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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 The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis.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 476 Documents
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Pengembangan Model CYBER CLUSTER E-COMMERCE Berbasis CMS dan SEO Produk UMKM Dwi Agus Diartono; Yohanes Suhari; Aji Supriyanto
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7543

Abstract

AbstrakPermasalahan yang sering dihadapi oleh UMKM adalah terbatasnya jumlah dan jangkauan pemasaran dan penjualan produknya. Begitu juga persaingan produk sejenis dapat terjadi oleh antar produk lokal atau produk yang datang dari luar. Hal ini disebabkan karena  pemasaran dan penjualan masih dilakukan secara konvensional dan dilakukan secara individual. Penelitian ini bermaksud melakukan implementasi  model sistem E-Commerce produk UMKM di suatu daerah atau Kabupaten dengan  model pemberdayaan partisipasi kelompok siber (cyber cluster partisipatif) dalam melakukan linking web yang dikembangkan menggunakan sistem Search Engine Optimisazion (SEO) dan Content Management System (CMS). Tujuannya adalah agar web yang dikembangkan dapat dengan mudah menempati ranking teratas pada halaman pencari web (search engine) dan selalu terupdate isi dan rankingnya. Manfaat dari penelitian ini adalah meningkatkan pemasaran dan penjualan produk UMKM hingga pasar global dan menjadikan alamat web tersebut mudah dicari dan dan ditemukan karena sering muncul pada posisi puncak pencarian di mesin pencari seperti google. Luaran penelitian ini adalah website produk UMKM berbasis CMS dan teroptimisasi dengan model link internal dan eksternal sehingga selalu muncul pada posisi top range pencarian. Metode penelitian ini menggunakan action research, dengan model pengembangan sistem terstruktur model air terjun (waterfall). Aplikasi webnya sendiri dikembangkan dengan model prototype, sesuai dengan kebutuhan penggunannya.  Kata kunci—Cyber-Cluster, SEO, CMS, UMKM AbstractProblems that are often faced by UMKM (SME) is the limited number and range of marketing and sales of its products. So is the competition of similar products can occur by inter-local products or products that come from outside. This is because marketing and sales are still done conventionally and done individually. This study intends to make the implementation of a model system of E-Commerce SME product in an area or district with the participation of empowerment models cyber group (cluster cyber participatory) in performing web linking system that was developed using Optimisazion Search Engine (SEO) and Content Management System (CMS). The goal is for the web that can be developed easily ranked the Web search page (search engines) and always updated content and rank. The benefit of this research is to improve the marketing and sale of products of SMEs to global market and making it easy to find the web address and and are found as often appear in top positions in the search engines like google. Outcomes of this research is based CMS website MSME products and optimized the model of internal and external links that always appears at the top position of the search range. Methods This study uses an action research, the model of structured systems development waterfall model (waterfall). Its own web application developed with prototype models, according to consumer needs.  Keywords—Cyber-Cluster, SEO, CMS, SME
Metode RCE-Kmeans untuk Clustering Data Izmy Alwiah Musdar; Azhari SN
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7544

Abstract

AbstrakTelah banyak metode yang dikembangkan untuk memecahkan berbagai masalah clustering. Salah satunya menggunakan metode-metode dari bidang kecerdasan kelompok seperti Particle Swarm Optimization (PSO). Metode Rapid Centroid Estimation (RCE) merupakan salah satu metode clustering yang berbasis PSO. RCE, seperti varian PSO clustering lainnya, memiliki kelebihan yaitu hasil clustering tidak tergantung pada inisialisasi pusat cluster awal. RCE juga memiliki waktu komputasi yang jauh lebih cepat dibandingkan dengan metode sebelumnya yaitu Particle Swarm Clustering (PSC) dan modified Particle Swarm Clustering (mPSC), tetapi metode RCE memiliki standar deviasi kualitas skema clustering yang lebih tinggi dibandingkan PSC dan mPSC dimana  ini berpengaruh terhadap variansi hasil clustering. Hal ini terjadi karena equilibrium state, yaitu kondisi dimana posisi partikel tidak mengalami perubahan lagi, kurang tepat pada saat kriteria berhenti tercapai. Penelitian ini mengusulkan metode RCE-Kmeans yaitu metode yang mengaplikasikan K-means setelah equilibrium state metode RCE tercapai untuk memperbarui posisi partikel yang dihasilkan dari metode RCE. Hasil penelitian menunjukkan bahwa dari sepuluh dataset, metode RCE-Kmeans memiliki nilai kualitas skema clustering yang lebih baik pada 7 dataset dibandingkan K-means dan lebih baik pada 8 dataset dibandingkan dengan metode RCE. Penggunaan K-means pada metode RCE juga mampu menurunkan nilai standar deviasi dari metode RCE.  Kata kunci—Clustering Data, Particle Swarm, K-means, Rapid Centroid Estimation.  Abstract There have been many methods developed to solve the clustering problem. One of them is method in swarm intelligence field such as Particle Swarm Optimization (PSO). Rapid Centroid Estimation (RCE) is a method of clustering based Particle Swarm Optimization. RCE, like other variants of PSO clustering, does not depend on initial cluster centers. Moreover, RCE has faster computational time than the previous method like PSC and mPSC. However, RCE has higher standar deviation value than PSC and mPSC in which has impact in the variance of clustering result. It is happaned because of improper equilibrium state, a condition in which the position of the particle does not change anymore, when  the stopping criteria is reached. This study proposes RCE-Kmeans which is a  method applying K-means after the equilibrium state of RCE  reached to update the particle's position which is generated from the RCE method. The results showed that RCE-Kmeans has better quality of the clustering scheme in 7 of 10 datasets compared to K-means and better in 8 of 10 dataset then RCE method. The use of K-means clustering on the RCE method is also able to reduce the standard deviation from RCE method. Keywords—Data Clustering, Particle Swarm, K-means, Rapid Centroid Estimation.    
Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword Wayan Gede Suka Parwita; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7545

