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Jurnal Ilmu Komputer dan Informasi

JIKI
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Published by Universitas Indonesia

ISSN : 20887051 EISSN : 25029274 DOI : 10.21609

Core Subject : Science,

Computer Science & IT Library & Information Science

Jurnal Ilmu Komputer dan Informasi is a scientific journal in computer science and information containing the scientific literature on studies of pure and applied research in computer science and information and public review of the development of theory, method and applied sciences related to the subject. Jurnal Ilmu Komputer dan Informasi is published by Faculty of Computer Science Universitas Indonesia. Editors invite researchers, practitioners, and students to write scientific developments in fields related to computer science and information. Jurnal Ilmu Komputer dan Informasi is issued 2 (two) times a year in February and June. This journal contains research articles and scientific studies. It can be obtained directly through the Library of the Faculty of Computer Science Universitas Indonesia.

Arjuna Subject : -

Articles 257 Documents

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OPERATING SYSTEM FOR WIRELESS SENSOR NETWORKS AND AN EXPERIMENT OF PORTING CONTIKIOS TO MSP430 MICROCONTROLLER Thang Vu Chien; Hung Nguyen Chan; Thanh Nguyen Huu
Jurnal Ilmu Komputer dan Informasi Vol 5, No 1 (2012): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Wireless Sensor Networks (WSNs) consist of a large number of sensor nodes, and are used for various applications such as building monitoring, environment control, wild-life habitat monitoring, forest fire detection, industry automation, military, security, and health-care. Each sensor node needs an operating system (OS) that can control the hardware, provide hardware abstraction to application software, and fill in the gap between applications and the underlying hardware. In this paper, researchers present OS for WSNs and an experiment of porting contikiOS to MSP430 microcontroller which is very popular in many hardware platforms for WSNs. Researchers begin by presenting the major issues for the design of OS for WSNs. Then, researchers examine some popular operating systems for WSNs including TinyOS, ContikiOS, and LiteOS. Finally, researchers present an experiment of porting ContikiOS to MSP430 microcontroller. Wireless Sensor Networks (WSNs) terdiri dari sejumlah besar sensor nodes, dan digunakan untuk berbagai aplikasi seperti pemantauan gedung, pengendalian lingkungan, pemantauan kehidupan habitat liar, deteksi kebakaran hutan, otomatisasi industri, militer, keamanan, dan kesehatan. Setiap sensor nodememerlukan sistem operasi (SO) yang dapat mengontrol hardware, menyediakan abstraksi hardware untuk aplikasi perangkat lunak, dan mengisi kesenjangan antara aplikasi dan hardware. Dalam penelitian ini, peneliti menyajikan SO untuk WSNs dan percobaan dari port contikiOS untuk MSP430 mikrokontroler yang sangat populer di platformhardware untuk WSNs. Peneliti memulai dengan menghadirkan isu utama yaitu desain SO untuk WSNs. Lalu, penelitimemeriksa beberapa sistem operasi populer untuk WSNs, termasuk TinyOS, ContikiOS, dan LiteOS. Akhirnya penelitimenyajikan sebuah percobaan dari port ContikiOS untuk MSP430 mikrokontroler.

PARTICLE SWARM OPTIMIZATION (PSO) FOR TRAINING OPTIMIZATION ON CONVOLUTIONAL NEURAL NETWORK (CNN) Arie Rachmad Syulistyo; Dwi Marhaendro Jati Purnomo; Muhammad Febrian Rachmadi; Adi Wibowo
Jurnal Ilmu Komputer dan Informasi Vol 9, No 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Neural network attracts plenty of researchers lately. Substantial number of renowned universities have developed neural network for various both academically and industrially applications. Neural network shows considerable performance on various purposes. Nevertheless, for complex applications, neural network’s accuracy significantly deteriorates. To tackle the aforementioned drawback, lot of researches had been undertaken on the improvement of the standard neural network. One of the most promising modifications on standard neural network for complex applications is deep learning method. In this paper, we proposed the utilization of Particle Swarm Optimization (PSO) in Convolutional Neural Networks (CNNs), which is one of the basic methods in deep learning. The use of PSO on the training process aims to optimize the results of the solution vectors on CNN in order to improve the recognition accuracy. The data used in this research is handwritten digit from MNIST. The experiments exhibited that the accuracy can be attained in 4 epoch is 95.08%. This result was better than the conventional CNN and DBN. The execution time was also almost similar to the conventional CNN. Therefore, the proposed method was a promising method.

