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All Journal Jurnal Teknik ITS Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics JAREE (Journal on Advanced Research in Electrical Engineering) Sewagati
Muhammad Attamimi
Institut Teknologi Sepuluh Nopember
Humanities Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Social Sciences

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Particle Filter with Integrated Multiple Features for Object Detection and Tracking Muhammad Attamimi; Takayuki Nagai; Djoko Purwanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.9466

Abstract

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.
Inclined Image Recognition for Aerial Mapping using Deep Learning and Tree based Models Muhammad Attamimi; Ronny Mardiyanto; Astria Nur Irfansyah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 6: December 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i6.10157

Abstract

One of the important capabilities of an unmanned aerial vehicle (UAV) is aerial mapping. Aerial mapping is an image registration problem, i.e., the problem of transforming different sets of images into one coordinate system. In image registration, the quality of the output is strongly influenced by the quality of input (i.e., images captured by the UAV). Therefore, selecting the quality of input images becomes important and one of the challenging task in aerial mapping because the ground truth in the mapping process is not given before the UAV flies. Typically, UAV takes images in sequence irrespective of its flight orientation and roll angle. These may result in the acquisition of bad quality images, possibly compromising the quality of mapping results, and increasing the computational cost of a registration process. To address these issues, we need a recognition system that is able to recognize images that are not suitable for the registration process. In this paper, we define these unsuitable images as “inclined images,” i.e., images captured by UAV that are not perpendicular to the ground. Although we can calculate the inclination angle using a gyroscope attached to the UAV, our interest here is to recognize these inclined images without the use of additional sensors in order to mimic how humans perform this task visually. To realize that, we utilize a deep learning method with the combination of tree-based models to build an inclined image recognition system. We have validated the proposed system with the images captured by the UAV. We collected 192 images and labelled them with two different levels of classes (i.e., coarse- and fine-classification). We compared this with several models and the results showed that our proposed system yielded an improvement of accuracy rate up to 3%.
Deep learning based facial expressions recognition system for assisting visually impaired persons Hendra Kusuma; Muhammad Attamimi; Hasby Fahrudin
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1306.358 KB) | DOI: 10.11591/eei.v9i3.2030

Abstract

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.
The study of attention estimation for child-robot interaction scenarios Muhammad Attamimi; Takashi Omori
Bulletin of Electrical Engineering and Informatics Vol 9, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (799.887 KB) | DOI: 10.11591/eei.v9i3.2035

Abstract

One of the biggest challenges in human-agent interaction (HAI) is the development of an agent such as a robot that can understand its partner (a human) and interact naturally. To realize this, a system (agent) should be able to observe a human well and estimate his/her mental state. Towards this goal, in this paper, we present a method of estimating a child's attention, one of the more important human mental states, in a free-play scenario of child-robot interaction (CRI). To realize attention estimation in such CRI scenario, first, we developed a system that could sense a child's verbal and non-verbal multimodal signals such as gaze, facial expression, proximity, and so on. Then, the observed information was used to train a model that is based on a Support Vector Machine (SVM) to estimate a human's attention level. We investigated the accuracy of the proposed method by comparing with a human judge's estimation, and obtained some promising results which we discuss here.
Sistem Otomatis Pendeteksi Wajah Bermasker Menggunakan Deep Learning Mufid Naufal Baay; Astria Nur Irfansyah; Muhammad Attamimi
Jurnal Teknik ITS Vol 10, No 1 (2021)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v10i1.59790

Abstract

COVID-19 merupakan virus yang telah dinyatakan sebagai pandemi oleh WHO, dan di indonesia sendiri menetapkan COVID-19 sebagai bencana nasional melalui Keputusan Presiden Nomor 12 Tahun 2020. Sumber utama transmisi dari virus ini berasal dari percikan pernapasan atau droplet yang salah satu pencegahan penyebarannya adalah dengan penggunaan masker. Saat ini, pemerintah sedang memberlakukan new normal. Walaupun beraktivitas di lingkungan luar, protokol kesehatan wajib diikuti dan seluruh masyarakat harus disiplin dalam menjalaninya. Pada studi ini dirancang sebuah sistem otomatis pendeteksi wajah bermasker menggunakan deep learning dalam menjalankan fungsinya. Sistem yang dirancang menggabungkan model deep learning, detektor wajah, dan program tracking dan counting menjadi sebuah sebuah sistem otomatis yang dibantu oleh Graphic User Interface (GUI) serta sebuah perangkat alarm dan platform Internet of Things dalam pemakaiannya. Berdasarkan hasil pengujian yang dilakukan mengikuti batasan masalah yang telah dirumuskan, model memiliki tingkat akurasi klasifikasi pada dataset test sebesar 99%. Implementasi pada Raspberry Pi 4 menunjukkan sistem berbasis model deep learning yang telah dibuat sukses melakukan deteksi, tracking dan counting yang datanya dikirimkan kepada alarm yang dirancang dan sebuah platform IoT, Ubidots. Performa deteksi maksimal dicapai saat objek deteksi bergerak 0,7 m/s, pencahayaan ≥ 100 lux, dan penggunaan modul TensorFlow Lite pada sistem dengan akurasi sebesar 85,7%. Hasil perbandingan dengan metode deteksi lain menunjukkan karakterisasi model deep learning memiliki akurasi deteksi sebesar 82%, lebih tinggi dari metode Haar Classifier dengan akurasi 53%
Kontrol Himpunan Panel Surya dengan Penyesuaian Diri Otomatis Menggunakan Aktuator dengan Dua Derajat Kebebasan Trulien Jeremiah Elmer; Astria Nur Irfansyah; Muhammad Attamimi
Jurnal Teknik ITS Vol 10, No 2 (2021)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v10i2.67445

