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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 8 Documents
 1 
Search results for , issue "Vol 4, No 2 (2019): Journal of Applied Intelligent System" : 8 Documents clear
Rabbit Type Classification Using Multi-SVM Based on Feature Extraction Ery Mintorini; Wildan Mahmud
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.v4i2.3381

Abstract

Rabbits reputation of being cute, fluffy, cuddly critters lend then to being a popular choice for children pets. But in raising a rabbit is not easy, this depends on the type of rabbit. Rabbits that commonly pet are Rex Rabbits, American Rabbits, and Giant Rabbits. Rex Rabbits itself has some species including Rex Amber and Rex Lilac species, Giant Rabbits rabbits includes Giant Chekered rabbit, Harleyquin, dan American White Rabbit. Classification technology can be used to help the classification process of rabbits are Multi-SVM method and image feature extraction to classify rabbit species. Feature extraction used in this study is mean, variance, skewness, kurtosis, entropy. The five features are classified with Multi-SVM. The data used in this study are 125 images, consisting of 100 training images and 25 test images. The accuracy of this method reached 92%. Keywords – Classification, Multi-SVM, Rabbit, Feature Extraction
Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison Ardytha Luthfiarta; Junta Zeniarja; Edi Faisal; Wibowo Wicaksono
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.v4i2.2772

Abstract

Banking system collect enormous amounts of data every day. This data can be in the form of customer information,  transaction  details,  risk profiles,   credit   card   details,   limits   and   collateral    details, compliance  Anti Money Laundering (AML) related information, trade  finance  data,  SWIFT  and  telex  messages. In addition,  Thousands  of decision  are  made in Banking system. For example, banks everyday creates credit decisions,  relationship  start  up,  investment   decisions, AML  and  Illegal  financing  related decision.  To create this decision, comprehensive review on various  reports  and drills  down  tools  provided  by the banking systems is needed.  However, this is a manual process which  is  error  prone  and  time  consuming  due  to  large volume of transactional  and historical  data available. Hence, automatic knowledge mining is needed to ease the decision making process.  This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms.  This research provides an overview of the data mining techniques and   procedures will be performed.   It also provides   an insight   into how these techniques can be used in deposit subscription  in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.
Expert System Of Nose Disease With Hybrid Method Yogi Wiyandra; Firna Yenila
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.v4i2.3079

Abstract

Nasal disease is a type of disease that is classified as often afflicting someone. And it is not uncommon among people to conduct a direct examination on ENT specialists to find out the type of disease they are experiencing. Thus an alternative is needed to find out early the problems that arise from the nose. The intended alternative is to build a system in the form of an expert system that uses one's expertise in providing knowledge in the form of a nose condition. The system was built using a hybrid method that combines the forward chaining method and the certainty factor. So, the results or knowledge given to the public is confirmed by the percentage given.
Cataract Disease Diagnosis System Using Artificial Neural Network Learning Vector Quantization Method Chrisani Waas; D. L. Rahakbauw; Yopi Andry Lesnussa
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.v4i2.3089

Abstract

Artificial Neural Network (ANN) is an information processing system that has certain performance characteristics that are artificial representatives based on human neural networks. ANN method has been widely applied to help human performance, one of which is health. In this research, ANN will be used to diagnose cataracts, especially Congenital Cataracts, Juvenile Cataracts, Senile Cataracts and Traumatic Cataracts based on the symptoms of the disease. The ANN method used is the Learning Vector Quantization (LVQ) method. The data used in this research were 146 data taken from the medical record data of RSUD Dr. M. Haulussy, Ambon. The data consists of 109 data as training data and 37 data as testing data. By using learning rate (α) = 0.1, decrease in learning rate (dec α) = 0.0001 and maximum epoch (max epoch) = 5, the accuracy rate obtained is 100%.
Diagnosis Of Heart Disease Using K-Nearest Neighbor Method Based On Forward Selection Junta Zeniarja; Anisatawalanita Ukhifahdhina; Abu Salam
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.v4i2.2749

Abstract

Heart is one of the essential organs that assume a significant part in the human body. However, heart can also cause diseases that affect the death. World Health Organization (WHO) data from 2012 showed that all deaths from cardiovascular disease (vascular) 7.4 million (42.3%) were caused by heart disease. Increased cases of heart disease require a step as an early prevention and prevention efforts by making early diagnosis of heart disease. In this research will be done early diagnosis of heart disease by using data mining process in the form of classification. The algorithm used is K-Nearest Neighbor algorithm with Forward Selection method. The K-Nearest Neighbor algorithm is used for classification in order to obtain a decision result from the diagnosis of heart disease, while the forward selection is used as a feature selection whose purpose is to increase the accuracy value. Forward selection works by removing some attributes that are irrelevant to the classification process. In this research the result of accuracy of heart disease diagnosis with K-Nearest Neighbor algorithm is 73,44%, while result of K-Nearest Neighbor algorithm accuracy with feature selection method 78,66%. It is clear that the incorporation of the K-Nearest Neighbor algorithm with the forward selection method has improved the accuracy result. Keywords - K-Nearest Neighbor, Classification, Heart Disease, Forward Selection, Data Mining
Securing Digital Color Image based on Hybrid Substitution Cipher Moch Sjamsul Hidajat; Ichwan Setiarso
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.v4i2.3380

Abstract

This study proposes securing digital color images with hybrid substitution cryptographic methods combined with the Vigenere and Beaufort methods. The hybrid process is carried out using the help of two randomly generated keys. The first key is a matrix with an 8-bits value and the second key is a matrix with a binary value. The binary key is used to determine the Vigenere or Beaufort process, while the 8-bit key is used for modulus operations based on the Vigenere or Beaufort algorithm. At the test stage used a standard image that has an RGB color channel as a dataset. The quality of the cryptographic method is measured by several measuring instruments such as MSE, PSNR, and SSIM to determine the quality of encryption visually and the perfection of decryption, besides that it is used Entropy, NPCR and UACI to determine the probability of encryption resistance and quality against differential attacks. The TIC TOC function is also used to measure the computing speed of the encryption and decryption process. Measurement results using all measuring instruments indicate that the proposed method has very satisfying results and has fast computing. Keywords – Cryptography, Substitution Cipher, Modulus Function, Encryption, Decryption, Image Transmission 
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

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