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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi)
Ibnu Utomo Wahyu Mulyono
Dian Nuswantoro University
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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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.
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.
Community Empowerment Through Financial Management Training and Business Development of The Borobudur Homestay in Candirejo Village, Magelang District Imang Dapit Pamungkas; Dian Indriana Hapsari; Emik Rahayu; Izza Ulumuddin Ahmad Asshofi; Joseph Aldo Irawan; Aji Kusumah Ramdhani Ramdhani; Karis Widyatmoko; Ibnu Utomo Wahyu Mulyono; Nurjanah Nurjanah
SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Vol. 4 No. 1 (2023)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/spekta.v4i1.7735

Abstract

Background: Community-based tourism is increasingly being considered a more sustainable alternative. Tourism Villages can be a step towards realizing sustainable tourism. This is because the tourism village has the opportunity to open up as much participation as possible for the local community. However, this does not mean anything if managers, including accommodation providers, need qualified organizational and financial management skills. Contribution: This activity aims to provide knowledge to homestay managers in Candirejo Village to properly manage business finances based on SAK EMKM (Standar Akuntansi Keuangan Entitas Mikro, Kecil, dan Menengah) Method: The method used by researchers is through training and empowerment of homestay managers Results: The result of this activity is an increased understanding of SAK EMKM in Candirejo Village. After the training, homestay managers have also started implementing SAK EMKM in their financial reports. As a result, the financial position can be monitored, financial leaks can be minimized, and homestay business management can be carried out more effectively and efficiently. Conclusion: The activities that were held went well. This is known from the excellent understanding of SAK EMKM after the training was given
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%.
Co-Authors Aji Kusumah Ramdhani Ramdhani Ajib Susanto Christy Atika Sari De Rosal Ignatius Moses Setiadi Dian Indriana Hapsari Eko Hari Rachmawanto Emik Rahayu Imang Dapit Pamungkas Izza Ulumuddin Ahmad Asshofi Joseph Aldo Irawan Md Kamruzzaman Sarker Md Kamruzzaman Sarker Musfiqur Rahman Sazal Nurjanah Nurjanah Widyatmoko Karis