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All Journal International Journal of New Media Technology Jurnal ULTIMATICS
Julio Christian Young
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (575.434 KB) | DOI: 10.31937/ijnmt.v1i1.1485

Abstract

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.
Marketing Communication Menggunakan Augmented Reality pada Mobile Platform Julio Christian Young
Ultimatics : Jurnal Teknik Informatika Vol 7 No 1 (2015): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (431.407 KB) | DOI: 10.31937/ti.v7i1.344

Abstract

Augmented Reality is a technology that can project objects from the virtual world to the real world. Augmented Reality continues to be developed so it can be easy to implement into various devices. However, the device must have a camera, VGA card, and the ability to process data that is high enough to be able to process and projecting graphical data that captured by the camera and displayed to the screen. Marker-based Augmented Reality is still better than Markerless Augmented Reality due to several issues such as disturbances in the geomagnetic sensor that is used to map the Y axis and Z device that belongs to the user.
Visualisasi Algoritma sebagai Sarana Pembelajaran K-Means Clustering Alethea Suryadibrata; Julio Christian Young
Ultimatics : Jurnal Teknik Informatika Vol 12 No 1 (2020): 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 (553.166 KB) | DOI: 10.31937/ti.v12i1.1523

Abstract

Algorithm Visualization (AV) is often used in computer science to represents how an algorithm works. Educators believe that visualization can help students to learn difficult algorithms. In this paper, we put our interest in visualizing one of Machine Learning (ML) algorithms. ML algorithms are used in various fields. Some of the algorithms are used to classify, predict, or cluster data. Unfortunately, many students find that ML algorithms are hard to learn since some of these algorithms include complicated mathematical equations. We hope this research can help computer science students to understand K-Means Clustering in an easier way.
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.
Implementasi Algoritma Complement dan Multinomial Naïve Bayes Classifier Pada Klasifikasi Kategori Berita Media Online Muhammad Naufal Randhika; Julio Christian Young; Alethea Suryadibrata; Hadian Mandala
Ultimatics : Jurnal Teknik Informatika Vol 13 No 1 (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.v13i1.1921

Abstract

Perkembangan teknologi dan penyebaran informasi di internet terus mengalami peningkatan. Salah satu bentuk informasi yang jumlahnya terus bertambah adalah berita. Media cetak dan elektronik yang kini telah dikemas dalam bentuk digital atau sering dikenal dengan portal berita online atau media online. PT Merah Putih Media merupakan media berita online. Berita yang disampaikan terdiri dari tiga kategori mulai dari berita tentang Indonesia, Hiburan dan Gaya Hidup, serta Olahraga. Namun, pembagian artikel berita ke dalam kategori dilakukan secara manual oleh kepala redaksi jurnalis. Text Mining adalah salah satu teknik yang dapat digunakan untuk melakukan klasifikasi sebuah dokumen. Pada penelitian ini dilakukan klasifikasi kategori otomatis dengan algoritma Multinomial Naïve Bayes, Complement Naïve Bayes, dan gabungan kedua model. Model yang memiliki performa terbaik dinilai dari metrik F1-Score dengan jumlah pembagian data latih dan data uji sebanyak 80:20, diperoleh keberhasilan performa sebesar 90,13% F1-Score.
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

Abstract

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.
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 Alethea Suryadibrata Alethea Suryadibrata Andre Rusli Andre Rusli Hadian Mandala Lulu Luthfiana Muhammad Naufal Randhika Samiaji Bintang Nusantara Septiyan Mudhiya Sadid Yehezkiel Gunawan