JAIS (Journal of Applied Intelligent System)
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.
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<![CDATA[Expert System for Diagnosing Potential Diabetes Attacks Using the Fuzzy Tsukamoto ]]>
<![CDATA[Christy Atika Sari]]>;
<![CDATA[Wellia Shinta Sari]]>;
<![CDATA[Andi Danang Krismawan]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6796
<![CDATA[Diabetes is one of the top three killers in Indonesia. According to the 2014 sample enrollment survey, the number of people with diabetes is increasing year by year. This is because the diagnosis of the disease is delayed. Also unhealthy lifestyle. In an era of fast and efficient technological advancement, this is a very good thing for advancement in various fields. More and more fields of knowledge are developing, one of which is expert systems. An expert system is a software or computer program that matches the ability of an expert, meaning that it can match humans with special abilities that ordinary people cannot solve. Expert systems aim to solve specific problems, such as in fields such as medicine, education, etc. This expert system takes as inputs several variables consisting of transient blood sugar (GDS), fasting blood sugar (GDP), frequent hunger, thirst, weight loss, and urine (BAK), the method used by the author is Fuzzy Tsukamoto. This Tsukamoto method states that every result of IF-Then must be described as a fuzzy set with an immutable or monotonic membership function, and uses PHP for programming. The results obtained in the study conducted by the authors were in the form of an expert system that detects diabetes and obtains results with 94% accuracy.]]>
<![CDATA[Classification of Arabica Coffee Green Beans Using Digital Image Processing Using the K-Nearest Neighbor Method ]]>
<![CDATA[Nurun Najmi Amanina]]>;
<![CDATA[Galuh Wilujeng Saraswati]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6449
<![CDATA[Arabica coffee is the largest commodity produced by farmers in Pagergunung Village, Bulu District, Temanggung Regency. Coffee production in recent years has increased rapidly by 80% with the increasing lifestyle of the Indonesian people marked by the number of coffee shop buildings in various regions, and of course the demand for Arabica coffee has also increased, therefore it must improve the quality or quality of the coffee itself. However, in determining and classifying the quality of coffee beans, errors often occur due to the lack of understanding of the farmers in processing coffee. Based on this, the purpose of this research is to classify using the K- Nearest Neighbor method and feature extraction using the average value of Red-Green-Blue (RGB) color in determining the quality and quality of coffee beans according to grade so that they can get a high selling price. In this study using as many as 150 training image data and 150 testing image data, the results of this classification accuracy are 80% using k=1.]]>
<![CDATA[Transfer Learning with Xception Architecture for Snakefruit Quality Classification ]]>
<![CDATA[Rismiyati Rismiyati]]>;
<![CDATA[Ardytha Luthfiarta]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6797
<![CDATA[Machine learning has been greatly used in the field of image classification. Several machine learning techniques perform very well in this task. The development of machine learning technique in recent years are in the direction of deep learning. One of the main challenge of deep learning is that it requires the number of the samples to be extremely large for the model to perform well. This is because the number of feature that trainable parameter are huge. One of the solution to overcome this is by introducing transfer learning. One of the architecture that is currently introduced is Xception architecture. This architecture is claimed to outperform VGG16, ResNet50, and inception in terms of model accuracy and model size. This research aims to classify snakefruit quality by using transfer learning with Xception architecture. This is to explore possibility to achieve better result as Xception architecture generally perform better than other available architecture in transfer learning. The snakefruit quality is classified into two classes. Hyperparameter value is optimized by several scenario to determine the best model. The best performance is achieved by using learning rate of 0.0005, momentum 0.9 and dropout value of 0 or 0.25. The accuracy achieved is 94.44%.]]>
<![CDATA[PUSPINDES E-Performance Information System for Monitoring the Performance ]]>
<![CDATA[Teguh Tamrin]]>;
<![CDATA[Akhmad Khanif Zyen]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6492
