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[SVM Optimization with Correlation Feature Selection Based Binary Particle Swarm Optimization for Diagnosis of Chronic Kidney Disease ]]>
<![CDATA[Doni Aprilianto]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.1
<![CDATA[Data mining has been widely used to diagnose diseases from medical data. In this study using chronic kidney disease dataset taken from UCI Machine Learning. The dataset has 25 attributes with 400 samples. With 25 attributes that allow redundant data. Redundant data in datasets can reduce computational efficiency and classification accuracy. To increase accuracy of classification algorithm can be done by reducing dimensions of dataset. Correlation-based Feature Selection (CFS) can quickly identify and filter redundant attributes. However, CFS has disadvantage that selected attribute is not necessarily the best attribute. These weaknesses can be overcome by Binary Particle Swarm Optimization (BPSO). BPSO chooses attributes based on the best fitness value. The purpose of this study is to improve accuracy of Support Vector Machine (SVM) by implementing combination of CFS and BPSO as feature selection. Accuracy of SVM in predicting CKD is 63.75%. Whereas, accuracy of SVM by applying CFS as feature selection is 88.75% and average accuracy of ten execution SVM algorithms by applying a combination of CFS and BPSO as feature selection is 95%. Thus, combination of CFS and BPSO as feature selection on the SVM algorithm can improve results of accuracy in diagnosing CKD by 31.25%.]]>
<![CDATA[Electrical Energy Monitoring System and Automatic Transfer Switch (ATS) Controller with the Internet of Things for Solar Power Plants ]]>
<![CDATA[Novi Kurniawan]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.2
<![CDATA[Internet of Things is a technology that connects communication devices with electronic devices that are used everyday using the internet as a medium to communicate between devices and users. The use of IoT technology can be implemented in solar power generation systems. The IoT technology implemented in this study is to monitor and control the use of batteries in solar power plants. Current technology, battery usage can only be monitored closely to get information about battery capacity and battery usage. When the battery is empty or cannot be used to meet electricity needs, it is not equipped with a diversion of existing electricity sources such as PLN electricity. So, we need a renewable technology to get information about batteries and transfer of electricity sources that can be accessed remotely and can be accessed via the internet. The design of this smart monitoring system has stages, namely planning,design , coding , and test . The results of this study are able to see data in the form of battery capacity, electric current and electric power used in Android applications. The data is obtained from sensors that are on smart monitoring connected to the internet network and stored on a database server. Then the data residing on the database server will be retrieved by the application to be displayed to users in the form of graphics and usage lists. Furthermore, the Automatic Transfer Switch system works if the battery capacity sensor has read less than 11.4V then the relay will automatically transfer electricity to PLN. The Android smartphone application is used as a monitoring tool to view data in realtime.]]>
<![CDATA[Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy ]]>
<![CDATA[Sulistiana]]>;
<![CDATA[Much Aziz Muslim]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.3
<![CDATA[Electronic Commerce (E-Commerce) is distributing, buying, selling, and marketing goods and services over electronic systems such as the Internet, television, websites, and other computer networks. E-commerce platforms such as amazon.com and Lazada.co.id offer products with various price and quality. Sentiment analysis used to understand the product’s popularity based on customers’ reviews. There are some approaches in sentiment analysis including machine learning. The part of machine learning that focuses on text processing called text mining. One of the techniques in text mining is classification and Support Vector Machine (SVM) is one of the frequently used algorithms to perform classification. Feature and parameter selection in SVM significantly affecting the classification accuracy. In this study, we chose unigram as the feature extraction and grid search as parameter optimization to improve SVM classification accuracy. Two customer review datasets with different language are used which is Amazon reviews that written in English and Lazada reviews in the Indonesian language. 10-folds cross validation and confusion matrix are used to evaluating the experiment results. The experiment results show that applying unigram and grid search on SVM algorithm can improve Amazon review accuracy by 26,4% and Lazada reviews by 4,26%.]]>
<![CDATA[Optimization of Naïve Bayes Classifier By Implemented Unigram, Bigram, Trigram for Sentiment Analysis of Hotel Review ]]>
<![CDATA[Ilham Esa Tiffani]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.4
<![CDATA[The information needed in its development requires that proper analysis can provide support in making decisions. Sentiment analysis is a data processing technique that can be completed properly. To make it easy to classify hotels based on sentiment analysis using the Naїve Bayes Classifier algorithm. As a classification tool, Naїve Bayes Classifier is considered efficient and simple. In this study consists of 3 stages of sentiment analysis process. The first stage is text pre-processing which consists of transform case, stopword removal, and stemming. The second stage is the implementation of N-Gram features, namely Unigram, Bigram, Trigram. The N-Gram feature is a feature that contains a collection of words that will be referred to in the next process. Next, the last click is the hotel review classification process using Na menggunakanve Bayes Classifier. OpinRank Hotels Review dataset on Naїve Bayes Classifier using N-Gram namely Unigram, Bigram, Trigram with research results that show Unigram can provide better test results than Bigram and Trigram with an average accuracy of 81.30%.]]>
<![CDATA[Improved Accuracy of Naive Bayes Classifier for Determination of Customer Churn Uses SMOTE and Genetic Algorithms ]]>
<![CDATA[Afifah Ratna Safitri]]>;
<![CDATA[Much Aziz Muslim]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.5
