Eko Hari Rachmawanto
Dian Nuswantoro University

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Implementasi Pengamanan Citra Digital Berbasis Metode Kriptografi Vernam Cipher Tan Samuel Permana; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Egia Rosi Subhiyakto
Techno.Com Vol 16, No 4 (2017): November 2017
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1334.622 KB) | DOI: 10.33633/tc.v16i4.1267

Abstract

Penggunaan media online dalam melakukan aktivitas telah semakin marak terjadi pada dinamika masyarakat modern. Salah satu obyek sasaran dalam aktivitas online adalah citra digital. Citra digital ini dapat diperuntukan untuk kalangan terbatas saja sehingga mudah menjadi sasaran oleh peretas, terutama jika data citra digital tersebut bersifat penting. Disinilah kriptografi mengambil peran penting dalam mengamankan citra digital. Dengan menggunakan teknik Vernam Cipher, pesan citra digital dapat diacak dengan kunci yang berbeda untuk setiap karakter, sehingga pesan citra digital hanya dapat dibaca oleh penerima saja. Hasil enkripsi akan menghasilkan citra baru dengan adanya perubahan pada intensitas warna piksel. Dari 12 gambar dengan ukuran kurang dari 100 KB, tingkat keberhasilannya adalah 100%. Algoritma ini sangat cepat, dengan kecepatan enkripsi rata-rata 0,007785 dan dekripsi 0,006903 untuk gambar berformat JPEG dan memiliki ukuran piksel 384x384 Berdasarkan penelitian tersebut maka dapat disimpulkan bahwa Algoritma Vernam Cipher adalah algoritma yang baik dan cepat.
Imperceptible and secure image watermarking using DCT and random spread technique Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Christy Atika Sari; Nova Rijati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 4: August 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i4.9227

Abstract

Watermarking is a copyright protection technique, while cryptography is a message encoding technique. Imperceptibility, robustness, and safety are aspects that are often investigated in watermarking. Cryptography can be implemented to increase watermark security. Beaufort cipher is the algorithm proposed in this research to encrypt watermark. The new idea proposed in this research is the utilization of Beaufort key for watermark encryption process as well as for spread watermark when inserted as PN Sequence substitute with the aim to improve imperceptibility and security aspects. Where PN Sequence is widely used in spread spectrum watermarking technique. Based on the experimental results and testing of the proposed method proved that imperceptibility and watermark security are increased. Improved imperceptibility measured by PSNR rose by about 5dB and so did the MSE score better. Robustness aspect is also maintained which has been proven by the excellent value of NCC.
Hoax classification and sentiment analysis of Indonesian news using Naive Bayes optimization Heru Agus Santoso; Eko Hari Rachmawanto; Adhitya Nugraha; Akbar Aji Nugroho; De Rosal Ignatius Moses Setiadi; Ruri Suko Basuki
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 2: April 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i2.14744

Abstract

Currently, the spread of hoax news has increased significantly, especially on social media networks. Hoax news is very dangerous and can provoke readers. So, this requires special handling. This research proposed a hoax news detection system using searching, snippet and cosine similarity methods to classify hoax news. This method is proposed because the searching method does not require training data, so it is practical to use and always up to date. In addition, one of the drawbacks of the existing approaches is they are not equipped with a sentiment analysis feature. In our system, sentiment analysis is carried out after hoax news is detected. The goal is to extract the true hidden sentiment inside hoax whether positive sentiment or negative sentiment. In the process of sentiment analysis, the Naïve Bayes (NB) method was used which was optimized using the Particle Swarm Optimization (PSO) method. Based on the results of experiment on 30 hoax news samples that are widely spread on social media networks, the average of hoax news detection reaches 77% of accuracy, where each news is correctly identified as a hoax in the range between 66% and 91% of accuracy. In addition, the proposed sentiment analysis method proved to has a better performance than the previous analysis sentiment method.
An improved security and message capacity using AES and Huffman coding on image steganography Christy Atika Sari; Giovani Ardiansyah; De Rosal Ignatius Moses Setiadi; Eko Hari Rachmawanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 5: October 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i5.9570

