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Qurrotin Ayunina
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jiki@cs.ui.ac.id
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"Faculty of Computer Science Universitas Indonesia Kampus Baru UI Depok - 16424"
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INDONESIA
Jurnal Ilmu Komputer dan Informasi
Published by Universitas Indonesia
ISSN : 20887051     EISSN : 25029274     DOI : 10.21609
Core Subject : 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 167 Documents
SIMPLE EXPERT VISION SYSTEM FOR RECOGNITION OF BEARING'S DEFECTS K. Herdianta, Agustian; Nasution, Aulia M.T.
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

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1091.482 KB) | DOI: 10.21609/jiki.v5i2.188

Abstract

Defects on a bearing is usually determined by observing its vibration characteristics. This method unfortunately can not detect the visual defects on the inner and outer ring bearing surface. A pattern recognition is implemented in this paper to solve the problem. A backpropagation neural network architecture is used to recognize the visual defect pattern. This architecture is integrated in a digital image processing chain. Recognition rate of good bearing is obtained at 92.93 %, meanwhile for defected bearing is obtained at 75 % respectively. This rate shows integrated artificial neural network with digital image processing can be implemented to detect the presence of visual bearing defect. Cacat pada bearing biasanya ditentukan dengan mengamati karakteristik getaran. Metode ini sayangnya tidak dapat mendeteksi kecacatan visual pada permukaan dalam dan luar cincin bearing. Sebuah pengenalan pola diimplementasikan dalam paper ini untuk memecahkan masalah tersebut. Sebuah arsitektur jaringan saraf backpropagation digunakan untuk mengenali pola kecacatan visual. Arsitektur yang diusulkan ini terintegrasi dalam sebuah alir pengolahan citra digital. Tingkat pengenalan bearing yang baik adalah 92.93%, sedangkan untuk bantalan yang cacat adalah 75%. Angka ini menunjukkan integrasi jaringan syaraf tiruan dengan pengolahan citra digital dapat diterapkan untuk mendeteksi kecacatan visual pada bearing.
DATA INTEGRATION SIMULATION USING DATA CONSOLIDATION Wasito, Ito; Sanabila, Hadaiq R.
Jurnal Ilmu Komputer dan Informasi Vol 4, No 1 (2011): 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 | Full PDF (2147.985 KB) | DOI: 10.21609/jiki.v4i1.156

Abstract

One of the data integration methods is data consolidation. This method captures data from multiple source systems/data and integrates it into a single persistent data. We examined the performance of data consolidation using k-means and Gaussian mixture clustering. Meanwhile, we use Silhouette index as cluster validation and measure how well of a clustering relative to others. The experiments analyses the data in various data duplication rate and actual number of data cluster. Based on the experimental result, there are two factors affecting the performance of data consolidation. These factors are the rate/percentage of duplicate data and the number of actual cluster contained in a data. The higher percentages of duplicate data and less number of clusters contained in the data would be increasing the performance of clustering algorithm. Salah satu metode dari integrasi data adalah konsolidasi data. Metode ini mengambil data dari beberapa sumber data untuk digabungkan menjadi data persisten tunggal. Peneliti memeriksa kinerja konsolidasi data menggunakan beberapa teknik clustering yaitu k-means dan gaussian mixture clustering. Penulis menggunakan Silhouette index sebagai metode validasi cluster untuk mengukur seberapa baik suatu pengelompokan relatif terhadap data lain. Penelitian ini melakukan analisis data terhadap jumlah rata-rata duplikasi data dan jumlah sebenarnya dari cluster data. Berdasarkan hasil percobaan, ada dua faktor yang mempengaruhi kinerja integrasi data dengan menggunakan konsolidasi data. Faktor-faktor tersebut antara lain adalah tingkat atau persentase dari duplikasi data dan jumlah cluster sebenarnya yang terkandung dalam data. Persentase duplikasi data yang tinggi dan data yang mengandung jumlah cluster yang rendah, akan meningkatkan kinerja dari algoritma clustering.
KOPYOR COCONUT DETECTION USING SOUND-BASED DYNAMIC TIME WARPING METHOD Dhelika, Radon; Nugroho, Firman Ady; Bayu, Muhamad; Kamil, Alfian
Jurnal Ilmu Komputer dan Informasi Vol 12, No 1 (2019): 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 | Full PDF (555.986 KB) | DOI: 10.21609/jiki.v12i1.631

