Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

Dewi, Candra; Sundari, Suci; Mardji, Mardji

doi:10.25126/jitecs.20194168

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Metode ini termasuk kedalam metode supervised learning yang dapat digunakan dalam permasalahan klasifikasi [22]. Backpropagation memiliki 3 tahap dalam pembelajarannya, yaitu perambatan maju, perambatan mundur dan penyesuaian bobot [23]. Pengujian yang dilakukan terhadap backpropagation adalah pengujian parameter.…”

Section: Gambar 3 Ilustrasi Cross Validation Dengan K = 10unclassified

Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja

Anshori,

Haris,

Teja Kusuma

2023

cicesj

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COVID-19 caused by the coronavirus disease-2019 has spread rapidly and attacked massively. As a precaution, a lockdown policy was issued. This policy limits the activities of schools, offices, shops, prohibits traveling at certain times, maintains distance from one another and reduces crowds in the public. During the lockdown period resulted in a new lifestyle where the use of smartphones increased. This increase is based on the fact that smartphones have many functions such as information, communication, education and entertainment. But excessive use of smartphones can cause addictive effects, especially in adolescents. Excessive use of smartphones makes teenagers become insomniac, different social behavior, low self-confidence, and even anxiety. The complexity of anxiety symptoms in adolescents tends to be difficult to understand, therefore a prediction of smartphone addiction with backpropagation is proposed. Parameter testing is done to get the right artificial neural network architecture. The results of testing the parameters that have been carried out are iterations = 50, the number of neurons in the hidden layer = 9 and the learning rate = 0.3. With this model, an accuracy of 99.49%, TPR of 99.5% and FPR of 0.08% is obtained. Keywords—Backpropagation, Artificial Neural Network, Smartphone Addiction, Machine Learning, Neural Network COVID-19 yang disebabkan oleh coronavirus disease-2019 telah menyebar dengan cepat dan menyerang secara masif. Sebagai tindakan pencegahan maka dikeluarkan kebijakan lockdown. Kebijakan ini membatasi kegiatan sekolah, perkantoran, pertokoan, melarang bepergian dalam waktu tertentu, saling menjaga jarak dan mengurangi kerumunan di publik. Selama masa lockdown menghasilkan gaya hidup yang baru dimana kegunaan smartphone meningkat. Peningkatan ini didasari karena smartphone memiliki banyak fungsi seperti informasi, komunikasi, edukasi dan hiburan. Tetapi penggunaan smartphone yang berlebihan dapat menimbulkan efek candu khususnya pada remaja. Berlebihan dalam menggunakan smartphone membuat anak remaja menjadi insomnia, Tingkah laku pergaulan yang berbeda, kepercayaan diri yang rendah, bahkan kecemasan. Kompleksnya gejala kecemasan pada anak remaja cenderung sulit untuk dipahami, oleh karena itu diusulkan prediksi kecanduan smartphone dengan backpropagation. Pengujian parameter dilakukan untuk mendapatkan arsitektur jaringan syaraf tiruan yang tepat. Hasil pengujian parameter yang telah dilakukan adalah iterasi = 50, jumlah neuron pada hidden layer = 9 dan nilai learning rate = 0.3. Dengan model tersebut, maka didapatkan akurasi sebesar 99.49%, TPR sebesar 99.5% dan FPR sebesar 0.08%. Kata Kunci—Backpropagation, Jaringan Saraf Tiruan, Kecanduan Smartphone, Pembelajaran Mesin, Jaringan Saraf

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Section: Gambar 3 Ilustrasi Cross Validation Dengan K = 10unclassified

Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja

Anshori,

Haris,

Teja Kusuma

2023

cicesj

View full text Add to dashboard Cite

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“…A neural network consists of an input layer, zero or more hidden layer, and an output layer. Information on neurons will be propagated through each layer, starting from the input layer, all the way to the output layer [14] [15]. The propagation is done by calculating the weighted sum on each neuron, which then used as the input value for the activation function used.…”

Section: A Neural Network and Backpropagationmentioning

confidence: 99%

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

Priambodo¹,

Mahmudy²,

Rahman³

2020

KEDS

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Even though it is inevitable, but it can be anticipated to minimize damage and casualties. Past research has been conducted to predict the level of impact caused by earthquakes in real-time [2]. One of those past research is about an early earthquake warning system (EEWS), which will give an alert when it detects an earthquake [3]. Numerous architectures and algorithms have been developed in those studies. Various research is the utilization of neural network trained with backpropagation algorithm and optimized using Levenberg (LOM) to predict hypocenter location, moment magnitude, and the expansion of the earthquake [4], modification of LOM to minimize error on EEWS [3], and utilization of neural tree to predict P and S waves [5]. Until now, to the knowledge of the author, the study of using a backpropagation (BP) algorithm to predict earthquake magnitude and grid-based location in Indonesia has not been conducted yet. This algorithm is chosen because it has been proven to perform well in broad types of problems, such as regression, pattern recognition, and prediction [6][7][8][9]. In this paper, the study aims to measure the performance of neural network trained using backpropagation algorithm in predicting earthquakes magnitude and grid-based location based on earthquakes magnitude and location data in Indonesia recorded from 2000 to 2019.

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“…GLCM is a matrix that describes the frequency of occurrence of pairs of two pixels with a certain intensity in a certain distance and direction in the image [8]. The direction of the orientation of the angle formed in four directions with an interval of 45°, which is 0°, 45°, 90°, and 135° [9] Fig. 6 Pseudo-code of calculating GLCM features 5.…”

Section: Feature Extraction By Gray Level Co-occurrence Matrixmentioning

confidence: 99%

K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

Johan

Utaminingrum

Budi

2020

JITeCS

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This study aims to analyze the k-value on K nearest neighbor classification. k-value is the distance used to find the closest data to label the class from the testing data. Each k-value can produce a different class label against the same testing data. The variants of k-value that we use are k=3, k=5 and k=7 to find the best k-value. There are 2 classes that are used in this research. Both classes are stairs descent and floor classes. The gray level co-occurrence matrix method is used to extract features. The data we use comes from videos obtained from the camera on the smart wheelchair taken by the frame. Refer to the results of our tests, the best k-value is obtained when using k=7 and angle 0° with accuracy is 92.5%. The stairs descent detection system will be implemented in a smart wheelchair

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Texture Feature On Determining Quantity of Soil Organic Matter For Patchouli Plant Using Backpropagation Neural Network

Cited by 3 publications

References 7 publications

Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja

Penerapan Backpropagation Neural Network (BPNN) Untuk Prediksi Kecanduan Smartphone Pada Remaja

Earthquake Magnitude and Grid-Based Location Prediction using Backpropagation Neural Network

K-Value Effect for Detecting Stairs Descent using Combination GLCM and KNN

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