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Journal : International Journal of Natural Science and Engineering

PEMETAAN AKTIFITAS KONSUMEN TOKO MENGGUNAKAN METODE BACKGROUND SUBTRACTION Listartha, I Made Edy; Indrawan, Gede; Aryanto, Kadek Yota Ernanda
International Journal of Natural Science and Engineering Vol 1, No 2 (2017)
Publisher : International Journal of Natural Science and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.695 KB)

Abstract

This study aims to heat mapping the consumer’s movement using background subtraction techniques. The mapping is built using the coordinate information obtained from the consumer location that detected from the video where the separation of the consumer object and the background is done by background subtraction technique. Tests were performed on eleven video of consumer data activity that have different activity characteristics that were created using Microsoft PowerPoint application. Simulated activities include walking straight, staying, walking back to the path that had been passed, pacing, disturbance from another object, the influence of color, the consumer walks meet and coincide with other consumers. From the test of video discovery is obtained accuracy of 96.07% for the detection process of consumer movement, where the lack of detection process occurs due to the absence of techniques used to perform the introduction of characteristics of consumer objects. The mapping process is very much in line with the number of coordinates generated in the motion detection process, but the inaccurate detection of movement in the entrance and exit areas makes the coordinates high. By filtering with Region of Interes (ROI) in the survey area, creating disturbances in the area of doors and areas with objects that produce movements other than consumers can be eliminated.
PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER Sanjaya, Kadek Oki; Indrawan, Gede; Aryanto, Kadek Yota Ernanda
International Journal of Natural Science and Engineering Vol 1, No 3 (2017)
Publisher : International Journal of Natural Science and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.964 KB)

Abstract

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object
PEMETAAN AKTIFITAS KONSUMEN TOKO MENGGUNAKAN METODE BACKGROUND SUBTRACTION Listartha, I Made Edy; Indrawan, Gede; Aryanto, Kadek Yota Ernanda
International Journal of Natural Science and Engineering Vol. 1 No. 2 (2017): July
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (516.695 KB) | DOI: 10.23887/ijnse.v1i2.12468

Abstract

This study aims to heat mapping the consumer’s movement using background subtraction techniques. The mapping is built using the coordinate information obtained from the consumer location that detected from the video where the separation of the consumer object and the background is done by background subtraction technique. Tests were performed on eleven video of consumer data activity that have different activity characteristics that were created using Microsoft PowerPoint application. Simulated activities include walking straight, staying, walking back to the path that had been passed, pacing, disturbance from another object, the influence of color, the consumer walks meet and coincide with other consumers. From the test of video discovery is obtained accuracy of 96.07% for the detection process of consumer movement, where the lack of detection process occurs due to the absence of techniques used to perform the introduction of characteristics of consumer objects. The mapping process is very much in line with the number of coordinates generated in the motion detection process, but the inaccurate detection of movement in the entrance and exit areas makes the coordinates high. By filtering with Region of Interes (ROI) in the survey area, creating disturbances in the area of doors and areas with objects that produce movements other than consumers can be eliminated.
PENDETEKSIAN OBJEK ROKOK PADA VIDEO BERBASIS PENGOLAHAN CITRA DENGAN MENGGUNAKAN METODE HAAR CASCADE CLASSIFIER Sanjaya, Kadek Oki; Indrawan, Gede; Aryanto, Kadek Yota Ernanda
International Journal of Natural Science and Engineering Vol. 1 No. 3 (2017): October
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (724.964 KB) | DOI: 10.23887/ijnse.v1i3.12938

