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INDONESIA
Indonesian Journal on Computing (Indo-JC)
Published by Universitas Telkom
ISSN : 24609056     EISSN : -     DOI : -
Core Subject : Science,
Computer Science & IT
Indonesian Journal on Computing (Indo-JC) is an open access scientific journal intended to bring together researchers and practitioners dealing with the general field of computing. Indo-JC is published by School of Computing, Telkom University (Indonesia).
Arjuna Subject : -
Articles 251 Documents
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Monitoring the Number of Vehicles on Highway Using Frame Difference Method M. Sofyan Bahrum Juniardi; Mahmud Imrona; Putu Harry Gunawan
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.202

Abstract

Indonesia is a country with the third largest motorcycle rider in the world after China and India. One of the biggest cities in Indonesia is Bandung is a city that often experience traffic congestion, especially during peak hours and weekends because of population growth, urbanization and transmigration has increased and not comparable with the growth and development of adequate infrastructure so that frequent traffic jams.In this study, the authors developed a traffic monitoring system to calculate the number of vehicles using the Frame Difference method. To implement the method contained in this paper in calculating the number of vehicles passing on a highway by using two different points of view. The assumption in this research is using static background assumption.Based on the result of the research, it is found that the performance of video from Toll Pasteur with the viewing angle has 8.21% error and Purbaleunyi Toll with vertical angle has 4.43% error by using some filter and morphological operation. Conversely, if without using filters and morphological operations have an error of 175.22% in Pasteur Toll and 115.44% at Purbaleunyi Toll.
Pengembangan dan Pengujian Sistem Grid Computing Menggunakan Globus Toolkit di Universitas Telkom Izzatul Ummah
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.19

Abstract

In this research, we build a grid computing infrastructure by utilizing existing cluster in Telkom University as back-end resources. We used middleware Globus Toolkit 6.0 and Condor 8.4.2 in developing the grid system. We tested the performance of our grid system using parallel matrix multiplication. The result showed that our grid system has achieved good performance. With the implementation of this grid system, we believe that access to high performance computing resources will become easier and the Quality of Service will also be improved.
Early Smoke Detection on Video Using Wavelet Energy Muhammad Zulfiqar Shafar; Tjokorda Agung Budi Wirayuda; Febryanti Sthevanie
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 2 (2017): September, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.2.180

Abstract

Most of the smoke detection system these days still using sensors that have to receive specific particles before it could give a warning. But, this system takes some time to react and quite difficult to place in spacious room or the outdoor. To overcome this, there is some research that build smoke detection system using many kind video processing technique that could provide early warning. In this research, wavelet energy was used to detect smoke in the video.  To determine candidate blocks in a frame that contain smoke, this research performed background subtraction and color analysis based on HSV color space. Then implementing spatial analysis and spatio-temporal analysis by using wavelet energy method and accumulative motion orientation to detect the smoke. This system using combination of dataset from previous research [1], downloaded from various sources and self-made dataset. Based on testing process using those dataset, this system reaches 91.05% accuracy for block-level and 72.22% accuracy for frame-level.Keywords: Accumulative motion orientation, smoke detection, spatial analysis, spatio-temporal analysis, video processing, wavelet energy
Implementasi Gabor Wavelet dan Support Vector Machine pada Deteksi Polycystic Ovary (PCO) Berdasarkan Citra Ultrasonografi Untari Novia Wisesty
Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 2 (2016): September, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2016.1.2.90

