Optimasi Model Artificial Neural Network untuk Klasifikasi Paket Jaringan

Klasifikasi jaringan internet dibutuhkan secara luas oleh berbagai pihak untuk penghematan, pengalokasian, pembatasan sumber daya internet. Berbagai teknik digunakan untuk hal tersebut, salah satunya adalah dengan pendekatan machine learning khususnya algoritma jaringan syaraf tiruan (artificial neural network) yang selanjutnya disingkat ANN. ANN bekerja dengan cara meniru cara kerja syaraf otak manusia. AAN pada penelitian ini digunakan untuk mengklasifikasikan paket jaringan. Dataset yang digunakan adalah terdiri dari 990.558 baris data, 4 kolom input(X) meliputi protocol, port, timestamp, packet length. Kolom output(Y) terdiri satu kolom 5 label (App, Sosmed, Game, Browsing, Streaming). Selanjutnya dataset ini dibagi menjadi 3 yaitu training, test, dan validation. Tujuan dari penelitian ini adalah, pertama, untuk mengetahui kemampuan algoritma ANN untuk pengklasifikasian paket berdasarkan per satu paket. Kedua, menemukan model ANN yang paling optimal untuk permasalahan diatas. Kesimpulan yang didapatkan adalah Algoritma neural network dapat digunakan pada klasifikasi paket jaringan, tetapi jika dataset yang digunakan memiliki karakteristik-karakteristik seperti jumlah variabel X kecil, data pada variabel X sangat lebar jaraknya seperti (port, packet size, time to previous packet, protocol) maka akurasi tinggi sulit untuk didapatkan. Kedua, jika permasalahan yang diselesaikan memiliki kemiripan dengan penelitian ini, maka arsitektur model ANN yang paling optimal adalah: jumlah neuron input layer adalah 7 kali jumlah variabel X, jumlah neuron hidden layer adalah 1/2,8 kali jumlah neuron input, jumlah hidden layer satu, nilai dropout 0,33, metode aktivasi tanh-tanh-softmax, metode optimasi adamax. Nilai accuracy stabil didapatkan pada iterasi (epoch) ke-600, nilai loss stabil didapatkan pada epoch ke-1000. Nilai accuracy yang didapatkan sebesar 0,8 dan nilai loss 0,32 pada iterasi ke seribu.

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The effect of Kurtosis on the accuracy of artificial neural network predictive model

MATEC Web of Conferences ◽

10.1051/matecconf/201820402018 ◽

2018 ◽

Vol 204 ◽

pp. 02018

Author(s):

Aisyah Larasati ◽

Anik Dwiastutik ◽

Darin Ramadhanti ◽

Aal Mahardika

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Predictive Model ◽

Predictive Models ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Misclassification Rates ◽

Hidden Layer ◽

Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

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Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

ASEAN Journal of Chemical Engineering ◽

10.22146/ajche.69521 ◽

2021 ◽

Vol 21 (2) ◽

pp. 241

Author(s):

Joselito Abierta Olalo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Optimization Techniques ◽

Fuel Oil ◽

Oil Yield ◽

Coconut Husk ◽

Input Layer ◽

Artificial Neural ◽

Predicted Values ◽

Hidden Layer

Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.

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Pengujian Algoritma Artificial Neural Network (ANN) Untuk Prediksi Kecepatan Angin

Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) ◽

10.32672/jnkti.v2i1.1056 ◽

2019 ◽

Vol 2 (1) ◽

pp. 43

Author(s):

Syukri Syukri ◽

Samsuddin Samsuddin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Angin memiliki peran yang penting dalam kehidupan manusia, antara lain pada pembangkit listrik, pelayaran dan penerbangan. Ketiga sektor tersebut erat kaitannya dengan kondisi angin. Angin dapat muncul setiap saat dan setiap waktu serta perubahan geografis pada suatu wilayah. Hal ini mengakibatkan sulitnya menentukan kecepatan angin, maka untuk mengatasi masalah tersebut diperlukan prediksi kecepatan angin. Saat ini berbagai metode prediksi telah banyak dikembangkan, salah satu metode yang dapat digunakan untuk melakukan prediksi dengan akurasi yang tinggi yaitu algoritma Artificial Neural Network (ANN) Backpropagation. Arsitektur ANN yang digunakan adalah 4 parameter input layer, hidden layer (5, 10, 15, 20, 25 dan 30) dan output layer (1 parameter). Data pembelajaran dan pengujian didapatkan dari stasiun BMKG Blang Bintang Aceh Besar, berupa data kecepatan angin jam per harian periode Januari 2011 sampai dengan Desember 2015 yang terdiri dari arah angin, suhu, tekanan, kelembaban dan suhu. Hasil pengujian menunjukkan bahwa metode ANN Backpropagation cukup baik diterapkan untuk proses prediksi, kemampuan ANN dalam melakukan prediksi memiliki tingkat akurasi rata – rata yang lebih baik yaitu 96 %. Sedangkan nilai rata – rata kerapatan daya angin jam per harian yaitu 45.030 W/m2

