Jurnal Ilmu Komputer dan Informasi
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.
Articles
257 Documents
<![CDATA[Interactive Image Segmentation using Neighborhood Spatial Information and Statistical Grey Level on Dental Panoramic Radiograph ]]>
<![CDATA[Shabrina Choirunnisa]]>;
<![CDATA[Ari Firmanto]]>;
<![CDATA[Agus Zaenal]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 12, No 1 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v12i1.622
<![CDATA[ In dental panoramic radiographs, grey-level intensity information has been widely used for object segmentation in digital image. However, low contrast in the radiograph image causes high ambiguity that can cause the inconsistency of classification result. Since the grey-level intensity of background and object is almost similar, so in order to improve the segmentation result, the spatial distance on neighborhod region is applied. In this paper, we proposed a novel strategy to measure the distance using neighborhod spatial information and statistical grey level technique for image segmentation. The proposed method starts by calculating adjacency matrix and measured spatial distance on neighborhood region. Since the value of both distances are not in the same range, then the normalization is needed. The distances merging is approached by tuning the weight using several constant values. The experiment results show that our proposed merging strategy has better segmentation result based on Relative Foreground Area Error value.]]>
<![CDATA[SISTEM KONTROL JARINGAN SYARAF TIRUAN BERBASIS SIMULASI PADA PENGELASAN PIPA ALUMINUM ]]>
<![CDATA[Ario Sunar Baskoro]]>;
<![CDATA[Masashi Kabutomori]]>;
<![CDATA[Yasuo Suga]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 4, No 1 (2011): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v4i1.155
<![CDATA[Dalam penelitian ini telah dikembangkan sistem pengelasan otomatis Tungsten Inert Gas (TIG) dengan menggunakan sensor vision pada pengelasan pipa aluminum. Penelitian ini mempelajari proses pengelasan cerdas pipa paduan aluminum 6063S-T5 dalam posisi tetap dengan obor las (welding torch) bergerak dan menggunakan mesin las AC. Model Jaringan Syaraf Tiruan (neural network) untuk pengendalian kecepatan pengelasan telah dikembangkan agar dapat bekerja secara otomatis. Untuk melatih Jaringan Syaraf Tiruan ini diperlukan cukup banyak data dari penelitian sehingga memerlukan waktu dan dana yang cukup besar. Penelitian ini menawarkan proses baru untuk memperkirakan dan mengendalikan penetrasi pengelasan dalam pengelasan pipa paduan aluminum. Penetrasi las diperkirakan dengan menggunakan metode perkiraan secara hibrida yaitu dengan mengombinasikan simulasi pengelasan dan pengamatan visual menggunakan sensor vision. Dari hasil eksperimen didapatkan bahwa sistem pengendalian cukup efektif untuk mendeteksi kolam las (molten pool) dan menghasilkan pengelasan yang baik. This research has developed an automatic welding system Tungsten Inert Gas (TIG) using sensor vision on aluminum pipe welding. This research studied the process of intelligent welding of alloy pipe aluminum 6063S-T5 in a fixed position with a welding torch to move and use the AC welding machines. The neural network model to control the speed of the welding has been developed in order to work automatically. The neural network train need quite a lot of data from studies that require time and substansial funds. This research offers a new process for estimating and controlling welding penetration in welding of aluminum alloy pipe. Weld penetration was estimated by using the approximate hybrid method that combines the simulations of welding and visual inspection using sensor vision. The experiment results that the control system is effective enough to detect the molten pool and produce a good weld.]]>
<![CDATA[AN INTELLIGENT DENGUE HEMORRHAGIC FEVER SEVERITY LEVEL DETECTION BASED ON DEEP NEURAL NETWORK APPROACH ]]>
<![CDATA[Dian Pratiwi]]>;
<![CDATA[Gatot Budi Santoso]]>;
<![CDATA[Leni Muslimah]]>;
<![CDATA[Raden Davin Rizki]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v12i2.642
<![CDATA[Dengue hemorrhagic fever is one of the most dangerous diseases which often leads to death for the sufferer due to delays or improper handling of the severity that has occurred. In determining that severity level, a specialist analyzes it from the symptoms and blood testing results. This research was developed to produce a system by applying Deep Neural Network approach that is able to give the same analytical ability as a doctor, so that it can give fast and precise decision of dengue handling. The research stages consisted of normalizing data to 0 – 1 intervals by Min-Max method, training data into multilayer networks with fully connected and partially connected schemes to produce the best weights, validating data and final testing. From the use of network parameters as much as 10 input units, 1 bias, 2 hidden layers, 2 output units, learning rate of 0.3, epoch 1000, tolerance rate 0.02, threshold 0.5, the system succeeded in generating a maximum accuracy of 95% in data learning (60 data), 87.5% on data learning and non-learning (40 data), 85% on non-learning data (20 data).]]>