Abstract

Kopyor coconut is a coconut that has genetic abnormalities which cause the coconut meat to have a unique texture and is detached from the coconut shell. Its uniqueness attracts many enthusiasts resulting in a high economic value, 4-5 times that of the ordinary coconut. From its external appearance, kopyor coconut does not differ with ordinary coconut and this poses a challenge in the detection stage. To date, both farmers and sellers use a traditional approach by listening to the sound of whisk from kopyor coconut to detect them. Unfortunately, this approach relies heavily on experience and expertise of the person. Therefore, a new detection approach is proposed based on sound recognition using Mel Frequency Cepstrum Coefficient (MFCC) as the method for feature extraction and Dynamic Time Warping (DTW) as the method for feature matching. Objects that will be detected are kopyor coconuts and ordinary coconut which has grown mature. By implementing both methods, a program has been developed to detect kopyor coconut with an accuracy of 93.8%.
KLASIFIKASI BEAT ARITMIA PADA SINYAL EKG MENGGUNAKAN FUZZY WAVELET LEARNING VECTOR QUANTIZATION Imah, Elly Matul; Basaruddin, T.
Jurnal Ilmu Komputer dan Informasi Vol 4, No 1 (2011): 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 | Full PDF (1099.07 KB) | DOI: 10.21609/jiki.v4i1.149

Abstract

Pengenalan pola beat dalam analisa rekaman elektrokardiogram (EKG) menjadi bagian yang penting dalam deteksi penyakit jantung terutama aritmia. Banyak metode yang dikembangkan terkait dengan pengenalan pola beat, namun sebagian besar masih mengunakan algoritma klasifikasi klasik di mana masih belum mampu mengenali outlier klasifikasi. Fuzzy Learning Vector Quantization (FLVQ) merupakan salah satu algoritma yang mampu untuk mengenali outlier klasifikasi tetapi juga memiliki kelemahan untuk sistem uji yang bukan data berkelompok. Dalam tulisan ini peneliti mengusulkan Fuzzy Wavelet LearningVector Quantization (FWLVQ), yaitu modifikasi FLVQ sehingga mampu mengatasi data crisp maupun data fuzzy dan juga memodifikasi inferensi sistemnya sebagai perpaduan model fuzzy Takagi Sugeno Kang dengan wavelet. Sinyal EKG diperoleh dari database MIT-BIH. Sistem pengenalan pola beat secara keseluruhan terbagi atas dua bagian yaitu data pra proses dan klasifikasi. Hasil percobaan diperoleh bahwa FWLVQ memiliki akurasi sebesar 90.20% untuk data yang tidak mengandung outlier klasifikasi dan 87.19% untuk data yang melibatkan outlier klasifikasi dengan rasio data uji outlier klasifikasi dengan data non-outlier sebesar 1:1. The recognition of beat pattern in analysis of recording an electrocardiogram (ECG) becomes an important detection of heart disease, especially arrhythmias. Many methods are developed related to the recognition of beat patterns, but most still use the classical classification algorithms which are still not able to identify outlier classification. Fuzzy Learning Vector Quantization (FLVQ) is one of the algorithms that can identify outlier classification but also has a weakness for test systems that are not grouped data. In this paper we propose a Fuzzy Wavelet Quantization Learning Vector (FWLVQ), which is modified so as to overcome FLVQ crisp data and fuzzy data and also modify the inference system as a combination of Takagi Sugeno Kang fuzzy model with the wavelet. ECG signal obtained from the MIT-BIH database. Beat pattern recognition system as a whole is divided into two parts: data pre-processing and classification. The experimental results obtained that FWLVQ has an accuracy 90.20% for data that does not contain outlier classification and 87.19% for the classification of data involving outlier ratio outlier test data classification with non-outlier data of 1:1.
Face Recognition Using Complex Valued Backpropagation Nafisah, Zumrotun; Rachmadi, Febrian; Imah, Elly Matul
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): 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 | Full PDF (405.143 KB) | DOI: 10.21609/jiki.v11i2.617

Abstract

Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition. Complex-Valued Backpropagation is an algorithm modified from Real-Valued Backpropagation algorithm where the weights and activation functions used are complex. The dataset used in this study consist of 250 images that is classified in 5 classes. The performance of face recognition using Complex-Valued Backpropagation is also compared with Real-Valued Backpropagation algorithm. Experimental results have shown that Complex-Valued Backpropagation performance is better than Real-Valued Backpropagation.
ALGORITMA PARALEL ODD EVEN TRANSPOSITION PADA MODEL JARINGAN NON-LINIER ., Ernastuti; Salim, Ravi A.; ., Haryanto
Jurnal Ilmu Komputer dan Informasi Vol 3, No 2 (2010): 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 | Full PDF (977.741 KB) | DOI: 10.21609/jiki.v3i2.144

