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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics International Journal of New Media Technology Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal ULTIMA Computing
Andre Rusli
Universitas Multimedia Nusantara
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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.
Rancang Bangun Aplikasi Face Tracking dan Filter Berdasarkan Raut Wajah Menggunakan Algoritma Fisher-Yates Berbasis iOS Malik Abdul Ghani; Andre Rusli; Ni Made Satvika Iswari
Ultima Computing : Jurnal Sistem Komputer Vol 11 No 1 (2019): Ultima Computing : Jurnal Sistem Komputer
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2134.646 KB) | DOI: 10.31937/sk.v11i1.1046

Abstract

Expressions of facial expressions in addition to providing important emotional indicators, are very important objects in our daily lives too. Real-time video processing on mobile devices is a hot topic and has a very broad application. Photos that have used the filter have 21% more possibilities to be seen and 45% more likely to be commented on by photo consumers. The use of the Fisher-Yates algorithm is used as a filter scrambler for each facial expression emotion. The application is made for the iOS operating system with the Swift programming language that utilizes the Core ML and Vision framework. Custom Vision is used as a tool for creating and training models. In making a model, this study uses a dataset from Cohn-Kanade AU-Coded Facial Expression Database and Karolinska Directed Emotional Faces. Custom Vision can provide performance result training and provide precision and recall values ​​for data that has been trained. The facial expression match with the model is determined by the confidence level value. The results of trials with Hedonic Motivation System Adoption Model method produce a percentage of pleasure in using the application (joy) of 79.39% of the users agree that the application provides joy.
Single object detection to support requirements modeling using faster R-CNN Nathanael Gilbert; Andre Rusli
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14838

Abstract

Requirements engineering (RE) is one of the most important phases of a software engineering project in which the foundation of a software product is laid, objectives and assumptions, functional and non-functional needs are analyzed and consolidated. Many modeling notations and tools are developed to model the information gathered in the RE process, one popular framework is the iStar 2.0. Despite the frameworks and notations that are introduced, many engineers still find that drawing the diagrams is easier done manually by hand. Problem arises when the corresponding diagram needs to be updated as requirements evolve. This research aims to kickstart the development of a modeling tool using Faster Region-based Convolutional Neural Network for single object detection and recognition of hand-drawn iStar 2.0 objects, Gleam grayscale, and Salt and Pepper noise to digitalize hand-drawn diagrams. The single object detection and recognition tool is evaluated and displays promising results of an overall accuracy and precision of 95%, 100% for recall, and 97.2% for the F-1 score.
User stories collection via interactive chatbot to support requirements gathering Ferliana Dwitama; Andre Rusli
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14866

Abstract

Nowadays, software products have become an essential part of human life. To build software, developers must have a good understanding of the requirements of the software. However, software developers tend to jumpstart system construction without having a clear and detailed understanding of the requirements. The user story concept is one of the practices of the requirements elicitation. This paper aims to present the work conducted to develop an Android chatbot application to support the requirements elicitation activity in software engineering, making the work less time-consuming and structured even for users not accustomed to requirements engineering. The chatbot uses Nazief & Adriani stemming algorithm to pre-process the natural language it receives from the users and artificial mark-up language (AIML) as the knowledge base to process the bot’s responses. A preliminary acceptance test based on the technology acceptance model results in an 83.03% score for users’ behavioral intention to use.
Co-Authors Dareen Halim Ferliana Dwitama Indah Noviasari Julio Christian Young Lulu Luthfiana Malik Abdul Ghani Nathanael Gilbert Ni Made Satvika Iswari Reza Setiabudi Samiaji Bintang Nusantara Seng Hansun Septiyan Mudhiya Sadid Shintia Trista Suryadibrata, Alethea Yehezkiel Gunawan