<![CDATA[Puspindes is a government institution built by the Pemalang government to provide competence in the field of Information and Communication Technology (ICT) development. The rural informatics empowerment center, hereinafter referred to as PUSPINDES, Pemalang Regency is a flagship program under the supervision and responsibility of BAPERMASDES (Community Empowerment Agency Village) Pemalang Regency this institution focuses on village development, especially in the field of Computer Information Technology and also Intern networks for villages. A system was created to assist in the administrative process carried out by PUSPINDES employees using the PHP Programming Language CodeIgniter package and data storage using a MySQL database.]]>
<![CDATA[Development of Android-Based 3d Animation Learning Applications to Support Distance Learning for the D4 Animation Study Program, Udinus Semarang ]]>
<![CDATA[Nur Rokhman]]>;
<![CDATA[Novi Hendriyanto]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6814
<![CDATA[The existence of the COVID-19 pandemic has become a big problem in practicum courses, especially the 3D1 Animation course at the Dian Nuswantoro University animation study program, Semarang. Lecturers cannot guide students directly when experiencing obstacles in the learning process such as during face-to-face learning. The purpose of this research is to create an android application for learning media 3d1 animation. In this application there are several menus including the semester learning plan menu, video tutorials, task collection, consultation with lecturers, remote desktop requests and othersRemote desktop features to make it easier for lecturers to guide students remotely. This study uses the waterfall method, namely software requirements analysis, design, development, testing, and maintenance with testing using the black box method. The test results show that each aspect has results that can be concluded as successful and feasible. This research succeeded in developing android-based 3d1 animation learning media.]]>
<![CDATA[GLCM Based Locally Feature Extraction On Natural Image ]]>
<![CDATA[Edi Faisal]]>;
<![CDATA[Agung Nugroho]]>;
<![CDATA[Ruri Suko Basuki]]>;
<![CDATA[Suharnawi Suharnawi]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6569
<![CDATA[GLCM is a feature extraction method that uses statistical analysis using a gray scale. Contrast, correlation, energy and entropy are feature features whose value will be sought as the basis for finding the threshold which can then be used to find the threshold value in image segmentation. In this study, a local-based GLCM method is used where the image that has been made into grayscale will be divided into 16 parts of the same size. Each section will look for the value of its GLCM features, namely Contrast, correlation, energy and entropy. The calculation of these four features will be applied to 16 parts of the grayscale image, which can then be used to find the threshold value. The results of the four features in the calculation with an angle of 0o are the contrast value = 0.0080, correlation = 0.619, energy : 0.00160 and entropy : 0.05591.]]>
<![CDATA[Visitor Prediction Decision Support System at Dieng Tourism Objects Using the K-Nearest Neighbor Method ]]>
<![CDATA[Eko Hari Rachmawanto]]>;
<![CDATA[Christy Atika Sari]]>;
<![CDATA[Heru Pramono]]>;
<![CDATA[Wellia Shinta Sari]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6821
<![CDATA[A tourist target is anything that attracts a visitor or tourist to come to visit a place or area. Tourism goods play an important role in a country or region, becoming a source of national foreign exchange, increasing human resources, and improving the economy of surrounding communities. The problem posed in this study is how to implement a decision support system in predicting visitor numbers for Dieng tourists using the k-nearest neighbor method. The purpose of this study is to help the local government and surrounding communities to improve facilities such as restaurants, places of worship, parking lots, clean toilets so that tourists can feel safe and comfortable when visiting Dieng. Helps manage tourism targets. is what you give. These attractions using a decision support system as a process to predict visitors. The number of visitors who visited in December 2017 was 421,394, which serves as a reference for predicting the number of visitors who will visit Dieng in the following year. The predicted result is 29569.25 visitors with a parameter value of k = 8 and a minimum RMSE value of k = 1/0.]]>
<![CDATA[Sentiment Analyst on Twitter Using the K-Nearest Neighbors (KNN) Algorithm Against Covid-19 Vaccination ]]>
