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JAIS (Journal of Applied Intelligent System)
Published by Universitas Dian Nuswantoro
ISSN : 25020493     EISSN : 25029401     DOI : -
Core Subject :
Journal of Applied Intelligent System (JAIS) is published by LPPM Universitas Dian Nuswantoro Semarang in collaboration with CORIS and IndoCEISS, that focuses on research in Intelligent System. Topics of interest include, but are not limited to: Biometric, image processing, computer vision, knowledge discovery in database, information retrieval, computational intelligence, fuzzy logic, signal processing, speech recognition, speech synthesis, natural language processing, data mining, adaptive game AI.
Arjuna Subject : -
Articles 10 Documents
 1 
Search results for , issue "Vol 7, No 1 (2022): Journal of Applied Intelligent System" : 10 Documents clear
An E-Library System Integrated with Bookshelf and Recommendation Components Folasade Olubusola Isinkaye; Tomiwa John Fred-Yusuff
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5791

Abstract

Apparently, most students in Nigeria are facing challenges as regards to the lack of portability, stress, time wastage and inadequate resources in terms of accessing the school libraries, as well as the inefficiency of the existing e-library, leading to the reduction in the number of students that do access the libraries. Research has shown that most students no longer believe in the physical libraries and have developed interest in electronic resources. Hence, an e-library system enhanced with book recommendation component could serve as a solution to these problems. Several researchers have repeatedly attempted to develop various solutions to this problem using various methodologies and approaches in order to provide a digital library that could address the aforementioned problems. In this work, an e-library system integrated with recommendation component was designed and implemented to help students locate relevant books. Also, an additional feature which adds books to the shelf for future reference was included to enhance accessibility and efficiency of the system. The web application was implemented on a live server (Namecheap) which is one of the most effective live servers in Nigeria. Furthermore, the system was evaluated with one hundred and fourteen (114) students, and the results of the evaluation carried out on the system emphasized its usefulness in terms user friendliness (77%), user experience (86%), interface appearance (75%), system loading speed (82%), platform compatibility (78%), recommendation accuracy (80%) and recommendation reliability (84%). Therefore, the system could be used to solve students’ problems with regards to the challenges faced with the use of physical and conventional e-libraries.
Gold Price Prediction Using Support Vector Regression Yupie Kusumawati; Karis Widyatmoko; Candra Irawan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.6124

Abstract

In this modern era, one of the businesses that continues to grow is investment. Gold has a more stable value. In Indonesia, there are futures exchange companies that offer gold investment with an online transaction system (E-Trade). The amount of demand and supply, the rate of inflation, economic conditions, and many more can affect the high and low prices of gold. Due to changes in the conditions above, the price of gold may increase, decrease, or remain constant every day. The price of gold that can go up and down causes the need for gold price predictions so that future gold trading investment prospects can be seen. In this final project, the accuracy of Support Vector Regression will be investigated to find out how accurate it is in predicting gold prices with High, Low, Open, Close, and Volume variables. Based on the calculation of the best RMSE in the study, it was found that the best RMSE was to use a Linear kernel with a C of 35 and using a Y variable dataset of 7.4615. The Support Vector Regression Algorithm can predict quite well, as evidenced by the acquisition of fairly good RMSE results. It is necessary to do a simulation of buying and selling gold based on the prediction results and comparing the advantages of the testing data and the actual data.
Mixed Reality Based User Acceptance Measurement on Primary School Age Children (Case Study: Introduction to Indonesian Native Fauna) Dimas Wahyu Wibowo; Muhammad Shulhan Khairy; Septian Enggar Sukmana
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5328

Abstract

One aspect of learning media technology sustainability is the level of user acceptance of the learning media itself. It is related to the user loyalty or willingness to continuously use the learning media application. For primary school age children, analyzing the level of acceptance of learning media using the unified theory of acceptance and use of technology (UTAUT) is challenging. In its application, the formulation of the questions is carried out in a simple way therefore children can understand each meaning of the questions. In this study, the object of technology used is a mixed reality-based learning application, which contains introduction to Indonesia Native fauna material. To ensure that the inter-view questions meet the children’s psychological, the reliability and validity measure were taken as the initial step. Secondly, hypothesis testing was carried out to analyze the children behavioral intentions based on the variables contained in UTAUT. The results of this study indicate that students do not find difficulties in using this mixed reality-based learning media application, both on the technical and psychological (techno-stress) sides.
Cross-Language Text Document Plagiarism Detection System Using Winnowing Method Mustika Mentari; Imam Fahrur Rozi; Maria Puji Rahayu
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5950

Abstract

Currently, there are many text documents such as journals scattered on the internet, both Indonesian and English-language journals. With this, it is possible to act plagiarism by copying from foreign journals that are translated into other languages or copying directly without being changed from the original language. One way that can suppress these actions is to build a plagiarism detection system for cross-language text documents. The method that can be used to detect document plagiarism is the Winnowing method. Winnowing method is a method where text input will be processed to produce a hash value called a fingerprint. This study aims to build a system that can detect plagiarism of text documents in different languages using the Winnowing method. Text documents that can be tested are input text and PDF files. Documents used in system testing are journals that have the same topic. The results of the highest level of accuracy produced between the calculation of the Jaccard Coefficient with the Plagiarism Checker X application are in the fourth scenario with an average percentage value of 84.7%.
New Image Texture Feature for Chest X-Ray Classification Prajanto Wahyu Adi; Fajar Agung Nugroho; Yani Parti Astuti
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5340

