Articles
<![CDATA[Implementation of Electronic Nose in Omni-directional Robot ]]>
<![CDATA[Harianto Harianto]]>;
<![CDATA[Muhammad Rivai]]>;
<![CDATA[Djoko Purwanto]]>
<![CDATA[ International Journal of Electrical and Computer Engineering (IJECE) Vol 3, No 3: June 2013 ]]>
Publisher : <![CDATA[Institute of Advanced Engineering and Science ]]>
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<![CDATA[Electronic nose (E-nose) is a device detecting odors which is designed to resemble the ability of the human nose. E-nose can identifying chemical elements that contained in the odors. E-nose is made of arrays of gas sensor, each of it could detect certain chemical element. When detects gases, each sensor will generate a specific pattern for each gas. These patterns could be classified using neural network algorithm. Neural network is a computational method based on mathematical models which has the structure and operation of neural networks which imitate the human brain. Neural network consists of a group of neurons conected to each other with a connection named weight. The weights will determine wether neural networks could compute given inputs to produce a specified output. To generate the appropriate weight, the neural network needs to be trained using a number of gasoline and alcohol samples. The training process to generate appropriate weights is done by using back propagation algorithm on a personal computer. The appropriate weight then transferred to omni-directional robot equipped with e-nose. The result shows that the robot could identify the trained gas.DOI:http://dx.doi.org/10.11591/ijece.v3i3.2531]]>
<![CDATA[Particle Filter with Integrated Multiple Features for Object Detection and Tracking ]]>
<![CDATA[Muhammad Attamimi]]>;
<![CDATA[Takayuki Nagai]]>;
<![CDATA[Djoko Purwanto]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i6.9466
<![CDATA[Considering objects in the environments (or scenes), object detection is the first task needed to be accomplished to recognize those objects. There are two problems needed to be considered in object detection. First, a single feature based object detection is difficult regarding types of the objects and scenes. For example, object detection that is based on color information will fail in the dark place. The second problem is the object’s pose in the scene that is arbitrary in general. This paper aims to tackle such problems for enabling the object detection and tracking of various types of objects in the various scenes. This study proposes a method for object detection and tracking by using a particle filter and multiple features consisting of color, texture, and depth information that are integrated by adaptive weights. To validate the proposed method, the experiments have been conducted. The results revealed that the proposed method outperformed the previous method, which is based only on color information.]]>
<![CDATA[Design of Electronic Nose System Using Gas Chromatography Principle and Surface Acoustic Wave Sensor ]]>
<![CDATA[Anifatul Faricha]]>;
<![CDATA[Suwito Suwito]]>;
<![CDATA[M. Rivai]]>;
<![CDATA[M.A. Nanda]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Rizki Anhar R.P.]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v16i4.7127
<![CDATA[Most gases are odorless, colorless and also hazard to be sensed by the human olfactory system. Hence, an electronic nose system is required for the gas classification process. This study presents the design of electronic nose system using a combination of Gas Chromatography Column and a Surface Acoustic Wave (SAW). The Gas Chromatography Column is a technique based on the compound partition at a certain temperature. Whereas, the SAW sensor works based on the resonant frequency change. In this study, gas samples including methanol, acetonitrile, and benzene are used for system performance measurement. Each gas sample generates a specific acoustic signal data in the form of a frequency change recorded by the SAW sensor. Then, the acoustic signal data is analyzed to obtain the acoustic features, i.e. the peak amplitude, the negative slope, the positive slope, and the length. The Support Vector Machine (SVM) method using the acoustic feature as its input parameters are applied to classify the gas sample. Radial Basis Function is used to build the optimal hyperplane model which devided into two processes i.e., the training process and the external validation process. According to the result performance, the training process has the accuracy of 98.7% and the external validation process has the accuracy of 93.3%. Our electronic nose system has the average sensitivity of 51.43 Hz/mL to sense the gas samples.]]>
<![CDATA[Performance Evaluation of MMA7260QT and ADXL345 on Self Balancing Robot ]]>
<![CDATA[Hany Ferdinando]]>;
<![CDATA[Handry Khoswanto]]>;
<![CDATA[Djoko Purwanto]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v11i1.876