<![CDATA[Suprayogi Suprayogi]]>;
<![CDATA[Christy Atika Sari]]>;
<![CDATA[Eko Hari Rachmawanto]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6734
<![CDATA[The corona virus (2019-nCoV), commonly known as COVID-19 has been officially designated as a global pandemic by the WHO. Twitter, is one of the social media used by many people and is popular among internet users in expressing opinions. One of the problems related to Covid-19 and causing a stir is the procurement of the Covid-19 vaccine. The procurement of the vaccine caused various opinions in Indonesian society, where the uproar was also quite busy being discussed on Twitter and even became a Trending Topic. The opinions that appear on Twitter will then be used as data for the Sentiment Analysis process. One of the members of the House of Representatives (DPR), namely RibkaTjiptaning was also included in the Trending Topic list on Twitter for refusing to receive the Covid-19 vaccine. Sentiment analysis itself is a computational study of opinions, sentiments and emotions expressed textually. Sentiment analysis is also a technique to extract information in the form of a person's attitude towards an issue or event by classifying the polarity of a text. Research related to Sentiment Analysis will be examined by dividing public opinion on Twitter social media into positive and negative sentiments, and using the K-Nearest Neighbor (KNN) algorithm to classify public opinion about COVID-19 vaccination. In the testing section, the Confusion Matrix method is used which then results in an accuracy of 85%, precision of 100%, and recall of 78.94%.]]>
<![CDATA[Predicting News Article Popularity with Multi Layer Perceptron Algorithm ]]>
<![CDATA[Arie Rachmad Syulistyo]]>;
<![CDATA[Vira Meliana Agustin]]>;
<![CDATA[Dwi Puspitasari]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6826
<![CDATA[Nowadays, news media seems to have been digitized. One of them is printed news which has now turned into online news. The increasing use of social media has made people interested in reading news online. News needs to attract readers with their headlines. Various online news media businesses want to know the future demand of readers, as well as whether the released news can reach more readers so that the news becomes popular. Therefore, with the increasing interest in online news today, this paper will analyze the performance of the Neural Network Algorithm and other artificial intelligence techniques in predicting the popularity of news articles that can help the media to know whether their news will become popular. The news article popularity prediction system can increase its revenue if there are advertisements in the news. The test results show that the accuracy of the Multi Layer Perceptron is 76% and Random Forest gives an accuracy of 70%.]]>
<![CDATA[Integration of Augmented Reality and Voice Recognition in Learning English for Children ]]>
<![CDATA[Dimas Wahyu Wibowo]]>;
<![CDATA[Ika Kusumaning Putri]]>;
<![CDATA[Leni Saputri]]>
<![CDATA[ Journal of Applied Intelligent System Vol 7, No 2 (2022): Journal of Applied Intelligent System ]]>
Publisher : <![CDATA[Universitas Dian Nuswantoro and IndoCEISS ]]>
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DOI: 10.33633/jais.v7i2.6119
<![CDATA[Application development by combining two technologies, namely Augmented Reality and Voice Recognition, can make learning media regarding object recognition at home interact directly with 3D virtual objects. The technology can also help the pronunciation or pronunciation of sentences in English. Natural Language Processing (NLP) is used to understand human language so that machines can understand and process it. This ability supports Voice Recognition to have intelligence and interact like humans. wit.ai is an open-source NLP platform that can support speech-to-text application development. The merging of the two technologies in this development using the wit.ai platform. With the wit.ai platform that is used to understand voice commands and perform tasks as needed for applications regarding object recognition at home, users will be able to interact with objects at home through the given voice commands. In the Black Box testing, each functionality got the results that all the features had functioned properly. User Acceptance Test was also carried out and the average test results were 95.77% and 93.26% on a Likert scale with test results on 13 respondents aged 6-9 years old who have tried the application and 14 respondents as observers when 13 respondents aged 6-9 years tried the application. These results show that the application can be accepted and used as a tool in learning media.]]>
