Convolutional Neural Network on Brain Concentration and Art of Reading the Qur'an

In Islam there is a belief that reading the Qur'an can increase one's concentration power in doing something. Concentration can be influenced by several factors, to be able to identify or characterize individuals, it is necessary to measure brain wave activity. Brain waves are one of the biometric properties that can be used to identify individuals based on their physical. An electroencephalogram (EEG) can be used to measure and capture brain wave activity. To be able to naturally record brain wave activity requires constant and emergent brain activity. The activities needed are in the form of giving assignments to get the thinking process and concentration needed for the Basic Cognitive Test. The object of this research is the students of the University in Yogyakarta. EEG data recording was carried out in two stages, the first stage the respondents were given 10 minutes to work on the test questions without reading the Qur'an before working on the questions, the second stage the respondents were given 10 minutes to work on the test questions by reading the Qur'an before doing the work. about. When the respondent was working on the test, the researcher recorded the EEG signal using Neurosky Mindwave Mobile 2, so that the data in the form of brain signals was obtained. The data acquisition will undergo a preprocessing process in the form of a domain transformation signal using Fast Fourier Transform (FFT). Then enter the labeling process and after that the next process will be carried out the classification process using the CNN algorithm. The results of this study showed that 10 of 30 students' concentration levels experienced an increase, with an accuracy rate of 85% and a significant F test is 0.00

Penerapan User-Based Collaborative Filtering Algorithm

Sistem online memanfaatkan website sebagai media pemasaran. Namun dengan perkembangan teknologi, pemasaran dilakukan dengan online terdapat kendala yaitu banyaknya produk yang tersedia dalam pemilihan produk. Sistem rekomendasi adalah sistem yang menyarankan informasi berguna atau menduga yang akan dilakukan user untuk mencapai tujuannya, seperti mencari teknik yang terbaik dalam memberikan rekomendasi bagi user. Menurut hasil survey yang telah dilakukan terhadap 17 orang pemakai website pemasaran produk Gadget Shield didapatkan 88,20% mengharapkan adanya penilaian user terhadap produk. Penelitian ini akan melakukan pengembangan sistem rekomendasi produk Gadget Shield pada toko Jackskins menggunakan metode User-Based Collaborative Filtering serta menggunakan Euclidean Distance untuk mengukur jarak kemiripan antar User dan Weighted Sum digunakan untuk mencari rekomendasi produk. Diharapkan dengan adanya sistem dapat memudahkan User dalam pencarian produk Gadget Shield terbaik. Guna menghasilkan produk rekomendasi,hasil nilai kemiripaan dilakukan perhitungan dengan algoritma Weighted Sum. Sistem rekomendasi Collaborative Filtering telah diuji menggunakan metode pengujian akurasi Root Mean Square Error (RMSE) dan pengujian User Acceptance Test (UAT). Hasil uji RMSE menunjukkan nilai 0,496 atau akurasinya 90,08%. Hasil pengujian UAT didapatkan 86,86% diterima. Informasi dari proses tersebutlah yang nantinya diharapkan akan bermanfaat sebagai dasar sumber rekomendasi yang akurat.&nbsp

K-Nearest Neighbor of Beta Signal Brainwave to Accelerate Detection of Concentration on Student Learning Outcomes

Intelligence, creativity, emotions, memory, and body movements are human activities controlled by the brain. While humans do an activity, the neural network in the brain produces an electrical current in the form of waves. Brainwaves are one of the biometric features that can be used to identify individual characteristics based on their activity and behavior patterns. Identifying individual characteristics requires a brain activity measurement using an Electroencephalogram (EEG). Measuring brainwaves requires a reliable, prominent, and constant activity stimulation by applying a series of cognitive tasks, such as the Culture Fair Intelligence Test (CFIT) and the Indonesian Competency Test (CT). This research aims to obtain relation patterns and accelerate the detection between brain concentration and learning outcomes. Beta signal acquisition is obtained from junior high school students while performing cognitive tasks. After data is obtained, the signal is extracted using the Fast Fourier Transform (FFT) to get its peak signal. The peak signal from FFT data on CFIT generated an average score of 0.214 with the category of Average. Meanwhile, the peak signal on CT generated an average score of 0.246 with the category "C+". K-Nearest Neighbor (KNN) algorithm is applied to identify patterns from extraction data with K-value=5; then, the accuracy is assessed using K-Fold Cross Validation with Kvalue=11. The resulting accuracy is 94.59%. Based on the KNN classification results, students' learning outcomes are influenced by their concentration. This research has successfully shortened the CFIT evaluation time from three days to one day

Implementation of Named Entity Recognition for Developing Question Answering System: Case Study Merapi Volcano Museum

