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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 191 Documents
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Sliding Modes Strategy Implementation for Controlling Nutrition in Hydroponics Based IoT Septian Enggar Sukmana; Nurul Anisa Sri Winarsih; Akmaludin Akbar
Journal of Applied Intelligent System Vol 4, No 2 (2019): 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.v4i2.2767

Abstract

To reduce unconsistenly of nutrition sensor data, an analysis which consists of mathemathical model and new control technique is required. In this paper, a simulation of smart garden is performed to simulate a smart green campus. However, the problem appears in this activity, the data form sensor is not consistent and it may harm the plant because sometime the plant may get a much nutrition and another time the plant will get less nutrition. Our propose is on the sensor circuit, we use additional circuit to our TDS meter so the data is normalized using this circuit.
Implementation of A* Algorithm for Solving Sokoban Logic Games Rosa Tri Setiani; Bonifacius Vicky Indriyono
Journal of Applied Intelligent System Vol 4, No 2 (2019): 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.v4i2.3409

Abstract

Sokoban is one of the puzzle category games that requires players to push boxes towards the top, bottom, left and right until the specified destination. Sometimes it takes a long time to complete a very difficult level so that a solution is needed. So that these difficulties can be overcome, we need an algorithm that can help find the right path.A * algorithm is an algorithm that minimizes the total cost of the track under the right conditions that will provide the best solution in optimal time. A * algorithm will find the distance of the fastest route to be taken by a starting point to the destination object by removing unnecessary steps.From the test results, this A * algorithm can help players in finding a solution to the road in the form of completion steps in the Sokoban game by determining the smallest F value.  Keywords - Artificial Intelligence, Games, Sokoban, A * algorithm
Android Base Rapid Application Development for Learning Yanbu'a Teguh Tamrin; Syamsul Ma’arif
Journal of Applied Intelligent System Vol 5, No 2 (2020): 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.v5i2.4375

Abstract

Learning to read the Al Quran is a very important basic knowledge, especially for early childhood, because it serves as a foundation to increase love for the Qur'an and an increase in the sense of faith in Allah Subhanahu Wata'ala. The current learning model is still very conventional. The method of learning the Yanbu'a book in general still uses the rote method by reading the book and listening to it from the teacher. The impact of learning like that causes the child to be less enthusiastic about learning so it is easy to bo-san. Seeing these problems, it is necessary to make mobile-based applications that can make it easier and fun for children to learn the Yanbu'a book. In this study, we will discuss the development of the Yanbu'a learning method with the Rapid Application Development Method on Android which was built using the Construct 2 application. Students are increasingly facilitated in learning and interest in learning is increasing. 
Classification of Student Aspiration Using Naïve Bayes Classifier Ifan Rizqa; Christy Atika Sari; Mohamed Doheir
Journal of Applied Intelligent System Vol 6, No 1 (2021): 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.v6i1.4459

Abstract

Students aspiration are various demands from the student that packed in creative idea to propose changing process of a thing. Mostly, aspiration delivered in complaints and expectation. Aspiration is used for evaluating the laxity and early detection in university quality system for the better. This activity took place in Dian Nuswantoro University, and Student Representative Council (SRC) is the unit to manage the students aspiration. Aspiration is obtained through predetermined mechanism such as manual questionnaire distribution and or using google form. The provided questionnaire requires student to fill the content according to the provided aspiration categories. However, the problem is sometimes the student choose the wrong category according to the content. Therefore it is needed to create an application that can classified the students aspiration automatically. Document text classification become the best way to determine the category based on the content of the students aspiration. Naïve bayes classifier method is used because it is capable to produce high accuracy. With 1000 data training document of each category, "facilities and infrastructure" (facilities), "lecturers" (attitudes, teaching methods, material delivered), "staffing and the academic system"(attitudes, ways of working, providing information), and "suggestions and feedback". This experiment achieved 90.20% accuracy. It can be said that this method is worth to implement in this research.
Automatic Power-up Items Placement on Shooter Game using Convolutional Neural Network Alvin Satria Nugraha; Abas Setiawan; Wijanarto Wijanarto
Journal of Applied Intelligent System Vol 5, No 1 (2020): 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.v5i1.4213

Abstract

- A shooter game is a popular game genre with various components. To make a shooter game more attractive, some power-ups items can support players to achieve their goals. Power-ups items provide more power to players, some of which include ammo, extra lives, and invulnerability. The location of power-ups items should be in a special place so that it neither too easy to find nor too difficult to find. Item placement could be done manually by a human or a technical artist. It will need a relatively long time and high cost. In this paper, we try to mimic technical artist vision when placing an item. Visual images have been collected by scanning spatially the forest terrain by using a virtual camera on top. Each image data comply with the item placement rules according to the Tomb Raider and Uncharted 4 games. Convolutional Neural Network (CNN) is used to find out which images can be occupied by power-up items or not. From several experimental scenarios, the use of the Global Average Pooling layer is proven to produce a model that is not overfitting. The best CNN models are developed and got an accuracy of 90.5% with an architecture that includes the Global Average Pooling layer. That model is applied to the new forest terrain so that power-up items can automatically be placed in an appropriate location.
Data Mining Applications for Violence Pattern Analysis with FP-Growth Algorithm Junta Zeniarja; Debrina Luna Arghata Mangkawa; Abu Salam
Journal of Applied Intelligent System Vol 6, No 1 (2021): 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.v6i1.4444

