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Agus Harjoko
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ijccs.mipa@ugm.ac.id
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+62274 555133
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ijccs.mipa@ugm.ac.id
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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 7 Documents
 1 
Search results for , issue "Vol 12, No 2 (2018): July" : 7 Documents clear
Motion Detection and Face Recognition for CCTV Surveillance System Ade Nurhopipah; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.18198

Abstract

Closed Circuit Television (CCTV) is currently used in daily life for a variety purpose. Development of the use of CCTV has transformed from a simple passive surveillance into an integrated intelligent control system. In this research, motion detection and facial recognation in CCTV video is done to be a base for decision making to produce automated, effective and efficient integrated system. This CCTV video processing provides three outputs, a motion detection information, a face detection information and a face identification information. Accumulative Differences Images (ADI) used  for motion detection, and Haar Classifiers Cascade used  for facial segmentation. Feature extraction is done with Speeded-Up Robust Features (SURF) and Principal Component Analysis (PCA). The features was trained by Counter-Propagation Network (CPN). Offline tests performed on 45 CCTV video. The test results obtained a motion detection success rate of 92,655%, a face detection success rate of 76%, and a face detection success rate of 60%. The results concluded that the process of faces identification through CCTV video with natural background have not been able to obtain optimal results. The motion detection process is ideal to be applied to real-time conditions. But in combination with face recognition process, there is a significant delay time.
An Expert System Using Certainty Factor for Determining Insomnia Acupoint Elizabeth Paskahlia Gunawan; Retantyo Wardoyo
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.26328

Abstract

In treating insomnia patients, acupuncturists who are not always in their clinics trust their patients to their assistants but because of their assistants limited knowledge, their assistants can not determine the right acupoints. Therefore, an application that able to store their knowledge about insomnia disease treatment is needed so that their assistants can handle the patients like they do.In this research, an expert system application using certainty factor method to determine the acupoint in dealing with insomnia disease was built. This research used certainty factor to accommodate uncertainty about symptoms and rules. The mechanism of certainty factor on symptoms used a measure of increased belief (MB) and a measure of increased disbelief (MD).The built expert system resulted acupoints based on symptoms experienced by insomnia patients. Accuracy value produced by the system that used certainty factor for determining acupoint dealing with insomnia is 0.933. It showed that the acupoint produced by the system is 93.3% relevant according acupuncturist expertise in treating insomnia patients.
Prioritization of Natural Dye Selection In Batik Tulis Using AHP and TOPSIS Approach Ahmad Abdul Chamid; Alif Catur Murti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.29813

Abstract

Batik is the most popular tradisional cloth made using the wax-resist dyeing technique. The fabric is found in various city in Indonesia, one of them is Lasem which popular with hand-drawn batik is called Batik Tulis Lasem. Natural dye selection is one of the most important priority for the batik tulis craftsmen. Natural dyes made from leaves and flowers. Proper selection of natural dye will impact on color, motif, and brightness on batik tulis fabric. AHP and TOPSIS methods can be used together to selecting natural dye especially the batik tulis lasem. AHP method is used in determining the weights of the criteria, and then TOPSIS method is needed for determining the best alternative on natural dye of batik tulis. According to the result of research, TOPSIS method is used to determine the priority of alternative on natural dye. Based on calculation with TOPSIS method , the fourth alternative (A4 is kayu secang) get priority value is 0.8478, so kayu secang is recommended to the craftsmen that will used this  material as the natural dye.
Local Triangular Kernel-Based Clustering (LTKC) for Case Indexing on Case-Based Reasoning Damar Riyadi; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.30423

