Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JATI (Jurnal Mahasiswa Teknik Informatika) MEANS (Media Informasi Analisa dan Sistem)
Saefurrahman, Saefurrahman
Unknown Affiliation
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
DETEKSI PHISHING WEBSITE MENGGUNAKAN SUPPORT VECTOR MACHINE, GRADIENT BOOSTING, DAN NEURAL NETWORKS Indriani, Vanyariska; Listiyono, Hersatoto; Saefurrahman, Saefurrahman
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 2 (2025): JATI Vol. 9 No. 2
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i2.13214

Abstract

Di era digital, phishing menjadi ancaman yang signifikan terhadap keamanan data pribadi pengguna internet. Website phishing yang meniru tampilan situs asli bertujuan untuk memperoleh informasi sensitif seperti data pribadi dan keuangan. Penelitian ini bertujuan untuk menguji efektivitas tiga algoritma machine learning, yaitu Support Vector Machine (SVM), Gradient Boosting, dan Neural Networks dalam mendeteksi URL phishing. Dataset PhiUSIIL yang terdiri dari 235.795 sampel data digunakan, yang mencakup URL phishing dan legitim dengan 54 fitur. Metode yang diterapkan meliputi preprocessing data, pembagian data latih dan uji, serta normalisasi menggunakan MinMaxScaler. Ketiga model dievaluasi berdasarkan akurasi, F1-Score, Confusion Matrix, dan waktu komputasi. Hasil penelitian menunjukkan bahwa model Gradient Boosting memperoleh akurasi dan F1-Score 100%, dengan waktu komputasi tercepat hanya 1 menit, menjadikannya pilihan terbaik untuk deteksi phishing. Sementara itu, SVM dan Neural Networks juga menunjukkan hasil yang sangat baik, masing-masing dengan akurasi 99,99%, namun dengan waktu komputasi yang lebih lama. Berdasarkan hasil tersebut, penelitian ini menyimpulkan bahwa Gradient Boosting adalah model yang paling efisien untuk mendeteksi phishing pada dataset yang digunakan.
Chatbot Telegram untuk Rekomendasi Pariwisata Daerah Semarang Menggunakan Framework Rasa Alvinnajmi, Abid Ridlo; Soelistijadi, R.; Saefurrahman, Saefurrahman
MEANS (Media Informasi Analisa dan Sistem) Volume 9 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/means.v9i1.3393

Abstract

Tourism is a leading industry in various countries because it can improve the country's economy. One city that has potential for tourism is Semarang, because this city has unique culture and beautiful nature. However, there are too many sources of information regarding tourist attractions such as the city of Semarang, which makes tourists sometimes confused about determining tourist destinations that suit their wishes at that time. Therefore, we need a system that can provide recommendations for tourism spots in a destination city. This research aims to develop a Question Answering System or digital question and answer system using a chatbot (ChatterBot). Chatbots are used as information service providers that can make it easier for tourists who are looking for information about tourist attractions. chat bot-based information service systems can work 24 hours or throughout the day, reducing the intensity of direct physical contact with officers and saving operational costs. The chatbot implementation is built on a Machine Learning Framework using RASA Open Source with the Python programming language. Basic knowledge of the chatbot system is drilled based on the FAQ (Frequently Asking Questions) dataset with tourism research objects in the Semarang area. The evaluation results and system performance based on data testing obtained a model accuracy level of 0.91. Furthermore, the weighted average value in the ConfusionMatrix produces a precision of 0.97, recall of 0.94, and an F1 score of 0.95. Training and processing models locally.
Co-Authors Alvinnajmi, Abid Ridlo Hersatoto Listiyono Indriani, Vanyariska R. Soelistijadi