Abstract

AbstrakRecommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun. Sedangkan, pemberian data identitas pada recommendation system dapat menimbulkan kekhawatiran penyalahgunaan data identitas.Hybrid recommendation system memanfaatkan algoritma penggalian frequent itemset dan perbandingan keyword dapat memberikan daftar rekomendasi tanpa menggunakan data identitas pengguna dan data peringkat item. Penggalian frequent itemset dilakukan menggunakan algoritma FP-Growth. Sedangkan perbandingan keyword dilakukan dengan menghitung similaritas antara dokumen dengan pendekatan cosine similarity.Hybrid recommendation system memanfaatkan kombinasi penggalian frequent itemset dan perbandingan keyword dapat menghasilkan rekomendasi tanpa menggunakan identitas pengguna dan data peringkat dengan penggunaan ambang batas berupa minimum similarity, minimum support, dan jumlah rekomendasi. Nilai pengujian yaitu precision, recall, F-measure, dan MAP dipengaruhi oleh besarnya nilai ambang batas yang ditetapkan. Kata kunci— Hybrid recommendation system, frequent itemset, cosine similarity.  AbstractRecommendation system was commonly built by manipulating item is ranking data and user is identity data. Item ranking data were  rarely available on newly constructed system. Whereas, giving identity data to the recommendation system causes concerns about identity data misuse.Hybrid recommendation system used frequent itemset mining algorithm and keyword comparison, it can provide recommendations without identity data and item ranking data. Frequent itemset mining was done using FP-Gwowth algorithm and keyword comparison with calculating document similarity value using cosine similarity approach.Hybrid recommendation system with a combination of frequent itemset mining and keywords comparison can give recommendations without using user identity and rating data.  Hybrid recommendation system using 3 thresholds ie minimum similarity, minimum support, and number of recommendations. With the testing data used, precision, recall, F-measure, and MAP testing value are influenced by the threshold value. Keywords— Hybrid recommendation system, frequent itemset, cosine similarity. 
Covert Channel Pada Aliran Data Websocket untuk Komunikasi Messaging XMPP Yoga Dwitya Pramudita; Reza Pulungan
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7546

