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All Journal Jurnal Sains dan Teknologi Semantik Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Creative Information Technology Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Teknologi Informasi Cyberku Tedc Jurnal Pengabdian Kesehatan Masyarakat Mulawarman Scientific Journal of Informatics
Guruh Fajar Shidik
Universitas Dian Nuswantoro
Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Deteksi Api dengan MultiColorFeatures, Background Subtraction dan Morphology Guruh Fajar Shidik; Fajrian Nur Adnan; Ricardus Anggi Pramunendar; Catur Supriyanto; Pulung Nurtantio Andono
Semantik Vol 3, No 1 (2013): Semantik 2013
Publisher : Semantik

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Abstract

Pentingnya  deteksi  api secara dini dapat membantu memberikan peringatan  serta  menghindari bencana yang menyebabkan kerugian ekonomi dan kehilangan nyawa manusia.  Teknik deteksi api dengan sensor konvensional  masih  memiliki keterbatasan, yakni  memerlukan waktu yang cukup lama dalam mendeteksi api pada ruangan yang besar serta tidak dapat bekerja di ruangan terbuka. Penelitian ini mengusulkan metode deteksi  api secara visual yang dapat digunakan pada  camera surveillance dengan  menggunakankombinasi  Multicolorfeatures  sepertiRGB,  HSV,YCbCr  dan  Background Subtraction  serta morphologyuntuk pendeteksian  pergerakan  api.  Evaluasi penelitian  dilakukan dengan menghitung tingkat error deteksi  area api.
ANALISA PENGARUH PERBEDAAN MEDIUM AIR DAN UDARA TERHADAP KALIBRASI KAMERA DENGAN MENGGUNAKAN METODE ZHANG Pulung Nurtantio Andono; Guruh Fajar Shidik; Ricardus Anggi Pramunendar; Catur Supriyanto; Mochamad Hariadi
Semantik Vol 2, No 1 (2012): Prosiding Semantik 2012
Publisher : Semantik

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Abstract

Pada paper ini kami melakukan penelitian untuk mencari pengaruh perbedaan medium terhadap kalibrasi kamera. Kami melakukan komparasi analisa kalibrasi kamera yang dilakukan di udara dan di air, untuk mengetahui tingkat perbedaannya. Pada studi komparasi analisa ini kami menggunakan tehnik kalibrasi Zhang yang sudah biasa digunakan untuk kalibrasi kamera. Hasil experimen yang diukur merupakan perbandingan nilai focal length. Dari hasil percobaan yang dilakukan, didapatkan bahwa adanya perbedaan nilai focal length kamera pada medium air dan udara sebesar 36%.Kata kunci : Kalibrasi Kamera, Metode Zhang
FRAMEWORK UNTUK MENDETEKSI BOTNET KRAKEN DAN CONFICKER PADA JARINGAN KOMPUTER GuruhFajar Shidik; Aisyatul Karima
Semantik Vol 1, No 1 (2011): Prosiding Semantik 2011
Publisher : Semantik

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Abstract

Botnet adalah malware yang dapat melakukan serangan terhadap suatu jaringan secara tergorganisir dimana malware ini juga dapat dikendalikan dari pusat atau command and control (C&C). Dengan membedakan kondisi normal dan abnormal traffic pada jaringan komputer, dapat digunakan sebagaiindikasi keberadaan Botnet khususnya Kraken dan Conficker. Untuk membedakan kondisi normal dan abnormal jaringan komputer, dapat dilakukan dengan menggunakan anomaly based detection. Dimana dengan anomaly based detection kita dapat mendeteksi Botnet secara dini dengan membandingkan suatu traffic pada jaringan komputer secara visual. Akan tetapi metode anomaly based detection masih belum dapat mendeteksi Botnet secara tepat, masih terdapat dugaan false rate yang tinggi. Untuk mengarahkan metode ini agar terfokus untuk mendeteksi Botnet, diperlukan sebuah framework yang dapat memberi penjelasan akan tahapan yang harus dilakukan. Paper ini memberikan sebuah framework yang berisilangkah-langkah kerja guna mendeteksi Botnet Kraken dan Conficker dengan memanfaatkan metode anomaly based detection.Kata kunci : Botnet, flow traffic, anomali traffic
Performance Evaluation of Bonding Techniques at Wireless 802.11n Guruh Fajar Shidik; Zul Azri bin Muhamad Noh
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.903

