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Journal of Soft Computing Exploration
Published by shm publisher
ISSN : 27467686     EISSN : 27460991     DOI : -
Core Subject : Science,
Computer Science & IT
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
Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak
Articles 146 Documents
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Implementation of a faster R-CNN algorithm for identification of metastatic tissue using lymphoma histopathological images Winata, Puja Aditya; Roysida, Isnaini
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.144

Abstract

Procedures for diagnosis of lymphoma includes blood tests, CT scan or MRI, and histopathological examination through a biopsy. Histopathological examination is the gold standard of diagnosis. Pathology diagnosis of lymphoma is challenging and difficult in the field of diagnostic pathology. This study aims to identify lymph node metastases using the Faster R-CNN algorithm using histopathological images of lymph nodes so that the Faster RCNN system design can help the medical team to make diagnostic decisions. Identification carried out by Faster R-CNN is by classifying histopathological images into normal classes and metastatic classes. Loss values that are not indicated for underfitting and overfitting are shown from the 10th epoch to the 20th epoch. The optimizer and the number of epochs for the optimal value of 83.3% accuracy and 71.8% recall are ADAM with 20 epochs. The accuracy and recall results obtained are quite good. 1113 metastatic images and 1478 normal images were predicted correctly, while 437 metastatic images and 82 normal images were predicted incorrectly.
Effect of fuzzy logic controller on voltage stability of parallel boost converter configuration Kusmantoro, Adhi; Hiyama, Takashi
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.153

Abstract

An increase in electricity load causes a change in grid voltage and current, causing losses to customers. In addition, the source of electricity from fossil energy has also decreased. Therefore this study aims to provide a stable DC voltage source from solar panels, with a Fuzzy Logic Controller (FLC). The proposed method is to design a boost converter in parallel with its output. The boost converter is used to increase the DC voltage from 24 V to 48 V. In this study, FLC is used to adjust the output voltage of each boost converter. This is so that if one of the boost converters fluctuates, the other boost converters will supply a voltage according to the load voltage. The results showed that the FLC can adjust the boost converter output voltage changes. Whereas when using the PI (Integral Proportional) controller, a voltage spike occurs in the range of 0 seconds to 0.6 seconds and the voltage stabilizes within 0.6 seconds to 1 second.
Crude oil price prediction using Artificial Neural Network-Backpropagation (ANN-BP) and Particle Swarm Optimization (PSO) methods Purwinarko, Aji; Amalia Langgundi, Fitri
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.159

Abstract

Crude oil price fluctuations significantly affect commodity market price fluctuations, so a sudden drop in oil prices will cause a slowdown in the economy and other commodities. This is very important for Indonesia, one of the world's oil-producing countries, to gain multiple benefits from oil exports when world oil prices increase and increase economic growth. Therefore, a system is needed to predict world crude oil prices. In this case, the Particle Swarm Optimization (PSO) algorithm is applied as the optimization of the weight parameters in the Artificial Neural Network-Backpropagation (ANN-BP) method. We compared the ANN-BP–PSO and ANN-BP methods to obtain the method with the best causation value based on the MAPE and MSE results. PSO aims to find the best weight value by iterating the process of finding and increasing position, speed, Pbest, and Gbest until the iteration is complete. The results showed that the ANN-BP-PSO process was classified as very good and had a lower predictive error rate than the ANN-BP method based on the MAPE and MSE values, which is 5.02007% and 7.15827% compared to 6.28323% and 13.86345.
Ensemble learning technique to improve breast cancer classification model Dullah, Ahmad Ubai; Apsari, Fitri Noor; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 4 No. 2 (2023): June 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i2.166