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Comprehensive Method of Botnet Detection Using Machine Learning

International Journal of Open Source Software and Processes ◽

10.4018/ijossp.287613 ◽

2021 ◽

Vol 12 (4) ◽

pp. 0-0

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Malicious Code ◽

Large Network ◽

Botnet Detection ◽

Comprehensive Method ◽

Input Layer ◽

Optimal Accuracy ◽

Artificial Neural ◽

Hidden Layer

The botnet interrupts network devices and keeps control of the connections with the command, which controls the programmer, and the programmer controls the malicious code injected in the machine for obtaining information about the machines. The attacker uses a botnet to commence dangerous attacks as DDoS, phishing, despoil of information, and spamming. The botnet establishes with a large network and several hosts belong to it. In the paper, the authors proposed the framework of botnet detection by using an Artificial Neural Network. The author research upgrading the extant system by comprising of cache memory to fast the process. Finally, for detection, the author used an analytical approach, which is known as an artificial neural network that contains three layers: the input layer, hidden layer, output layer, and all layers are connected to correlate and approximate the results. The experiment result determines that the classifier with 25 epochs gives optimal accuracy is 99.78 percent and shows the detection rate is 99.7 percent.

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PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

Jurnal Gaussian ◽

10.14710/j.gauss.v7i1.26636 ◽

2018 ◽

Vol 7 (1) ◽

pp. 64-72

Author(s):

Ekky Rosita Singgih Wigati ◽

Budi Warsito ◽

Rita Rahmawati

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Processing System ◽

Training Data ◽

Neural Network Modeling ◽

Feed Forward Neural Network ◽

Output Layer ◽

Input Layer ◽

Artificial Neural ◽

Hidden Layer

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE

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Modelling, Simulation and Optimization of Diagnosis Cardiovascular Disease Using Computational Intelligence Approaches

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.2996 ◽

2020 ◽

Vol 10 (5) ◽

pp. 1005-1022 ◽

Cited By ~ 1

Author(s):

Shahan Yamin Siddiqui ◽

Atifa Athar ◽

Muhammad Adnan Khan ◽

Sagheer Abbas ◽

Yousaf Saeed ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Cardiovascular Disease ◽