Abstract

Information security is very important and has been widely implemented. Cryptography and steganography are two common methods that can be implemented to secure and conceal the information. In this research, the proposed AES algorithm for cryptography and DWT for steganography. However, in case of implementing DWT as steganography, there is a weakness which is a lower capacity. Based on DWT’s problem, proposed Huffman Coding to reduce the total of the message’s bit and increase the capacity. In the implementation, a message will be processed by using AES and compressed by using Huffman Coding then conceal in a cover using DWT. After doing several experiments using a 128x128 pixel message image and a 512x512 pixel of the cover image, achieved the average of MSE is 1.5676 and the average of PSNR result is above 40db which is 46.1878.
Orchid types classification using supervised learning algorithm based on feature and color extraction Pulung Nurtantio Andono; Eko Hari Rachmawanto; Nanna Suryana Herman; Kunio Kondo
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3118

Abstract

Orchid flower as ornamental plants with a variety of types where one type of orchid has various characteristics in the form of different shapes and colors. Here, we chosen support vector machine (SVM), Naïve Bayes, and k-nearest neighbor algorithm which generates text input. This system aims to assist the community in recognizing orchid plants based on their type. We used more than 2250 and 1500 images for training and testing respectively which consists of 15 types. Testing result shown impact analysis of comparison of three supervised algorithm using extraction or not and several variety distance. Here, we used SVM in Linear, Polynomial, and Gaussian kernel while k-nearest neighbor operated in distance starting from K1 until K11. Based on experimental results provide Linear kernel as best classifier and extraction process had been increase accuracy. Compared with Naïve Bayes in 66%, and a highest KNN in K=1 and d=1 is 98%, SVM had a better accuracy. SVM-GLCM-HSV better than SVM-HSV only that achieved 98.13% and 93.06% respectively both in Linear kernel. On the other side, a combination of SVM-KNN yield highest accuracy better than selected algorithm here.
Triple layer image security using bit-shift, chaos, and stream encryption Ajib Susanto; De Rosal Ignatius Moses Setiadi; Eko Hari Rachmawanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Md Kamruzzaman Sarker; Musfiqur Rahman Sazal
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (851.826 KB) | DOI: 10.11591/eei.v9i3.2001

Abstract

One popular image security technique is image encryption. This research proposes an image encryption technique that consists of three encryption layers, i.e. bit-shift encryption, chaos-based encryption, and stream encryption. The chaos algorithm used is Arnold's chaotic map, while the stream cipher algorithm used is RC4. Each layer has different cryptology characteristics in order to obtain safer image encryption. The characteristics of cryptology are permutation, confusion, diffusion, and substitution. The combination of the proposed encryption method aims to secure images against various attacks, especially attacks on statistics and differentials. The encryption method testing is done by various measuring instruments such as statistical analysis, i.e. entropy information, avalanche effect, and histogram, differential analysis, i.e. UACI and NPCR, visual analysis using PSNR and SSIM, and bit error ratio. Based on the results of experiments that the encryption method that we propose can work excellently based on various measurement instruments. The decryption process can also work perfectly this is evidenced by the ∞ value based on PSNR, and zero value based on SSIM and BER.
GOOD PERFORMANCE IMAGES ENCRYPTION USING SELECTIVE BIT T-DES ON INVERTED LSB STEGANOGRAPHY Christy Atika Sari; Eko Hari Rachmawanto; Edi Jaya Kusuma
Jurnal Ilmu Komputer dan Informasi Vol 12, No 1 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (940.59 KB) | DOI: 10.21609/jiki.v12i1.646

