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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 7 Documents
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Search results for , issue "Vol. 4 No. 1 (2023): March 2023" : 7 Documents clear
Current trend in control of artificial intelligence for health robotic manipulator Suwarno, Iswanto; Cakan, Abdullah; Raharja, Nia Maharani; Baballe, Muhammad Ahmad; Mahmoud, Magdi S.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.96

Abstract

The increasing utilization of artificial intelligence and robots in various services in healthcare makes robots as preferred intelligent agent model. Robotic evolution produces the optimal robotic innovation in the robotic system or its subsystems, morphology, kinematics, and control. An intelligent algorithm is programmed into the control of the robotic manipulator. This paper aims to identify the control of artificial intelligence and identify comparisons of artificial intelligence algorithms control for healthcare robotic manipulators. This study uses a systematic literature review using the Preferred Reporting Items for Systematic Review (PRISMA). The potential for further articles is explored related to the theme of the research carried out. The conclusion obtained many studies have been carried out to optimize the work and tasks of the robotic arm manipulator, specifically developing various types of manipulator control (algorithms) combined with neural networks to get the right and appropriate algorithm.
cARica: enhancing travelling experiences in wonosobo through location-based mobile augmented reality Saputra, Dhanar Intan Surya; Murjiatiningsih, Lilis; Hermawan, Hellik; Handani, Sitaresmi Wahyu; Wijanarko, Andik
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.97

Abstract

Wonosobo, as a Regency in Central Java Province, Indonesia, has attractions including the Dieng Plateau Theater Kalianget, and Menjer Lake. The research is intended to provide more experience for tourists who visit the tour through Location-Based Mobile Augmented Reality (MAR), an application we developed, cARica. This application includes experience travelling in Wonosobo and is aware of other information displayed through AR content. It was an alternative medium for tourism promotion to be easy, attractive, and inexpensive. It is a practical guide to attract tourists to visit tourist sites. In its development, we use the prototyping method so that each stage is carried out under the procedures that have been prepared. To get the point of Interest (PoI) points of tourist sites, use Global Positioning System (GPS) data taken through Google Maps to get the Latitude and Longitude of each object. The results of this study present that cARica is a Location-Based Mobile Augmented Reality service platform that can be accessed using an Android smartphone and has three-dimensional animated character content with the Wonosobo regency icon. cARica is a form of innovation in providing exceptional services and experiences for tourists and has the potential to be continuously developed.
Mix histogram and gray level co-occurrence matrix to improve glaucoma prediction machine learning Jumanto, Jumanto; Nugraha, Faizal Widya; Harjoko, Agus; Muslim, Much Aziz; Alabid, Noralhuda N.
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.99

Abstract

Glaucoma is an eye disease that is the second leading cause of blindness. Examination of glaucoma by an ophthalmologist is usually done by observing the retinal image directly. Observations from one doctor to another may differ, depending on their educational background, experience, and psychological condition. Therefore, a glaucoma detection system based on digital image processing is needed. The detection or classification of glaucoma with digital image processing is strongly influenced by the feature extraction method, feature selection, and the type of features used. Many researchers have carried out various kinds of feature extraction for glaucoma detection systems whose accuracy needs to be improved. In general, there are two groups of features, namely morphological features and non-morphological features (image-based features). In this study, it is proposed to detect glaucoma using texture features, namely the GLCM feature extraction method, histograms, and the combined GLCM-histogram extraction method. The GLCM method uses 5 features and the Histogram uses 6 features. To distinguish between glaucoma and non-glaucoma eyes, the multi-layer perceptron (MLP) artificial neural network model serves as a classifier. The data used in this study consisted of 136 fundus images (66 normal images and 70 images affected by glaucoma). The performance obtained with this approach is an accuracy of 93.4%, a sensitivity of 86.6%, and a specificity of 100%.
Augmented reality development using multimedia development life cycle (MDLC) method in learning media Solehatin, Solehatin; Aslamiyah, Sulaibatul; Pertiwi, Dwika Ananda Agustina; Santosa, Kevin
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.118