Abstract

Demands for high throughput bandwidth, encourage Point to Point wireless to serve more bandwidth for many kind application such as real-time multimedia services. We conduct research with testbed experimental at Point to Point topology use wireless 802.11n in LAB environment. The aim is to studying the performance that would be achieved by Interface Bonding and Channel Bonding techniques. We proposed experiment process and design to evaluate the performance of those techniques. Several parameters such as delay, jitter, data loss rate and throughput applied on TCP/UDP protocols with different Packet Sizes and Directional Traffic Flows. The results experiment showed that Channel Bonding has significant throughput improvement. However, the Interface Bonding results are far from expectation, we found that the performance is least than single normal link. As our finding we analyze it caused by Media Independent Interface (MII), and Scheduling Algorithm unable to work properly at wireless 802.11n using Point to Point connection.
Host Overloading Detection pada Dynamic VM Consolidation Menggunakan Fuzzy Mamdani Chaerul Umam; Guruh Fajar Shidik
Creative Information Technology Journal Vol 4, No 2 (2017): Februari - April
Publisher : UNIVERSITAS AMIKOM YOGYAKARTA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (386.6 KB) | DOI: 10.24076/citec.2017v4i2.101

Abstract

Perkembangan Cloud Computing telah mengakibatkan pembangunan data center skala besar di seluruh dunia yang berisi ribuan node. Data Center Cloud mengkonsumsi energi listrik yang besar yang tentunya mengakibatkan biaya operasi yang tinggi. Konsumsi energi di Data Center akan terus tumbuh pesat kecuali dengan mengembangkan dan menerapkan manajemen resource yang hemat energi. Dynamic VM consolidation bisa menjadi strategi efektif untuk mengatasi masalah pemborosan energi pada data center cloud. Strategi ini dapat diuraikan ke dalam empat tugas pengambilan keputusan, yaitu Host overloading detection (memutuskan kapan host harus dianggap sebagai kelebihan beban), Host underloading detection (memutuskan kapan host harus dianggap underloaded / kekurangan beban), VM selection (memutuskan VMs mana yang harus pindah dari host yang kelebihan beban), dan VM placement (memutuskan tentang host mana yang harus dipilih untuk menerima migrasi VM). Penelitian ini mengusulkan metode fuzzy logic dalam proses host overloading detection. Dataset untuk menguji metode menggunakan data workload dari PlanetLab. Hasil dari pengujian metode yang diusulkan menunjukkan hasil yang menjanjikan dengan peningkatan efisiensi energi 2,24%.
ENHANCEMENT OF 3D SURFACE RECONSTRUCTION OF UNDERWATER CORAL REEF BASE ON SIFT IMAGE MATCHING USING CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION AND OUTLIER REMOVAL Pulung Nurtantio Andono; Ricardus Anggi Pramunendar; Catur Supriyanto; Guruh Fajar Shidik; I Ketut Eddy Purnama; Mochamad Hariadi
Jurnal Ilmiah Kursor Vol 7 No 1 (2013)
Publisher : Universitas Trunojoyo Madura