Abstract

This study proposes a new feature extraction model to identify CXR images of covid-19 and pneumonia has a high visual resemblance. The feature extraction model starts by using histogram equalization and average filters as lowpass features and high pass features obtained through Laplacian and LoG filters. In the next step, covariance matrix of image along with the entire features are used to produce an eigen vector that will be used as a feature vector in the classification process. The final stage is the process of testing features on the classification algorithms KNN, SVM, LDA, Naïve Bayes, and Decision Tree through a 10-foldcross validation scheme with 0.9 training data and 0.1 test data. The first experiment for the Covid-19 and normal classes shows that the proposed model is able to produce an accuracy of 96% as the comparison model with GLCM texture extraction have an accuracy value of 91%. The second test is conducted for the class Covid-19 and pneumonia and obtained an accuracy value of 89% for the proposed model and 73% for the GLCM texture extraction. Experiments proved that the proposed model successfully outperformed the GLCM texture extraction model in all of classification algorithms used.
A Covid-19 Sentiment Analysis on Twitter Using K-Nearest Neighbours Castaka Agus Sugianto; Shandy Tresnawati
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5984

Abstract

In December 2019, an outbreak named Corona Virus (SARS-CoV-2) occurred in the city of Wuhan, China which was later known as COVID-19. News of the development of the virus spread through various media, one of which was through the well-known platform Twitter. Twitter is one of the widely used media platforms to communicate about Covid-19. Information related to Covid-19 circulating in the community can be in the form of news or opinions or opinions. Then, the circulating information will be classified into three classes, namely positive, negative or neutral. The method used to calculate the prediction of text classification on Twitter is K-nearest neighbors (KNN). The dataset used in grouping on twitter by using the account name Covid19. Firstly, the dataset by crawling data or information on twitter. Secondly, the text mining stage to determine the class distance value and calculate the Euclidean distance formula based on all the training data to be tested. After the training process is complete, the evaluation model used will be used, the Euclidean results are taken based on the value of the closest distance. The accuracy of the model will be calculated using the previous Euclidean method. The results of this study he obtained with the highest value, one of which was 78% using a 50:50 sample comparison with k-5 and k-9 values.
Classification of X-Ray Images of Normal, Pneumonia, and Covid-19 Lungs Using the Fuzzy C-Means (FCM) Algorithm Dini Rohmayani; Ayu Hendrati Rahayu
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5512

Abstract

Lung disease has a very serious impact on the respiratory system and can be dangerous if not treated immediately. At this time, lung diseases that are often encountered by the public include pneumonia and 2019 coronavirus. Many people mistake the disorder that occurs to him because the symptoms of Covid-19 and pneumonia are very similar. Thus, it is very important to know the difference between the two diseases so that early treatment can be carried out. Based on the problems that have been described, the author will propose a study entitled "Classification of X-ray Images of Normal Lungs, Pneumonia, and Covid-19 Using the Fuzzy C-Means (FCM) Algorithm". The aim of this study is to assist in classifying normal, pneumonia, and Covid-19 lungs. The reason for choosing this algorithm is that this algorithm has advantages in grouping cluster centers which are more optimal than other methods.
Indonesian Language Hoax News Classification Basedn on Naïve Bayes Ari Sudrajat; Ratna Rizky Wulandari; Elvathna Syafwan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5985

Abstract

Hoax news in Indonesia causes various problems, therefore it is necessary to classify whether a news is in the hoax category or is valid. Naive Bayes is an algorithm that can perform classification but has a weakness, namely the selection of attributes that can affect accuracy so that it needs to be optimized by giving weights to attributes using the TF-IDF method. Classification using Naive Bayes and using TF-IDF as attribute weighting on a dataset of 600 data resulted in 82% accuracy, 84% precision, and 89% recall. The suggestion put forward is that it is better to use a larger number of datasets in order to produce higher accuracy.
Facial Skin Color Segmentation Using Otsu Thresholding Algorithm Aris Haris Rismayana; Henny Alfianti; Dadan Saepul Ramdan
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5513

Abstract

The development of technology and information is currently very fast. One of the fields of technology and information that is experiencing development is the field of digital image processing. There are many technologies today that utilize digital images such as facial recognition, object detection and many others. Skin is one of the largest components of the human body. Currently, technology in the identification of skin color is widely used in recognizing the human race. In this study, skin color detection uses the YCbCr color space, which in this study only uses the range of Cb and Cr values, and ignores the Y value. Where Y is the lighting in the image. So if not changed, the image will contain light effects that can change the characteristics of skin color. However, problems were found because the detected images were not segmented properly, such as clothes and hair from the tested images were still detected as skin. Therefore, the HCbCr color space method is proposed where the Hue value will represent the color of visible light. While the Otsu Thresholding method will separate the background from the object in the digital image.
A Good Relative Percentage Increase (RPI) of Variant Job Scheduling Using Artificial Bee Colony (ABC) Riri Damayanti Apnena; Firdhani Faujiyah
Journal of Applied Intelligent System Vol 7, No 1 (2022): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v7i1.5989

Abstract

Artificial Bee Colony (ABC) which is a development of the intelligent swarm model and is a branch of artificial intelligence based on self-organization systems. Artificial Bee Colony (ABC) is an intelligent algorithm that is inspired by the food search process carried out by bees. This is like what is done when there are many jobs that need to find the optimal value, where each job to be processed has a specific route of operations to be performed on a set of machines, and a different flow shop and variant: all jobs follow the same machine sequences. We will focus on the latter. In this study, ABC is implemented to optimize work scheduling, in this case 7 different variations are used with mxn values between 10x3 to 40x15 on 10 to 40 jobs. To evaluate the results, the Relative Percentage Increase (RPI) has been used in the test with an achievement range between 1.9 to 18.9.

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