities do not stop after the product is deployed but continues as the users use the product and provide feedbacks to the system and matter how decent the functionalities of a product are, if it cannot address the correct problem and/or opportunities of the stakeholders or users, the product cannot be considered useful. That being said, not all stakeholders are willing to participate in providing useful feedbacks to improve the product after deployment, for many reasons. Gamification is considered as an opportunity that can be utilized to improve the motivation of user to use a product by implementing game design elements into an existing software product, thus increasing user participation to contribute in providing useful feedbacks and evolving requirements of a software product. This research proposes a model to support engineers in motivating users to provide feedbacks using gamification and also Naïve Bayes Classifier to classify user feedbacks into categories needed by the developer to extract the requirements stated in the feedback, such as bug reports, feature request, user experiences, etc. Kata Kunci—requirements engineering, gamification, Naïve Bayes, user feedback
Implementasi Algoritma Support Vector Machine dan Chi Square untuk Analisis Sentimen User Feedback Aplikasi Lulu Luthfiana; Julio Christian Young; Andre Rusli
Ultimatics : Jurnal Teknik Informatika Vol 12 No 2 (2020): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v12i2.1828

Abstract

In order to adapt with evolving requirements and perform continuous software maintenance, it is essential for the software developers to understand the content of user feedback. User feedback such as bug report could provide so much information regarding the product from user’s point of view, especially parts that need improvements. However, it is often difficult to read all the feedback for products with enormous number of users as manually reading and analyzing each feedback could take too much time and effort. This research aims to develop a model for automatic feedback classification by implementing Support Vector Machine for the classifier’s algorithm and Chi-square method for feature selection. The model is developed using Python programming language and is then evaluated under different scenarios in order to measure its performance. Using a ratio of training and testing set of 80:20, our model achieved 77% accuracy, 50% precision, 55% recall, and 73% F1-score with 6.63 critical value and C=100 and gamma 0.001 as the SVM hyperparameters.
Spam Filtering On User Feedback Via Text Classification Using Multinomial Naïve Bayes And TF-IDF Septiyan Mudhiya Sadid; Julio Christian Young; Andre Rusli
Ultimatics : Jurnal Teknik Informatika Vol 13 No 2 (2021): Ultimatics : Jurnal Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31937/ti.v13i2.2149

Abstract

User feedback could give developer an information on what should be fixed or should be improved. But there are many user feedback that are actually spam. In user feedback, spam contents are more likely to be an inappropriate feedback, a feedback that is not actually a feedback, just some random comment or even a question. Reading and choosing feedback manually could be costly, especially in terms of time and energy. Therefore, this research focuses in building a spam filtering model using Multinomial Naïve Bayes that implement a TF/IDF approach to detect spam automatically. For text classification, Multinomial Naïve Bayes proved on having better speed and having good performance. With TF/IDF, word that highly occurred in many documents has less impact than other so it could help increasing performance from imbalanced dataset. This research aims to implement Multinomial Naïve Bayes for spam filtering in user feedback and to measure performance of the model. Best performance of this classifier was obtained when using up-sampling method and typo corrector with 70:30 ratio of train and test set resulting in 89.25% for accuracy, 45% for precision, 56% for recall, and 50% for F1-Score.
Co-Authors Julio Christian Young Lulu Luthfiana Septiyan Mudhiya Sadid