Design to Build E-learning Application in SMP N 2 Busalangga
Jimi Asmara,
Gregorius Rinduh Iriane,
Edwin Ariesto Umbu Malahina
Issue:
Volume 6, Issue 2, December 2021
Pages:
8-16
Received:
20 August 2021
Accepted:
30 September 2021
Published:
19 November 2021
Abstract: The development of technology today is growing so rapidly, almost all aspects of human life all use technology. The need for it-based teaching and learning concepts and mechanisms is increasingly advanced which then became known as E-learning brought the influence of the transformation of conventional education into digital forms, both in content (contens) and systems. E-learning is a learning model that utilizes information and communication technology facilities. With this information technology can act as the provision of information between students and students, learning resources and a means to streamline learning evaluation. SMP Negeri 2 Busalangga as one of the state schools whose teaching and learning activities process still use conventional means. To educate students or students in the face of national exams, teachers or schools always hold tutoring or try out, national pre-exams outside of school hours. A system is needed that can facilitate the teaching and learning process in schools between students and teachers, namely e-learning. The results obtained in the form of e-learning website media that can be used for teaching and learning activities where teachers and students can access the subject matter easily and quickly and can be accessed from anywhere and will be a plus because the system is based on information and technology.
Abstract: The development of technology today is growing so rapidly, almost all aspects of human life all use technology. The need for it-based teaching and learning concepts and mechanisms is increasingly advanced which then became known as E-learning brought the influence of the transformation of conventional education into digital forms, both in content (...
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A Genetic Neuro-Fuzzy System for Diagnosing Clinical Depression
Adegboyega Adegboye,
Imianvan Anthony Agboizebeta
Issue:
Volume 6, Issue 2, December 2021
Pages:
17-23
Received:
1 April 2021
Accepted:
11 November 2021
Published:
23 November 2021
Abstract: Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform feature selection and extraction on the dataset used in training the systems and this has a huge implication on the classification accuracy of the system. The objective of this research work is to develop a depression diagnosis system, that takes into consideration feature selection and extraction of dataset using Genetic-Neuro-Fuzzy techniques. Feature selection and extraction, will enable identification of key symptoms and hidden traits which are vital in diagnosis of depression. In this work, a Genetic Neuro-Fuzzy Model which is capable of handling feature selection and extraction on depression dataset was proposed and designed for diagnosing clinical depression. The GA component optimizes the clinical dataset which consist of series of diagnosed depression cases by performing feature selection and extraction, while ANFIS is used in training the optimized dataset obtained from the GA. The system had 92.5% prediction accuracy. This is a significant improvement over the best related model in literature that achieved a prediction accuracy of 92.4%. The system is recommended for psychiatrist hospital to aid in depression diagnosis. The research is limited to the diagnosis of clinical depression; future work should focus on the other forms of depression and treatments. The model has incorporated feature selection and feature extraction for the prediction of clinical depression with significant results established with performance indicators.
Abstract: Depression is a serious illness that affects millions each year and if left untreated, it may lead to the deaths of many. It comes in many flavors that can be very different among people and this makes diagnosing it very difficult. A lot of artificial intelligence systems have been designed to diagnosing depression but they failed to perform featur...
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