scholarly journals PREDIKSI TINGKAT KESUKSESAN PROMOSI BANK DENGAN ALGORITMA DNN

2021 ◽  
Vol 21 (1) ◽  
pp. 23-33
Author(s):  
Oscar Oscar ◽  
Nurlaelatul Maulidah ◽  
Annida Purnamawati ◽  
Destiana Putri ◽  
Hilman F Pardede
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Success Rate ◽  
Deep Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Bank Marketing ◽  
Fold Cross Validation ◽  
Better Than

Telemarketing is one effective way for promoting products. However, it is often difficult to measure the success of telemarketing. Therefore, a way to predict the success rate of telemarketing, and hence strategies could be planned to increase the success rate. In this study, we evaluate several implementations of machine learning for prediction the success of telemarketing. The evaluated methods are Deep Neural Network (DNN), Random Forest, and K-nearest neighbor (K-NN). We validate our experiments using 10-fold cross validation and our experiments show that DNN with 3 hidden layers outperforms other methods. Accuracy of 90% is achieved with the DNN. It is better than Random Forest and KNN that achieve accuracies of algorithm and 88% and 89%.Keywords— Bank Marketing, DNN, KNN, Random Forest.

scholarly journals Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection

2020 ◽  
Vol 37 (4) ◽  
pp. 563-569
Author(s):  
Dželila Mehanović ◽  
Jasmin Kevrić
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Security Threats ◽  
Selection Methods ◽  
K Nearest Neighbor ◽  
Machine Learning Classifiers ◽  
Time To Build ◽  
Fold Cross Validation

Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.

scholarly journals Recommender System for Term Deposit Likelihood Prediction using Cross-validated Neural Network

2021 ◽  
pp. 21-28
Author(s):  
Shawni Dutta ◽  
Samir Kumar Bandyopadhyay
Keyword(s):  
Neural Network ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Automated System ◽  
K Nearest Neighbor ◽  
Decision Tree Classifier ◽  
Proposed Model ◽  
Tree Classifier ◽  
Customer Perspective ◽  
Fold Cross Validation

For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer details are influential while considering possibilities of taking term deposit subscription. An automated system is provided in this paper that approaches towards prediction of term deposit investment possibilities in advance. Neural network along with stratified 10-fold cross-validation methodology is proposed as predictive model which is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concluded that proposed model provides significant prediction results over other baseline models with an accuracy of 88.32% and MSE of 0.1168.

scholarly journals Himawari-8 Aerosol Optical Depth (AOD) Retrieval Using a Deep Neural Network Trained Using AERONET Observations

2020 ◽  
Vol 12 (24) ◽  
pp. 4125
Author(s):  
Lu She ◽  
Hankui K. Zhang ◽  
Zhengqiang Li ◽  
Gerrit de Leeuw ◽  
Bo Huang
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Land Surface ◽  
Deep Neural Network ◽  
Cross Validation ◽  
Japan Meteorological Agency ◽  
Pixel Location ◽  
Fold Cross Validation

Spectral aerosol optical depth (AOD) estimation from satellite-measured top of atmosphere (TOA) reflectances is challenging because of the complicated TOA-AOD relationship and a nexus of land surface and atmospheric state variations. This task is usually undertaken using a physical model to provide a first estimate of the TOA reflectances which are then optimized by comparison with the satellite data. Recently developed deep neural network (DNN) models provide a powerful tool to represent the complicated relationship statistically. This study presents a methodology based on DNN to estimate AOD using Himawari-8 Advanced Himawari Imager (AHI) TOA observations. A year (2017) of AHI TOA observations over the Himawari-8 full disk collocated in space and time with Aerosol Robotic Network (AERONET) AOD data were used to derive a total of 14,154 training and validation samples. The TOA reflectance in all six AHI solar bands, three TOA reflectance ratios derived based on the dark-target assumptions, sun-sensor geometry, and auxiliary data are used as predictors to estimate AOD at 500 nm. The DNN AOD is validated by separating training and validation samples using random k-fold cross-validation and using AERONET site-specific leave-one-station-out validation, and is compared with a random forest regression estimator and Japan Meteorological Agency (JMA) AOD. The DNN AOD shows high accuracy: (1) RMSE = 0.094, R2 = 0.915 for k-fold cross-validation, and (2) RMSE = 0.172, R2 = 0.730 for leave-one-station-out validation. The k-fold cross-validation overestimates the DNN accuracy as the training and validation samples may come from the same AHI pixel location. The leave-one-station-out validation reflects the accuracy for large-area applications where there are no training samples for the pixel location to be estimated. The DNN AOD has better accuracy than the random forest AOD and JMA AOD. In addition, the contribution of the dark-target derived TOA ratio predictors is examined and confirmed, and the sensitivity to the DNN structure is discussed.

scholarly journals An Improved Coronary Heart Disease Predictive System Using Random Forest

