Analisa Dan Prediksi Iklan Lowongan Kerja Palsu Dengan Metode Natural Language Programing  Dan Machine Learning

Sabita, Hary; Fitria, Fitria; Herwanto, Riko

doi:10.30873/ji.v21i1.2865

Cited by 3 publications

(5 citation statements)

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“…The results show that XGBoost gives the highest R-Squared value of 81% in the prediction of all metrics used in this study. Research conducted by Hary [17] predicted a false job vacancy click-through rate. The evaluation and validation model used Naïve Bayes as the baseline model and Stochastic Gradient Descent as the final model.…”

Section: Related Workmentioning

confidence: 99%

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Deep Neural Network for Click-Through Rate Prediction

RIANA¹

2022

IJSECS

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Predicted click-through rate is one of the most frequently used criteria to determine the effectiveness of an ads. In advertising production, click-through predictions are very influential for the company that places the ads. Click-through rates need to be predicted accurately because accurate prediction results determine whether the click-through rate is exactly clicked or not by the viewing consumer. Predicted click-through can be done on advertising and social network datasets. The use of these two datasets is intended to make the comparison results more convincing from the proposed method. The purpose of this study is to compare two advertising and social network datasets, by proposing the application of the Deep Neural Network (DNN) model by testing hyperparameter variations to find a better architecture in predicting whether or not users click on an advertisement. The hyperparameter variations include 3 variations of the hidden layer, 2 variations of the activation function, namely ReLuand Sigmoid, 3 variations of the optimization (RMSprop, Adam, and Adagrad),and 3 variations of the learning rate (0.1, 0.01, and 0.001). The results of experiments conducted with the advertising parameter dataset with hidden layer of 3, learning rate of 0.01,and Adam optimization with an accuracy value of 99.90%, AUC of 99.90% and Precision-Recallof99.89% while the data for social network ads parameters with hidden layer of 5, learning rate of 0.1 and Adam optimization with accuracy of 92.25%, AUC of 92.72%,andPrecision-Recallof 89.70%.

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Section: Related Workmentioning

confidence: 99%

“…The XGBoost method has the highest R-Squared value of 81% Hary, sabita (2021) [17] Job vacancies (17.880K)…”

Section: Related Workmentioning

confidence: 99%

Deep Neural Network for Click-Through Rate Prediction

RIANA¹

2022

IJSECS

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“…Tahap ini juga berfungsi untuk mendeteksi data-data bermasalah dan data-data null. Melalui hasil ekplorasi data ini akan ditentukan proses yang akan dilakukan terhadap data yang tersedia [7].…”

Section: B Exploreunclassified

Prediksi Kualitas Air Menggunakan Algoritma Naïve Bayes Dan Random Forest

Christian

Jacky

Winata

et al. 2022

jkt

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Air sangat penting bagi kehidupan semua organisme termasuk manusia, tumbuhan, atau hewan. Kualitas air yang baik sangat penting, pencemaran air dapat menimbulkan risiko yang berbahaya. Saat ini untuk mendeteksi kualitas air perlu dilakukan uji laboratorium. Pengujian ini membutuhkan analisis yang kompleks sehingga membutuhkan waktu yang lebih lama untuk mendeteksi kualitas air. Oleh karena itu, peneliti akan melakukan penelitian tentang prediksi kualitas air dengan melihat beberapa parameter yang mengandung informasi air tersebut. Dataset kualitas air berasal dari Kaggle. Metode data mining yang digunakan adalah algoritma Random Forest dan algoritma Naïve Bayes. Kedua algoritma ini merupakan algoritma klasifikasi yang dapat membantu kita untuk memprediksi kualitas air. Dengan menggunakan kedua algoritma tersebut didapatkan akurasi 79% untuk algoritma Random Forest dan akurasi 55% untuk algoritma Naïve Bayes. Setelah itu kami mengimplementasikan kedua algoritma tersebut ke website sederhana dengan framework flask. Penelitian selanjutnya dapat mencoba menggunakan algoritma lain seperti ANN, K-NN, atau Decision Tree.

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“…Job recruitment or employment scams have increased during the Covid-19 pandemic. According to Consumer News and Business Channel (CNBC), job scams have doubled since 2017 [14]. As a result, many victims are deceived by fake job recruitment because fraudsters provide very tempting job offers to job seekers [15].…”

Section: Introductionmentioning

confidence: 99%

“…As a result, many victims are deceived by fake job recruitment because fraudsters provide very tempting job offers to job seekers [15]. Scams can occur in various ways: (1) Job advertisements with attractive amounts of income to collect applicant personal data, including address, bank account, social security number, and more (2) Asking applicants to take an online test for a few minutes and then direct them to fake application sites to collect banking information, payments for specific purposes, and (3) downloading any virus or malware on the applicant's computer or mobile device to obtain survey system history [14]. This makes it necessary to detect fake job recruitment on the internet for the safety of many people.…”

Section: Introductionmentioning

confidence: 99%

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

Rofik,

Hakim,

Unjung

et al. 2024

matrik

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Online job searching is one of the most efficient ways to do this, and it is widely used by people worldwide because of the automated process of transferring job recruitment information. The easy and fast process of transferring information in job recruitment has led to the rise of fake job vacancy fraud. Several studies have been conducted to predict fake job vacancies, focusing on improving accuracy. However, the main problem in prediction is choosing the wrong parameters so that the classification algorithm does not work optimally. This research aimed to increase the accuracy of fake job vacancy predictions by tuning parameters using GridSearchCV. The research method used was SVM and Gradient Boosting with parameter adjustments to improve the parameter combination and align it with the predicted model characteristics. The research process was divided into preprocessing, feature extraction, data separation, and modeling stages. The model was tested using the EMSCAD dataset. This research showed that the SVM algorithm can achieve the highest accuracy of 98.88%, while gradient enhancement produces an accuracy of 98.08%. This research showed that optimizing the SVM model with GridSearchCV can increase accuracy in predicting fake job recruitment.

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Analisa Dan Prediksi Iklan Lowongan Kerja Palsu Dengan Metode Natural Language Programing Dan Machine Learning

Cited by 3 publications

References 0 publications

Deep Neural Network for Click-Through Rate Prediction

Deep Neural Network for Click-Through Rate Prediction

Prediksi Kualitas Air Menggunakan Algoritma Naïve Bayes Dan Random Forest

Optimization of SVM and Gradient Boosting Models Using GridSearchCV in Detecting Fake Job Postings

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