Abstract

Cancer is a disease characterized by abnormal cell growth and is not contagious, such as breast cancer which can affect both men and women. breast cancer is one of the cancer diseases that is classified as dangerous and takes many victims. However, the biggest problem in this study is that the classification method is low and the resulting accuracy is less than optimal. the purpose of this study is to improve the accuracy of breast cancer classification. Therefore, a new method is proposed, namely ensemble learning which combines logistic regression, decision tree, and random forest methods, with a voting system. This system is useful for finding the best results on each parameter that will produce the best prediction accuracy. The prediction results from this method reached an accuracy of 98.24%. The resulting accuracy rate is more optimal by using the proposed method.
Lung cancer classification using convolutional neural network and DenseNet Damayanti, Nabila Putri; Ananda, Mohammad Nabiel Dwi; Nugraha, Faizal Widya
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.177

Abstract

Lung cancer is a condition that has a major impact on public health. Convolutional Neural Network (CNN) and DenseNet approaches are suggested in this study to aid lung cancer detection and classification. In various fields of pattern recognition and medical imaging, CNN and DenseNet have demonstrated their efficacy. In this study, radiology images from individuals with lung cancer were used to create a set of medical lung images. The findings show that lung cancer can be accurately classified into malignant and benign from radiological images using CNN and DenseNet architectures, with a parameter accuracy of 99.48%. This research contributes to the creation of a deep learning-based system for detecting and classifying lung cancer. The findings can be the basis for creating a more accurate and productive lung cancer diagnostic system.
Unveiling unmasked faces: a novel model for improved mask detection using haar cascade technique Kumar, Sanjeev; Kumar, Mohit; Dubey, Kriti; Sharma, Kaushal
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.179

Abstract

In response to the urgent need to enforce mask-wearing compliance during the COVID-19 pandemic, this "Face Mask Detection" project introduces a robust model for identifying individuals not wearing face masks in videos. Leveraging computer vision's Haar Cascade technique, the project achieves rapid face detection within video streams, facilitating accurate mask usage assessment. This initiative holds paramount importance due to the pivotal role of masks in curbing virus spread. The model finds practical applications in monitoring mask adherence in public settings, pinpointing potential COVID-19 hotspots through data analysis, and bolstering safety via integration into surveillance systems. By effectively addressing the intricate challenge of precise mask detection, this project makes significant contributions to public health endeavors and the mitigation of COVID-19 hazards. The proposed algorithm showcases remarkable performance across various metrics. With an impressive detection rate of 98.4%, it surpasses established methods such as CNN (91.26%), PCA+SVM (93.4%), and Adaboost (96.1%), signifying its potential to revolutionize mask detection technology.
Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding Ningsih, Maylinna Rahayu; Wibowo, Kevyn Aalifian Hernanda; Dullah, Ahmad Ubai; Jumanto, Jumanto
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.193

Abstract

The issue of the Global Recession is hitting various countries, including Indonesia. Many Indonesians have expressed their opinions on the issue of the global recession in 2023, one of which is from Twitter. By understanding public sentiment, we can assess the impact felt by the public on the issue itself. Sentiment analysis in this research is a form of support to evaluate Indonesia's sustainability in dealing with the issue of Global Recession in accordance with the Sustainable Development Goals (SDGs). However, in previous research, it is still rare to find a model that has good performance in conducting Global Recession Sentiment Analysis. Therefore, the purpose of this research is to propose a machine learning model that is expected to provide good performance in sentiment analysis. The existing sentiment dataset is labeled with the Valence Aware Dictionary for Social Reasoning (VADER) algorithm, then an Ensemble Learning method is designed which is composed of Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine (SVM) algorithms. After that, the Countvectorizer feature extraction with N-Gram, Best Match 25 (BM25), and Word Embedding is carried out to convert sentences in the dataset into numerical vectors so as to improve model performance. The research results provide a more optimal accuracy performance of 95.02% in classifying sentiment. So that the proposed model successfully performs sentiment analysis better than previous research.
Application of the KNN method to check soil compatibility using a microcontroller for android-based banyuwangi citrus fruit plants Solehatin, Solehatin; Hadiq, Hadiq; Pertiwi, Dwika Ananda Agustina
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.197

