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Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair Adikara, Putra Pandu; Wihandika, Randy Cahya; Utaminingrum, Fitri; Sari, Yuita Arum; Fauzi, M Ali; Syauqy, Dahnial; Maulana, Rizal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): Januari-Juni (3/7)
Publisher : Prodi Sistem Informasi - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1160.804 KB) | DOI: 10.26594/register.v3i1.587

Abstract

 Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.   This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchair’s pathway based on the line shape of reflected line laser. Obstacle’s distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm.
PENGGUNAAN MULTISTAGE FILTER ADAPTIVE WIENER UNTUK MENINGKATKAN KUALITAS CITRA DIGITAL Utaminingrum, Fitri; Prijono, Wahyu Adi
JURNAL TEKNOLOGI TECHNOSCIENTIA Academia Ista Vol 12 No 01 Agustus 2007
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (235.995 KB) | DOI: 10.34151/technoscientia.v0i0.1983

Abstract

One way to enhance the quality of digital image from gaussian noise disturbance is trough a combined Adaptive Filter and Wiener one. This then called the Adaptive Wi-ener Filter is capable of estimating the noise, first by finding the adaptation parameters mean and variance calculated from the noisy image data.The Adaptive Wiener Filter performs better in a multistage scheme, so that higher resulting signal to noise ratio can be achieved. The associated analysis and evaluation is based on the corresponding MSE (Mean Square Error) and PSNR (Peak to Signal Noise Ratio) for comparing with those obtained the comman Median Filter and Average Filter.With two representative digital images used, which are black and white and color, respec-tively, the results show significant of MSE of 0.0029562 and 0.0010456 for low noise of variance 0.001, 0.0093262 and 0.0022968 for higher noise of variance 0,035 and in-crement of PSNR of 73.4235 and 77.9335 for the same low noise and of 68.4338 and 74.5195 the same higher noise, due to the multistaging scheme.
Newton’s Method for Distance Optimization in Firefly Algorithm in Determining Optimum Nutrition for Laying Hens Burhan, M.Shochibul; Utaminingrum, Fitri
INKOM Journal Vol 11, No 1 (2017)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (711.793 KB) | DOI: 10.14203/j.inkom.509

Abstract

An accurate calculation of feed nutrition and more affordable price is an extremely complex. Firefly algorithm is an algorithm designed for optimization calculation whose output is highly dependent on light intensity (β), which is influenced by distance (r). Therefore, in order to produce maximum output values, an optimization of firefly distance should be done. The most appropriate method is Newton’s Method as it has the capability of solving roots of equations accurately. From the testing of distance optimization in firefly algorithm, a fairly good increase in the fitness value was obtained.Keywords: Newton Method, Firefly Algorithm
PENGGUNAAN MULTISTAGE FILTER ADAPTIVE WIENER UNTUK MENINGKATKAN KUALITAS CITRA DIGITAL Utaminingrum, Fitri; Prijono, Wahyu Adi
JURNAL TEKNOLOGI TECHNOSCIENTIA Academia Ista Vol 12 No 01 Agustus 2007
Publisher : Lembaga Penelitian & Pengabdian Kepada Masyarakat (LPPM), IST AKPRIND Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34151/technoscientia.v0i0.1983

Abstract

One way to enhance the quality of digital image from gaussian noise disturbance is trough a combined Adaptive Filter and Wiener one. This then called the Adaptive Wi-ener Filter is capable of estimating the noise, first by finding the adaptation parameters mean and variance calculated from the noisy image data.The Adaptive Wiener Filter performs better in a multistage scheme, so that higher resulting signal to noise ratio can be achieved. The associated analysis and evaluation is based on the corresponding MSE (Mean Square Error) and PSNR (Peak to Signal Noise Ratio) for comparing with those obtained the comman Median Filter and Average Filter.With two representative digital images used, which are black and white and color, respec-tively, the results show significant of MSE of 0.0029562 and 0.0010456 for low noise of variance 0.001, 0.0093262 and 0.0022968 for higher noise of variance 0,035 and in-crement of PSNR of 73.4235 and 77.9335 for the same low noise and of 68.4338 and 74.5195 the same higher noise, due to the multistaging scheme.
Regresi linier berbasis clustering untuk deteksi dan estimasi halangan pada smart wheelchair Adikara, Putra Pandu; Wihandika, Randy Cahya; Utaminingrum, Fitri; Sari, Yuita Arum; Fauzi, M Ali; Syauqy, Dahnial; Maulana, Rizal
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 3, No 1 (2017): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v3i1.587

