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Agus Harjoko
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INDONESIA
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Published by Universitas Gadjah Mada
ISSN : 19781520     EISSN : 24607258     DOI : https://doi.org/10.22146/ijccs
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering
Indonesian Journal of Computing and Cybernetics Systems (IJCCS), a two times annually provides a forum for the full range of scholarly study . IJCCS focuses on advanced computational intelligence, including the synergetic integration of neural networks, fuzzy logic and eveolutionary computation, so that more intelligent system can be built to industrial applications. The topics include but not limited to : fuzzy logic, neural network, genetic algorithm and evolutionary computation, hybrid systems, adaptation and learning systems, distributed intelligence systems, network systems, human interface, biologically inspired evolutionary system, artificial life and industrial applications. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 8 Documents
 1 
Search results for , issue "Vol 1, No 2 (2007): July" : 8 Documents clear
Face Detection Using Backpropagation Neural Networks Anifuddin Azis; Muhamad Haikal
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2274

Abstract

AbstractThis paper summarizes a research effort in human face detection. A system to locate human faces in images, especially when used as a front-end for a human face identification system, could have many applications in the law enforcement and security professions. The approach presented here is a hybrid system using an edge deletion preprocessor and back-propagation neural networks. The method proposed successfully detected multiple faces. The results obtained are reported along with a discussion for improving the system.Keywords: Backpropagation neural networks, Edge Detection
An Application of Expert System to Identify Trees Utilizing Leaf Images Muhammad lhsan Sarita; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2275

Abstract

AbstractTree identification is a very important to support almost all activities in the forest sector. Unfortunately, the inavailability of data and computer programs that is user friendly have caused ineficiency in tree identification. This research tries to make an expert system to identify trees by using the leaf images. To store the data in the knowledge base one must choose one of the some leaf images that are in the data base available in the program according the characteristic of the leaf. Each leaf image has a code and the accumulation of all codes build a tree code then this code is saved in the knowledge base. The tree code is used to identify a tree by making the comparison between input chosen by user and the tree code in the knowledge base using forward chaining. User who has information about a tree can add to the knowledge base but this information must be validated by an expert before it is used in the system. Another task of an expert is to give a CF (certainty factor) for each tree.The result of this research shows that no more errors are found due to input mistakes and the program is more user friendly. Another advantage is that the knowledge base is more flexible, dynamic and well organized Validation of knowledge base by experts can increase the quality and accuracy of using the knowledge base system.Keywords : expert system, leaf image, knowledge base, forward chaining, CF
An Application of Expert System For Diagnozing Endoparsitism Gastrointestinal Disease In Livestock Animals Rusdi Efendi; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2276

Abstract

AbstractThe goal of this research is to make an expert system as a tool for diagnozing endoparsitism gastrointestinal disease for cows and sheep. The knowledge base of the system has been acquired from some interviews with some doctors from the internal diseases animal's unit at Animal's Hospital, Gadjah Mada University Jogjakarta, some text books, journals, and research papers. The inference machine of the system uses Forward Chaining and uncertainty data methods using Dempster-Shafer Theory.The system has a consultation session with an interactive dialog that can be used by the users. A user gives information such as user's data, and answers the questions about the endoparsitism gastrointestinal symptoms that might be had by his animal. From the answers, the system computes the possibility of the animal to suffer from endoparsitism gastrointestinal, informs the life cycle parasites, and suggests a therapy for it.Keywords : Expert System, diagnoze, endoparsitism gastrointestinal disease, Forward chaining method, Dempster-Shafer Teary
Artificial Intelligence on Computer Based Chess Game: An Implementation of Alpha-Beta-Cutoff Search Method Albert Dian Sano; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2277

Abstract

AbstractA chess program usually consists of three main parts, that is, a move generator to generate all legal moves, an evaluation function to evaluate each move, and a search function to select the best move. The search function is the core of thinking process. The goal of this research is to implement the alpha beta cutoff as a search method. This method is derived from minimax search method and is more optimal than the minimax search method.In minimax, all nodes is searched and compared one by one to get the best value. On the other hand, the alpha beta cut of methd only searches nodes which make contribution to the previous value and cuts off nodes which are not useful. It means that the alpha beta method will not search and compare all nodes. The new node will be better than the previous one and replace the old value with the new one. This will make the alpha beta method requires smaller search time.The proposed method is tested by doing a series of matches between humans and a computer. The results show that the computer has ability to think well and performs a good artcial intelligence though it is very open to be modified and more optimized.Keywords: move generator function, evaluation function, search function, minimax, alpha beta cutoff
Fuzzy C-Means Clustering Model Data Mining For Recognizing Stock Data Sampling Pattern Sylvia Jane Annatje Sumarauw; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2278

