The Implementation of Backtracking Algorithm on Crossword Puzzle Games Based on Android

Nurdin, Nurdin; Azhar, Rizki; Abdullah, Dahlan; Erliana, Cut Ita; Nurdiawan, Odi; Dikananda, Arif Rinaldi; Rinaldi, Ade Rizki; Sudrajat, Dadang; Susanti, Elva; Hasibuan, Abdurrozzaq; Yuliwati,; Rahman, Arief Aulia; Sudarsana, I Ketut

doi:10.1088/1742-6596/1363/1/012075

Cited by 3 publications

(2 citation statements)

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“…The testing stage is carried out to validate the model that has been formed at the training stage. Testing is carried out at each cross validation for the Convolutional neural network algorithm [22] Convolutional neural network algorithm model The configuration parameters used in the Convolutional neural network algorithm are the number of epochs, the convolution model, the type of activation function, the number of dense layers, and the number of batch sizes [23].…”

Section: Second Stagementioning

confidence: 99%

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

Nurdiawan,

Susana,

Faqih

2024

jitk

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Polycystic Ovarian Syndrome (PCOS) is the main cause of infertility in women. This condition results in abnormal hormone levels. Women who experience this syndrome will have irregular hormone levels and experience irregular menstrual cycles as well, thereby affecting the reproductive system. Symptoms that arise as a result of the increase in these hormones can be seen from the growth of hair on the legs, weight gain which results in not being ideal, irregular menstruation, unusual acne growth, and oily skin. The problem of Polycystic Ovarian Syndrome can cause disturbances in ovulation and cause infertility in women. Urgency This research requires a classification that has good accuracy in diagnosing early to minimize the rate of pregnancy failure. The aim of the research is to be able to model early detection of Polycystic Ovarian Syndrome with high accuracy so that it can help the health team in detecting Polycystic Ovarian Syndrome or not having Polycystic Ovarian Syndrome. The research stage has 3 stages including the first stage of identifying problems and collecting datasets from Telkom University dataverse in the form of images and literature reviews of various sources. The second stage is Pre Processing of image data, Data Training, modeling design by managing image data and classifying using the Convolutional Neural Network Algorithm deep learning model and testing. The third stage is evaluating the test results and discussing the results of accuracy in determining the status of Normal Polycystic Ovarian Syndrome or PCOS. The results of training and validation on the ovarian xray image dataset using the CNN architecture that has been made, 40 iterations (epochs), and 4 step_per_epochs show an accuracy value of 0.8947 or 89.47% and a loss value of 0.2684.

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Section: Second Stagementioning

confidence: 99%

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

Nurdiawan,

Susana,

Faqih

2024

jitk

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“…Neural Network [20]. Pola informasi yang dihasilkan dari proses data mining evaluasi harus disajikan dalam format yang dapat dengan mudah dipahami oleh para pemangku kepentingan [21][22] [23].…”

Section: Data Mining Pada Penelitian Ini Merupakan Penerapan Model Al...unclassified

Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron

Anwar

Kurnia²,

Faqih³

et al. 2022

Jur. Ris. Kom.

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The implementation of online distance learning affects the psychosocial aspects of students so that a combination of offline and face-to-face learning is carried out using Hybrid Learning. The problem in this research is the analysis of the competencies that will be generated, student characteristics, face-to-face interactions, online learning strategies, and lecturers by calculating the data from the Hybrid Learning evaluation to get the best level of accuracy using the Machine Learning technique method. The purpose of this study is to evaluate the results of Hybrid learning using the Artificial Neural Network (ANN) method which is expected to have the right level of accuracy and prediction and tolerate errors so that it can produce good predictions and can be used to model good relationships in finding patterns in the data. . Prediction of Hybrid Learning learning outcomes during the Covid 19 pandemic using Machine Learning techniques consisting of 12 attributes with a total of 1,231 datasets of Hybrid Learning learning outcomes in 2022. The Artificial Neural Network algorithm model uses Retrive operators, Set Roles, optimization parameters, Cross Validation, Apply Models, Performance and Logs. Accuracy results show 99.35% meaning that the results of the learning evaluation using Hybrid Learning with predictions that match and turn out to be appropriate are 1039, then those that do not match are 8. The predictions are very suitable and turn out to be very suitable in 104. Artificial Neural Network with Multilayer Perceptron, with two hidden layer and the format of the first hidden layer is 2 nodes, then the second hidden layer is 5 nodes with an output of 3 nodes

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Comparison of the K-Nearest Neighbor algorithm and the decision tree on moisture classification

Nurdiawan

Kurnia

Solihudin

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

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Soil moisture is a parameter needed by plants in terms of plant growth. In determining the appropriate soil moisture for plants requires a control system. This study uses a comparison of KNN and decision tree algorithms with the aim of being able to determine soil calcification with yield parameters namely moist and dry, so that it has good accuracy compared to the accuracy of the Decision Tree algorithm with an accuracy of 55.77% with dry class recall of 62.69% moist 51.92% dry precision class 58.33% humid 47.37% and K-Nearest Neighbor with 72.69% accuracy dry class recall 80.60% humid 63.16% dry precision class 72.00% humid 73.47%. The results of the above model testing can be concluded that the K-Nearest Neighbor is the most accurate algorithm for classification of moist soil

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The Implementation of Backtracking Algorithm on Crossword Puzzle Games Based on Android

Cited by 3 publications

References 0 publications

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

Deep Learning for Polycystic Ovarian Syndrome Classification Using Convolutional Neural Network

Prediksi Hasil Belajar Hybrid Menggunakan Artificial Neural Network Dengan Multilayer Perceptron

Comparison of the K-Nearest Neighbor algorithm and the decision tree on moisture classification

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