Abstract

AbstrakLayanan komunikasi Instant Messaging menyediakan berbagai fitur komunikasi yang bisa digunakan oleh pengguna, diantaranya adalah text messaging (pesan teks) baik online maupun offline. Salah satu standar protokol yang mendukung layanan ini adalah XMPP (Extensible Messaging and Presence Protocol). Aliran komunikasi XMPP menggunakan potongan dokumen XML, sehingga rentan terhadap serangan pasif monitoring konten paket komunikasi. Untuk mengatasi kelemahan ini solusinya adalah menggunakan komunikasi yang terenkripsi. Selain itu ada solusi lain yang coba ditawarkan dalam penelitian ini, yaitu penggunaan covert channel untuk mengirim pesan secara tersembunyi. Dalam penelitian ini akan dibuat sebuah aplikasi klien XMPP berbasis web browser yang mampu melakukan komunikasi XMPP dan juga menyediakan komunikasi covert channel. Komunikasi XMPP agarbisa berjalan diatas aplikasi berbasis web browser maka digunakanlah protokol WebSocket. Protokol inilah yang nantinya akan dieksploitasi pada sisi header, khususnya pada field masking-key untuk memuat pesan covert channel yang dikirimkan pada saat sesi komunikasi XMPP berlangsung. Dari hasil ujicoba, aplikasi klien covert channel mampu menghasilkan komunikasi dengan lebar data 3 byte perpaket. Aplikasi Klien juga mampu melakukan komunikasi covert channel pada kondisi link komunikasi dengan tingkat probabilitas packet loss dibawah 10%. Kata kunci— WebSocket, XMPP, masking-key, Covert Channel, aplikasi klien berbasis browser. AbstractInstant Messaging communication services provide a variety of communication features that can be used by the user, such as text messaging (text messages) both online and offline. One of the standard protocol that supports this service is XMPP (Extensible Messaging and Presence Protocol). XMPP communication using XML documents, making it vulnerable to passive attacks monitoring content of  communications. To overcome this drawback the solution is encrypted communications. The other solutions that try to offer in this research is the use of a covert channel to send hidden messages. In this research will create a browser based XMPP client application that is capable to deliver XMPP communication and also provide covert channel communication. XMPP communication can be built on a web-based application using WebSocket protocol. This protocol will exploit field masking-key to load the covert channel messages that is sent during the session XMPP communication takes place. From the test results, the client application is able to produce a covert channel communication with a data width of 3 bytes in each packet. The client application is also able to perform covert communication channel in a communication link with the condition of the probability of packet loss rate below 10%. Keywords— WebSocket, XMPP, masking-key, Covert Channel, browser based application. 
Identifikasi Gangguan Neurologis Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS) Jani Kusanti; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7547

Abstract

AbstrakPenggunaan metode Adaptive Neuro Fuzzy Inference System (ANFIS) dalam proses identifikasi salah satu gangguan neurologis pada bagian kepala yang dikenal dalam istilah kedokteran stroke ischemic dari hasil ct scan kepala dengan tujuan untuk mengidentifikasi lokasi  yang terkena stroke ischemik. Langkah-langkah yang dilakukan dalam proses identifikasi antara lain ekstraksi citra hasil ct scan kepala dengan menggunakan histogram. Citra hasil proses histogram ditingkatkan intensitas hasil citranya dengan menggunakan threshold otsu sehingga didapatkan hasil pixel yang diberi nilai 1 berkaitan dengan obyek sedangkan pixel yang diberi nilai 0 berkaitan dengan background. Hasil pengukuran digunakan untuk proses clustering image, untuk proses cluster image digunakan fuzzy c-mean (FCM). Hasil clustering merupakan deretan pusat cluster, hasil  data digunakan untuk membangun fuzzy inference system (FIS). Sistem inferensi fuzzy yang diterapkan adalah inferensi fuzzy model Takagi-Sugeno-Kang. Dalam penelitian ini ANFIS digunakan untuk mengoptimalkan hasil penentuan lokasi penyumbatan stroke ischemic. Digunakan recursive least square estimator (RLSE) untuk pembelajaran. Hasil RMSE yang didapat pada proses pelatihan sebesar 0.0432053, sedangkan pada proses pengujian dihasilkan tingkat akurasi sebesar 98,66% Kata kunci—stroke ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE  Abstract            The use of Adaptive Neuro Fuzzy Inference System (ANFIS) methods in the process of identifying one of neurological disorders in the head, known in medical terms ischemic stroke from the ct scan of the head in order to identify the location of ischemic stroke. The steps are performed in the extraction process of identifying, among others, the image of the ct scan of the head by using a histogram. Enhanced image of the intensity histogram image results using Otsu threshold to obtain results pixels rated 1 related to the object while pixel rated 0 associated with the measurement background. The result used for image clustering process, to process image clusters used fuzzy c-mean (FCM) clustering result is a row of the cluster center, the results of the data used to construct a fuzzy inference system (FIS). Fuzzy inference system applied is fuzzy inference model of Takagi-Sugeno-Kang. In this study ANFIS is used to optimize the results of the determination of the location of the blockage ischemic stroke. Used recursive least squares estimator (RLSE) for learning. RMSE results obtained in the training process of 0.0432053, while in the process of generated test accuracy rate of 98.66% Keywords— Stroke Ischemik, Global threshold, Fuzzy Inference System model Sugeno, ANFIS, RMSE 
Sistem Pendukung Keputusan untuk Memilih Budidaya Ikan Air Tawar Menggunakan AF-TOPSIS Hence Beedwel Lumentut; Sri Hartati
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7548

