#NonDicevoSulSerio at SemEval-2018 Task 3: Exploiting Emojis and Affective Content for Irony Detection in English Tweets.

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DESIGNING UI/UX FOR A MEDITERRANEAN DIET APP TO MINIMIZE TYPE 2 DIABETES RISK

The industrial revolution has had a significant impact in health field, where the crucial disease in Indonesia is Type 2 Diabetes Mellitus (T2DM). One of the effective ways to minimize the increase in T2DM is by developing a diet application using Mediterranean diet Method. The Mediterranean diet involves controlling certain types of foods and beverages, characterized by high intake of fruits, vegetables, whole grains, nuts, legumes, moderate consumption of fish, low intake of red/processed meat, and low intake of high-fat dairy products. In this research and design, the Author applied User Centered Design (UCD) method, which encompasses User Interface (UI) and User Experience (UX). The author also applied the UX law (Hick’s Law, Law of Proximity, dan Aesthetic-usability Effect) on designing the user interface. This article included a A/B Testing and System Usability Scale (SUS) result that showed an interesting findings, where the design that achieved a higher success rate had a lower user satisfaction score and vice versa. However, both designs have an acceptable score and C grade

DEVELOPMENT OF SCHEDULING SYSTEM WITH GENETIC ALGORITHM IN WEBSITE-BASED SMK NEGERI 1 SINE

Scheduling is an information that has limited conditions that must be met. Preparation of the schedule will take quite a long time if it is done using conventional media such as writing on paper or books. Scheduling optimization is needed to provide effectiveness and efficiency so that the implementation of learning activities can run more optimally. The genetic algorithm approach method is used to get the optimum schedule. This algorithm produces the best combination for subject pairs and teaching teachers as a whole by determining the initial population and initializing the chromosomes, determining the fitness value, then carrying out crossover selection, and carrying out mutations to produce the best fitness value which will be used to determine the final value of scheduling. The results of the entire algorithm process are consistent with the original prediction data, and the same teacher is not scheduled to teach more than once at the same time. The results of the subject scheduling process using the genetic algorithm obtain a fairly good optimization in subject scheduling

Performance Improvement of Business Process Similarity Calculation using Word Sense Disambiguation

Similarity calculation between Business Process Models (BPM) has an important role in the process of managing BPM repository. One of its uses is to facilitate the searching process of a model in the repository. Similarity calculation between business processes is closely related with semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database, such as WordNet, to find the semantic meaning of words. The problem in WordNet is that this lexical database contains terms wich have more than one meaning or polysemous. Selecting the wrong meaning will  decrease the accuracy of similarity calculation process. In this study, we will try to improve the accuracy of similarity calculation of business processes using Word Sense Disambiguation (WSD). The main purpose is to eliminate the ambiguity of polysemous words before calculating the similarity value. WSD is performed by unsupervised methods based on the value of graph connectivity. Then, we used a lexical database that is focused in the business and industry field. The results from this study is able to achieve higher accuracy of the sense selection process for terms especially terms that are related to business and industrial domains. It will also increase the accuracy of similarity value calculation between the business process models

B-BabelNet: Business-Specific Lexical Database for Improving Semantic Analysis of Business Process Models

Similarity calculation between business process models has an important role in managing repository of business process model. One of its uses is to facilitate the searching process of models in the repository. Business process similarity is closely related to semantic string similarity. Semantic string similarity is usually performed by utilizing a lexical database such as WordNet to find the semantic meaning of the word. The activity name of the business process uses terms that specifically related to the business field. However, most of the terms in business domain are not available in WordNet. This case would decrease the semantic analysis quality of business process model. Therefore, this study would try to improve semantic analysis of business process model. We present a new lexical database called B-BabelNet. B-BabelNet is a lexical database built by using the same method in BabelNet. We attempt to map the Wikipedia page to WordNet database but only focus on the word related to the domain of business. Also, to enrich the vocabulary in the business domain, we also use terms in the business-specific online dictionary (businessdictionary.com). We utilize this database to do word sense disambiguation process on business process model activity’s terms. The result from this study shows that the database can increase the accuracy of the word sense disambiguation process especially in particular terms related to the business and industrial domains

Stance Classification for Rumour Analysis in Twitter: Exploiting Affective Information and Conversation Structure

Analysing how people react to rumours associated with news in social media is an important task to prevent the spreading of misinformation, which is nowadays widely recognized as a dangerous tendency. In social media conversations, users show different stances and attitudes towards rumourous stories. Some users take a definite stance, supporting or denying the rumour at issue, while others just comment it, or ask for additional evidence related to the veracity of the rumour. On this line, a new shared task has been proposed at SemEval-2017 (Task 8, SubTask A), which is focused on rumour stance classification in English tweets. The goal is predicting user stance towards emerging rumours in Twitter, in terms of supporting, denying, querying, or commenting the original rumour, looking at the conversation threads originated by the rumour. This paper describes a new approach to this task, where the use of conversation-based and affective-based features, covering different facets of affect, has been explored. Our classification model outperforms the best-performing systems for stance classification at SemEval-2017 Task 8, showing the effectiveness of the feature set proposed.Comment: To appear in Proceedings of the 2nd International Workshop on Rumours and Deception in Social Media (RDSM), co-located with CIKM 2018, Turin, Italy, October 201