ADAPTIVE CLUSTER BASED ROUTING PROTOCOL WITH ANT COLONY OPTIMIZATION FOR MOBILE AD-HOC NETWORK IN DISASTER AREA Enrico Budianto; A Hafidh; M.S. Alvissalim; A Wibowo
Jurnal Ilmu Komputer dan Informasi Vol 5, No 2 (2012): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

In post-disaster rehabilitation efforts, the availability of telecommunication facilities takes important role. However, the process to improve telecommunication facilities in disaster area is risky if it is done by humans. Therefore, a network method that can work efficiently, effectively, and capable to reach the widest possible area is needed. This research introduces a cluster-based routing protocol named Adaptive Cluster Based Routing Protocol (ACBRP) equipped by Ant Colony Optimization method, and its implementation in a simulator developed by author. After data analysis and statistical tests, it can be concluded that routing protocol ACBRP performs better than AODV and DSR routing protocol. Pada upaya rehabilitasi pascabencana, ketersediaan fasilitas telekomunikasi memiliki peranan yang sangat penting. Namun, proses untuk memperbaiki fasilitas telekomunikasi di daerah bencana memiliki resiko jika dilakukan oleh manusia. Oleh karena itu, metode jaringan yang dapat bekerja secara efisien, efektif, dan mampu mencapai area seluas mungkin diperlukan. Penelitian ini memperkenalkan sebuah protokol routing berbasis klaster bernama Adaptive Cluster Based Routing Protocol (ACBRP), yang dilengkapi dengan metode Ant Colony Optimization, dan diimplementasikan pada simulator yang dikembangkan penulis. Setelah data dianalisis dan dilakukan uji statistik, disimpulkan bahwa protokol routing ACBRP beroperasi lebih baik daripada protokol routing AODV maupun DSR.

DYNAMIC AND INCREMENTAL EXPLORATION STRATEGY IN FUSION ADAPTIVE RESONANCE THEORY FOR ONLINE REINFORCEMENT LEARNING Budhitama Subagdja
Jurnal Ilmu Komputer dan Informasi Vol 9, No 2 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

One of the fundamental challenges in reinforcement learning is to setup a proper balance between exploration and exploitation to obtain the maximum cummulative reward in the long run. Most protocols for exploration bound the overall values to a convergent level of performance. If new knowledge is inserted or the environment is suddenly changed, the issue becomes more intricate as the exploration must compromise the pre-existing knowledge. This paper presents a type of multi-channel adaptive resonance theory (ART) neural network model called fusion ART which serves as a fuzzy approximator for reinforcement learning with inherent features that can regulate the exploration strategy. This intrinsic regulation is driven by the condition of the knowledge learnt so far by the agent. The model offers a stable but incremental reinforcement learning that can involve prior rules as bootstrap knowledge for guiding the agent to select the right action. Experiments in obstacle avoidance and navigation tasks demonstrate that in the configuration of learning wherein the agent learns from scratch, the inherent exploration model in fusion ART model is comparable to the basic E-greedy policy. On the other hand, the model is demonstrated to deal with prior knowledge and strike a balance between exploration and exploitation.

DECENTRALIZED SOCIAL NETWORK SERVICE USING THE WEB HOSTING SERVER FOR PRIVACY PRESERVATION Yoonho Nam; Changhoon Lee; Youngman Jung; Woongryul Jeon; Dongho Won
Jurnal Ilmu Komputer dan Informasi Vol 6, No 1 (2013): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

In recent years, the number of subscribers of the social network services such as Facebook and Twitter has increased rapidly. In accordance with the increasing popularity of social network services, concerns about user privacy are also growing. Existing social network services have a centralized structure that a service provider collects all the userâ€™s profile and logs until the end of the connection. The information collected typically useful for commercial purposes, but may lead to a serious user privacy violation. The userâ€™s profile can be compromised for malicious purposes, and even may be a tool of surveillance extremely. In this paper, we remove a centralized structure to prevent the service provider from collecting all usersâ€™ information indiscriminately, and present a decentralized structure using the web hosting server. The service provider provides only the service applications to web hosting companies, and the user should select a web hosting company that he trusts. Thus, the userâ€™s information is distributed, and the userâ€™s privacy is guaranteed from the service provider.