Abstract

 Penelitian ini bertujuan untuk mengusulkan sebuah pendekatan dalam mendeteksi halangan dan memperkirakan jarak halangan untuk diterapkan pada kursi roda pintar (smart wheelchair) yang dilengkapi kamera dan line laser. Kamera menangkap sinar line laser yang jatuh di depan kursi roda untuk mengenali adanya halangan pada lintasan berdasarkan bentuk citra line laser tersebut. Estimasi jarak halangan dihitung dari hasil Regresi Linier. Metode Regresi Linier yang digunakan dalam penelitian ini adalah model bertingkat dengan k-Means clustering. Metode Regresi Linier model bertingkat digunakan untuk merepresentasikan korelasi antara jarak line laser pada citra dan jarak halangan secara aktual. Hasil metode Regresi Linier model bertingkat dengan k-Means clustering yang diujicobakan memberikan hasil yang lebih baik dengan RMSE sebesar 3.541 cm dibanding dengan Regresi Liner sederhana dengan RMSE sebesar 5.367 cm.   This research aim to propose a new approach to detect obstacles and to estimate the distance of the obstacle which is in this case applied to smart wheelchair equipped with camera and line laser. The camera capture the image of line laser reflected in front of the wheelchair to detect any existing obstacle on the wheelchair’s pathway based on the line shape of reflected line laser. Obstacle’s distance is estimated using Linier Regression. Linier Regression method used in this research is stepwise model using k-Means clustering. Linear Regression method with stepwise model will be used to represent the correlation between the distance of the line laser in the image and the actual distance of the obstacle in real world. The result of Linear Regression with stepwise model using k-Means clustering gave better result with RMSE of 3.541 cm than simple Linear Regression with RMSE of 5.367 cm.
Fast Obstacle Distance Estimation using Laser Line Imaging Technique for Smart Wheelchair Fitri Utaminingrum; Hurriyatul Fitriyah; Randy Cahya Wihandika; M Ali Fauzi; Dahnial Syauqy; Rizal Maulana
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 4: August 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (497.611 KB) | DOI: 10.11591/ijece.v6i4.pp1602-1609

Abstract

This paper presents an approach of obstacle distance estimation for smart wheelchair. A smart wheelchair was equipped with a camera and a laser line. The camera was used to capture an image from the environment in order to sense the pathway condition. The laser line was used in combination with camera to recognize an obstacle in the pathway based on the shape of laser line image in certain angle. A blob method detection was then applied on the laser line image to separate and recognize the pattern of the detected obstacles. The laser line projector and camera which was mounted in fixed-certain position ensured a fixed relation between blobs-gap and obstacle-to-wheelchair distance. A simple linear regression from 16 obtained data was used to respresent this relation as the estimated obstacle distance. As a result, the average error between the estimation and the actual distance was 1.25 cm from 7 data testing experiments. Therefore, the experiment results show that the proposed method was able to estimate the distance between wheelchair and the obstacle.
Image Processing for Rapidly Eye Detection based on Robust Haar Sliding Window Fitri Utaminingrum; Renaldi Primaswara Praetya; Yuita Arum Sari
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 2: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v7i2.pp823-830

Abstract

Object Detection using Haar Cascade Clasifier widely applied in several devices and applications as a medium of interaction between human and computer such as a tool control that utilizes the detection of eye movements. Obviously speed and precision in the detection process such as eyes, has an effect if implemented on a device. If the eye could not detect accurately, controlling device systems could reach bad detection as well. The proposed method can be used as an approach to detect the eye region of eye based on haar classifier method by means of modifying the direction of sliding window. In which, it was initially placed in the middle position of image on facial area by assuming the location of eyes area in the central region of the image. While the window region of conventional haar cascade scan the whole of image start from the left top corner. From the experiment by using our proposed method, it can speed up the the computation time and improve accuracy significantly reach to 92,4%.
Autonomous Robot System Based on Room Nameplate Recognition Using YOLOv4 Method on Jetson Nano 2GB Muhammad Pandu Dwi Cahyo; Fitri Utaminingrum
JOIV : International Journal on Informatics Visualization Vol 6, No 1 (2022)
Publisher : Politeknik Negeri Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1.785