<![CDATA[IMPLEMENTATION OF IMAGE PROCESSING ALGORITHMS AND GLVQ TO TRACK AN OBJECT USING AR.DRONE CAMERA ]]>
<![CDATA[Muhammad Nanda Kurniawan]]>;
<![CDATA[Didit Widiyanto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v7i2.264
<![CDATA[Abstract In this research, Parrot AR.Drone as an Unmanned Aerial Vehicle (UAV) was used to track an object from above. Development of this system utilized some functions from OpenCV library and Robot Operating System (ROS). Techniques that were implemented in the system are image processing al-gorithm (Centroid-Contour Distance (CCD)), feature extraction algorithm (Principal Component Analysis (PCA)) and an artificial neural network algorithm (Generalized Learning Vector Quantization (GLVQ)). The final result of this research is a program for AR.Drone to track a moving object on the floor in fast response time that is under 1 second.]]>
<![CDATA[DETEKSI DISTORSI BLOK PADA GAMBAR DIGITAL TERKOMPRESI ]]>
<![CDATA[Irwan Prasetya Gunawan]]>;
<![CDATA[Antony Halim]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 4, No 2 (2011): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v4i2.166
<![CDATA[Pada penelitian ini, dikemukakan sebuah metode baru berbasis analisis multiresolusi untuk mendeteksi distorsi blok pada gambar digital terkompresi. Gambar digital terkompresi cenderung memiliki artefak codingyang mungkin muncul ketika gambar dikodekan dengan tingkat kompresi yang tinggi. Penelitian ini berfokus pada distorsi blok yang dirasakan signifikan dalam gambar digital terkompresi berbasis blok seperti JPEG. Pada penelitian ini, transformasi Wavelet Haar digunakan untuk mendekomposisi sebuah gambar dan menganalisis karakteristik tepian dari gambar tersebut. Berdasarkan dekomposisi ini, peneliti menyusun sebuah algoritma untuk mendeteksi distorsi blok dengan menganalisis koefisien hasil transformasi wavelet. Hasil eksperimen algoritma terhadap database gambar LIVE menunjukkanhasil yang sangat memuaskan dengan tingkat kesalahan yang rendah. In this study, presented a new method based on multiresolution analysis to detect the distortion of the block in a compressed digital image. Compressed digital image tend to have coding artifacts that may arise when an image is encoded with a high compression rate. This study focuses on a block distortion that significantly perceived in the block-based compressed digital images such as JPEG. In this study, Wavelet Haar transformation is used to decompose an image and analyze the characteristics of the edge of the picture. Based on this decomposition, the researchers compiled an algorithm for detecting a block distortion by analyzing the coefficients of the wavelet transformation. The results of experimental algorithms for image database LIVE shows very satisfactory results with low error rates.]]>
<![CDATA[Analyzing Depthwise Convolution Based Neural Network: Study Case in Ship Detection and Land Cover Classification ]]>
<![CDATA[Kuntoro Adi Nugroho]]>;
<![CDATA[Yudi Eko Windarto]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 12, No 2 (2019): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v12i2.752
<![CDATA[Various methods are available to perform feature extraction on satellite images. Among the available alternatives, deep convolutional neural network (ConvNet) is the state of the art method. Although previous studies have reported successful attempts on developing and implementing ConvNet on remote sensing application, several issues are not well explored, such as the use of depthwise convolution, final pooling layer size, and comparison between grayscale and RGB settings. The objective of this study is to perform analysis to address these issues. Two feature learning algorithms were proposed, namely ConvNet as the current state of the art for satellite image classification and Gray Level Co-occurence Matrix (GLCM) which represents a classic unsupervised feature extraction method. The experiment demonstrated consistent result with previous studies that ConvNet is superior in most cases compared to GLCM, especially with 3x3xn final pooling. The performance of the learning algorithms are much higher on features from RGB channels, except for ConvNet with relatively small number of features.]]>
<![CDATA[PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING ]]>
<![CDATA[Khadijah Fahmi Hayati Holle]]>;
<![CDATA[Agus Zainal Arifin]]>;
<![CDATA[Diana Purwitasari]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v8i1.283
<![CDATA[In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively.]]>
<![CDATA[CORTICAL BONE SEGMENTATION USING WATERSHED AND REGION MERGING BASED ON STATISTICAL FEATURES ]]>
<![CDATA[Mamluatul Hani`ah]]>;
<![CDATA[Christian Sri Kusuma Aditya]]>;
<![CDATA[Aryo Harto]]>;
<![CDATA[Agus Zainal Arifin]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 8, No 2 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v8i2.305