Abstract

Practical classroom learning in the multimedia department requires props, where props range from damage. To address this need, learning media are made by applying Augmented Reality. Learning media presents actual images without holding and seeing the objects in real terms so that there is no damage to the props. This research was conducted to create learning media for students of SMK Negeri 1 Banyuwangi majoring in multimedia as an Android-based teaching aid. Stages of research using the development method in the form of Multimedia Model Life Cycle (MDLC). The concept stage analyzes and applies the Augmented Reality (AR) method, the design stage performs application planning according to the needs of learning media. The data collection stage conducted interviews with teachers and students while the stages of making learning media used Blender, Unity and Visual Studio software. At the trial stage of the application by making a guidebook, it was carried out for students at SMK Negeri 1 Banyuwangi. For the stages of distributing learning media using the Likert scale method through distributing questionnaires. The results of the application trials and questionnaire distribution, the responses of students about learning media, the results show the interpretation of respondents by combining a value of 72.22%, which means students accept this learning media. The results of this research can create learning media for multimedia majors that can reduce the risk of damage to props and provide cool and fun learning media.
An expert system on diagnosis of mental diseases Jain, Somay; Aggarwal, Mukul; Singhal, Yash; Lestari, Apri Dwi
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.100

Abstract

Mental disorder is one of the most serious problems in today's time. Mental disorders can be classified into different sub-disorders according to changes in human behavior and mental condition. According to reports one out of seven people suffered from mental disorders. In this research paper, our main emphasis is to build an expert system that diagnoses people based on their symptoms, so people can diagnose themselves early before going to the doctor. Expert Systems are one of the most important applications in artificial intelligence that solves complex problems without human help. We provide different rules, facts, and relationships among different symptoms in our knowledge base, from which users can query their problems and get their results. We used SWI-prolog to build an expert system. There are a few types of disorders, such as mental disorders, neurodevelopmental disorders, eating disorders, etc.
Developed an expert system for analysis of covid-19 affected Mishra, Shashank; Yadav, Shivam; Aggarwal, Mukul; Sharma, Yashika; Muzayanah, Rini
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.113

Abstract

The expert system solves problems within a specific area of the knowledge base. Prolog is a logical programming language which works on its knowledge base and effectively can be used to develop an expert system. Covid 19 is a pandemic deices and an expert system can be developed to diagnose this disease with the help of its symptoms that can be used as a knowledge base in Prolog. This expert system can make a fast diagnosis process for the covid 19 which is important to prevent the spread of the virus. Here we developed an expert system using prolog for diagnosis purposes. Like humans, these systems can get better with time as they gain more experience. Expert systems combine their experiences and expertise into a knowledge base that is then used by an inference or rules engine, a set of rules that the software employs, to apply to certain scenarios. Prolog is ideal for use with intelligent systems for a few reasons. Prolog can be viewed as a straightforward theorem prover or inference engine that derives from predefined rules. With the help of Prolog's built-in search and backtracking techniques, simple expert systems can be created.
Hoax classification in indonesian language with bidirectional temporal convolutional network architecture Maulana, Fajar; Asih, Tri Sri Noor
Journal of Soft Computing Exploration Vol. 4 No. 1 (2023): March 2023
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v4i1.116

Abstract

The increasingly massive rate of information dissemination in cyberspace has had several negative impacts, one of which is the increased vulnerability to the spread of hoaxes. Hoax has seven classifications. Classification problems such as hoax classification can be automated using the application of the Deep Learning model. Bidirectional Temporal Convolutional Network (Bi-TCN) is a type of Deep Learning architectural model that is very suitable for text classification cases. Because the architecture uses dilation factors in its feature extraction so it can generate exceptionally large receptive fields and is supported by Bidirectional aggregation to ensure that the model can learn long-term dependencies without storing duplicate context information. The purpose of this study is to evaluate the performance of Bi-TCN architecture combined with pre-trained FastText embedding model for hoax classification in Indonesian and implement the resulting model on website. Based on the research that has been done, the model with Bi-TCN architecture has satisfactory performance with an accuracy score of 92.98% and a loss value that can be reduced to 0.191. Out of a total of 13,673 data tested with this model, only 414 data or in other words around 3% of the total data were incorrect predictions.

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