Abstract

AbstrakPotensi perikanan budidaya air tawar semakin meningkat, hal tersebut disebabkan produksi ikan sektor penangkapan mendekati “overfishing”. Budidaya perikanan air tawar memiliki beberapa alternatif ikan yang memiliki nilai ekonomis tinggi yaitu ikan Mas, ikan Mujair, ikan Nila, ikan Gurame, ikan Lele dan ikan Patin. Alternatif ikan ini memiliki karakteristik yang berbeda untuk masing-masing jenis pembudidayaannya. Parameter-parameter yang mempengaruhi proses budidaya ikan air tawar tersebut diantaranya: faktor kesesuaian air meliputi: suhu, kecerahan, DO (derivater oksigen), keasaman (pH). Sedangkan pemilihan budidaya perikanan yang menguntungkan bisa dinilai dari faktor finansial yaitu: NPV (Net Present Value), ROI (Return on Investment), BCR (Benefit Cost Ratio), PBP (Pay Back Period) dan BEP (Break Event Point). Sedangkan metode yang dipergunakan untuk pengambilan keputusan yaitu Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) sebagai salah satu model decision dapat digunakan untuk memberikan preferensi kepada para petani budidaya ikan, karena alternatif yang terpilih tidak hanya memiliki jarak terpendek dari solusi ideal positif tetapi juga jarak terpanjang dari solusi ideal negatif. Hasil dari penelitian ini menunjukkan bahwa sistem penunjang keputusan yang mempertimbangkan parameter kondisi lingkungan air dan faktor finansial dapat membantu petani budidaya ikan untuk menentukan jenis budidaya ikan air tawar yang akan dijalankan. Kata kunci—Ikan air tawar, Analisis Finansial, TOPSIS, SPK. AbstractFreshwater aquaculture potential is increasing, one of the reason is production of fishing over the sea is almost deal with "overfishing". Freshwater aquaculture fish have few alternatives such as Carp, Mossambique, Tilapia, Gouramy, Catfish and Pangacius. Each has different type of cultivation. The requirement parameters that influence the process of freshwater cultive is water suitability factors include: Temperature, Brightness, DO (derivated oxygen), acidity (pH) etc. While the selection of profitable aquaculture can be determind from financial bussines as: NPV (Net Present Value), ROI (Return on Investment), BCR (Benefit Cost Ratio), PBP (Payback Period) and BEP (Break Event Point). The methods that used to help the decision-making process that Method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as one of the decision models can be used to give preference to farmers fish farming, because the alternative is chosen not only have the shortest distance from a solution positive ideal but also the longest distance from the negative ideal solution. The results of this study show that decision support systems that take into account the environmental condition of water parameters and financial bussines can help fisherman to determine the type of freshwater Aquaculture culture to be run. Keywords— Fresh Water Fish, Financial Analysis, TOPSIS, SPK
Pengenalan Spesies Gulma Berdasarkan Bentuk dan Tekstur Daun Menggunakan Jaringan Syaraf Tiruan Herman Herman; Agus Harjoko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 9, No 2 (2015): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.7549

Abstract

AbstrakGulma merupakan tanaman pengganggu yang merugikan tanaman budidaya dengan menghambat pertumbuhan tanaman budidaya. Langkah awal dalam melakukan pengendalian gulma adalah mengenali spesies gulma pada lahan tanaman budidaya. Cara tercepat dan termudah untuk mengenali tanaman, termasuk gulma adalah melalui daunnya. Dalam penelitian ini, diusulkan pengenalan spesies gulma berdasarkan citra daunnya dengan cara mengekstrak ciri bentuk dan ciri tekstur dari citra daun gulma tersebut. Untuk mendapatkan ciri bentuk, digunakan metode moment invariant, sedangkan untuk ciri tekstur digunakan metode lacunarity yang merupakan bagian dari fraktal. Untuk proses pengenalan berdasarkan ciri-ciri yang telah diekstrak, digunakan metode Jaringan Syaraf Tiruan dengan algoritma pembelajaran Backpropagation. Dari  hasil pengujian pada penelitian ini, didapatkan tingkat akurasi pengenalan tertinggi sebesar 97.22% sebelum noise dihilangkan pada citra hasil deteksi tepi Canny. Tingkat akurasi tertinggi didapatkan menggunakan 2 ciri moment invariant (moment  dan ) dan 1 ciri lacunarity (ukuran box 4 x 4 atau 16 x 16). Penggunaan 3 neuron hidden layer pada Jaringan Syaraf Tiruan (JST) memberikan waktu pelatihan data yang lebih cepat dibandingkan dengan menggunakan 1 atau 2 neuron hidden layer. Kata kunci—3-5 gulma, daun ,moment invariant, lacunarity, jaringan syaraf tiruan AbstractWeeds are plants that harm crops by inhibiting the growth of cultivated plants. The first step to take control of weeds is by identifying weed among the cultivating plant. The fastest and easiest way to identify plants, including weeds is by its leaves. This research proposing weed species recognition based on weeds leaf images by extracting its shape and texture features. Moment invariant method is used to get the shape and Lacunarity method for the texturel.  Neural Network with backpropagation learning algorithm are implements for the extracted features recognition proses. The result of this research achievement shows the highest level of recognition accuracy of 97.22% before the noise is eliminated in the image of the Canny edge detection. Highest level of accuracy is obtained using two features from moment invariant (moment  and  ) and 1 lacunarity’s feature (size box 4 x 4 or 16 x 16). The use of 3 neurons in the hidden layer of Artificial Neural Network (ANN) provide training time data more quickly than by using 1 or 2 hidden layer neurons. Keywords— weed, leaf, moment invariant, lacunarity, artificial neural network 
Pengembangan Data Warehouse Menggunakan Pendekatan Data-Driven untuk Membantu Pengelolaan SDM Mujiono Mujiono; Aina Musdholifah
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 1 (2016): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.11184