Abstract

This study aims to improve the performance of Case-Based Reasoning by utilizing cluster analysis which is used as an indexing method to speed up case retrieval in CBR. The clustering method uses Local Triangular Kernel-based Clustering (LTKC). The cosine coefficient method is used for finding the relevant cluster while similarity value is calculated using Manhattan distance, Euclidean distance, and Minkowski distance. Results of those methods will be compared to find which method gives the best result. This study uses three test data: malnutrition disease, heart disease, and thyroid disease. Test results showed that CBR with LTKC-indexing has better accuracy and processing time than CBR without indexing. The best accuracy on threshold 0.9 of malnutrition disease, obtained using the Euclidean distance which produces 100% accuracy and 0.0722 seconds average retrieval time. The best accuracy on threshold 0.9 of heart disease, obtained using the Minkowski distance which produces 95% accuracy and 0.1785 seconds average retrieval time. The best accuracy on threshold 0.9 of thyroid disease, obtained using the Minkowski distance which produces 92.52% accuracy and 0.3045 average retrieval time. The accuracy comparison of CBR with SOM-indexing, DBSCAN-indexing, and LTKC-indexing for malnutrition diseases and heart disease resulted that they have almost equal accuracy.
The MapReduce Model on Cascading Platform for Frequent Itemset Mining Nur Rokhman; Amelia Nursanti
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.34102

Abstract

The implementation of parallel algorithms is very interesting research recently. Parallelism is very suitable to handle large-scale data processing. MapReduce is one of the parallel and distributed programming models. The implementation of parallel programming faces many difficulties. The Cascading gives easy scheme of Hadoop system which implements MapReduce model.Frequent itemsets are most often appear objects in a dataset. The Frequent Itemset Mining (FIM) requires complex computation. FIM is a complicated problem when implemented on large-scale data. This paper discusses the implementation of MapReduce model on Cascading for FIM. The experiment uses the Amazon dataset product co-purchasing network metadata.The experiment shows the fact that the simple mechanism of Cascading can be used to solve FIM problem. It gives time complexity O(n), more efficient than the nonparallel which has complexity O(n2/m).
Shortest Path Search Futsal Field Location With Dijkstra Algorithm Delpiah Wahyuningsih; Erzal Syahreza
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.34513

Abstract

Pangkalpinang City is a city where futsal field rentals are experiencing growth and improvement. The number of lovers of futsal sport from outside Pangkalpinang city, especially those who are less aware of the streets in Pangkalpinang city will have little difficulty in accessing futsal field places in this city because they do not know in detail information about the route to the futsal field. This research can facilitate futsal players in searching shortest path futsal field with algorithm dijkstra. The dijkstra algorithm determines the shortest path by computing the nodes passed from the initial node to the destination node. Dijkstra algorithm by forming the node graph, the new node then perform the calculation of the number of nodes that will form a new node for the determination of the node to be passed so that the algorithm dijkstra find the smallest node that will form the shortest path in the geographic information system. This system displays the shortest route from the user position to the futsal field which is the destination in the city of Pangkalpinang and surrounding areas.
An Optimal Stock Market Portfolio Proportion Model Using Genetic Algorithm Wahyono Wahyono; Chasandra Puspitasari; Muhammad Dzulfikar Fauzi; Kasliono Kasliono; Wahyu Sri Mulyani; Laksono Kurnianggoro
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 2 (2018): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.36154

Abstract

To reduce the amount of loss due to investment risk, an investor or stockbroker usually forms an optimal stock portfolio. This technique is done to get the maximum return of investment on shares to be purchased. However, in forming a stock portfolio required a fairly complex calculations and certain skills. This work aims to provide an alternative solution in the problem of forming the optimal and efficient stock portfolio composition by designing a system that can help decision making of investors or stockbrokers in preparing stock portfolio in accordance with the policy and risk investment. In this work, determination of optimal stock portfolio composition is constructed by using Genetic Algorithm. The data used in this work are the 4 selected stocks listed on the LQ45 index in 2017. Meanwhile, the calculation of profit and loss rate utilizes a single index model theory. The efficiency of the algorithm has been examined against the population size and crossover and mutation probabilities. The experimental results show that the proposed algorithm can be used as one of solutions to select the optimal stock portfolio.

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