1 10 4 https://repository.horizon.ac.id/files/original/fd6776b03f0d40a7bd717ef019feaf03.pdf c98019ef602634a7423c4ab5b3469ac7 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 4 (2021) JULY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Comparative Study of Gameplay of Different Sets of Players in an Engineering Mapping Game Subject The topic of the resource Educational games, Engineering games, Mapping, Games with a purpose, UNTANGLED III Description An account of the resource Educators in the STEM (science, technology, engineering, and mathematics) field are constantly employing different tools to make the process of education streamlined and fun. The digital gaming platform also called e-gaming platform has evolved as one of the key tools to make STEM education more accessible to students. UNTANGLED III is such an e-gaming platform that is based on STEM concepts and aims to bring in players from all educational backgrounds under a common platform. The data obtained from the game gave us insights on how males and females play the game. It has answered whether there are any significant differences in the gameplay strategies between males and females. The data pertaining to the types of puzzles that players, from both genders, chose and played, was also obtained. Males and females had no stark differences in the strategies that they used in solving the puzzles. They used similar kinds of moves and in fact solved similar kinds of puzzles of similar difficulty levels. During their gameplay sessions, both the males and the females visualized similar patterns in the puzzles as evident in their final solution. The performance of players from both the genders, based on the gameplay data was at par. Suggestions obtained from the current players and outreach events hold the key to increasing the overall participation in the game. Creator An entity primarily responsible for making the resource Anirban Chakraborty (Student Member, IEEE)*, Zachary Simpson, Rani Deepika Balavendran Joseph (Member, IEEE), and Gayatri Mehta (Senior Member, IEEE) Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource July 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/310797d048716cfd1dd1a141b01db273.pdf 776401de9618abf5526643404effed1d Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 4 (2021) JULY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Systematic Literature Review of Hausa Natural Language Processing Subject The topic of the resource Machine Learning, Hausa Language Processing, Natural Language Processing, Artificial Intelligence, Speech Recognition Description An account of the resource The processing of natural languages is an area of computer science that has gained growing attention recently. NLP helps computers recognize, in other words, the ways in which people use their language. NLP research, however, has been performed predominantly on languages with abundant quantities of annotated data, such as English, French, German and Arabic. While the Hausa Language is Africa's second most commonly used language, only a few studies have so far focused on Hausa Natural Language Processing (HNLP). In this research paper, using a keyword index and article title search, we present a systematic analysis of the current literature applicable to HNLP in the Google Scholar database from 2015 to June 2020. A very few research papers on HNLP research, especially in areas such as part-of-speech tagging (POS), Name Entity Recognition (NER), Words Embedding, Speech Recognition and Machine Translation, have just recently been released. This is due to the fact that for training intelligent models, NLP depends on a huge amount of human-annotated data. HNLP is now attracting researchers' attention after extensive research on NLP in English and other languages has been performed. The key objectives of this paper are to promote research, to define likely areas for future studies in the HNLP, and to assist in the creation of further examinations by researchers for relevant studies. Creator An entity primarily responsible for making the resource Rufai Yusuf Zakari, Zaharaddeen Karami Lawal, Idris Abdulmumin Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource July 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/115839499819c877f39044b39fa45701.pdf 4c8f5a8795fcf1363afd66cce2e3550a Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 4 (2021) JULY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource A Vision of the Internet of Things: A Review of Critical Challenges Subject The topic of the resource The Internet of Things, Challenges Description An account of the resource Today, Information Communication Technology has brought many benefits to have a better life. Meanwhile, the concept of the Internet of Things (IoT), which has transformed the traditional lifestyle into a modern lifestyle and is growing rapidly, is of great importance. This research deals with the critical challenges of IoT. Although not much time has passed since the advent of the concept of the IoT, today the Internet of Things has faced a great deal of complexity in the industry, which requires in-depth studies to realise its potential and challenges. This study introduces and examines IoT challenges including security and privacy, scalability, interoperability, mobility, protocol & standardisation, and energy consumption. In this study, the relationship between these challenges has been clearly defined. Finally, based on the research, some main challenges or sub-challenges considered for these challenges. Creator An entity primarily responsible for making the resource Zahra Valadkhani, Branka Rodić, Farhad Lotfi Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource July 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text https://repository.horizon.ac.id/files/original/16423591cd51a57444b7e67c61d388a9.pdf d3c182e26ace5c9c4a5471790147eb7b Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource VOL. 10 NO. 4 (2021) JULY 2021 Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource The Accuracy Improvement of Text Mining Classification on Hospital Review through The Alteration in The Preprocessing Stage Subject The topic of the resource accuracy improvement; preprocessing; text classification Description An account of the resource Sentiment analysis is a part of text mining used to dig up information from a sentence or document. This study focuses on text classification for the purpose of a sentiment analysis on hospital review by customers through criticism and suggestion on Google Maps Review. The data of texts collected still contain a lot of nonstandard words. These nonstandard words cause problem in the preprocessing stage. Thus, the selection and combination of techniques in the preprocessing stage emerge as something crucial for the accuracy improvement in the computation of machine learning. However, not all of the techniques in the preprocessing stage can contribute to improve the accuracy on classification machine. The objective of this study is to improve the accuracy of classification model on hospital review by customers for a sentiment analysis modeling. Through the implementation of the preprocessing technique combination, it can produce a highly accurate classification model. This study experimented with several preprocessing techniques: (1) tokenization, (2) case folding, (3) stop words removal, (4) stemming, and (5) removing punctuation and number. The experiment was done by adding the preprocessing methods: (1) spelling correction and (2) Slang. The result shows that spelling correction and Slang method can assist for improving the accuracy value. Furthermore, the selection of suitable preprocessing technique combination can fasten the training process to produce the more ideal text classification model. Creator An entity primarily responsible for making the resource Triyas Hevianto Saputro, Arief Hermawan Source A related resource from which the described resource is derived www.ijcit.com Date A point or period of time associated with an event in the lifecycle of the resource July 2021 Contributor An entity responsible for making contributions to the resource peri irawan Format The file format, physical medium, or dimensions of the resource pdf Language A language of the resource english Type The nature or genre of the resource text