Social media platforms have enabled users to share their thoughts, ideas, and opinions on different subject matters and meanwhile generate lots of information which can be adopted to understand people's emotion towards certain products. This information can be effectively applied for Aspect Category Detection (ACD). Similarly, people's emotions and recommendation based Artificial Intelligence (AI) powered systems are in trend to assist vendors and other customers to improve their standards. These systems have applications in all sorts of business available on multiple platforms. However, the current conventional approaches fail in providing promising results. Thus, in this paper, we propose novel convolutional attention based bidirectional modified LSTM by combining the techniques of the next word, next sequence, and pattern prediction with ACD. The proposed approach extracts significant features from public reviews to detect entity and attributes pair, which are treated as a sequence or pattern from a given opinion. Next, we trained our word vectors with the proposed model to strengthen the ACD process. Empirically, we compare the approach with the state-of-theart ACD models that use SemEval-2015, SemEval-2016, and SentiHood datasets. Results show that the proposed approach effectively achieve 78.96% F1-Score on SemEval-2015, 79.10% F1-Score on SemEval-2016, and 79.03% F1-Score on Sen-tiHood which is higher than the existing approaches.

The selection process for scholarship recipients in higher education requires a measurable system. The problem that has been happening is that the procedures carried out by the scholarship provider still use a manual file examination system. Composite performance index is the method used in this study. The purpose of this study is to create a decision support system for the selection of scholarship recipients to be more systematic and time efficient in the process. There are 10 alternatives and 4 criteria, namely parental income, GPA, electricity usage ,and semesters. The results of this study were obtained 5 highest values are A7 t with a value of 235.00 rank 1, A4 with a value of 200.00 rank 2, A1 with a value of 134.14 rank 3 sequences, A5 with a value of 120.00 ranks 4, and A8 with a value of 91.67 sequences 5.

Student publication organizing application is an application used to assist officers in the process of managing web-based student publication journals. The mechanism of this research was made iteratively from the process of making software requirements specifications, analyzing system requirements, then proceed to the system design stage, use case and flowchart design, database design, and interface design, then followed by developing applications and closed with testing. The programming languages used are PHP, HTML, and MySQL databases. The results of this study are the document design and application organizing of student publications. The application of this student publication organizing application can improve the quality of the scientific journal organizing process at the South Aceh Polytechnic more systematic and organized to achieve efficiency and improve publication to be global.

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