Abstract

 The basis of bureaucratic reform is the reform of human resources management. One supporting factor is the development of an employee database. To support the management of human resources required including data warehouse and business intelligent tools. The data warehouse is an integrated concept of reliable data storage to provide support to all the needs of the data analysis. In this study developed a data warehouse using the data-driven approach to the source data comes from SIMPEG, SAPK and electronic presence. Data warehouses are designed using the nine steps methodology and unified modeling language (UML) notation. Extract transform load (ETL) is done by using Pentaho Data Integration by applying transformation maps. Furthermore, to help human resource management, the system is built to perform online analytical processing (OLAP) to facilitate web-based information. In this study generated BI application development framework with Model-View-Controller (MVC) architecture and OLAP operations are built using the dynamic query generation, PivotTable, and HighChart to present information about PNS, CPNS, Retirement, Kenpa and Presence
Peningkatan Kinerja Siakad Menggunakan Metode Load Balancing dan Fault Tolerance Di Jaringan Kampus Universitas Halu Oleo Alimuddin Alimuddin; Ahmad Ashari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 1 (2016): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.11185

Abstract

The application of academic information system (siakad) a web-based college is essential to improve the academic services. Siakad the application has many obstacles, especially in dealing with a high amount of access that caused the overload. Moreover in case of hardware or software failure caused siakad inaccessible. The solution of this problem is the use of many existing servers where the load is distributed in the respective server. Need a method of distributing the load evenly in the respective server load balancing is the method by round robin algorithm so high siakad scalability. As for dealing with the failure of a server need fault tolerance for the availability siakad be high. This research is to develop methods of load balancing and fault tolerance using software linux virtual server and some additional programs such as ipvsadm and heartbeat that has the ability to increase scalability and availability siakad. The results showed that with load balancing to minimize the response time to 5,7%, increase throughput by 37% or 1,6 times and maximize resource utilization or utilization of 1,6 times increased, and avoid overload. While high availability is obtained from the server's ability to perform failover or move another server in the event of failure. Thus implementing load balancing and fault tolerance can improve the service performance of siakad and avoid mistakes.
Sistem Pencarian Informasi Berbasis Ontologi untuk Jalur Pendakian Gunung Menggunakan Query Bahasa Alami dengan Penyajian Peta Interaktif Fadhila Tangguh Admojo; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 10, No 1 (2016): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.11186

Abstract

Mountain climbing path information has been widely available on the internet. However, to get information that suits the needs of climbers take time to browse and compare all the available information. The diversity of the search results content actually confuse the climbers.            This research aims to provide a solution to the problems faced by climbers, by developing an information retrieval system for mountain climbing path using semantic technology (ontology) based approach .   The system is developed by using two knowledge base (ontology), ontology Bahasa represents linguistic knowledge and ontology Mountaineering represents mountaineering knowledge. The system is designed to process and understand natural language input form. The process of understanding the natural language based on syntactic and semantic analysis using the rules of Indonesian grammar.            The results of the research that has been conducted shows that the system is able to understand natural language input and is capable of detecting input that is not in accordance with the rules of Indonesian grammar both syntactically and semantically. The system is also able to use a thesaurus of words in the search process. Quantitative test results show that the system is able to understand 69% of inputs are taken at random from the respondents.

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