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Alamsyah
Computer Science Department, Faculty Of Mathematics And Natural Sciences, Universitas Negeri Semarang, Indonesia
Computer Science & IT

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The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease Hestu Aji Prihanditya; Alamsyah
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : Surya Hijau Manfaat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.8

Abstract

In the health sector, data mining can be used as a recommendation to predict a disease from the collection of patient medical record data or health data. One of the techniques can be applied is classification with the C4.5 algorithm. The increasing accuracy can be conducted in data transformation using zscore normalization method. In addition, the implementation of the ensemble method can also improve accuracy of C4.5 algorithm, namely boosting or adaboost. The purpose of this study was determinin the implementation of zscore normalization in the pre-processing and adaboost stages of the C4.5 algorithm and determing the accuracy of the C4.5 algorithm after applying zscore and adaboost normalization in diagnosing chronic kidney disease. In this study, the mining process used k-fold cross validation with the default value k = 10. The implementation of the C4.5 algorithm obtained an accuracy of 96% while the accuracy of the C4.5 algorithm with the zscore normalization method obtained an accuracy of 96.75%. The highest accuracy was obtained from the addition of the boosting method to the C4.5 algorithm and zscore normalization obtained the accuracy of 97.25%. The increasing accuracy was obtained of 1.25% which compared to the accuracy C4.5 algorithm.
Data Security System of Text Messaging Based on Android Mobile Devices Using Advanced Encrytion Standard Dynamic S-BOX Akhmad Sahal Mabruri; Alamsyah
Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020
Publisher : Surya Hijau Manfaat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v1i1.10

Abstract

Most of the recent technologies are turning to mobile platforms, Android becames one of the most widely used OS. Eventhough it has complete features, even it's not safe enough such like Chat Messenger. The security of messages distribution is a challenge to increase of vulnerable distribution of information through the network today. Therefore, a data security or cryptographic algorithm is needed to secure the messages so that it cannot be read by irresponsible people. National Institute of Standard and Technology (NIST) established the Advanced Encrytion Standard (AES) cryptographic algorithm as a standard encryption algorithm that is safe and can be used globally. AES algorithm is included in block cipher cryptography that uses substitution boxes (S-BOX) in its operations, so that algorithmically can make input and output unrelated. So, it can provide more varied output in the process, we need a dynamic S-BOX. In this research, dynamic S-BOX generalized using XOR operations from affine transformations with 8-bit binary element matrices arranged and randomly to produce as many as 256 S-Boxes. The application of dynamic AES with S-BOX algorithm on Android-based messenger chat application is built using the Java programming language and database hierarchy for data storage. The implementation results showed that the algorithm was running well and could encrypt the text of the message to ciphertext and decrypt the ciphertext to the original message. This research can be used as a reference so that further researchers can merge the AES algorithm with other algorithms to improve the security of encryption in text files, documents, images, videos or other types of files.
Security Improvement Of Aes Algorithm Using S-Box Modification Based On Strict Avalanche Criterion On Image Encryption David Topanto; Alamsyah Alamsyah
Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i1.70

Abstract

Communication is something that cannot be separated from humans as social creatures. Images are the most commonly used visual communication in today's era. On the other hand, sending images via wireless networks is very vulnerable to piracy. AES, as one of the best cryptographic algorithms, can be applied as a solution. Even so, the AES algorithm still has weaknesses, which are weak against linear attacks and differential cryptanalysis. One solution to overcome the weaknesses of the AES algorithm is to use a stronger S-box. One of the methods to measure the strength of an S-box is the Strict Avalanche Criterion (SAC). The dataset is divided into four categories based on the image type and size of the pixels. Data that has been encrypted using the proposed algorithm will be compared with data that has been encrypted using the standard AES algorithm. Cipherimages (encrypted data) are tested using histogram analysis, information entropy, and sensitivity analysis. The results obtained from cipher image testing are differences in histogram analysis testing in grayscale and color images. The information entropy value is 0.000131583% better than the AES standard, the NPCR is 0.17613% better than the AES standard, and the UACI value. 0.211148% better than AES standard in sensitivity analysis testing. Based on these data, the proposed algorithm has a higher level of security than the standard AES algorithm on image encryption.
Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers Pahala Bima Pramudya; Alamsyah Alamsyah
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.80

Abstract

Security is an important factor in today's digital era. In a network, implementing a security system is the focus of a network developer. One of the most basic network securities is in the form of access. To manage the security of a system must be known in advance who is involved in the system and what activities are carried out. Just like a security alarm, which monitors work conditions, this is the function of the Intrusion Detection System (IDS). IDS has several effective methods for detecting threats, one of which is the Signature-based method. IDS can be implemented through the open-source SNORT application, and the method works with rules which are commands to IDS to recognize various attacks. IDS rules will be included in the signature matching process, which means matching between rules and incoming attacks and views of both protocols, then the IDS will generate alerts that contain notifications. This study conducted a reading of the MIT-DARPA 1999 dataset on 1,252,412 packages and tested alerting with Network Scanning and DoS attacks. Analyze Package Data runs at a speed of 83,494 packets /second and gets a true positive percentage reaching 100% and an accuracy of 98.10%.
Restricted boltzmann machine and softmax regression for acute respiratory infections disease identification Afrizal Rizqi Pranata; Alamsyah Alamsyah; Budi Prasetiyo; Hilda Vember
Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v3i2.90

Abstract

Restricted boltzmann machines (RBM) have attracted much attention lately after being proposed as building blocks of deep learning blocks. RBM is an algorithm that belongs to the artificial neural network (ANN) algorithm. Deep learning models can be used in the health field to identify diseases using medical data records. Acute Respiratory Infection (ARI) is a disease that infects the respiratory tract. A patient infected by ARI diseases is high. To identify ARI can use the symptoms that the patient had experienced. Based on this background, this study aims to help identify ARI disease using its symptoms. The method used for identification is the deep learning model, which was built using the RBM and softmax regression. Three steps were used in this research, which are training, testing, and implementation. The trained deep learning model will be implemented to identify ARI disease. This research will use ARI data from Puskemas Warungasem, Indonesia. From the research result, the deep learning model can get an accuracy of 96%. The deep learning configuration used in this research has 4 RBM layers, 1 Softmax layer as the output layer, and a learning rate value of 0.01 and 1000 iterations. This research can be used as a reference so that the next researcher can add other algorithms to Deep learning to improve accuracy.
Co-Authors Afrizal Rizqi Pranata Akhmad Sahal Mabruri Budi Prasetiyo David Topanto Hestu Aji Prihanditya Hilda Vember Pahala Bima Pramudya