Abstract

Object detection is a topic widely studied by the scientists as a special study in image processing. Although applications of this topic have been implemented, but basically this technology is not yet mature, futher research is needed to developed to obtain the desired result. The aim of the present study is to detect cigarette objects on video by using the Viola Jones method (Haar Cascade Classifier). This method known to have speed and high accuracy because of combining some concept (Haar features, integral image, Adaboost, and Cascade Classifier) to be a main method to detect objects. In this research, detection testing of cigarettes object is in samples of video with the resolution 160x120 pixels, 320x240 pixels, 640x480 pixels under condition of on 1 cigarette object and condition 2 cigarettes object. The result of this research indicated that percentage of average accuracy highest 93.3% at condition 1 cigarette object and 86,7% in the condition 2 cigarette object that was detected on the video with resolution 640x480 pixels, while the percentage of accuracy lowest 90% at condition 1cigarette object, and 81,7% at the condition 2 cigarette objects, detected on the video with the lowest resolution 160x120 pixels. The percentage of average errors at detection cigarettes object was inversely with percentage of accuracy. So that the detection system is able to better recognize the object of the cigarette, then the number of samples in the database needs to be improved and able to represent various types of cigarettes under various conditions and can be added new parameters related to cigarette object
Co-Authors Ade Prayoga, I Made Ade Surya Indrawan Aditya, Eka Adnyana, I Putu Iwan Krisna Agus Adiarta,ST,MT . Agustini, Ni Wayan Eva Ahmad Asroni Ahmad Asroni, Ahmad Ambara, Made Anak Agung Candra Widyaningsih Anandita, Ida Bagus Gede Andika, I Gede Anop Sudiatmika Arditaloka, I Wayan Angga Arimbawa, Gusti Putu Arya Arsa, Putu Suka Artayasa, I Kadek Dwi Artha, I Gede Mony Aryani, Wayan Astawa, I Gede Karya Benhard Sitohang Christina Purnama Yanti Cokorda Oka Birawidya Damiati Dananjaya, Md Wira Putra Daniel Eka Saputra Daniel Kevin Alexander Dea Sillviari, Ni Putu Dede Desnantha Putra Denny Nathaniel Chandra Dewa Gede Hendra Divayana, Dewa Gede Hendra Dewi, Ni Wayan Jeri Kusuma Dewi, Suzy Puspita Dhruvayoti Tiirtheshvara Dika Anggara, I Made Diva Palguna Erna Supriathi, Ni Kadek Gede Rasben Dantes Gede Suweken Gemara Adhiyasa Parahita Nugraha Gusti Ngurah Joniartawan Hakimi, Musawer Hendra Trinium Jaya, I Komang Herdiana, I Kayan Hery Heryanto I Gede Andika I Gede Aris Gunadi I Gede Bara Yuda Gautama I Gede Indra Suwardika I Gusti Agung Istri Pradnya Prameswari I Gusti Ngurah Bagus Putra Asmara I Gusti Putu Agung Arka Putra I Kadek Juni Arta I Kadek Wahyu Sudiatmika I Kadek Wihendradinata I Kayan Herdiana I Ketut Paramarta I Ketut Pramarta I Ketut Suja I Komang Adyanata I Komang Deny Supanji I Made Agus Oka Gunawan I Made Agus Widiana Putra I Made Candiasa I Made Edy Listartha I Made Wahyu Dwi Wismayana I Made Windu Segara Kurniawan I N. Jampel I Nyoman Saputra I Nyoman Suarka I Nyoman Sukajaya I Nyoman Sukaraja I Nyoman Triadi Wiguna I Putu Aris Sanjaya I Putu Okta Priyana I Putu Prima Ananda I Putu Yoga Indrawan I W. Widiana I Wayan Adi Wiratama I Wayan Aditya Wiguna I Wayan Dodi Putra Artawan I Wayan Rosiana Ida Ayu Mirah Cahya Dewi Ida Bagus Gede Anandita Ida Bagus Nyoman Wijana Manuaba Ida Bagus Prayoga Bhiantara Ida Putu Ayu Hemy Eka Yani Joniartawan, Gusti Ngurah Juni Arta, I Kadek Juniastra, Made Gde Kadek Enny Rusmala Dewi Kadek Teguh Wahyu Dewantara Kadek Wibawa Kadek Yota Ernanda Aryanto Ketut Agustini Ketut Nila Arta Komang Gde Hendra Kusuma Putra Kurniawan, I Made Windu Segara Kusuma Wardana, Kadek Lemes, I Nyoman Limbong, Kevin Gary Luh Joni Erawati Dewi M.Cs S.Kom I Made Agus Wirawan . M.T. S.T. I Wayan Sutaya . Made Agus Panji Sujaya Made Ambara Made Hery Santosa Made Santo Gitakarma Made Windu Antara Kesiman Made Yuda Sadewa Mahadewi, Luh Putu Putrini Mahadewi, Luh Putu Putrini Moh. Heri Setiawan Muhammad Alwan Nursuhaida Ni Luh Made Uti Tiasmi Ni Made Ayu Juli Astari Ni Made Rai Arini Permatasari Ni Nyoman Harini Puspita Ni Putu Dea Sillviari Ni Putu Ria Anggreni Ni Wayan Jeri Kusuma Dewi Ni Wayan Wardani Nugraha, I Gede Pradipta Adi Nyoman Jampel Nyoman Santiyadnya Palguna, Diva Pande Made Mahendri Pramadewi Pande Putu Ode Juliantara. KW Parahita Nugraha, Gemara Adhiyasa Praja Setiawan Pramadewi, Pande Made Mahendri Pramarta, I Ketut Pranata, Putu Ade Pratama, Putu Aditya Purnama, M. Rokhman Putu Ade Pranata Putu Eka Parianthana Putu Suka Arsa Raditya Pramita, I Putu Agus Ratna Mei Vidya Richo, Rolando Alex Sandhiyasa, I Made Subrata Sanjaya, Kadek Oki Sanjaya, Kadek Oki Saputra, Daniel Eka Sariyasa . seftian rusditya Setemen , Komang Setiawan, Kadek Reda Setiawan, Praja Sogen, Afrianto T.L Sudestra, I Made Ardi Sudiatmika, Anop Sujaya, Made Agus Panji Sukla Mandika, Ketut Gde Sumarno, I Wayan Supanji, I Komang Deny Suparsa, I Made Surata, I Nyoman Susena, I Gede Ardika Suzy Puspita Dewi Taufik Akbar Taufik Akbar Tjahyanti, L.P.A.S Utami, Ni Luh Putu Sri Wardani, Ni Wayan Wayan Andre Pratama Wayan Eka Ariawan Wibawa, Kadek Wiguna, I Kadek Arta Wijaya, Putu Agung Ananta Wikanta, I Made Indra Adhi Wirawan Nathaniel Chandra Yani Yani