Artificial Neural Network ◽

Fuzzy Logic ◽

Fuzzy Inference ◽

Learning Approaches ◽

Inference System ◽

Artificial Neural ◽

Hidden Layer

Background: To provide ease to diagnose that serious sickness multi-technique model is proposed. Data Analytics and Machine intelligence are involved in the detection of various diseases for human health care. The computer is used as a tool by experts in the medical field, and the computer-based mechanism is used to diagnose different diseases in patients with high Precision. Due to revolutionary measures employed in Artificial Neural Networks (ANNs) within the research domain in the medical area, which appear to be in the data-driven applications usually described in the domain of health care. Cardio sickness according to name is a type of an ailment that is directly connected to the human heart and blood circulation setup, so it should be diagnosed on time because the delay of diagnosing of that disease may lead the sufferer to death. The research is mainly aimed to design a system that will be able to detect cardiovascular sickness in the sufferer using machine learning approaches. Objective: The main objective of the research is to gather information of the six parameters that is age, chest pain, electrocardiogram, systolic blood pressure, fasting blood sugar and serum cholesterol are used by Mamdani fuzzy expert to detect cardiovascular sickness. To propose a type of device which will be successfully used in overcoming the cardiovascular diseases. This proposed model Diagnosis Cardiovascular Disease using Mamdani Fuzzy Inference System (DCD-MFIS) shows 87.05 percent Precision. To delineate an effective Neural Network Model to predict with greater precision, whether a person is suffering from cardiovascular disease or not. As the ANN is composed of various algorithms, some will be handed down for the training of the network. The main target of the research is to make the use of three techniques, which include fuzzy logic, neural network, and deep machine learning. The research will employ the three techniques along with the previous comparisons, and given that, the results will be compared respectively. Methods: Artificial neural network and deep machine learning techniques are applied to detect cardiovascular sickness. Both techniques are applied using 13 parameters age, gender, chest pain, systolic blood pressure, serum cholesterol, fasting blood sugar, electrocardiogram, exercise including angina, heart rate, old peak, number of vessels, affected person and slope. In this research, the ANN-based research is one of the algorithms collections, which is the detection of cardiovascular diseases, is proposed. ANN constitutes of many algorithms, some of the algorithms are employed in the paper for the training of the network used, to achieve the prediction ratio and in contrast of the comparison of the mutual results shown. Results: To make better analysis and consideration of the three frameworks, which include fuzzy logic, ANN, Deep Extreme Machine Learning. The proposed automated model Diagnosis Cardiovascular Disease includes Fuzzy logic using Mamdani Fuzzy Inference System (DCD-MFIS), Artificial Neural Network (DCD–ANN) and Deep Extreme Machine Learning (DCD–DEML) approach using back propagation system. These frameworks help in attaining greater precision and accuracy. Proposed DCD Deep Extreme Machine Learning attains more accuracy with previously proposed solutions that are 92.45%. Conclusion: From the previous comparisons, the propose automated Diagnosis of Cardiovascular Disease using Fuzzy logic, Artificial Neural Network, and deep extreme machine learning approaches. The automated systems DCDMFIS, DCD–ANN and DCD–DEML, the framework proposed as effective and efficient with 87.05%, 89.4% and 92.45 % success ratios respectively. To verify the performance which lies in the ANNs and computational analysis, many indicators determining the precise performance were calculated. The training of the neural networks is made true using the 10 to 20 neurons layers which denote the hidden layer. DEML reveals and indicates a hidden layer containing 10 neurons, which shows the best result. In the last, we can conclude that after making a consideration among the three techniques fuzzy logic, Artificial Neural Network and Proposed DCD Deep Extreme Machine, the Proposed DCD Deep Extreme Machine Learning based solution give more accuracy with previously proposed solutions that are 92.45%.

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Artificial neural network models for coronary artery disease

Current Bioinformatics ◽

10.2174/1574893615666200214102837 ◽

2020 ◽

Vol 15 ◽

Author(s):

Elham Shamsara ◽

Sara Saffar Soflaei ◽

Mohammad Tajfard ◽

Ivan Yamshchikov ◽

Habibollah Esmaili ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Coronary Artery Disease ◽

Pattern Recognition ◽

Artificial Neural Network ◽

Coronary Artery ◽

Diagnostic Model ◽

Early Prediction ◽

Artificial Neural ◽

Artery Disease

Background: Coronary artery disease (CAD) is an important cause of mortality and morbidity globally. Objective : The early prediction of the CAD would be valuable in identifying individuals at risk, and in focusing resources on its prevention. In this paper, we aimed to establish a diagnostic model to predict CAD by using three approaches of ANN (pattern recognition-ANN, LVQ-ANN, and competitive ANN). Methods: One promising method for early prediction of disease based on risk factors is machine learning. Among diﬀerent machine learning algorithms, the artificial neural network (ANN) algo-rithms have been applied widely in medicine and a variety of real-world classifications. ANN is a non-linear computational model, that is inspired by the human brain to analyze and process complex datasets. Results: Diﬀerent methods of ANN that are investigated in this paper indicates in both pattern recognition ANN and LVQ-ANN methods, the predictions of Angiography+ class have high accuracy. Moreover, in CNN the correlations between the individuals in cluster ”c” with the class of Angiography+ is strongly high. This accuracy indicates the significant diﬀerence among some of the input features in Angiography+ class and the other two output classes. A comparison among the chosen weights in these three methods in separating control class and Angiography+ shows that hs-CRP, FSG, and WBC are the most substantial excitatory weights in recognizing the Angiography+ individuals although, HDL-C and MCH are determined as inhibitory weights. Furthermore, the effect of decomposition of a multi-class problem to a set of binary classes and random sampling on the accuracy of the diagnostic model is investigated. Conclusion : This study confirms that pattern recognition-ANN had the most accuracy of performance among diﬀerent methods of ANN. That’s due to the back-propagation procedure of the process in which the network classify input variables based on labeled classes. The results of binarization show that decomposition of the multi-class set to binary sets could achieve higher accuracy.

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Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

Journal of Marine Science and Engineering ◽

10.3390/jmse8100766 ◽

2020 ◽

Vol 8 (10) ◽

pp. 766

Author(s):

Dohan Oh ◽

Julia Race ◽

Selda Oterkus ◽

Bonguk Koo

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Network Model ◽

Neural Network Model ◽

Deep Neural Network ◽

Machine Learning Techniques ◽

Burst Pressure ◽

Comparison Results ◽

Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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