Abstract

Violence is a crime that is one of the problems the principal experienced by each country. Violence can be interpreted as a behavior that causes harm to someone. According to the results of DP3AKB research in Central Java Province in 2017, there are less many than 200 people in Central Java province experienced acts of violence. By because of the many acts of violence that occur in various forms of violence, it requires definite information about the form of violence that happens most often, in obtaining that information Data mining techniques are needed by using the FP-Growth algorithm. The application of the FP-Growth algorithm to produce form association patterns violence. Hardness data is 420 data, the best 7 rules have been obtained with min value support 50% and min value support 60%. On the best rule results have given a recommendation (solution) so that the DP3AKB can handle the problem of violence well and on target.
5th Hyphotesis Consideration of UTAUT for IOT By Exploiting ACO based Classification Rudy Ariyanto; Vivin Ayu Lestari; Septian Enggar Sukmana
Journal of Applied Intelligent System Vol 5, No 1 (2020): 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.v5i1.3523

Abstract

Internet of things (IoT) application needs to be evaluated to gain better improvement and innovation. The evaluation can be examined from user acceptance. The unified theory of acceptance and use of technology (UTAUT) can be used as a model to identify user acceptance in using technology, including IoT application. However, the ease of use of technology must be included, so the determining of easy of use from negative aspects must be included, so the 5th hypothesis of UTAUT (hindering condition) must be included. Before this hypothesis is formulated and included in evaluation by the user, obtaining data to identify the real condition of the user is performed using forensic analysis and ACO based classification. To evaluate this activity, this 5th hypothesis is measured by reliability and validity test, also hypothesis testing itself.
Sentiment Analysis on Indonesia Twitter Data Using Naïve Bayes and K-Means Method Ajib Susanto; Muhammad Atho’il Maula; Ibnu Utomo Wahyu Mulyono; Md Kamruzzaman Sarker
Journal of Applied Intelligent System Vol 6, No 1 (2021): 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.v6i1.4465

Abstract

This study focuses on the analysis of sentiments on Indonesian twitter data. Twitter data on Indonesian simultaneous pilkada used to get its sentiments using Naïve Bayes Classifier method as a method of classification and K-means method to get Label on the data train process. Combining the two methods is expected to get high accuracy results. The results obtained from the research shows a pretty good accuracy of 74.5%.
Particle Swarm Optimization For Improved Accuracy of Disease Diagnosis Suamanda Ika Novichasari; Iwan Setiawan Wibisono
Journal of Applied Intelligent System Vol 5, No 2 (2020): 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.v5i2.4242

Abstract

The increasing number of patients suffering from various diseases and the impact and high cost of medical treatment for the community has made the government or health communities seek solutions for prevention from an early age. This valuable information can be found using artificial intelligence and data mining. Most diseases are dangerous; if detected early and adequate diagnosis and treatment are available, there will be a chance for a cure. The main objective of this study was to use Particle Swarm Optimization (PSO) to improve the accuracy of several classification methods, namely Naive Bayes, C4.5, Support Vector Machine (SVM), and Neural Network (NN) to detect heart disease, hepatitis, kidney, and breast cancer. The method used in this research is the CRISP-DM model, with 5 stages. The data used were four disease data from UCI Machine Learning. The result of this research is that PSO can improve the accuracy of Naive Bayes, C4.5, SVM, and NN.
Comparation of Dice Similarity and Jaccard Coefficience Against Winnowing Algorithm For Similarity Detection of Indonesian Text Documents Santi Purwaningrum; Agus Susanto; Nur Wachid Adi Prasetya
Journal of Applied Intelligent System Vol 6, No 1 (2021): 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.v6i1.4453

Abstract

Plagiarism is the act of imitating and quoting and even copying or acknowledging other people's work as one's own work. Plagiarism is currently growing rapidly, especially in the world of education. So that plagiarism detection is needed to prevent plagiarism from growing rapidly. In response to this, this paper intends to conduct research that compares the dice similarity and the jaccard coefficient to find the best document similarity value level against the Winnowing algorithm which functions to find the fingerprint value of each document. The test results show that the winnowing algorithm is quite good at using the dice similarity level with the results of an average similarity value of 71.17615%  than testing using jaccard coefficient with the resulting value 35,58837%.

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