Analisis Perbandingan Korelasi Spearman Dan Maximal Information Coefficient Dalam Seleksi Fitur Website Phishing Menggunakan Algoritma Machine Learning

Moedjahedy, Jimmy; Setyanto, Arief; Aryasa, Komang

doi:10.22303/csrid.12.2.2020.107-116

Cited by 3 publications

(2 citation statements)

References 14 publications

(14 reference statements)

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“…Pada penelitian ini, nilai akurasi Random Forest masih di bawah algoritma Multilayer Perceptron. Sedangkan pada penelitian Moedjahedy nilai akurasi Random Forest dengan menggunakan seleksi fitur Spearman dan MICe TICe lebih tinggi daripada Logistic Regression [8].…”

Section: Pendahuluanunclassified

Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection

Farida¹,

Mustopa

2023

SISTEMASI

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Dunia pada saat ini tengah mengalami perkembangan pada bidang teknologi dan komunikasi secara massif, terlebih pada masa pandemi saat ini yang mengharuskan semua proses pembelajaran bahkan bekerja secara daring. Hal ini yang memicu banyaknya kejahatan di dunia internet. Salah satunya yaitu mencuri data pengguna internet melalui sebuah website palsu yang dibangun seperti asli atau disebut juga website phising. Pada penelitian ini, untuk mengatasi maraknya website phising di dunia maya, maka diperlukan suatu model klasifikasi untuk mendeteksi website yang terindikasi phising dengan menggunakan kinerja terbaik dari salah satu algoritma klasifikasi logistic regression dan random forest. Untuk meningkatkan kinerja algoritma klasifikasi dilakukan seleksi fitur menggunkan metode correlation-based feature selection (CFS) untuk menyeleksi atribut yang paling berpengaruh dalam mendeteksi web phising. Berdasarkan hasil uji coba, penerapan algoritma klasifikasi logistic regression dan random forest dalam klasifikasi web phising dihasilkan akurasi sebesar 93,035 % dan 96.834 %, setelah dilakukan seleksi fitur dengan CFS akurasi yang dihasilkan menjadi 92.718 % dan 97.015 %. Dari uji coba terjadi peningkatan akurasi pada random forest sebesar 0,181 % dan terjadi penurunan yang tidak signifikan pada logistic regression. Hasil uji coba membuktikan bahwa seleksi fitur dengan CFS dapat menghilangkan atribut redundan dan dihasilkan akurasi algoritma klasifikasi yang tidak jauh berbeda ketika atribut lengkap dan Random Forest memiliki akurasi lebih baik setelah menggunakan CFS .

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Section: Pendahuluanunclassified

Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection

Farida¹,

Mustopa

2023

SISTEMASI

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“…Tahap kinerja, tahap ini bisa juga dikatakan dengan tahap Eksekusi, dimana pada tahap ini pelaku hacker akan melakukan serangan phishing dengan cara menggunakan tools phishing kemudian memberikan link kepada korban. sehingga hacker dapat mencuri data korban [5]. Tools yang dianalisis meliputi ZPhisher, Shellphish dan WhPhisher, akan ada analisis, pengukuran dan pengujian dalam metode penelitian ini.…”

Section: Analisis Kerentanan Kejahatan Online Phising Menggunakan Too...unclassified

Analisis Kerentanan Kejahatan Online Phising Menggunakan Tools Zphisher, Shellphish Dan Whphisher

Wahyuni,

Putu Putri Cahayani,

I Gusti Ngurah Yogi Wicaksana

et al. 2023

teknik

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In the era of increasingly advanced and widespread technology as it is today, personal data is an asset that really needs to be protected by an organization or individual, personal data information that is private is very important to be protected so that it cannot be accessed by unauthorized parties. Phishing attacks are currently still increasing, this happens because the perpetrators of phishing attacks understand to trick victims where the victim is still weak to understand the security system on personal data information. The purpose of this article is to explain how these phishing attacks can still be carried out, so that technology users can better understand phishing attacks and further improve the security of users' personal data themselves. Website phishing attack technique is one type of phishing attack that can be done with several types of tools including Zphisher, Shellphish and WhPhisher. With the exposure of this article, technology users get information regarding phishing website attacks and actions to deal with them. Keywords: website phishing, Zphisher, Shellphish, WhPhisher

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Exploratory Data Analysis (EDA) of Social Aid Recipients in Murung Raya District Central Kalimantan Province with Machine Learning Approach

Regina,

M. Riko Anshori Prasetya,

Ahmad Hidayat

2024

Information System and Technology

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Various kinds of problems often occur in programs implemented by the government, one of which is the problem that often arises is the uneven distribution of social assistance carried out to people with low economic levels, poverty is the main problem being faced by all countries in the world, including Indonesia. The purpose of implementing the dataset of social assistance recipients using EDA. To find the correlation of 4 variables, namely Number of Population, Number of Recipients, Occupation, and Age in the dataset of social assistance recipients in Murung Raya District, Central Kalimantan Province, with a machine learning approach. Then the research method used here is quantitative methods and experiments with machine learning approaches. So by using the EDA method on data on social assistance recipients in Murung Raya Regency, Central Kalimantan Province, it can be seen that the variable Number of population has.

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Analisis Perbandingan Korelasi Spearman Dan Maximal Information Coefficient Dalam Seleksi Fitur Website Phishing Menggunakan Algoritma Machine Learning

Cited by 3 publications

References 14 publications

Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection

Comparison of Logistic Regression and Random Forest using Correlation-based Feature Selection for Phishing Website Detection

Analisis Kerentanan Kejahatan Online Phising Menggunakan Tools Zphisher, Shellphish Dan Whphisher

Exploratory Data Analysis (EDA) of Social Aid Recipients in Murung Raya District Central Kalimantan Province with Machine Learning Approach

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