Abstract

Odd-even-transposition adalah suatu algoritma paralel yang merupakan pengembangan dari algoritma sekuensial “bubble sort”. Algoritma odd-even-transposition ini didesain khusus untuk model jaringan array linier (homogen). Untuk n elemen data, kompleksitas waktu dari algoritma bubble sort adalah O(n2), sedangkan pada odd-even-transposition yang bekerja di atas n prosesor adalah (n). Ada peningkatan kecepatan waktu pada kinerja algoritma paralel ini sebesar n kali dibanding algoritma sekuensialnya. Hypercube dimensi k adalah model jaringan non-linier (non-homogen) terdiri dari n = 2k prosesor, di mana setiap prosesor berderajat k. Model jaringan Fibonacci cube dan extended Lucas cube masing-masing merupakan model subjaringan hypercube dengan jumlah prosesor < 2k prosesor dan maksimum derajat prosesornya adalah k. Pada paper ini, diperlihatkan bagaimana algoritma odd-even-transposition dapat dijalankan juga pada model jaringan komputer cluster non-linier hypercube, Fibonacci cube, dan extended Lucas cube dengan kompleksitas waktu O(n). Odd-even-transposition is a parallel algorithm which is the development of sequential algorithm “bubble sort”. Odd-even transposition algorithm is specially designed for linear array network model (homogeneous). For n data elements, the time complexity of bubble sort algorithm is O(n2), while the odd-even-transposition that works with n processor is (n). There in an increase in the speed of time on the performance of this parallel algorithms for n times than its sequential algorithm. K-dimensional hypercube is a non-linear network model (non-homogeneous) consists of n = 2k processors, where each processor has k degree . Network model of Fibonacci cube and extended Lucas cube are the hypercube sub-network model with the number of processors
Batik Classification using Deep Convolutional Network Transfer Learning Gultom, Yohanes; Arymurthy, Aniati Murni; Masikome, Rian Josua
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): 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 | Full PDF (504.497 KB) | DOI: 10.21609/jiki.v11i2.507

Abstract

Batik fabric is one of the most profound cultural heritage in Indonesia. Hence, continuous research on understanding it is necessary to preserve it. Despite of being one of the most common research task, Batik’s pattern automatic classification still requires some improvement especially in regards to invariance dilemma. Convolutional neural network (ConvNet) is one of deep learning architecture which able to learn data representation by combining local receptive inputs, weight sharing and convolutions in order to solve invariance dilemma in image classification. Using dataset of 2,092 Batik patches (5 classes), the experiments show that the proposed model, which used deep ConvNet VGG16 as feature extractor (transfer learning), achieves slightly better average of 89 ± 7% accuracy than SIFT and SURF-based that achieve 88 ± 10% and 88 ± 8% respectively. Despite of that, SIFT reaches around 5% better accuracy in rotated and scaled dataset.
PEMISAHAN BANYAK SUMBER SUARA MESIN MENGGUNAKAN INDEPENDENT COMPONENT ANALYSIS (ICA) UNTUK DETEKSI KERUSAKAN Atmaja, B.T.; Aisyah, A. S.; Arifianto, D.
Jurnal Ilmu Komputer dan Informasi Vol 3, No 1 (2010): 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 | Full PDF (1595.306 KB) | DOI: 10.21609/jiki.v3i1.139