<![CDATA[A self balancing robot (SBR) controller needs to detect platform inclination. For this purpose, an accelerometer is used. From various types of accelerometer, we can divide into digital and analog ones. The problem is how to select the right type for the SBR. This paper evaluates the performance of the ADXL345, 3-axis digital output accelerometer and the MMA7260QT, 3-axis analog output accelerometer. The Arduino is used to read data from the sensor and send it to PC for plotting. Both sensors use the lowest sensitivity. The sensors are evaluated with three criteria, i.e. stationary, dynamical response and collaborating with ITG3200 3-axis gyroscope for Kalman filter fusion. For stationary criterion, the ADXL345 is better than the other sensor for all stationary position. For dynamical response, both sensors suffer from the noise due to acceleration of the platform. The sensors do not only sense the gravity but also the acceleration of the platform when it is moved. But the noise level for the ADXL345 is lower than the other. Using Kalman filter makes both sensors show good performance for a SBR application. If three criteria are combined with hardware aspect, then the authors recommend using the ADXL345. Besides, it has several useful features to handle abrupt acceleration.]]>
<![CDATA[Electronic Nose using Gas Chromatography Column and Quartz Crystal Microbalance ]]>
<![CDATA[Muhammad Rivai]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Hendro Juwono]]>;
<![CDATA[Hari Agus Sujono]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 2: August 2011 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v9i2.703
<![CDATA[The conventional electronic nose usually consists of an array of dissimilar chemical sensors such as quartz crystal microbalance (QCM) combined with pattern recognition algorithm such as Neural network. Because of parallel processing, the system needs a huge number of sensors and circuits which may emerge complexity and inter-channel crosstalk problems. In this research, a new type of odor identification which combines between gas chromatography (GC) and electronic nose methods has been developed. The system consists of a GC column and a 10-MHz quartz crystal microbalance sensor producing a unique pattern for an odor in time domain. This method offers advantages of substantially reduced size, interferences and power consumption in comparison to existing odor identification system. Several odors of organic compounds were introduced to evaluate the selectivity of the system. Principle component analysis method was used to visualize the classification of each odor in two-dimensional space. This system could resolve common organic solvents, including molecules of different classes (aromatic from alcohols) as well as those within a particular class (methanol from ethanol) and also fuels (premium from pertamax). The neural network can be taught to recognize the odors tested in the experiment with identification rate of 85 %. It is therefore the system may take the place of human nose, especially for poisonous odor evaluations. ]]>
<![CDATA[Multiple Moving Obstacles Avoidance of Service Robot using Stereo Vision ]]>
<![CDATA[Widodo Budiharto]]>;
<![CDATA[Ari Santoso]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Achmad Jazidie]]>
<![CDATA[ TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 9, No 3: December 2011 ]]>
Publisher : <![CDATA[Universitas Ahmad Dahlan ]]>
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DOI: 10.12928/telkomnika.v9i3.733
<![CDATA[In this paper, we propose a multiple moving obstacles avoidance using stereo vision for service robots in indoor environments. We assume that this model of service robot is used to deliver a cup to the recognized customer from the starting point to the destination. The contribution of this research is a new method for multiple moving obstacle avoidance with Bayesian approach using stereo camera. We have developed and introduced 3 main modules to recognize faces, to identify multiple moving obstacles and to maneuver of robot. A group of people who is walking will be tracked as a multiple moving obstacle, and the speed, direction, and distance of the moving obstacles is estimated by a stereo camera in order that the robot can maneuver to avoid the collision. To overcome the inaccuracies of vision sensor, Bayesian approach is used for estimate the absense and direction of obstacles. We present the results of the experiment of the service robot called Srikandi III which uses our proposed method and we also evaluate its performance. Experiments shown that our proposed method working well, and Bayesian approach proved increasing the estimation perform for absence and direction of moving obstacle.]]>
<![CDATA[Rancang Bangun Kendali Jarak Jauh Robot Servis Pembersih Debu Berbasis Internet of Things ]]>
<![CDATA[Adrie Sentosa]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Rudy Dikairono]]>
<![CDATA[ Jurnal Teknik ITS Vol 5, No 2 (2016) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v5i2.16281
<![CDATA[Robot servis otonom, khususnya robot pembersih debu otonom, yang melakukan pekerjaan secara mandiri ketika pengguna tidak berada di rumah merupakan impian sebagian besar masyrakat. Berbagai perusahaan dan institusi penelitian telah melakukan usaha yang baik dalam merancang robot servis pembersih debu otonom. Robot servis pembersih debu otonom yang dikembangkan saat ini, khususnya robot pembersih debu Chuwi iLife v5, dikendalikan dengan tombol ataupun remote control berbasis infrared. Hal ini menjadi permasalahan ketika pengguna tidak berada di lokasi robot servis sehingga pengguna tidak dapat memberikan perintah kepada robot servis secara langsung. Maka dari itu, dirancanglah kendali jarak jauh robot servis berbasis Internet of Things yang memungkinkan robot servis untuk dikendilakan pada jarak jauh. Robot servis akan diintegrasikan dengan perangkat smartphone atau komputer berbasis internet untuk menggantikan fungsi remote control sehingga pengguna dapat melakukan perintah dimanapun mereka berada selama memiliki koneksi internet. Hasil pengujian yang dilakukan pada pengujian tugas akhir ini adalah sistem dapat mengendalikan robot servis pembersih debu dengan tingkat kehandalan sebesar 100%. Dengan menggunakan spesifikasi sistem yang digunakan pada tugas akhir ini, robot servis dapat menjalankan seluruh perintah yang diberikan oleh pengguna.]]>