<![CDATA[Research on biomedical image is a subject that attracted many researchers’ interest. This is because the biomedical image could contain important information to help analyze a disease. One of the existing researches in his field uses dental panoramic radiographs image to detect osteoporosis. The analyzed area is the width of cortical bone. To analyze that area, however, we need to determine the width of the cortical bone. This requires proper segmentation on the dental panoramic radiographs image. This study proposed the integration of watershed and region merging method based on statistical features for cortical bone segmentation on dental panoramic radiographs. Watershed segmentation process was performed using gradient magnitude value from the input image. The watershed image that still has excess segmentation could be solved by region merging based on statistical features. Statistical features used in this study are mean, standard deviation, and variance. The similarity of adjacent regions is measured using weighted Euclidean distance from the statistical feature of the regions. Merging process was executed by incorporating the background regions as many as possible, while keeping the object regions from being merged. The segmentation result has succeeded in forming the contours of the cortical bone. The average value of accuracy is 93.211%, while the average value of sensitivity and specificity is 93.858% and respectively.]]>
<![CDATA[ANUGA SOFTWARE FOR NUMERICAL SIMULATIONS OF SHALLOW WATER FLOWS ]]>
<![CDATA[Sudi Mungkasi]]>;
<![CDATA[Stephen Gwyn Roberts]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 5, No 1 (2012): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v5i1.180
<![CDATA[Shallow water flows are governed by the shallow water wave equations, also known as the Saint-Venant system. This paper presents a finite volume method used to solve the two-dimensional shallow water wave equations and how the finite volume method is implemented in ANUGA software. This finite volume method is the numerical method underlying the software. ANUGA is open source software developed by Australian National University (ANU) and Geoscience Australia (GA). This software uses the finite volume method with triangular domain discretisation for the computation. Four test cases are considered in order to evaluate the performance of the software. Overall, ANUGA is a robust software to simulate two-dimensional shallow water flows. Arus air dangkal diatur dalam persamaan gelombang air dangkal, dikenal sebagai sistem Saint-Venant. Penelitian ini menyajikan metode finite volumeyang digunakan untuk menyelesaikan persamaan gelombang air dangkal dua dimensi dan bagaimana metode finite volumediimplementasikan dalam perangkat lunak ANUGA. Metode finite volumeadalah metode numerik yang mendasari perangkat lunakANUGA. ANUGA sendiri adalah perangkat lunak open source yang dikembangkan oleh Australian National University(ANU) dan Geoscience Australia (GA). Perangkat lunak ini menggunakan metode finite volumedengan diskritisasi domain segitiga dalam proseskomputasi. Empat uji kasus digunakan untuk mengevaluasi kinerja perangkat lunak. Secara keseluruhan, ANUGA adalah perangkat lunak yang robust untuk mensimulasikan dua dimensi aliran arus air dangkal.]]>
<![CDATA[WEB NEWS DOCUMENTS CLUSTERING IN INDONESIAN LANGUAGE USING SINGULAR VALUE DECOMPOSITION-PRINCIPAL COMPONENT ANALYSIS (SVDPCA) AND ANT ALGORITHMS ]]>
<![CDATA[Arif Fadllullah]]>;
<![CDATA[Dasrit Debora Kamudi]]>;
<![CDATA[Muhamad Nasir]]>;
<![CDATA[Agus Zainal Arifin]]>;
<![CDATA[Diana Purwitasari]]>
<![CDATA[ Jurnal Ilmu Komputer dan Informasi Vol 9, No 1 (2016): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information) ]]>
Publisher : <![CDATA[Faculty of Computer Science - Universitas Indonesia ]]>
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DOI: 10.21609/jiki.v9i1.362
<![CDATA[Ant-based document clustering is a cluster method of measuring text documents similarity based on the shortest path between nodes (trial phase) and determines the optimal clusters of sequence document similarity (dividing phase). The processing time of trial phase Ant algorithms to make document vectors is very long because of high dimensional Document-Term Matrix (DTM). In this paper, we proposed a document clustering method for optimizing dimension reduction using Singular Value Decomposition-Principal Component Analysis (SVDPCA) and Ant algorithms. SVDPCA reduces size of the DTM dimensions by converting freq-term of conventional DTM to score-pc of Document-PC Matrix (DPCM). Ant algorithms creates documents clustering using the vector space model based on the dimension reduction result of DPCM. The experimental results on 506 news documents in Indonesian language demonstrated that the proposed method worked well to optimize dimension reduction up to 99.7%. We could speed up execution time efficiently of the trial phase and maintain the best F-measure achieved from experiments was 0.88 (88%).]]>