Abstract

Ketidaksuburan adalah kondisi pasangan suami istri yang susah memiliki keturunan. Salah satu pemeriksaan kesuburan yang dianjurkan di bidang kesehatan adalah USG (Ultrasonografi). Untuk memeriksa kesuburan wanita dilakukan USG rahim dengan memeriksa keberadaan penyakit di rahim yang menyebabkan kemandulan, salah satunya adalah PCO (Polycystic Ovary), dengan melihat jumlah dan ukuran folikel dalam ovarium. Namun, sampai saat ini penentuan hasil USG rahim masih dilakukan secara manual oleh Dokter Spesialis Kandungan. Penelitian ini bermaksud untuk membantu ahli medis dalam mendiagnosa kesuburan wanita berdasarkan keberadaan PCO secara terkomputerisasi, sehingga hasil diagnosa dapat dilakukan dengan cepat dan akurat. Proses pendektesian diawali dengan pemrosesan awal pada citra USG dan ekstraksi ciri menggunakan Gabor Wavelet. Selanjutnya, pada tahap klasifikasi PCO digunakan metode Support Vector Machine (SVM). Kernel SVM yang digunakan sebagai classifier adalah fungsi kernel Linear, RBF, Kuadratik, dan Polinomial sesuai dengan kebutuhan persebaran data, dengan nilai parameter C kelipatan 10 dari rentang 0 hingga 300. Dengan menggunakan metode-metode tersebut, pencapaian akurasi tertinggi didapatkan dengan menggunakan parameter Gabor Wavelet dan SVM yang terbaik yaitu kernel polynomial, C=160, mask 17x17, frekuensi 2, 3, 4, 5 Hz dan sudut orientasi [π/6; π/6; π] dengan akurasi uji 78.4661% dan akurasi latih 75.5480% berdasarkan pengujian per-folikel.
Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm Putri Tsatsabila Ramadhani; Untari Novia Wisesty; Annisa Aditsania
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 2 (2017): September, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.2.173

Abstract

Di beberapa tahun terakhir, pemanfaatan teknologi microarray memiliki pengaruh besar dalam menentukan gen informatif yang menyebabkan kanker. Micorarray mampu menentukan ekspresi ribuan gen dan secara simultan memantau proses bilogis yang sedang berlangsung. Dengan melakukan analisa terhadap data micorarray, selanjutnya ekspresi dari ribuan gen yang merepresentasikan suatu jaringan pada manusia, akan diklasifikasikan sebagai jaringan kanker atau bukan. Dalam penulisan penelitian penelitian, penulis meng-implementasikan Functional Link Neural Network dengan fungsi basis Legendre Polynomial untuk klasifikasi data yang akurat dan menggunakan Genetic Algorithm sebagai seleksi fitur untuk mereduksi data berdimensi tinggi yang sering ditemukan pada data microarray. Dengan serangkaian proses yang telah dilakukan, maka diperoleh kinerja tertinggi terhadap klasifikasi data microarray Colon Tumor sebesar 92.3% dan Leukemia sebesar 87.5%. Perbedaan kinerja yang diperoleh disebabkan oleh perbedaan karakteristik masing-masing data.
Pengenalan Huruf Isyarat Tangan Menggunakan Ekstraksi Ciri Local Binary Pattern M. Adhi Satria; Kurniawan Nur Ramadhani; Anditya Arifianto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.215

Abstract

Pada penelitian ini dibangun sistem pengenalan huruf isyarat tangan menggunakan metode ekstraksi ciri Local Binary Patterns (LBP). Metode LBP memiliki kehandalan dalam melakukan analisis tekstur, mengatasi penskalaan dan citra yang kabur. Untuk algoritma klasifikasi, digunakan metode k-Nearest Neighbour (KNN) dan Support Vector Machine (SVM). Parameter LBP terbaik didapatkan untuk nilai R=10 dan P=16 menggunakan SVM dengan kernel Gaussian. Performansi terbaik dalam penelitian ini didapatkan untuk nilai F1-Score 99,84%.
Implementation of Evolution Strategies for Classifier Model Optimization Mahmud Dwi Sulistiyo; Rita Rismala
Indonesia Journal on Computing (Indo-JC) Vol. 1 No. 2 (2016): September, 2016
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2016.1.2.43