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Abstract

ENHANCEMENT OF 3D SURFACE RECONSTRUCTION OF UNDERWATER CORAL REEF BASE ON SIFT IMAGE MATCHING USING CONTRAST LIMITED ADAPTIVE HISTOGRAM EQUALIZATION AND OUTLIER REMOVAL aPulung Nurtantio Andono, bRicardus Anggi Pramunendar, cCatur Supriyanto, dGuruh Fajar Shidik,e I Ketut Eddy Purnama, fMochamad Hariadi a,b,c,dFaculty of Computer Science, Dian Nuswantoro University Jalan Imam Bonjol, No. 207, Semarang 50131, Indonesia e,fFaculty of Industrial Technology, Dept. of Electrical Engineering, ITS, Surabaya, Indonesia Email: a pulung@research.dinus.ac.id Abstrak Penelitian ini menggambarkan peningkatan kualitas rekonstruksi 3D permukaan terumbu karang bawah laut menggunakan sistem kamera stereo. Algoritma Contrast Limited Adaptive Histogram image Equalization (CLAHE) diusulkan untuk meningkatkan kualitas citra bawah laut tersebut, karena menurunnya kualitas citra bawah laut dapat disebabkan oleh penyerapan dan hamburan sinar matahari. Dalam mengembangkan rekonstruksi 3D permukaan bawah laut, pasangan citra stereo diekstrak secara manual dari rekaman video yang diperoleh, yang kemudian dilakukan proses pencocokan citra stereo menggunakan algoritma SIFT. Kelebihan algoritma SIFT tersebut adalah tahan terhadap perubahan skala, transformasi, dan rotasi dari sepasang citra tersebut. Banyaknya matching point antar 2 citra stereo dijadikan ukuran untuk mengetahui kinerja CLAHE terhadap algoritma SIFT. Hasil penelitian menunjukan bahwa penggunaan CLAHE dan outlier removal mampu meningkatkan jumlah matching point sebesar 56%. Keberhasilan CLAHE tersebut perlu diujikan ke beberapa algoritma matching point yang lain. Perbandingan beberapa algoritma matching point yang menerapkan CLAHE dapat membuktikan bahwa CLAHE sangat cocok dalam meningkatkan kinerja algoritma matching point dan rekonstruksi permukaan 3D citra bawah laut. Kata kunci: Rekonstruksi 3D, Citra Bawah Laut, SIFT, CLAHE. Abstract This research describes an enhancement of 3D Reconstruction coral reef images using stereo camera system. Contrast Limited Adaptive Histogram image Equalization (CLAHE) algorithm was proposed to enhance the image quality in preprocessing area, since the quality of underwater images degrades by the absorption and scattering of light. To develop a 3D-representation of the seafloor, image-pairs were first extracted from the video footage manually, then corresponding points are automatically extracted from the stereo-pairs by SIFT matching algorithm, which is invariant to scale, translation, and rotation. Number of matching points is used to evaluate the performance of SIFT with and without CLAHE. As a result, the promising techniques provides better 3D reconstruction details of coral reef imagesin total, the combination of CLAHE and outlier removal performs the enhancement for 56%. For further, CLAHE need to be performed to other image matching techniques. The comparison of different image matching techniques with and without CLAHE can prove that CLAHE is appropriate as image enhancement method for image matching and 3D surface reconstruction. Key words: 3D Reconstruction, Underwater Image, SIFT, CLAHE.
FEATURE RECOGNITION BERBASIS CORNER DETECTION DENGAN METODE FAST, SURF, DAN FLANN TREE UNTUK IDENTIFIKASI LOGO PADA AUGMENTED REALITY MOBILE SYSTEM Rastri Prathivi; Vincent Suhartono; Guruh Fajar Shidik
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Logo is a graphical symbol that is the identity of an organization, institution, or company. Logo isgenerally used to introduce to the public the existence of an organization, institution, or company.Through the existence of an agency logo can be seen by the public. Feature recognition is one of theprocesses that exist within an augmented reality system. One of uses augmented reality is able torecognize the identity of the logo through a camera. The first step to make a process of feature recognitionis through the corner detection. Incorporation of several method such as FAST, SURF, and FLANN TREEfor the feature detection process based corner detection feature matching up process, will have the betterability to detect the presence of a logo. Additionally when running the feature extraction process there areseveral issues that arise as scale invariant feature and rotation invariant feature. In this study theresearch object in the form of logo to the priority to make the process of feature recognition. FAST, SURF,and FLANN TREE method will detection logo with scale invariant feature and rotation invariant featureconditions. Obtained from this study will demonstration the accuracy from FAST, SURF, and FLANNTREE methods to solve the scale invariant and rotation invariant feature problems
Optimasi Keseimbangan Lintasan Perakitan Two-Sided Menggunakan Algoritma Simulated Annealing Riri Damayanti Apnena; Guruh Fajar Shidik
Jurnal TEDC Vol 13 No 2 (2019): Jurnal TEDC
Publisher : UPPM Politeknik TEDC Bandung

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Abstract

Two-sided line is line series consists of the left side and right are dealing simultaneously. Elements of work to be done the left or right of the transport system (conveyors) and there are also elements of work that can be done on both sides and presented to the assembly of products has a high complexity (eg: product automobiles). Algorithm simulated annealing including the algorithm metaheuristic, ie, an algorithm that allows reception of the solution space larger because considering all the solutions that emerge though not a better solution and take advantage of structural changes occurring in the neighborhood to address issues if the search solution trapped in local minimum. Simulated annealing consists of phase generation solutions initials using the algorithm J-Wagon and stage repair solutions initials in search of local search (exchange and insert).
Optimizing Parameters for Earthquake Prediction Using Bi-LSTM and Grey Wolf Optimization on Seismic Data Shidik, Guruh Fajar; Pramunendar, Ricardus Anggi; Purwanto, Purwanto; Hasibuan, Zainal Arifin; Dolphina, Erlin; Kusumawati, Yupie; Sriwinarsih, Nurul Anisa
Journal of Robotics and Control (JRC) Vol 5, No 4 (2024)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v5i4.22199