Abstract

Pemeliharaan kondisi mesin di industri membutuhkan kecepatan dan kemudahan, salah satu metodenya adalah dengan analisis getaran. Getaran mesin menyebabkan pola suara yang diemisikan mesin, di mana suara mesin satu bercampur dengan mesin lainnya. Blind Source Separation (BSS) merupakan teknik memisahkan sinyal campuran berdasarkan sifat kebebasan statistik antar sumber. Melalui simulasi dengan beberapa motor dan susunan mikrofon sebagai sensor, didapatkan data suara campuran dari beberapa motor yang terekam melalui tiap mikrofon. Intensitas sinyal yang diterima mikrofon berbeda satu sama lain, tergantung pada jarak dan sudut datangnya. Tujuan penelitian ini adalah untuk memisahkan sinyal campuran dari tiap mikrofon sehingga didapatkan sinyal estimasi sumber untuk mendeteksi kerusakan motor. Berdasarkan hasil penelitian, diperoleh pemisahan sinyal terbaik dalam Time-Domain ICA. Sinyal estimasi tersebut dianalisis untuk menentukan kondisi kerusakan mesin berdasarkan pola frekuensi sesaatnya. Maintenance of engine conditionin the industry requires speed and convenience, one of the method is by vibration analysis. Machine’s vibration causes the machine emitted sound pattern, in which an engine sound mixed with other machine’s. Blind Source Separation (BSS) is a technique to separate mixed signals based on the statistical independence properties between the sources. Through simulation with several motors and the composition of the microphones as the sensor, noise mixture data obtained from some motors recorded by each microphone. The signal intensity received by microphone are different from each other, depending on the distance and angle of arrival. The purpose of this study is to separate the mixed signals from each microphone to obtain estimation of the signal source to detect the motor damage . Based on the research, obtained the best signal separation in the Time-Domain ICA. Signal estimation is analyzed to determine the condition of an engine failure patterns based on instantaneous frequency.
Detecting Controversial Articles on Citizen Journalism Wicaksono, Alfan Farizki; Herdiyana, Sharon Raissa; Adriani, Mirna
Jurnal Ilmu Komputer dan Informasi Vol 11, No 1 (2018): 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 | Full PDF (404.521 KB) | DOI: 10.21609/jiki.v11i1.478

Abstract

Someone's understanding and stance on a particular controversial topic can be influenced by daily news or articles he consume everyday. Unfortunately, readers usually do not realize that they are reading controversial articles. In this paper, we address the problem of automatically detecting controversial article from citizen journalism media. To solve the problem, we employ a supervised machine learning approach with several hand-crafted features that exploits linguistic information, meta-data of an article, structural information in the commentary section, and sentiment expressed inside the body of an article. The experimental results shows that our proposed method manages to perform the addressed task effectively. The best performance so far is achieved when we use all proposed feature with Logistic Regression as our model (82.89\% in terms of accuracy). Moreover, we found that information from commentary section (structural features) contributes most to the classification task.
METODE LOKALISASI ROBOT OTONOM DENGAN MENGGUNAKAN ADOPSI ALGORITMA HEURISTIC SEARCHING DAN PRUNING UNTUK PEMBANGUNAN PETA PADA KASUS SEARCH-AND-SAFE Jatmiko, Wisnu; Alvissalim, M. S.; Febrian, A.; Y.S., Dhiemas R.
Jurnal Ilmu Komputer dan Informasi Vol 2, No 2 (2009): 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 | Full PDF (482.183 KB) | DOI: 10.21609/jiki.v2i2.134

Abstract

Permasalahan search-and-safe merupakan salah satu contoh robot otonom dapat disimulasikan untuk menggantikan pekerjaan manusia di lingkungan berbahaya, misalnya pada kegiatan evakuasi manusia dari ruang tertutup yang terbakar. Dalam contoh ini, robot otonom harus dapat menemukan objek manusia untuk diselamatkan, serta objek api untuk dipadamkan. Lebih jauh lagi, untuk dapat menyelesaikan permasalahan seperti ini dengan baik, robot otonom harus dapat mengetahui keberadaannya, bukan hanya posisi dalam sistem koordinat global saja tetapi juga posisi relatif terhadap posisi tujuan dan keadaan lingkungan itu sendiri. Permasalahan ini kemudian dikenal juga sebagai lokalisasi yang menjadi bagian penting dari proses navigasi pada robot otonom. Salah satu metode yang dapat digunakan untuk menyelesaikan permasalahan lokalisasi adalah dengan menggunakan representasi internal peta lingkungan kerja dalam pengetahuan robot otonom. Pada kondisi ketika tidak tersedia informasi mengenai konfigurasi lingkungan, atau informasi yang tersedia sifatnya terbatas, robot harus dapat membangun sendiri representasi petanya dengan dibantu oleh komponen sensor yang dimilikinya. Pada paper ini kemudian dibahas salah satu metode yang dapat diterapkan dalam proses pembangunan peta seperti yang dijelaskan, yaitu melalui adopsi algoritma heuristic searching dan pruning yang sudah dikenal pada bidang kecerdasan buatan. Selain itu juga akan dijabarkan desain robot otonom yang digunakan, serta konfigurasi lingkungan yang digunakan pada studi kasus search-and-safe ini. Diharapkan nantinya hasil yang diperoleh dari penelitian ini dapat diterapkan untuk skala yang lebih besar.

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