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All Journal Journal of Information Systems Engineering and Business Intelligence Proceeding of the Electrical Engineering Computer Science and Informatics
Wahyudi Agustiono
University of Trunojoyo Madura
Computer Science & IT Electrical & Electronics Engineering

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Academic Business Intelligence: Can a Small and Medium-sized University Afford to Build and Deploy it within Limited Resources? Wahyudi Agustiono
Journal of Information Systems Engineering and Business Intelligence Vol. 5 No. 1 (2019): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1481.456 KB) | DOI: 10.20473/jisebi.5.1.1-12

Abstract

Background: For many years, researches on Business Intelligence (BI) development have been popular in primary industry (trading, telecommunication, and manufacturing). Nevertheless, the academic sector has not been the primary beneficiary. This lack of practices also means there has been limited knowledge relating to the development of BI in the academic sectorObjective: This study presents the development of an Academic Business Intelligence (ABI). Taking an actual ABI development project in a small and medium-sized university in Indonesia context, it specifically sought to understand as to why the university needed an ABI and how it could be developed within the limited resources (funding, IT infrastructure and expertise).Methods: Following the business intelligence development roadmap, this study was able to develop an ABI as an attempt to provide a smart way for generating valuable information from scattered data interactively. It also successfully deployed the newly developed ABI into the existing IT legacy and then run a series of pilot testing involving the intended users.Results: The results showed the acceptance rate was high (87.25%) and suggested that the system found to be usable for conducting students' performance assessment and decision making faster. In short, this study contributes to the growing body of BI development literature by providing empirical evidence on how to successfully develop a BI within the unique context of the academic sector.Conclusion: Considering the findings, this study also draws practical recommendations and highlights a few limitations from which future study could address, especially when developing BI or similar ABI in particular.
Technologies, methods, and approaches on detection system of plant pests and diseases Devie Rosa Anamisa; Muhammad Yusuf; Wahyudi Agustiono; Mohammad Syarief
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 6: EECSI 2019
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eecsi.v6.1954

Abstract

This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease.
Co-Authors Devie Rosa Anamisa Mohammad Syarief Muhammad Yusuf