Abstract

The prediction of COVID-19 cases will continue to experience a surge, inseparable from the presence of a new variant of the coronavirus in the world. One of the best ways to prevent transmission of the virus is to avoid or limit contact with people showing symptoms of COVID-19 or any respiratory infection. The number of medical personnel infected when interacting with patients directly also needs to be an essential concern. Hence, an autonomous robot based on room nameplate recognition systems is a solution. It can be used as an intermediary medium for medical personnel with patients to reduce the intensity of direct contact primarily can be implemented in the hospital. It is expected to reduce the spread of the COVID-19 virus, especially among health workers. Each patient room in the hospital has its room nameplate to be used as a robot reference in navigating. This research aims to make a room nameplate recognition system using the YOLOv4 method on NVIDIA Jetson Nano 2GB that produces an output for 4-wheeled robot navigation control to move. This system is designed to detect rooms within a range of 1-3 meters using 5W and 10W power modes. The testing results based on recognition is obtained an average accuracy value of 95.34%. The system performance test results based on the power mode resulted in the best average computing time of 0.149 seconds. The average value of the accuracy of output integration with the system is 94.73%.
Hybrid Head Tracking for Wheelchair Control Using Haar Cascade Classifier and KCF Tracker Fitri Utaminingrum; Yuita Arum Sari; Putra Pandu Adikara; Dahnial Syauqy; Sigit Adinugroho
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.6595

Abstract

Disability may limit someone to move freely, especially when the severity of the disability is high. In order to help disabled people control their wheelchair, head movement-based control is preferred due to its reliability. This paper proposed a head direction detector framework which can be applied to wheelchair control. First, face and nose were detected from a video frame using Haar cascade classfier. Then, the detected bounding boxes were used to initialize Kernelized Correlation Filters tracker. Direction of a head was determined by relative position of the nose to the face, extracted from tracker’s bounding boxes. Results show that the method effectively detect head direction indicated by 82% accuracy and very low detection or tracking failure.
Road surface classification based on LBP and GLCM features using kNN classifier Arthur Ahmad Fauzi; Fitri Utaminingrum; Fatwa Ramdani
Bulletin of Electrical Engineering and Informatics Vol 9, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (667.28 KB) | DOI: 10.11591/eei.v9i4.2348

