No abstract

No abstract

No abstract

User review of mobile application is an valuable data that can be used by developer to improve their application or to build similar application. User can give feedback such as reporting errors, asking for new or improved feature, explain their experience of using certain feature and also praise or dispraise. User review or opinion data is very large in amount and difficult to analyze. It is time consuming and labour expensive to do it manually. Recent study has tried to extract product feature using word collocation. In this work, we try to improve the aspect extraction process by using only informative data. We took user opinion of 3 mobile application from application distribution platform. The experiment result indicate that our approach is able to improve the performance of collocation finding method.. General TermsInformation retrieval

No abstract

No abstract

This study aims to provide an overview of the current research on detecting abusive language in Indonesian social media. The study examines existing datasets, methods, and challenges and opportunities in this field. The research found that most existing datasets for detecting abusive language were collected from social media platforms such as Twitter, Facebook, and Instagram, with Twitter being the most commonly used source. The study also found that hate speech is the most researched type of abusive language. Various models, including traditional machine learning and deep learning approaches, have been implemented for this task, with deep learning models showing more competitive results. However, the use of transformer-based models is less popular in Indonesian hate speech studies. The study also emphasizes the importance of exploring more diverse phenomena, such as islamophobia and political hate speech. Additionally, the study suggests crowdsourcing as a potential solution for the annotation approach for labeling datasets. Furthermore, it encourages researchers to consider code-mixing issues in abusive language datasets in Indonesia, as it could improve the overall model performance for detecting abusive language in Indonesian data. The study also suggests that the lack of effective regulations and the anonymity afforded to users on most social networking sites, as well as the increasing number of Twitter users in Indonesia, have contributed to the rising prevalence of hate speech in Indonesian social media. The study also notes the importance of considering code-mixed language, out-of-vocabulary words, grammatical errors, and limited context when working with social media data.

Code Smell mengacu pada konsep mengenai pola atau aspek desain pada sistem perangkat lunak yang dapat menimbulkan masalah dalam proses pengembangan, penggunaan, atau perawatan sebagai dampak dari implementasi yang buruk dari desain perangkat lunak. Code Smell dapat menurunkan aspek understandability dan maintainability program. Program yang mengandung God Class juga cenderung lebih sulit untuk dirawat dibandingkan dengan program yang sama namun tidak mengandung God Class. God Class atau dapat juga disebut Blob merupakan sebuah kelas yang terlalu banyak berisi fungsionalitas didalamnya. Kelas-kelas seperti ini mengolah dan mengakses banyak informasi sehingga sulit dipahami. Pada penelitian ini akan dibahas metodemetode untuk mendeteksi adanya God Class. Selain itu juga dibandingkan kelebihan serta kekurangan metodemetode yang telah dianalisa. Dari pencarian literatur yang dilakukan, didapatkan 3 buah metode, metode pertama menggunakan cara deteksi dalam bentuk rule card, metode kedua menggunakan rule card dan catatan histori perubahan pada sebuah perangkat lunak, dan metode ketiga adalah pendeteksian berdasarkan contoh kelas yang dideteksi manual sebagai kecacatan perangkat lunak. Dari ketiga metode tersebut, metode ketiga dinilai sebagai yang terbaik berdasarkan nilai presisi dan recall-nya.