<![CDATA[With increasing competition in the business world, many companies use data mining techniques to determine the level of customer loyalty. The customer data used in this study is the german credit dataset obtained from UCI. Such data have an imbalance problem of class because the amount of data in the loyal class is more than in the churn class. In addition, there are some irrelevant attributes for customer classification, so attributes selection is needed to get more accurate classification results. One classification algorithm is naive bayes. Naive Bayes has been used as an effective classification for years because it is easy to build and give an independent attribute into its structure. The purpose of this study is to improve the accuracy of the Naive Bayes for customer classification. SMOTE and genetic algorithm do for improving the accuracy. The SMOTE is used to handle class imbalance problems, while the genetic algorithm is used for attributes selection. Accuracy using the Naive Bayes is 47.10%, while the mean accuracy results obtained from the Naive Bayes with the application of the SMOTE is 78.15% and the accuracy obtained from the Naive Bayes with the application of the SMOTE and genetic algorithm is 78.46%.]]>
<![CDATA[Increasing Accuracy of C4.5 Algorithm Using Information Gain Ratio and Adaboost for Classification of Chronic Kidney Disease ]]>
<![CDATA[Aprilia Lestari]]>;
<![CDATA[Alamsyah]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.6
<![CDATA[Data information that has been available is very much and will require a very long time to process large amounts of information data. Therefore, data mining is used to process large amounts of data. Data mining methods can be used to classify patient diseases, one of them is chronic kidney disease. This research used the classification tree method classification with the C4.5 algorithm. In the pre-processing process, a feature selection was applied to reduce attributes that did not increase the results of classification accuracy. The feature selection used the gain ratio. The Ensemble method used adaboost, which well known as boosting. The datasets used by Chronic Kidney Dataset (CKD) were obtained from the UCI repository of learning machine. The purpose of this research was applying the information gain ratio and adaboost ensemble to the chronic kidney disease dataset using the C4.5 algorithm and finding out the results of the accuracy of the C4.5 algorithm based on information gain ratio and adaboost ensemble. The results obtained for the default iteration in adaboost which was 50 iterations. The accuracy of C4.5 stand-alone was obtained 96.66%. The accuracy for C4.5 using information gain ratio was obtained 97.5%, while C4.5 method using information gain ratio and adaboost was obtained 98.33%.]]>
<![CDATA[Improving Algorithm Accuracy K-Nearest Neighbor Using Z-Score Normalization and Particle Swarm Optimization to Predict Customer Churn ]]>
<![CDATA[Muhammad Ali Imron]]>;
<![CDATA[Budi Prasetyo]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.7
<![CDATA[Due to increased competition in the business world, many companies use data mining techniques to determine the loyalty level of customers. In this business, data mining can be used to determine the loyalty level of customers. Data mining consists of several research models, one of which is classification. One of the most commonly used methods in classification is the K-Nearest Neighbor algorithm. In this study, the data which used are from German Credit Datasets obtained from UCI machine learning repository. The purpose of this study is to find out how Z-Score works to normalize the data and Particle Swarm Optimization to find the most optimal K value parameters, so the performance of the K-Nearest Neighbor algorithm is more optimal during the classification. The methods which were used to normalize the data are Z-score and Particle Swarm Optimization to determine the most optimal K value. The classification was tested using confusion matrix to determine the generated accuracy. From the finding of this study, the application of Z-score normalization and Particle Swarm Optimization with the K Nearest Neighbor algorithm succeed in increasing the accuracy up to 14%. The initial accuracy was 68.5%, and after applying the normalization of Z-Score and Particle Swarm Optimization, the accuracy became 82.5%.]]>
<![CDATA[The Implementation of Z-Score Normalization and Boosting Techniques to Increase Accuracy of C4.5 Algorithm in Diagnosing Chronic Kidney Disease ]]>
<![CDATA[Hestu Aji Prihanditya]]>;
<![CDATA[Alamsyah]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.8
<![CDATA[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.]]>
<![CDATA[Improve the Accuracy of C4.5 Algorithm Using Particle Swarm Optimization (PSO) Feature Selection and Bagging Technique in Breast Cancer Diagnosis ]]>
<![CDATA[Raka Hendra Saputra]]>;
<![CDATA[Budi Prasetyo]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.9
<![CDATA[Breast cancer is the second leading cause of death due to cancer in women currently. It has become the most common cancer in recent years. In early detection of cancer, data mining can be used to diagnose breast cancer. Data mining consists of several research models, one of which is classification. The most commonly used method in classification is the decision tree. C4.5 is an algorithm in the decision tree that is often used in the classification process. In this study, the data used was the Breast Cancer Wisconsin (Original) Data Set (1992) obtained from the UCI Machine Learning Repository. The purpose of this study was to select features that will be used and overcome class imbalances that occur, so that the performance of the C4.5 algorithm worked more optimal in the classification process. The methods used as feature selection are PSO and bagging to overcome class imbalances. Classification was tested using the confusion matrix to determine the accuracy that was generated. From the results of this study, the application of PSO as a feature selection and bagging to overcome class imbalances with the C4.5 algorithm succeeded in increasing accuracy by 5.11% with an initial accuracy of 93.43% to 98.54%.]]>
<![CDATA[Data Security System of Text Messaging Based on Android Mobile Devices Using Advanced Encrytion Standard Dynamic S-BOX ]]>
<![CDATA[Akhmad Sahal Mabruri]]>;
<![CDATA[Alamsyah]]>
<![CDATA[ Journal of Soft Computing Exploration Vol. 1 No. 1 (2020): September 2020 ]]>
Publisher : <![CDATA[Surya Hijau Manfaat ]]>
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DOI: 10.52465/joscex.v1i1.10
<![CDATA[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.]]>