Abstract

Autonomous Ground Vehicle (UGV) technology has shown a fast development this past year and proven to be useful. The use of UGV technology is restricted on a particular road condition. Classification of the road is an essential process in UGV, especially to control the autonomous vehicle. For example, the speed could be adjusted by referring to the road type, these process require a fast computational time. This research focuses on finding the most discriminant feature while keeping the number of features into a minimum to obtain fast computational time and accurate classification result. One can experiences difficulties because the condition of the road varies, this research proposes a combination of Gray Level Co-occurrence Matrix (GLCM) a statistical method to extract feature and Local Binary Pattern (LBP) feature to improve the robustness of the features. The kNN classifier is used to do the classification with the accuracy of 98% and 12 picture processed per second.
Co-Authors Abdan Idza Hurmuzi Abiyyu Herwanto Achmad Dinda Basofi Sudirman Achmad Jafar Al Kadafi Achmad Rizqi Ilham Shaleh Adinugroho, Sigit Aditia Reza Nugraha Afdy Clinton Afrizal Rivaldi Agung Setia Budi Agung Setia Budi Agung Setia Budi Agus Wahyu Widodo Ahmad Wali Satria Bahari Johan Ahmad Wildan Farras Mumtaz Ainandafiq Muhammad Alqadri Aisyah Awalina Akbar Dicky Purwanto Akbar Wira Bramantya Alfan Rafi'uddin Ardhani Alfianto Palebangan Aliffandi Purnama Putra Alrynto Alrynto Alvin Evaldo Darmawan Amalia Septi Mulyani Andika Bayhaki Al Rasyid Syah Andika Kalvin Simarmata Andrika Wahyu Wicaksono Anugrah Zeputra Arthur Ahmad Fauzi Asep Ranta Munajat Asfar Triyadi Audrey Athallah Asyam Fauzan Aufa Nizar Faiz Auliya Firdaus Bagas Nur Rahman Bagus Septian Aditya Wijayanto Barlian Henryranu Prasetio Beryl Labique Ahmadie Blessius Sheldo Putra Laksono Budi Atmoko Burhan, M.Shochibul Choirul Huda Constantius Leonardo Pratama Dahnial Syauqy Daris Muhammad Yafi Desy Marinda Oktavia Sitinjak Dewi Amalia Dimas Rizqi Firmansyah Dony Satrio Wibowo Duwi Purnama Sidik Dzakwan Daffa Ramdhana Edita Rosana Widasari Eko Setiawan Eko Setiawan Enny Trisnawati Ervin Yohannes Ervin Yohannes Ester Nadya Fiorentina Lumban Gaol Faris Chandra Febrianto Fatwa Ramdani, Fatwa Figo Ramadhan Hendri Fitra A. Bachtiar Fitra A. Bachtiar Fitra Abdurrachman Bachtiar Fitrahadi Surya Dharma Fitria Indriani Fitriyatul Qomariyah Frihandhika Permana Gabe Siringoringo Gagana Ghifary Ilham Gembong Edhi Setyawan Guruh Adi Purnomo Herman Tolle Herman Tolle Herman Tolle Hernanda Agung Saputra Hilman Syihan Ghifari Hilmy Bahy Hakim Huda Ahmad Hidayatullah Hurriyatul Fitriyah Hurriyatul Fitriyah Ichlasuning Diah Amaliah Ichsan Ali Rachimi Ida Yusnilawati Ihwanudien Hasan Robbani Ikhsan Rahmad Ilham Imam Cholissodin Imam Faris Intan Fatmawati Irnayanti Dwi Kusuma Issa Arwani Joan Chandra Kustijono Juniman Arief Kelvin Himawan Eka Maulana Kezia Amelia Putri Kirana Sekar Ayu Krisna Pinasthika Lailil Muflikhah Laksono Trisnantoro Leina Alimi Zain Lilo Nofrizal Akbar Linda Silvya Putri Lita Nur Fitriani M. Ali Fauzi M. Fiqhi Hidayatulah M.Shochibul Burhan Marsha Nur Shafira Masyita Lionirahmada Meidiana Adinda Prasanty Mela Tri Audina Misran Misran Mochammad Bustanul Ilmi Mochammad Hannats Hanafi Ichsan Mohammad Andy Purwanto Mohammad Isya Alfian Mohammad Sezar Nusti Ilhami Muchlas Muchlas Muhamad Fauzan Alfiandi Muhammad Amin Nurdin Muhammad Arga Farrel Arkaan Muhammad Fadhel Haidar Muhammad Hafid Khoirul Muhammad Ibrahim Kumail Muhammad Nazrenda Ramadhan Muhammad Pandu Dwi Cahyo Muhammad Rafi Zaman Muhammad Raihan Wardana Budiarto Muhammad Rizky Rais Muhammad Sulthon Yazid Basthomi Muhammad Tri Buwana Zulfikar Ardi Muhammad Wafi Muzammilatul Jamiilah Nico Dian Nugraha Niko Aji Nugroho Noza Trisnasari Alqoria Nugraheny Wahyu Try Nyoman Kresna Aditya Wiraatmaja Olivia Rumiris Sitanggang Onky Soerya Nugroho Utomo Ovy Rochmawanti Paulus Ojak Parasian Putra Pandu Adikara Putra, Firnanda Al Islama Achyunda Qonita Luthfiyani Qurrotul A'yun Rachmad Jibril Al Kautsar Rahma Tiara Puteri Rahmatul Bijak Nur Kholis Rakhmadina Noviyanti Randy Cahya Wihandika Randy Cahya Wihandika Renaldi Primaswara Praetya Renita Leluxy Sofiana Rhaka Gemilang Sentosa Ringga Aulia Primahayu Riyandi Banovbi Putera Irsal Rizal Maulana Rizal Maulana, Rizal Rizdania Dermawi Rizka Husnun Zakiyyah Rizky Haris Risaldi Rizky Teguh Nursetyawan Samuel Andika Slamet Arifmawan Sri Mayena Syahrul Yoga Pradana Tiara Sri Mulati Tibyani Tibyani Timothy K. Shih Tobias Sion Julian Versa Christian Wijaya Virza Audy Ervanda Wahyu Adi Prijono Waskitha Wijaya waskitha wijaya Wayan Firdaus Mahmudy Wijaya Kurniawan William Hutamaputra Willy Andika Putra Wisik Dewa Maulana Yoke Kusuma Arbawa Yongki Pratama Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Zamaliq Zamaliq Zhuliand Rachman Zulfina Kharisma Frimananda