Irregular Grid Interpolation using Radial Basis Function for Large Cylindrical Volume Syam Budi Iryanto; Furqon Hensan Muttaqien; Rifki Sadikin
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Irregular grid interpolation is one of the numerical functions that often used to approximate value on an arbitrary location in the area closed by non-regular grid pivot points. In this paper, we propose method for achieving efficient computation time of radial basis function-based non-regular grid interpolation on a cylindrical coordinate. Our method consist of two stages. The first stage is the computation of weights from solving linear RBF systems constructed by known pivot points. We divide the volume into many subvolumes. At second stages, interpolation on an arbitrary point could be done using weights calculated on the first stage. At first, we find the nearest point with the query point by structuring pivot points in a K-D tree structure. After that, using the closest pivot point, we could compute the interpolated value with RBF functions. We present the performance of our method based on computation time on two stages and its precision by calculating the mean square error between the interpolated values and analytic functions. Based on the performance evaluation, our method is acceptable.

SEPARATION OF OVERLAPPING OBJECT SEGMENTATION USING LEVEL SET WITH AUTOMATIC INITALIZATION ON DENTAL PANORAMIC RADIOGRAPH Safri Adam; Agus Zainal Arifin
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

Visual Recognition Of Graphical User Interface Components Using Deep Learning Technique Agyl Ardi Rahmadi; Aris Sudaryanto
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v13i1.845

Graphical User Interface (GUI) building in software development is a process which ideally need to go through several steps. Those steps in the process start from idea or rough sketch of the GUI, then refined into visual design, implemented in coding or prototype, and finally evaluated for its function and usability to discover design problem and to get feedback from users. Those steps repeated until the GUI considered satisfactory or acceptable by the user. Computer vision technique has been researched and developed to make the process faster and easier; for example generating code for implementation, or automatic GUI testing using component images. But among those techniques, there are still few for usability testing purpose. This preliminary research attempted to make the foundation for usability testing using computer vision technique by built minimalist dataset which has images of various GUI components and used the dataset in deep learning experiment for GUI components visual recognition. The experiment results showed deep learning technique suitable for the intended task, with accuracy of 95% for recognition of two different types of components, and accuracy of 72% for six different types of component.

STUDENT ATTENDANCE SYSTEM USING WIFI DIRECT AND TEMPORARY WI-FI HOTSPOT Doni Setio Pambudi; Taufiqotul Bariyah
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21609/jiki.v13i1.750

Manual attendance recording throws away a lot of teaching and administration time from the university. Research on automatic attendance recording that has been done can be divided into biometrics and non-biometrics uses. Almost all methods require additional device that it is costly and inflexible for class changes. The proposed method solves the problems by utilizing the standard features of smartphones that are owned by all student, this method uses Wi-Fi direct for class broadcasting process and temporary Wi-Fi hotspot for verification process. The experimental results show that the proposed method produces the time needed for the initialization process is 14980 ms and the verification process is 3640 ms.

Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection System Herminarto Nugroho; Meredita Susanty; Ade Irawan; Muhamad Koyimatu; Ariana Yunita
Jurnal Ilmu Komputer dan Informasi Vol 13, No 1 (2020): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

This paper proposes a fully convolutional variational autoencoder (VAE) for features extraction from a large-scale dataset of fire images. The dataset will be used to train the deep learning algorithm to detect fire and smoke. The features extraction is used to tackle the curse of dimensionality, which is the common issue in training deep learning with huge datasets. Features extraction aims to reduce the dimension of the dataset significantly without losing too much essential information. Variational autoencoders (VAEs) are powerfull generative model, which can be used for dimension reduction. VAEs work better than any other methods available for this purpose because they can explore variations on the data in a specific direction.

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Nurul Fazriah

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Home Page

OAI Link

Editorial Team

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Google Scholar

Contact Name Nurul Fazriah

Contact Email jiki@cs.ui.ac.id

Phone +62217863419

Journal Mail Official jiki@cs.ui.ac.id

Editorial Address "Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

Location Kota depok, Jawa barat INDONESIA

Abstract

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Contact Name
Nurul Fazriah

Contact Email
jiki@cs.ui.ac.id

Phone
+62217863419

Journal Mail Official
jiki@cs.ui.ac.id

Editorial Address
"Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"

Location
Kota depok,

Jawa barat

INDONESIA