Journal of Soft Computing Exploration
Journal of Soft Computing Exploration is a journal that publishes manuscripts of scientific research papers related to soft computing. The scope of research can be from the theory and scientific applications as well as the novelty of related knowledge insights. Soft Computing: Artificial Intelligence Applied Algebra Neuro Computing Fuzzy Logic Rough Sets Probabilistic Techniques Machine Learning Metaheuristics And Many Other Soft-Computing Approaches Area Of Applications: Data Mining Text Mining Pattern Recognition Image Processing Medical Science Mechanical Engineering Electronic And Electrical Engineering Supply Chain Management, Resource Management, Strategic Planning Scheduling Transportation Operational Research Robotics
Articles
82 Documents
<![CDATA[Security Improvement Of Aes Algorithm Using S-Box Modification Based On Strict Avalanche Criterion On Image Encryption ]]>
<![CDATA[David Topanto]]>;
<![CDATA[Alamsyah Alamsyah]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i1.70
<![CDATA[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.]]>
<![CDATA[Multi-objective optimization for multi-satellite scheduling task: Multi-objective optimization for multi-satellite scheduling task ]]>
<![CDATA[Heba Abdulrahman Khojah]]>;
<![CDATA[Mohamed Atef Mosa]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 1 (2022): March 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i1.71
<![CDATA[The satellites scheduling mission play an effective role in enhancing the role of ground station control and monitoring systems. In this search, SGSEO is re-formulated into a multi-objective optimization task. Therefore, the Gravitational Search Algorithm GSA is exploited to attain several essential objectives for generating tight scheduling. Moreover, particle swarm optimization model PSO is consolidated with GSA in a novel form for strengthening its ability of local search and slow the speed of convergence. On the other side, to make the most of the satellite resources in the right direction, we have observed targets that have fewer observational opportunities to keep them from being lost. The PageRank algorithm is used to fulfil this issue by ranking the candidate's strips. Finally, the effect of different parameters of the proposed approach was studied by experimental outcomes and compared with previous methods. It has shown that the performance of the proposed approach is superior to its peers from other methods.]]>
<![CDATA[Application of pest detection on vegetable crops using the cnn algorithm as a smart farm innovation to realize food security in the 4.0 era ]]>
<![CDATA[Apri Dwi Lestari]]>;
<![CDATA[Nur Afan syarifudin]]>;
<![CDATA[Yopi Julia Nurriski]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.72
<![CDATA[Pests and diseases are one of the factors that become obstacles in the cultivation of vegetables because they can cause a decrease in the quality and quantity of production. The more varied types of pests have different impacts on crops, so if farmers incorrectly identify the class of pests, the treatment will be ineffective. Therefore, we need a technology that can classify the types of pests on vegetable crops to maintain the quality and quality of the product as well as the abundant harvest. The classification model of pests on vegetables using the deep learning method using the Convolutional Neural Network (CNN) algorithm with a high level of accuracy is the solution to this problem. The application of artificial intelligence in the agricultural sector also supports smart agriculture in Indonesia. Based on the research that has been carried out, the application of pest classification on vegetable crops made by applying the CNN model using the Inception V3 - k-fold cross-validation method has a test accuracy rate of 99%, meaning that the application can perform pest classification correctly.]]>
<![CDATA[Optimization of breast cancer classification using feature selection on neural network ]]>
<![CDATA[Jumanto Jumanto]]>;
<![CDATA[M Fadil Mardiansyah]]>;
<![CDATA[Rizka Nur Pratama]]>;
<![CDATA[M. Faris Al Hakim]]>;
<![CDATA[Bibek Rawat]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.78
<![CDATA[Cancer is currently one of the leading causes of death worldwide. One of the most common cancers, especially among women, is breast cancer. There is a major problem for cancer experts in accurately predicting the survival of cancer patients. The presence of machine learning to further study it has attracted a lot of attention in the hope of obtaining accurate results, but its modeling methods and predictive performance remain controversial. Some Methods of machine learning that are widely used to overcome this case of breast cancer prediction are Backpropagation. Backpropagation has an advantage over other Neural Networks, namely Backpropagation using supervised training. The weakness of Backpropagation is that it handles classification with high-dimensional datasets so that the accuracy is low. This study aims to build a classification system for detecting breasts using the Backpropagation method, by adding a method of forward selection for feature selection from the many features that exist in the breast cancer dataset, because not all features can be used in the classification process. The results of combining the Backpropagation method and the method of forward selection can increase the detection accuracy of breast cancer patients by 98.3%.]]>
<![CDATA[Implementation of signature-based intrusion detection system using SNORT to prevent threats in network servers ]]>
<![CDATA[Pahala Bima Pramudya]]>;
<![CDATA[Alamsyah Alamsyah]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.80
<![CDATA[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%.]]>
<![CDATA[Business process modeling at steak restaurant using business process model and notation ]]>
<![CDATA[Alya Aulia Nurdin]]>;
<![CDATA[Aisyah Nungky Pristanti]]>;
<![CDATA[Nikita Samantha]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.84