Abstract

Classification becomes one of the classic problems that are often encountered in the field of artificial intelligence and data mining. The problem in classification is how to build a classifier model through training or learning process. Process in building the classifier model can be seen as an optimization problem. Therefore, optimization algorithms can be used as an alternative way to generate the classifier models. In this study, the process of learning is done by utilizing one of Evolutionary Algorithms (EAs), namely Evolution Strategies (ES). Observation and analysis conducted on several parameters that influence the ES, as well as how far the general classifier model used in this study solve the problem. The experiments and analyze results show that ES is pretty good in optimizing the linear classification model used. For Fisher’s Iris dataset, as the easiest to be classified, the test accuracy is best achieved by 94.4%; KK Selection dataset is 84%; and for SMK Major Election datasets which is the hardest to be classified reach only 49.2%.
Modifikasi Headstega berdasarkan Penyisipan Karakter Hasmawati Hasmawati; Ari Moesriami Barmawi
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.145

Abstract

AbstractHead steganography or Headstega is one of noiseless steganography paradigm, or Nostega.This method utilizes the email header as a media of  message concealment. There are several problems that can be enhanced in Headstega, i.e. low embedding capacity and high level of suspicion. Modified Headstega based on Character Hiding uses a combination of consonant vowel to embed the secret messages into email address. The messages embedding process using four consonant vowel combination that represented one character in Indonesian language.  From the experiments conducted, the results obtained that the Modified Headstega has a better performance than the Original Headstega in term of embedding capacity and also in suspicion level. Keyword : Steganography, Nostega, Headstega, Character Hiding
Factors Influencing the Adoption of E-Tilang; Empirical Evidence from the UTAUT Model Eva Yumami; Djoko Budiyanto Setyohadi; Suyoto Suyoto
Indonesia Journal on Computing (Indo-JC) Vol. 3 No. 1 (2018): Maret, 2018
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2018.3.1.207

Abstract

Mid-Year 2017 The National Police of the Republic of Indonesia publishes E-tilang technology innovation. E-tilang is a mobile app that is used online by traffic police to take action against traffic violators on the highway. E-tilang aims to improve service to the public and reduce the misuse fines of traffic violations. This research factor influences acceptance and use of e-ticket by using UTAUT model. This research was conducted in Bengkulu area with 152 traffic policemen. The findings of this study indicate that performance expectancy, effort expectancy, and social influences positively affect the use of E-tilang. Facilitating conditions has no significant effect on the intention of using E-tilang. The results of this study are important steps to improve e-tilang services.
Prediksi Indeks Harga Saham dengan Metode Gabungan Support Vector Regression dan Jaringan Syaraf Tiruan Lisbeth Evalina Siahaan
Indonesia Journal on Computing (Indo-JC) Vol. 2 No. 1 (2017): Maret, 2017
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/INDOJC.2017.2.1.45

Abstract

Harga suatu saham berubah secara cepat dari waktu ke waktu. Pergerakan indeks harga saham menjadi tolak ukur para pemilik saham untuk membuat keputusan kapan sebaiknya saham dibeli, dijual atau dipertahankan. Untuk itu diperlukan suatu model yang dapat memprediksi indeks harga saham untuk memantau pergerakan tersebut dan membantu para pemilikk saham dalam mengambil keputusan. Penelitian ini mengusulkan metode untuk memprediksi pergerakan harga saham dengan menggunakan metode gabungan Support Vector Regression (SVR) pada tahap dan Jaringan Syaraf Tiruan (JST) pada tahap kedua. Pada penelitian ini, Algoritma Genetika atau Genetic Algorithm (GA) akan digunakan untuk melakukukan optimasi parameter SVR. Prediksi dibuat untuk 1, 3, 5, 7, 10, 15, dan 30 hari kedepan. Dari serangkaian uji coba yang dilakukan, SVR-JST (SVR dioptimasi GA) memberikan tingkat kesalahan lebih kecil dibandingkan dengan metode JST.

Page 5 of 26 | Total Record : 251

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