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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 6 Documents
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Search results for , issue "Vol. 4 No. 2 (2023): June 2023" : 6 Documents clear
Game design documents for mobile elementary school mathematic educative games Roy Jordy jordy; Hendra Marcos; Jaka Wijaya Kusuma; Dhanar Intan Surya Saputra; Purwadi Purwadi
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.v4i2.129

Abstract

Mobile games or games in this era are very much in demand by young people and small children as a medium of entertainment. Even the elderly are still often encountered playing this mobile game. This has prompted many game developer programmers to want to make mobile-based games. This aims to add insight, especially at the age of children, so that they are more enthusiastic about learning through this educational game. The academic side of this game comes from simple and fun math puzzles. From within this game, players can enjoy games that have 2D animations and are based on Android, as well as enriching children's knowledge and learning basic mathematical calculations by answering using games or this educational game. The assessment of this educational game is assessed when based on the number of correct answers. The method used in this research is in the form of collecting information and data, which includes recording and studying the literature and will conduct searches using the internet, as well as data sources relating to the problems in this research game Development. Lifecycle (GDLC) is used as a system development method. GDLC is a guide or guidelines that can regulate the rules in making this educational game. The results of this research will be the realization of mobile or android-based games with construct 2 for elementary school children from grades 3, 4, and 5. This android-based educational game is expected to provide experience to children in the world of learning and can increase elementary school children's interest in arithmetic, especially in counting.
News text classification using Long-Term Short Memory (LSTM) algorithm Indra Triyadi; Budi Prasetiyo; Tiara Lailatul Nikmah
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.v4i2.136

Abstract

Over the past few years, the classification of texts has become increasingly important. Because knowledge is now available to users through various sources namely electronic media, digital media, print media, and many more. One of them is the development of so much news every day. LSTM is one of the algorithms of deep learning methods that can classify a text. This research proves for the LSTM algorithm on the classification of news text sentences. The data used is the news text from the Kaggle data center set i.e. aggregator news data. The results of the LSTM experiment from 10 epochs obtained with an accuracy value of 93,15% on the classification of texts into four categories, namely entertainment, bussines, science, and health.
Implementation of a faster R-CNN algorithm for identification of metastatic tissue using lymphoma histopathological images Puja Aditya Winata; Isnaini Roysida
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 Adhi Kusmantoro; Takashi Hiyama
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 Aji Purwinarko; Fitri Amalia Langgundi
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 Ahmad Ubai Dullah; Fitri Noor Apsari; 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.

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