<![CDATA[The complexity of business processes occurring today makes the company try to find ways to describe its business processes. Business processes are not only an operational standard but also become one of the determining factors for the smooth use of time and costs in a business unit to be more efficient. With good business processes, it makes the flow of information faster so that it can help in making the best decisions in the organization. The business process modeling that will be explained further in this study is the order and procurement business process at steak restaurant using the Business Process Model Notation (BPMN) approach. This research was conducted using a qualitative descriptive method with the aim of observing the business unit to help analyze and make improvements to its business processes. Several series of processes were carried out, namely business identification and modeling with bizagi modeler and process reengineering to produce recommended new business process models that could be beneficial for business units, namely the recommended automation in the form of the use of mobile applications, remote, and database systems to support the effectiveness of the order to cash and procure to pay processes.]]>
<![CDATA[Comparison of LSTM, SVM, and naive bayes for classifying sexual harassment tweets ]]>
<![CDATA[Tiara Lailatul Nikmah]]>;
<![CDATA[Muhammad Zhafran Ammar]]>;
<![CDATA[Yusuf Ridwan Allatif]]>;
<![CDATA[Rizki Mahjati Prie Husna]]>;
<![CDATA[Putu Ayu Kurniasari]]>;
<![CDATA[Andi Syamsul Bahri]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.85
<![CDATA[Twitter is now a very open and extensive social media; anyone can freely express their opinion on any topic on social media. The content or discussion on Twitter is also quite diverse and unlimited. However, because it is unlimited, many misuse it for negative things. One of them is verbal sexual harassment through Twitter. This research aims to identify sexual harassment in an Indonesian tweet using sentiment analysis using the LSTM, SVM, and naive bayes methods with text normalization. In this study, 2990 tweets in the Indonesian language were tested from 4th to 6th in May 2022. The Twitter data shows that tweets included in sexual harassment are more than those not included in sexual harassment, totaling 2026 data. From the results of the evaluation of tweet data classification using text normalization with LSTM, the accuracy is 84.62%, SVM is 86.54%, and naive bayes is 85.45%. Using the SVM algorithm with text normalization gets the highest accuracy compared to LSTM and naive bayes in classifying Indonesian sexual harassment tweets.]]>
<![CDATA[Implementation of the data encryption using caesar cipher and vernam cipher methods based on CrypTool2 ]]>
<![CDATA[Gading Nur Salmi]]>;
<![CDATA[Farhan Siagian]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.86
<![CDATA[Information has become precious and essential for all fields, so it is crucial to carry out information security. The principle of information security is to protect and safeguard information with the aim that the information is not entitled to be read, modified, or deleted by anyone who does not have rights to it. The purpose of our research is to analyze how the caesar cipher and vernam cipher methods are jointly used in the cryptographic process and are expected to produce a high level of data encryption so that it can increase the security of data or messages. The research applies the combination of the caesar cipher and vernam cipher methods to encrypt text data or messages. Using the secret key value will convert the input message into an encrypted message that is difficult to crack and cannot be decrypted again. The input text and the encrypted data have no resemblance to maintain the confidentiality of the information or data contents.]]>
<![CDATA[Analysis of public opinion sentiment against COVID-19 in Indonesia on twitter using the k-nearest neighbor algorithm and decision tree ]]>
<![CDATA[Ryo Pambudi]]>;
<![CDATA[Faiq Madani]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.88
<![CDATA[COVID-19 has become an ongoing disease pandemic across the globe. The need for information makes social media such as twitter a place to exchange information. This tweet can be used to see public sentiment towards COVID-19 in Indonesia. Sentiment analysis classifies opinions from tweets that have been processed and classified into different sentiments, namely negative, neutral, or positive. The aim of this paper is to find the algorithm that has the best accuracy. The researcher proposes to compare the K-Nearest Neighbors (KNN) and decision tree algorithms to be used in the classification of sentiment data from tweets related to COVID-19 that took place in Indonesia. The results of the evaluation of performance metrics concluded that the decision tree algorithm has a higher level of accuracy than KNN. Decision tree produces accuracy = 0.765, error = 0.235, recall = 0.76, and precision = 0.767 which is better when compared to KNN which produces accuracy = 0.69, error = 0.31, recall = 0.66, and precision = 0.702.]]>
<![CDATA[Classification of potential customers using C4.5 and k-means algorithms to determine customer service priorities to maintain loyalty ]]>
<![CDATA[Nur Hazimah Syani]]>;
<![CDATA[Afif Amirullah]]>;
<![CDATA[Meidika Bagus Saputro]]>;
<![CDATA[Ilham Alzahdi Tamaroh]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 3 No. 2 (2022): September 2022 ]]>
Publisher : <![CDATA[SHM Publisher ]]>
Show Abstract
|
Download Original
|
Original Source
|
Check in Google Scholar
|
DOI: 10.52465/joscex.v3i2.89
<![CDATA[The increasing competition among Middle-Class Micro Enterprises (MSMEs) is a problem because business actors must improve techniques and strategies to maintain customer satisfaction, and the number of customers continues to increase. Customers are an essential asset for the company. To maintain customer loyalty with promising prospects for the company, a strategy is needed to support this. Strategies such as service prioritization can be used to maintain customer loyalty. This research was conducted to classify customers who are estimated to have good prospects for the company so that service priorities are not mistargeted by utilizing 1683 data from store By.SIRR, a fashion store in Semarang, Indonesia contains five attributes, and customers are classified and are estimated to have promising prospects for the company. Data mining methods use the C4.5 and K-Means algorithms to classify the classification process. The research resulted in the grouping of customers into four categories: potential lover, flirting, faithful lover, and spiritual friend. From the validation test conducted using the Confusion Matrix Validation method, the classification results get an Accuracy of 97.70%.]]>
