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All Journal Jurnal TIMES Sinkron : Jurnal dan Penelitian Teknik Informatika Journal Information System Development METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Informatika Kaputama (JIK) Jurnal Pengabdian Pada Masyarakat METHABDI TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Resianta Perangin-Angin
Universitas Methodist Indonesia
Religion Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Medicine & Pharmacology Physics Public Health

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Journal : METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi

 1 
OPTIMASI KETAHANAN WATERMARKING AUDIO DIGITAL MENGGUNAKAN ALGORITMA RSA DAN MSB Rumahorbo, Benget; Perangin-angin, Resianta
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 7 No. 1 (2021): Maret 2021
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v7i1.409

Abstract

Copyright is a serious problem in the digital world, the process of sending and distributing digital media is so easy nowadays, copyright in the digital world is very detrimental to those who feel that their digital rights are copied and pasted or taken without the consent of the creator. Therefore we need a way where when a digital file can be identified as original as a product, one of the right ways is to use a watermark technique. But often this watermark process can be lost or cannot be extracted because the digital file has gone through a compression process, duplicate, or something else. So in this study, it will be tried to increase the durability of a watermark in digital audio as a solution for identifying copyrighted digital works. Where the watermark process will use the RSA and MSB algorithms to enter information into a digital audio file, later this information can be extracted to view copyright ownership information from the digital audio. And it is hoped that this watermarking is resistant to various digital audio processes such as compression, duplication, and editing carried out on the file. the information that is inserted into is maintained without compromising the quality of the digital audio.
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 Bangun, Joy Erivan Pratama Berlian Juni R Marpaung Br. Batubara, Anggi Natasya Br. Karo, Selli Afnita Br. Sembiring Pelawi, Pindi Alpioninta Darwis Robinson Manalu Delvi Natalina Br Tarigan Elisabeth, Duma Megaria Elvika Rahmi Emma Rosinta Simarmata Eva Julia G. Harianja Eva Julia Gunawati Harianja Eva Julia Gunawati Harianja, Eva Julia Gunawati Eviyanti N. Purba Fenina Twince Tobing Ginting Babo, Aris Franata Giska Yufani Gortap Lumbantoruan Harianja, Eva J. G. Harianja, Eva Julia G. Hutagalung, Estri Aprilia Hutagaol, Ryan Philip Hutapea, Marlyna I. Ijonris, Yusuf Ika Yusnita Sari Indra Kelana Jaya Jamaluddin Jamaluddin Jamaluddin Jepriyanta N. Brahmana Jimmy F. Naibaho Jonathan H. Saragih Jonathan Hamonangan Saragih Jujur Marentha Nababan Junika Napitupulu Lyna M. N. Hutapea Mahendra Tlapta Sitepu Marpaung, Berlian Juni R Marpaung, Flora Moris Raichel Sitanggang Mufria J. Purba Nainggolan, Rena Napitupulu, Thomson Januari Paiman Nababan Panjaitan, Calvin Nicolas Petty Exclesia Pardosi Purba, Eviyanti N. Purba, Eviyanti Novita Rasmulia Sembiring Rena Nainggolan Reynaldi Pantun Sianturi Rijois I. E. Saragih Rimbun Siringoringo Rimbun Siringoringo, Rimbun Robert Simangunsong Rumahorbo, Benget Siboro, Yohana Natalia Sidabalok, Valentino Sihaloho, Senta Egrioni Simanjuntak, Stevani L. Z. Simanullang, Sanco Siringoriongo, Rimbun Sitepu, Fernanda Jekita F. Sitorus, Hegi Audria Sofya C. Sitompul Tobing, Fenina A. T. Torong, Yepta Efraim Yohana Angelita Manullang Zalukhu, Delianus