The Merapi Volcano Museum is one of the places used as a means of knowledge and information about the mountain with the website address, namely mgm.slemankab.go.id. Generally, the information provided causes website visitors to be dissatisfied with the information. The number of visitors who are dissatisfied with the information on the website is evidenced by the results of a questionnaire from 40 respondents, 50.55% of visitors do not get information that is not in accordance with what is desired. Therefore, a system is implemented using the Question Answering System (QAS) with the Named Entity Recognition (NER) method. The implementation of the system uses a telegram based on the NER methodology. Testing using White Box Testing. The results of testing and analysis of tests carried out with white box testing the system has 3 regions and 3 independent path, with path 1 = 1-2-3-4-11, path 2 = 1-2-3- 4-5 -6-7-8-11, and path 3 = 1-2-3-4-5-6-7-9-10-11. The 3 paths are able to return the right answer after being tested using test scenarios for each independent path

A New Artificial Intelligence Approach Using Extreme Learning Machine as the Potentially Effective Model to Predict and Analyze the Diagnosis of Anemia

The procedure to diagnose anemia is time-consuming and resource-intensive due to the existence of a multitude of symptoms that can be felt physically or seen visually. Anemia also has several forms, which can be distinguished based on several characteristics. It is possible to diagnose anemia through a quick, affordable, and easily accessible laboratory test known as the complete blood count (CBC), but the method cannot directly identify different kinds of anemia. Therefore, further tests are required to establish a gold standard for the type of anemia in a patient. These tests are uncommon in settings that offer healthcare on a smaller scale because they require expensive equipment. Moreover, it is also difficult to discern between beta thalassemia trait (BTT), iron deficiency anemia (IDA), hemoglobin E (HbE), and combination anemias despite the presence of multiple red blood cell (RBC) formulas and indices with differing optimal cutoff values. This is due to the existence of several varieties of anemia in individuals, making it difficult to distinguish between BTT, IDA, HbE, and combinations. Therefore, a more precise and automated prediction model is proposed to distinguish these four types to accelerate the identification process for doctors. Historical data were retrieved from the Laboratory of the Department of Clinical Pathology and Laboratory Medicine, Faculty of Medicine, Public Health, and Nursing, Universitas Gadjah Mada, Yogyakarta, Indonesia for this purpose. Furthermore, the model was developed using the algorithm for the extreme learning machine (ELM). This was followed by the measurement of the performance using the confusion matrix and 190 data representing the four classes, and the results showed 99.21% accuracy, 98.44% sensitivity, 99.30% precision, and an F1 score of 98.84%

K-Nearest Neighbor of Beta Signal Brainwave to Accelerate Detection of Concentration on Student Learning Outcomes

—Intelligence, creativity, emotions, memory, and body movements are human activities controlled by the brain. While humans do an activity, the neural network in the brain produces an electrical current in the form of waves. Brainwaves are one of the biometric features that can be used to identify individual characteristics based on their activity and behavior patterns. Identifying individual characteristics requires a brain activity measurement using an Electroencephalogram (EEG). Measuring brainwaves requires a reliable, prominent, and constant activity stimulation by applying a series of cognitive tasks, such as the Culture Fair Intelligence Test (CFIT) and the Indonesian Competency Test (CT). This research aims to obtain relation patterns and accelerate the detection between brain concentration and learning outcomes. Beta signal acquisition is obtained from junior high school students while performing cognitive tasks. After data is obtained, the signal is extracted using the Fast Fourier Transform (FFT) to get its peak signal. The peak signal from FFT data on CFIT generated an average score of 0.214 with the category of Average. Meanwhile, the peak signal on CT generated an average score of 0.246 with the category "C+". K-Nearest Neighbor (KNN) algorithm is applied to identify patterns from extraction data with K-value=5; then, the accuracy is assessed using K-Fold Cross Validation with K-value=11. The resulting accuracy is 94.59%. Based on the KNN classification results, students' learning outcomes are influenced by their concentration. This research has successfully shortened the CFIT evaluation time from three days to one day. © 2022, International Association of Engineers. All rights reserved

Naive Bayes for Thesis Labeling

The thesis preparation in the Department of Informatics Universitas Ahmad Dahlan is divided into two areas of interest, namely Intelligent Systems and Software and Data Engineering. Existing thesis title data is only used as an archive and has never been processed or classified to determine the trend of thesis topics based on student interest each year. The stages include data collection, the data is divided into two parts (training data and test data), manual labeling of training data, text preprocessing, and classification using Naive Bayes. The results show the trend of thesis title taking from 2013 to 2018 shows the thesis trend in the field of Intelligent Systems and Software. Accuracy testing uses Confusion Matrix and K-Fold Cross Validation with a k value is 10, has a value of 94.60%, a precision of 97.30%, and a recall of 85.70%