2021 ◽  
pp. 17-27
Author(s):  
Abdulraheem Abdul ◽  
Rafiu M. Isiaka ◽  
Ronke S. Babatunde ◽  
Jumoke F. Ajao
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor

Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but  no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.

scholarly journals Studi Komparasi Algoritma Klasifikasi Mental Workload Berdasarkan Sinyal EEG

2020 ◽  
Vol 3 (2) ◽  
pp. 133-143
Author(s):  
Dessy Kusumaningrum ◽  
Elly Matul Imah
Keyword(s):  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Mental Workload ◽  
Principal Component ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
K Nearest Neighbor ◽  
Electroencephalogram Eeg ◽  
Fold Cross Validation

Kondisi psikologis dan fisik manusia dapat memengaruhi proses berpikir. Apabila kondisi individu mengalami kelelahan, maka dapat memengaruhi penurunan tingkat produktivitas maupun penurunan proses berpikir yang menyebabkan timbulnya mental workload. Workload yang dimiliki harus seimbang terhadap kemampuan dan keterbatasan yang dimiliki. Mental workload yang berlebih berdampak buruk bagi individu karena menimbulkan penurunan produktivitas kerja. Perangkat khusus yang dapat digunakan untuk mengetahui tingkat mental workload seorang individu adalah Electroencephalogram (EEG). EEG adalah perangkat khusus yang digunakan untuk mengukur sinyal potensi listrik dari otak. Dataset yang digunakan dalam penelitian ini adalah STEW: Simultaneous Task EEG Dataset dengan 45 subjek. Dalam penelitian ini, telah dilakukan studi komparasi algoritma Random Forest, K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), dan Support Vector Machine (SVM) untuk klasifikasi mental workload berdasarkan sinyal EEG. Studi dilakukan untuk menentukan algoritma terbaik dalam klasifikasi dilihat dari segi nilai akurasi dan penggunaan memori saat proses klasifikasi. Dataset telah melalui beberapa tahapan, diantaranya pra-pemrosesan data, ekstraksi fitur, dan proses klasifikasi. Pra-pemrosesan data menerapkan pembagian data menjadi beberapa chunk. Untuk mendapatkan ciri dalam ekstraksi fitur, diterapkan metode Principal Component Analysis (PCA). Pada proses klasifikasi menggunakan pendekatan k-fold cross validation. Hasil studi penelitian ini adalah algoritma terbaik dari sisi akurasi adalah algoritma KNN, algoritma terbaik dari sisi waktu pembuatan model adalah algoritma Random Forest, serta algoritma terbaik dari sisi penggunaan memori adalah algoritma MLP.

scholarly journals KLASIFIKASI STATUS PEMBAYARAN PREMI MENGGUNAKAN ALGORITMA NEIGHBOR WEIGHTED K-NEAREST NEIGHBOR (NWKNN) (STUDI KASUS: PT. BUMIPUTERA KOTA SAMARINDA)

2020 ◽  
Vol 1 (2) ◽  
pp. 56-63
Author(s):  
Grassella Gunsyang ◽  
Ika Purnamasari ◽  
Fidia Deny Tisna Amijaya
Keyword(s):  
Cross Validation ◽  
Nearest Neighbor ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Algoritma Neighbor Weighted K-Nearest Neighbor (NWKNN) merupakan pengembangan dari algoritma K-Nearest Neighbor (KNN), dengan memberikan bobot pada setiap kelas yang akan diklasifikasikan. Penelitian ini membahas tentang klasifikasi menggunakan algoritma NWKNN yang diaplikasikan pada data status pembayaran premi. Tujuannya untuk mengetahui nilai eksponen (E) dan nilai ketetanggaan (K) yang optimal, serta nilai akurasi dari klasifikasi data status pembayaran Premi di PT. Bumiputera Kota Samarinda. Tahapan dalam penelitian ini yaitu menentukan nilai E dan nilai K menggunakan k-fold cross validation, menghitung jarak euclidean, menghitung bobot dan skor setiap kelas, melihat nilai skor terbesar untuk menentukan hasil klasifikasi, kemudian menghitung nilai akurasi klasifikasi. Hasil penelitian menunjukkan bahwa nilai K dan nilai E yang optimal untuk klasifikasi status pembayaran premi di PT. Bumiputera Kota Samarinda menggunakan NWKNN sebesar K=3 dan E=6 dengan nilai akurasi sebesar 75%.

scholarly journals Komparasi Algoritma Klasifikasi Data Mining untuk Memprediksi Tingkat Kematian Dini Kanker dengan Dataset Early Death Cancer

2019 ◽  
Vol 4 (2) ◽  
pp. 63
Author(s):  
Panny Agustia Rahayuningsih
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Random Forest ◽  
Cross Validation ◽  
Naive Bayes ◽  
Early Death ◽  
Naïve Bayes ◽  
T Test ◽  
Fold Cross Validation