Abstract

AbstractCapital market has been beneficial to companies and investor. For investors, the capital market provides two economical advantages, namely deviden and capital gain, and a non-economical one that is a voting .} hare in Shareholders General Meeting. But, it can also penalize the share owners. In order to prevent them from the risk, the investors should predict the prospect of their companies. As a consequence of having an abstract commodity, the share quality will be determined by the validity of their company profile information. Any information of stock value fluctuation from Jakarta Stock Exchange can be a useful consideration and a good measurement for data analysis. In the context of preventing the shareholders from the risk, this research focuses on stock data sample category or stock data sample pattern by using Fuzzy c-Me, MS Clustering Model which providing any useful information jar the investors. lite research analyses stock data such as Individual Index, Volume and Amount on Property and Real Estate Emitter Group at Jakarta Stock Exchange from January 1 till December 31 of 204. 'he mining process follows Cross Industry Standard Process model for Data Mining (CRISP,. DM) in the form of circle with these steps: Business Understanding, Data Understanding, Data Preparation, Modelling, Evaluation and Deployment. At this modelling process, the Fuzzy c-Means Clustering Model will be applied. Data Mining Fuzzy c-Means Clustering Model can analyze stock data in a big database with many complex variables especially for finding the data sample pattern, and then building Fuzzy Inference System for stimulating inputs to be outputs that based on Fuzzy Logic by recognising the pattern.Keywords: Data Mining, AUz..:y c-Means Clustering Model, Pattern Recognition
Stock Data Clustering of Food and Beverage Company Shofwatul Uyun; Subanar Subanar
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2279

Abstract

AbstractCluster analysis can be defined as identifying groups of similar objects to discover distribution of patterns and interesting correlations in large data sets. Clustering analysis is important in the fields of pattern recognition and pattern classification. Over the years many methods have been developed for clustering data. In general, clustering methods can be categoried into two categories, i.e., fuzzy clustering and hard clustering. Fuzzy C-means is one of many methods of clustering based on fuzzy approach, while K-Means and K-Medoid are methods clustering based on crisp approach.This study aims to apply Fuzzy C-Means, K-Means and K-Medoid methods for clustering stock data in a jbod and beverage company. The main goal is to find a clustering method that can produce optimal clusters, The resulting clusters are validated using Dunn'• Index (DI). It is expected that the result of this reseach can be used to support decision making in the food and beverage company.Keywords : Clustering, Fuzzy C-Means, K-Means, K-Medoid, Cluster Validity, Dunn's Index (Dl)
An Implementation of Audio Security Using DES Algorithm Abdul Wahid; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2280

Abstract

AbstractData security is an important problem in computer technology. This paper discusses security system for audio data. This technology is crucial because the multimedia technology has been improved very fast. One of the common audio format forms is wave audio format. The wave format is an uncompressed file format which is for RIFF specification owned by Microsoft. It is used for saving multimedia file. By using DES algorithm, the wave data could be encrypted for hiding information contained in the data. DES algorithm is chosen in this research because DES algorithm is one of the best symmetrical cryptography algorithms and it has been used world wide. This research is expected to give contribution to the audio security concept, especially for audio data security using wave file format.Keywords : audio security, DES algorithm, wave Omar
Asymmetric Watermarking Scheme Based on Correlation Testing Rinaldi Munir; Bambang Riyanto; Sarwono Sutikno; Wiseto P. Agung
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 2 (2007): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.2281

Abstract

AbstractAsymmetric watermarking is the second generation of watermarking scheme which uses different keys for embedding and detecting watermark. Key for embedding is private or secret, but key for detecting can be available publicly and everyone who has the key can detect watermark Watermark detection does not need to be original multimedia data. Detection of watermark is realized using correlation test between public key and multimedia data received. In most of schemes, private key is the watermark itself; public key is public watermark which correlates to the private watermark This paper presents concept of asymmetric watermarking scheme that based on correlation test and reviews some schemes of asymmetric watermarking that have been proposed by researchers.Keywords: asymmetric watermarking, private key, public key, watermark., multimeelia,:correlation.

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