<![CDATA[Rancang Bangun Sistem Pengukuran Posisi Target dengan Kamera Stereo untuk Pengarah Senjata Otomatis ]]>
<![CDATA[Anas Maulidi Utama]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Ronny Mardianto]]>
<![CDATA[ Jurnal Teknik ITS Vol 5, No 2 (2016) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v5i2.16301
<![CDATA[Salah satu hal yang penting dalam mengarahkan senjata secara otomatis ke target adalah informasi posisi dari target terhadap senjata. Terdapat banyak metode untuk mengetahui posisi target. Salah satunya adalah dengan metode pengukuran triangulasi. Metode ini membutuhkan minimal dua citra untuk medapatkan informasi jarak target terhadap kamera. Kemudian, informasi jarak tersebut bisa diolah untuk mengetahui posisi target terhadap senjata. Di dalam sistem ini, stereo visual digunakan untuk mendukung proses pengukuran triangulasi. Stereo visual menggunakan dua kamera untuk menghasilkan dua citra. Dalam sistem ini, salah satu kamera bertindak sebagai pemilih target. Citra yang ditangkap dua kamera tersebut akan diproses oleh processing unit untuk mendapatkan informasi posisi target terhadap senjata. Informasi ini digunakan untuk menggerakkan motor pada platform senjata agar senjata mengarah ke target. Hasil pengujian yang dilakukan pada sistem ini adalah sistem dapat menentukan posisi target yang dipilih oleh operator dan juga dapat mengarahkan senjata ke arah target tersebut. Akurasi tertinggi dalam penentuan posisi target dicapai ketika jarak antar dua kamera sekitar 30 cm.]]>
<![CDATA[Penerjemahan Bahasa Isyarat Menggunakan Kamera pada Telepon Genggam Android ]]>
<![CDATA[Muhammad Yunus Andrian]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Ronny Mardiyanto]]>
<![CDATA[ Jurnal Teknik ITS Vol 5, No 2 (2016) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v5i2.16313
<![CDATA[Penginderaan visual atau machine vision merupakan suatu proses manipulasi data citra. Data tersebut dapat digunakan untuk melakukan intepretasi banyak hal, salah satunya yaitu pengenalan gesture. Pengenalan gesture adalah antarmuka yang dapat mengenali gerak-isyarat seorang manusia dan mentranslasikan gerakan tersebut sebagai instruksi yang dapat dipahami oleh komputer. Pengenalan gesture dapat digunakan untuk penerjemahkan bahasa isyarat pada orang tunawicara. Hal ini karena banyaknya orang yang tidak mengerti bahasa tangan orang tunawicara. Sehingga, orang tunawicara kesulitan dalam berinteraksi di masyarakat. Pada tugas akhir ini pengenalan gesture untuk penerjemahan bahasa isyarat lebih mengarah pada hand recognition, yaitu pendeteksian perubahan gerak tangan, dengan menggunakan android mobile phone sebagai divaisnya. Android mobile phone memiliki kamera untuk menangkap citra orang tuna wicara saat berkomunikasi menggunakan bahasa isyarat berupa gerakan tangan. Selanjutnya, citra diproses oleh processing unit android untuk melakukan proses hand recognition. Setelah proses tersebut selesai, maka layar display akan memunculkan huruf atau kata dari perubahan posisi gerak tangan yang dilakukan orang tunawicara yang berada di depan kamera.]]>
<![CDATA[Pengenalan Bahasa Isyarat Tangan Menggunakan Depth Image ]]>
<![CDATA[Try Yuliandre Pajar]]>;
<![CDATA[Djoko Purwanto]]>;
<![CDATA[Hendra Kusuma]]>
<![CDATA[ Jurnal Teknik ITS Vol 7, No 1 (2018) ]]>
Publisher : <![CDATA[Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS ]]>
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DOI: 10.12962/j23373539.v7i1.28567
<![CDATA[Sistem pengenalan bahasa isyarat menggunakan pengolahan visual digital. Pada tugas akhir ini sistem mengambil citra gambar menggunakan depth sensor. Depth sensor digunakan untuk mendapatkan gambar tangan sehingga tahap pengambilan kontur berbeda dibandingkan dengan kamera RGB. Depth sensor yang diatur jarak pembacaanya dapat menghasilkan gambar kontur tangan. Menggunakan metode convex hull, convexity defects, dan pusat massa gambar dapat menghasilkan nilai-nilai yang dapat dilatih untuk melakukan pengenalan pada tahap uji cobanya. Sistem ini dapat menangkap citra tangan dari jarak 50cm hingga 65cm. Sistem ini dilatih menggunakan artificial neural network dengan dua kondisi percobaan. Percobaan pertama menggunakan delapan output berdasarkan koordinat yang didapat. Percobaan kedua menggunakan tiga input berdasarkan panjang garis dan luas. Hasil yang dicapai sistem ini yaitu dapat mengenali gestur bahasa isyarat tangan berdasarkan hasil pelatihan. Hasil pelatihan ditentukan dari elemen penyusun neural network dan banyaknya iterasi yang dilakukan, pada ragam huruf yang sedikit akurasi hasil pelatihan dapat memenuhi target output sebaliknya jika ragam huruf bertambah banyak]]>