Abstract

The city of Banyuwangi needs a touch of information technology in the agricultural sector, namely in the process of planting orange fruit, because orange fruit planting is carried out continuously to meet export needs. Citrus fruit planting is sometimes carried out without paying attention to the existing soil nutrient content, this condition can result in less than optimal harvest results. The research was carried out by creating a soil nutrient detection application with the aim of providing information to farmers about the soil nutrient content including nitrogen, calcium, phosphorus, pH and moisture resistance before planting citrus fruit. From the results of trials conducted by researchers with farmers based on various types of soil used as trial data, the information shows a match of 89.6%. The results of the research produced an Android-based soil nutrient checking application that farmers can use to check soil nutrients when planting citrus fruit. In conducting the research, the researcher created an application by applying the KNN method and utilizing a microcontroller to input the data. By combining methods and tools, microcontrollers can assist the implementation process so as to provide information in the form of soil suitability for planting citrus fruit based on the nutrient content of the soil being examined. The contribution made from the research results is the application of a KNN method which is used to check soil nutrients so that it can maximize the results of the detection carried out. Meanwhile, another contribution is the use of a tool in the form of a microcontroller which is used to automatically input data which can be obtained using the Bluetooth service in the soil nutrient check application.
The implementation of mamdani fuzzy logic in determining student concentration in the computer engineering program Liku, Antika Diana; Adiba, Fhatiah; Kartika, A.Amalia
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.211

Abstract

The selection of a concentration is an important stage for students in the Computer Engineering program at the State University of Makassar before entering the fifth semester. Each student must choose one of the concentrations in the fields of networking, embedded systems, or smart systems. This concentration selection has a significant impact on academic activities and future career abilities. However, the lack of awareness among students about their talents and interests has resulted in many students having difficulty in choosing the right concentration. To address this issue, this research proposes using the Mamdani fuzzy logic method to assist students in selecting the appropriate concentration based on their talents and interests. The approach is carried out by collecting information through questionnaires filled out by students who have completed the fourth semester of the Computer Engineering program. The collected data is then processed using the concepts of Mamdani fuzzy logic in the MATLAB environment to generate concentration scores for each field. The research results show the effectiveness of Mamdani fuzzy logic in determining the concentration of students in networking, embedded systems, and smart systems, with an accuracy rate of up to 80%. By using the appropriate linguistic variables, students' levels of interest and abilities in each field can be accurately represented. This research has benefits for students and the university in identifying the right concentration that aligns with the interests and abilities of students at the State University of Makassar.
Optimization of support vector machine using information gain and adaboost to improve accuracy of chronic kidney disease diagnosis Listiana, Eka; Muzayanah, Rini; Muslim, Much Aziz; Sugiharti, Endang
Journal of Soft Computing Exploration Vol. 4 No. 3 (2023): September 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i3.218

Abstract

Today's database is growing very rapidly, especially in the field of health. The data if not processed properly then it will be a pile of data that is not useful, so the need for data mining process to process the data. One method of data mining used to predict a decision in any case is classification, where in the classification method there is a support vector machine algorithm that can be used to diagnose chronic kidney disease. The purpose of this study is to determine the level of accuracy of the application of information gain and AdaBoost on the support vector machine algorithm in diagnosing chronic kidney disease. The use of information gain is to select the attributes that are not relevant while AdaBoost is used as an ensemble method commonly known as the method of classifier combination. In this study the data used are chronic kidney disease (CKD) dataset obtained from UCI repository of machine learning. The result of experiment using MATLAB applying information gain and AdaBoost on vector machine support algorithm with k-fold cross validation default k = 10 shows an accuracy increase of 0.50% with the exposure of the result as follows, the support vector machine algorithm has accuracy of 99.25 %, if by applying AdaBoost on the support vector machine has an accuracy of 99.50%, whereas if applying AdaBoost and information gain on the support vector machine has an accuracy of 99.75%.

Page 7 of 15 | Total Record : 146

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