Abstract

Earthquakes pose a significant threat to societies worldwide, underscoring the urgent need for advanced prediction technologies. This study introduces an optimization technique aimed at reducing the error rate in earthquake prediction by selecting the most suitable parameters for a Bi-LSTM (Bidirectional Long Short-Term Memory) model. Despite Bi-LSTM's promising outcomes, variations in parameters can impact performance, necessitating careful parameter selection. This research employs Grey Wolf Optimization (GWO) to optimize parameters and evaluates its effectiveness against other group optimization approaches to identify the most efficient parameters for earthquake prediction. Additionally, a multiple input multiple output (MIMO) architecture is implemented to enhance prediction accuracy. The evaluation results demonstrate that GWO outperforms other optimization techniques, achieving a reduced loss score of 0.364. The ANOVA method yields a p-value approaching 0, indicating statistical significance. This study contributes to the development of early warning systems for earthquake disasters by emphasizing the importance of parameter optimization in earthquake prediction and showcasing the effectiveness of Bi-LSTM and GWO methodologies.
Comparing Haar Cascade and YOLOFACE for Region of Interest Classification in Drowsiness Detection Andrean, Muhammad Niko; Shidik, Guruh Fajar; Naufal, Muhammad; Zami, Farrikh Al; Winarno, Sri; Azies, Harun Al; Putra, Permana Langgeng Wicaksono Ellwid
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7167

Abstract

Driver drowsiness poses a serious threat to road safety, potentially leading to fatal accidents. Current research often relies on facial features, specific eye components, and the mouth for drowsiness classification. This causes a potential bias in the classification results. Therefore, this study shifts its focus to both eyes to mitigate potential biases in drowsiness classification.This research aims to compare the accuracy of drowsiness detection in drivers using two different image segmentation methods, namely Haar Cascade and YOLO-face, followed by classification using a decision tree algorithm. The dataset consists of 22,348 images of drowsy driver faces and 19,445 images of non-drowsy driver faces. The segmentation results with YOLO-face prove capable of producing a higher-quality Region of Interest (ROI) and training data in the form of eye images compared to segmentation results using the Haar Cascade method. After undergoing grid search and 10-fold cross-validation processes, the decision tree model achieved the highest accuracy using the entropy parameter, reaching 98.54% for YOLO-face segmentation results and 98.03% for Haar Cascade segmentation results. Despite the slightly higher accuracy of the model utilizing YOLO-face data, the YOLO-face method requires significantly more data processing time compared to the Haar Cascade method. The overall research results indicate that implementing the ROI concept in input images can enhance the focus and accuracy of the system in recognizing signs of drowsiness in drivers.
Co-Authors Abdussalam Abdussalam, Abdussalam Affandy Affandy Aisyatul Karima Andrean, Muhammad Niko Andreas Wilson Setiawan Anggraini, Fitria Anhsori, Khusman Astuti, Yani Parti Azzahra, Tarissa Aura Budi Harjo Cahaya Jatmoko Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto Chaerul Umam Chaerul Umam Christy Atika Sari Dewi Pergiwati Dliyauddin, Muhammad Doheir, Mohamed Dwi Eko Waluyo Dwi Puji Prabowo, Dwi Puji Dzaky, Azmi Abiyyu Edi Noersasongko Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Elkaf Rahmawan Pramudya Erlin Dolphina Erna Zuni Astuti Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Firmansyah, Rusmal Harun Al Azies Hayu Wikan Kinasih Heru Lestiawan I Ketut Eddy Purnama Ika Pantiawati Islam, Hussain Md Mehedul Junta Zeniarja Kusuma, Edi Jaya Kusumawati, Yupie L. Budi Handoko Lenci Aryani Megantara, Rama Aria Mochamad Hariadi Muhammad Huda, Alam Muhammad Naufal, Muhammad Ningrum, Amanda Prawita Nurmandhani, Ririn Paramita, Cinantya Pergiwati, Dewi Praskatama, Vincentius Pujiono Pujiono Pulung Nurtantio Andono Purwanto Purwanto Putra, Permana Langgeng Wicaksono Ellwid Rafsanjani, Muhammad Ivan Rahadian, Arief Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Rastri Prathivi Ratmana, Danny Oka Ricardus Anggi Pramunendar Riri Damayanti Apnena Rohman, Muhammad Syaifur Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Sarker, Md. Kamruzzaman Savicevic, Anamarija Jurcev Shier Nee Saw Sinaga, Daurat Sindhu Rakasiwi Soeleman, M. Arief Sri Winarno Swanny Trikajanti Widyaatmadja Vincent Suhartono Wahyu Adi Nugroho Wellia Shinta Sari Winarsih, Nurul Anisa Sri Yaacob, Noorayisahbe Mohd Yani Parti Astuti Zainal Arifin Hasibuan Zami, Farrikh Al Zul Azri bin Muhamad Noh