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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

Author-ID : 2453412

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

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Articles

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

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

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.

Enhancing text classification performance by preprocessing misspelled words in Indonesian language Reza Setiabudi; Ni Made Satvika Iswari; Andre Rusli
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 4: August 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i4.20369

Supervised learning using shallow machine learning methods is still a popular method in processing text, despite the rapidly advancing sector of unsupervised methodologies using deep learning. Supervised text classification for application user feedback sentiments in Indonesian Language is one of the applications which is quite popular in both the research community and industry. However, due to the nature of shallow machine learning approaches, various text preprocessing techniques are required to clean the input data. This research aims to implement and evaluate the role of Levenshtein distance algorithm in detecting and preprocessing misspelled words in Indonesian language, before the text data is then used to train a user feedback sentiment classification model using multinomial Naïve Bayes. This research experimented with various evaluation scenarios, and found that preprocessing misspelled words in Indonesian language using the Levenshtein distance algorithm could be useful and showed a promising 8.2% increase on the accuracy of the model’s ability to classify user feedback text according to their sentiments.

HistoriAR: Experience Indonesian history through interactive game and augmented reality Shintia Trista; Andre Rusli
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

History has a vital function in shaping the personality of the nation, the quality of humans, and the people of a country. However, one factor that influences learning behavior that could be improved is the students’ interest in learning. The use of game-based learning has been proven to be effective in making activities to be more fun to do. Moreover, augmented reality technology also shows enormous potential in the world of education. This research developed a game-based historical learning application using augmented reality to enhance user experience in learning history. The application is built using the Unity Game Engine and Vuforia. Furthermore, the application was tested and evaluated by measuring the perceived usefulness and perceived ease of use following the guidance in the Technology Acceptance Model. The result shows that the application achieves 89.5% for perceived usefulness and 86.33% for perceived ease of use.

A Comparison of Traditional Machine Learning Approaches for Supervised Feedback Classification in Bahasa Indonesia Andre Rusli; Alethea Suryadibrata; Samiaji Bintang Nusantara; Julio Christian Young
IJNMT (International Journal of New Media Technology) Vol 7 No 1 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

The advancement of machine learning and natural language processing techniques hold essential opportunities to improve the existing software engineering activities, including the requirements engineering activity. Instead of manually reading all submitted user feedback to understand the evolving requirements of their product, developers could use the help of an automatic text classification program to reduce the required effort. Many supervised machine learning approaches have already been used in many fields of text classification and show promising results in terms of performance. This paper aims to implement NLP techniques for the basic text preprocessing, which then are followed by traditional (non-deep learning) machine learning classification algorithms, which are the Logistics Regression, Decision Tree, Multinomial Naïve Bayes, K-Nearest Neighbors, Linear SVC, and Random Forest classifier. Finally, the performance of each algorithm to classify the feedback in our dataset into several categories is evaluated using three F1 Score metrics, the macro-, micro-, and weighted-average F1 Score. Results show that generally, Logistics Regression is the most suitable classifier in most cases, followed by Linear SVC. However, the performance gap is not large, and with different configurations and requirements, other classifiers could perform equally or even better.

Indoor Positioning berbasis Wi-Fi untuk Notifikasi Pintar Berbasis Lokasi Dareen Halim; Andre Rusli
IJNMT (International Journal of New Media Technology) Vol 7 No 1 (2020): IJNMT (International Journal of New Media Technology)
Publisher : Universitas Multimedia Nusantara

Indoor localization has been researched widely in the recent two decades due to its wide range of applications such as navigation, elder care, advertising. This work presents a utilization of indoor positioning for a location-based smart notification purposed, deployed in a meeting room booking application. Our localization method is based on Wi-Fi fingerprint, tailored to our application needs to alleviate the drawback of its tedious offline phase. The initial implementation is done with limited number of recorded locations. The testing shows that the meeting room booking application works well, with the localization detecting user’s location correctly aside from when poor signal condition occurs.

Penerapan Algoritma ACO untuk Penjadwalan Kuliah Pengganti pada Perguruan Tinggi (Studi Kasus: Program Studi Informatika, Universitas Multimedia Nusantara) Indah Noviasari; Andre Rusli; Seng Hansun
ULTIMA InfoSys Vol 9 No 2 (2018): Ultima InfoSys : Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Students and scheduling are both essential parts in a higher educational institution. However, after schedules are arranged and students has agreed to them, there are some occasions that can occur beyond the control of the university or lecturer which require the courses to be cancelled and arranged for replacement course schedules. At Universitas Multimedia Nusantara, an agreement between lecturers and students manually every time to establish a replacement course. The agreement consists of a replacement date and time that will be registered to the division of BAAK UMN which then enter the new schedule to the system. In this study, Ant Colony Optimization algorithm is implemented for scheduling replacement courses to make it easier and less time consuming. The Ant Colony Optimization (ACO) algorithm is chosen because it is proven to be effective when implemented to many scheduling problems. Result shows that ACO could enhance the scheduling system in Universitas Multimedia Nusantara, which specifically tested on the Department of Informatics replacement course scheduling system. Furthermore, the newly built system has also been tested by several lecturers of Informatics UMN with a good level of perceived usefulness and perceived ease of use. Keywords—scheduling system, replacement course, Universitas Multimedia Nusantara, Ant Colony Optimization

FastText Word Embedding and Random Forest Classifier for User Feedback Sentiment Classification in Bahasa Indonesia Yehezkiel Gunawan; 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.v13i2.2124

User feedback nowadays become a platform for software developer to identify and understand user requirements, preferences, and user’s complaints. It is important for the developer to identify the problem that exist in user feedback. According to software growth, user amount also growth. Read and classify one by one manually are wasting time and energy. As the solution for the problem, sentiment analysis system using Random Forest Classifier which use word embedding as the feature extraction is made to help to classify which feedback is positive, neutral, or negative. Random Forest Algorithm is chosen because it gives the best performance, even its need the larger resources. Furthermore, with word embedding, the words which has semantic or syntactic similarities will be detected. Word embedding does not need stemming and stop word removal, so the context of the sentences keep remains. This research is made to implement word embedding to classify sentiment of user feedbacks using Random Forest Classifier. 70.27% accuracy, 80% precision, 54 recall and 54% F1 score is reached when BYU dataset (200 dimension) as embedding dataset with the train and test ratio 80:20.

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

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.

Co-Authors Alethea Suryadibrata Dareen Halim Ferliana Dwitama Indah Noviasari Julio Christian Young Malik Abdul Ghani Nathanael Gilbert Ni Made Satvika Iswari Reza Setiabudi Samiaji Bintang Nusantara Seng Hansun Shintia Trista Yehezkiel Gunawan

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