Abstract

Transmitting image through the internet needs to be secured because of risk to be stolen. Security techniques that can be used for securing data especially image are cryptography and steganography. Combine these techniques can provide double protection in image security. In this research, we proposed the used of T-DES encryption with a selective bit to improve the time performance because time aspect is one of the important aspects of data transmission process. Four MSB of the secret image will be selected, then it will be encrypted using T-DES. After that, this encrypted results will be combined with other 4 LSB. This encryption scheme result will be embedded into a cover image using inverted LSB because inverted LSB can produce high imperceptible value. From 6 testing images which encrypted using proposed scheme present that proposed encryption scheme is twice faster than classic triple DES and slightly faster than double DES. While the embedding scheme can produce PSNR value above 40 dB with the range between 51 dB to 61 dB as well as SSIM which close to 1. This result denoted that proposed scheme generated good quality of stego images.
Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination Suprayogi Suprayogi; Christy Atika Sari; Eko Hari Rachmawanto
Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i2.6734

Abstract

The corona virus (2019-nCoV), commonly known as COVID-19 has been officially designated as a global pandemic by the WHO. Twitter, is one of the social media used by many people and is popular among internet users in expressing opinions. One of the problems related to Covid-19 and causing a stir is the procurement of the Covid-19 vaccine. The procurement of the vaccine caused various opinions in Indonesian society, where the uproar was also quite busy being discussed on Twitter and even became a Trending Topic. The opinions that appear on Twitter will then be used as data for the Sentiment Analysis process. One of the members of the House of Representatives (DPR), namely RibkaTjiptaning was also included in the Trending Topic list on Twitter for refusing to receive the Covid-19 vaccine. Sentiment analysis itself is a computational study of opinions, sentiments and emotions expressed textually. Sentiment analysis is also a technique to extract information in the form of a person's attitude towards an issue or event by classifying the polarity of a text. Research related to Sentiment Analysis will be examined by dividing public opinion on Twitter social media into positive and negative sentiments, and using the K-Nearest Neighbor (KNN) algorithm to classify public opinion about COVID-19 vaccination. In the testing section, the Confusion Matrix method is used which then results in an accuracy of 85%, precision of 100%, and recall of 78.94%.
Handwritten Javanese script recognition method based 12-layers deep convolutional neural network and data augmentation Ajib Susanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Md Kamruzzaman Sarker
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1448-1458

Abstract

Although numerous studies have been conducted on handwritten recognition, there is little and non-optimal research on Javanese script recognition due to its limitation to basic characters. Therefore, this research proposes the design of a handwritten Javanese Script recognition method based on twelve layers deep convolutional neural network (DCNN), consisting of four convolutions, two pooling, and five fully connected (FC) layers, with SoftMax classifiers. Five FC layers were proposed in this research to conduct the learning process in stages to achieve better learning outcomes. Due to the limited number of images in the Javanese script dataset, an augmentation process is needed to improve recognition performance. This method obtained 99.65% accuracy using seven types of geometric augmentation and the proposed DCNN model for 120 Javanese script character classes. It consists of 20 basic characters plus 100 others from the compound of basic and vowels characters.
Improved Javanese script recognition using custom model of convolution neural network Ajib Susanto; Ibnu Utomo Wahyu Mulyono; Christy Atika Sari; Eko Hari Rachmawanto; De Rosal Ignatius Moses Setiadi; Md Kamruzzaman Sarker
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp6629-6636

Abstract

Handwriting recognition in Javanese script is not widely developed with deep learning (DL). Previous DL and machine learning (ML) research is generally limited to basic characters (Carakan) only. This study proposes a deep learning model using a custom-built convolutional neural network to improve recognition accuracy performance and reduce computational costs. The main features of handwritten objects are textures, edges, lines, and shapes, so convolution layers are not designed in large numbers. This research maximizes optimization of other layers such as pooling, activation function, fully connected layer, optimizer, and parameter settings such as dropout and learning rate. There are eleven main layers used in the proposed custom convolutional neural network (CNN) model, namely four convolution layers+activation function, four pooling layers, two fully connected layers, and a softmax classifier. Based on the test results on the Javanese script handwritten image dataset with 120 classes consisting of 20 basic character classes and 100 compound character classes, the resulting accuracy is 97.29%.