Penyakit Kanker merupakan sepuluh besar penyakit pembunuh di dunia. Kanker merupakan penyakit yang ganas dan sulit disembuhkan jika penyebarannya sudah terlalu luas. Akan tetapi, pendeteksian sel kanker sedini mungkin dapat mengurangi resiko kematian. Penelitian ini bertujuan untuk memprediksikan tingkat kematian dini kanker pada penduduk Eropa dengan menggunakan 5algoritma klasifikasi yaitu: Desecion Tree, Naïve Bayes, k-Nearset Neighbour, Random Forest dan Neural Network dari algoritma tersebut algoritma mana yang dianggap paling baik untuk penelitian ini. Pengujian dilakukan dengan beberapa tahapan penelitian antara lain: dataset (pengumpulan data), pengolahan data awal, metode yang diusulkan, pengujian metode menggunakan 10-fold cross validation, evaluasi hasil dan uji beda t-test. Nilai alpha yang digunakan adalah 0.05. jika probabilitasnya >0.05 maka H0 diterima. Sedangkan jika probabilitasnya <0.05 maka Ho ditolak.Hasil dari penelitian yang mendapatkan performe terbaik dengan nilai akurasi sebesar 98,35% adalah algoritma Neural Network. Sedangkan, hasil penelitian menggunakan uji t-test algoritma dengan model terbaik yaitu: algoritma Random Forest dan Neural Network, algoritma Naïve Bayes lumanyan baik, algoritma Desecion Tree cukup baik dan algoritma yang kurang baik adalah algoritma K-Nearset Neighbour (K-NN).

scholarly journals Perbandingan Akurasi dan Waktu Proses Algoritma K-NN dan SVM dalam Analisis Sentimen Twitter

2019 ◽  
Vol 6 (2) ◽  
pp. 226-235
Author(s):  
Muhammad Rangga Aziz Nasution ◽  
Mardhiya Hayaty
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fold Cross Validation

Salah satu cabang ilmu komputer yaitu pembelajaran mesin (machine learning) menjadi tren dalam beberapa waktu terakhir. Pembelajaran mesin bekerja dengan memanfaatkan data dan algoritma untuk membuat model dengan pola dari kumpulan data tersebut. Selain itu, pembelajaran mesin juga mempelajari bagaimama model yang telah dibuat dapat memprediksi keluaran (output) berdasarkan pola yang ada. Terdapat dua jenis metode pembelajaran mesin yang dapat digunakan untuk analisis sentimen:  supervised learning dan unsupervised learning. Penelitian ini akan membandingkan dua algoritma klasifikasi yang termasuk dari supervised learning: algoritma K-Nearest Neighbor dan Support Vector Machine, dengan cara membuat model dari masing-masing algoritma dengan objek teks sentimen. Perbandingan dilakukan untuk mengetahui algoritma mana lebih baik dalam segi akurasi dan waktu proses. Hasil pada perhitungan akurasi menunjukkan bahwa metode Support Vector Machine lebih unggul dengan nilai 89,70% tanpa K-Fold Cross Validation dan 88,76% dengan K-Fold Cross Validation. Sedangkan pada perhitungan waktu proses metode K-Nearest Neighbor lebih unggul dengan waktu proses 0.0160s tanpa K-Fold Cross Validation dan 0.1505s dengan K-Fold Cross Validation.

scholarly journals Smartphone assist deep neural network model to recognize the high-quality tea using leaf maturity and its effect on leaf chlorophyll

2021 ◽  
Vol 13 (4) ◽  
pp. 1249-1255
Author(s):  
Utpal Barman ◽  
Ridip Dev Choudhury ◽  
Bipul Kumar Talukdar ◽  
George Bhokta ◽  
Sahrul Alom Choudhari ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Tea Leaves ◽  
High Quality ◽  
Leaf Maturity ◽  
Tea Leaf

Immature and tender tea leaves always produce high-quality tea than mature tea leaves. Depending on the maturity and age of the leaf, the colour and texture of the tea leaf are different. The photosynthesis capacity of the tea leaf also changes with the change of leaf maturity. Though the tea farmer plucks, classifies, and recognizes the best tea leaves (immature and tender) by viewing the visual symptoms and position of the leaves, the method is not authentic all time and leads to the overall degradation of the tea quality. The present study presents a smartphone assist tea leaf recognition system by analyzing the colour and texture properties of the tea leaf. The six different colour features and 4 Haralick texture features were extracted in the colour and grey domain of the leaf images. Three types of tea leaves, i.e., mature, immature, and tender, were classified using Deep Neural Network (DNN) with ADAM (Adaptive Moment Estimation) optimizer. With an accuracy of 97%, the DNN outperformed the Support Vector Machine (SVM) and K Nearest Neighbor (KNN). The SVM and KNN reported a total of 94.42% and 95.53% accuracy, respectively. The investigated system using DNN with an average precision and recall value of 98.67 and 98.34, respectively, may detect and classify the tea leaf maturity status. The system also can be used in AI-based tea plucking robotic systems or machines.

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