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All Journal Jurnal Ilmiah Sinus Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara)

Yustina Retno Wahyu Utami

STMIK Sinar Nusantara

Author-ID : 3669067

Computer Science & IT

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STEGANOGRAFI PADA CITRA BITMAP MENGGUNAKAN METODE LEAST SIGNIFICANT BIT BERSILANG UNTUK TEKS TERENKRIPSI BASE64 Albert Christie Giovani; Yustina Retno Wahyu Utami; Teguh Susyanto
Jurnal Ilmiah SINUS Vol 17, No 1 (2019): Vol. 17 No. 1 Januari 2019
Publisher : STMIK Sinar Nusantara

The development of internet has become one of the most popular data communication media. The ease of use and complete facilities are the advantages possessed by the internet. However, along with the development of internet media and applications that use the Internet, crime on information system increases as well. With various illegal information-gathering techniques developing, many are trying to access information that is not their right. There are several security techniques for sending messages confidentially and securedly, one of which is known as steganography. This study combined steganography and cryptography. The message was encrypted first using base64 then inserted using the LSB Crossed method. This method was aimed at making the process of extracting messages by unauthorized ones not easy. Embedding message into images was using the last binary number of the RGB value of an image by randomizing the placement of binary numbers by integrating base64 coding so that it combined base64 messages which next the text messages would be encrypted. The measurement results in the stego image using PSNR (Peak Signal to Noise Ratio) showed that the image quality after the insertion process was > 50 db

Penerapan Agglomerative Hierarchical Clustering Untuk Segmentasi Pelanggan Widyawati Widyawati; Wawan Laksito Yuly Saptomo; Yustina Retno Wahyu Utami
Jurnal Ilmiah SINUS Vol 18, No 1 (2020): Vol 18, No 1, Januari 2020
Publisher : STMIK Sinar Nusantara

As more businesses emerge, companies need to have the right marketing strategy to provide the best service to customers. The first step is to know the type of customer and make appropriate marketing strategies according to the type of customer. In this research, it is proposed for clustering customers so that an appropriate strategy for that customer group can be determined. The method used for cluster formation uses Agglomerative Hierarchical Clustering with Average Linkage approach and distance determination using Manhattan Distance. The variables in this research are Recency, Frequency, and Monetary (RFM). The results of testing using the Silhouette coefficient show that the results of 7 clusters are the best results when compared with 2 clusters up to 20 clusters because they have the smallest minus value. Based on the results of the Silhoutte coefficient, customer segmentation uses 7 clusters with each cluster representing the existing customer type.

Co-Authors Albert Christie Giovani Calvin Ivan Wiryawan Didik Nugroho Didik Nugroho Fajar Pamungkas Kustanto Kustanto Ricky Yandy Darmawantoro Siskak Akatani Kaudsar Sonia Ratnaning Pertiwi Sri Hariyati Fitriasih Sri Harjanto Suprihatin Suprihatin Suprihatin Suprihatin Taratya Indra Pratiwi Teguh Susyanto Wawan Laksito Yuly Saptomo Widyawati Widyawati

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