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All Journal SMATIKA Rainstek : Jurnal Terapan Sains dan Teknologi BIMASAKTI KOPEMAS Smatika Jurnal : STIKI Informatika Jurnal
Akhmad Zaini
Universitas PGRI Kanjuruhan Malang
Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Mathematics Social Sciences

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Journal : BIMASAKTI

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INVESTIGASI PERBANDINGAN COSINE SIMILARITY DAN EUCLIDEAN DISTANCE DALAM DETEKSI PHISHING ATTACK MENGGUNAKAN METODE K-NEAREST NEIGHBOR Da Frosa, Bibiana; Akhmad Zaini; Muhammad Priyono Tri Sulistyanto
Jurnal Fakultas Teknologi Informasi Vol 6 No 2 (2024): BIMASAKTI
Publisher : Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v6i2.10107

Abstract

The development of information technology affects various aspects of life. However, this positive impact also opens up opportunities for growing cybercrime, known as cybercrime (Iman et al., 2020). These crimes, such as carding, hacking, and phishing, threaten security in the digital realm (Gulo et al., 2021). Phishing, as a form of cybercrime, involves sending fake links to steal victim information (Wibowo & Fatimah, 2017). In the midst of the development of information technology systems, data mining has emerged as a solution, enabling all valuable information from big data. K-Nearest Neighbors (KNN) is a machine learning algorithm used for classification and regression (Dewi Obert & Gusmana, 2018). In K-Nearest Neighbor, distance methods such as euclidean distance, Manhattan distance, cosine similarity, and jaccard similarity are commonly used. The focus of this research is on euclidean distance and cosine similarity which are considered efficient and commonly used. The evaluation results show that the second method, cosine similarity and Euclidean distance, has a similar level of accuracy and speed in detecting phishing attacks. However, Euclidean distance stands out in phishing detection with an accuracy rate of 87.70% and a speed of 0.0172. Meanwhile, cosine similarity reaches an accuracy rate of 87.57% with a speed of 0.0360. Looping analysis consistently confirms the Euclidean distance speed advantage. In phishing attack detection, Euclidean distance is proven to be more effective in accuracy and speed.
IMPLEMENTASI METODE FUZZY TSUKAMOTO BERBASIS WEBSITE PADA PENERIMAAN KARYAWAN BARU DI PT JAVA INDOSINERGI CREATIVE Rohman, Ahmad Zainur; Moh Ahsan; Akhmad Zaini
Jurnal Fakultas Teknologi Informasi Vol 5 No 2 (2023): BIMASAKTI
Publisher : Fakultas Sains dan Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/bimasakti.v5i2.8978

Abstract

Challenges that must be overcome to help PT. Java Indosinergi Creative with its employee selection process developed a decision support system. Currently, companies rely on a manual approach, which can produce ineffective results and poor accuracy. Tsukamoto's Fuzzy method is used in Indosinergi Creative's Java decision support system software which assists in the hiring process, enabling faster processing and providing recommendations and considerations for decision making at later stages of the hiring process. By comparing expert ratings with those generated by other ranking algorithms, this study using Fuzzy Tsukamoto provides useful information for making hiring decisions. The authors' questionnaire study found that, on average, respondents scored 79.38% when using this decision aid system.
Co-Authors Abdul Aziz Da Frosa, Bibiana Danang Aditya Nugraha Diana Pungki Heri Santoso Heri Santoso Hestiningtyas Yuli Pratiwi Kusumawati, Enike Dwi Moh Ahsan Nur Hikmah Pieter Sahertian Rohman, Ahmad Zainur Sulistyanto, Muhammad Priyono Tri Windy Syarifudin