Article Per Year (5 Year)
p-Index From 2019 - 2024
0.444
P-Index
This Author published in this journals
All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi
Simanullang, Sanco
Unknown Affiliation
Computer Science & IT

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

Found 2 Documents
Search

 1 
PENGAMANAN DATA DALAM JARINGAN LAN DENGAN MENGGUNAKAN ALGORITMA CHIPER TRANSPOSISI Simanullang, Sanco
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 5 No. 2 (2019): September 2019
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v5i2.421

Abstract

Data security is important in the implementation of information technology, especially in the field of computers, which allows thousands of people and computers around the world to be connected in a virtual world known as cyberspace or the internet. This can create new challenges and demands for the availability of a data security system that is as sophisticated as the advances in computer technology itself. In cryptography, data sent over the network will be disguised in such a way that even if the data can be read by third parties, it should not be understood by unauthorized parties. Data to be sent and has not been encrypted which produces ChipperTtext. The implementation of the transposition algorithm for securing data flowing in the Local Area Network (LAN), the transposition process changes the arrangement of letters from the source text (plaintext), with a column transposition cipher, to obtain the words in a barred manner. In the transposition cipher, the plaintext is the same, but the sequence is changed. . This algorithm transposes a series of characters in the text. Another name for this method is permutation because transpose each character in the text is the same as permutating the characters so that this application generates encrypted data on the network stream and returns it to plaintext at the final destination. So that it can be ensured that the information and data sent to other parties are safe from unauthorized parties.
PENINGKATAN PERFORMA ALGORITMAK-NEAREST NEIGBORD DALAM KELASIFIKASI DATA TIDAK SEIMBANG MENGGUNAKAN METODE SPIDER-2 Perangin-angin, Resianta; Simanullang, Sanco; Manalu, Darwis Robinson
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 6 No. 2 (2020): September 2020
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v6i2.426

Abstract

Class imbalance has become an ongoing problem in the field of Machine Learning and Classification. The group of data classes that are less known as the minority group, the other data class group is called the majority group (majority). In essence real data, data that is mined directly from the database is unbalanced. This condition makes it difficult for the classification method to perform generalization functions in the machine learning process. Almost all classification algorithms such as Naive Bayes, Decision Tree, K-Nearest Neighbor and others show very poor performance when working on data with highly unbalanced classes. The classification methods mentioned above are not equipped with the ability to deal with class imbalance problems. Many data processing methods are often used in cases of data imbalance, in this case research will be carried out using the Spider2 method. In this study, the Ecoli dataset was used, while for this study, 5 (five) different Ecoli datasets were used for each dataset for the level of data imbalance. After testing datasets with different levels of Inbalancing Ratio (IR), starting from the smallest 1.86 to 15.80, the results that explain that the KNN algorithm can improve its performance even better in terms of unbalanced data classification by adding the SPIDER- method 2 as a tool in dataset processing. In the 5 trials, the performance of the KNN algorithm can increase GM by 5.81% and FM 14.47% by adding the SPIDER-2 method to KNN.
Co-Authors Darwis Robinson Manalu Resianta Perangin-Angin