Abstract

Untuk mendapatkan energi listrik dari sinar matahari, kita menggunakan panel surya sebagai alat untuk mengubah energi dari sinar matahari menjadi arus listrik yang dapat mencatu alat-alat elektronika yang kita gunakan sehari-hari. Agar kita dapat membuat suatu sistem yang efisien, kita dapat menggunakan berbagai metode untuk dapat meningkatkan energi yang dapat diserap oleh seluruh sistem. Salah satu metode yang dapat digunakan adalah menggunakan pelacak arah sinar matahari agar sistem dapat mengarahkan panel surya ke arah sinar matahari supaya panel surya dapat terus terpapar dalam kurun waktu yang lebih lama. Ada beberapa metode umum untuk melacak sinar matahari, dua diantaranya adalah dengan mengalkulasikan arah matahari berdasarkan posisi geografis dan waktu setempat dan melacak sinar matahari berdasarkan arah pancaran sinar matahari. Untuk menggerakkan sinar mataharinya sendiri, kita memerlukan aktuator. Tergantung sistemnya, jumlah aktuator yang digunakan akan menentukan energi yang dikeluarkan oleh sistem yang juga menentukan seberapa efisien kinerja dari sistem tersebut. Asumsi awal yang kita bisa dapat adalah semakin banyak aktuator yang digunakan maka semakin banyak energi yang dikeluarkan. Akan tetapi, penelitian ini akan menunjukkan bahwa asumsi tersebut hanya berlaku dalam situasi dimana beban kerja dari keseluruhan sistem memang dapat ditanggung oleh jumlah aktuator yang sedikit, bukan dalam keadaan dimana beban kerja dari aktuator ditumpangkan pada satu atau dua aktuator dalam sistem. Faktor ini lebih dipengaruhi oleh rancangan alat dan jenis aktuator yang digunakan ketimbang jumlah aktuator itu sendiri.
A Visual Sensor for Domestic Service Robots Muhammad Attamimi; Takayuki Nagai
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 2, No 1 (2018): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v2.i1.37

Abstract

In this study, we present a visual sensor for domestic service robots, which can capture both color information and three-dimensional information in real time, by calibrating a time of flight camera and two CCD cameras. The problem of occlusions is solved by the proposed occlusion detection algorithm. Since the proposed sensor uses two CCD cameras, missing color information of occluded pixels is compensated by one another. We conduct several evaluations to validate the proposed sensor, including investigation on object recognition task under occluded scenes using the visual sensor. The results revealed the effectiveness of proposed visual sensor. Keywords: Time of flight camera, visual sensor, camera calibration, occlusion detection, object recognition.
Implementation of Ichiro Teen-Size Humanoid Robots For Supporting Autism Therapy Muhammad Attamimi; Muhtadin Muhtadin
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 3, No 1 (2019): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v3.i1.75

Abstract

The humanoid robot is a robot which has humanlikeshapes and/or functions. For instance, a humanoid robot hasa neck that connect the head to the body, two legs to supportthe body, and has two arms on the right- and left-side of itsbody. According to the RoboCup competition, the humanoid robotcan be classified into several types based on their sizes, i.e.,kid-size, teen-size, and adult-size. In this study, we developeda teen-size humanoid robot with the aim of approaching thesize of children’s bodies with autism to facilitate the interactionsbetween the robot and the children. In general, the autism personis difficult to communicate with a normal person because there isa virtual wall that limits the world of the autism with the normalperson. As long as the wall is standing upright, communicationwill be difficult, so that inconvenience occurred on both sides.Especially in children, the process learning will be hampered ifcommunication is blocked. In many cases, the autism childrenmore actively interact and/or communicate with objects such asbooks, toys, and so forth. This motivated us to use a humanoidrobot as a mediator of interactions and/or communication withthe autism to support their therapy. Of course the choice ofhumanoid robots must also be considered both financially andfunctionally. At present there are many commercial humanoidrobots such as: NAO, Darwin-OP, and so forth. However, theprice offered is relatively expensive and also inflexible capabilitiesbecause existing hardware and software can no longer be freelydeveloped. Flexibility in hardware and software is very importantfor the implementation of a system that can be used in supportingthe therapy for autism. These facts motivated us to develop theIchiro teen-size robot. In this study, we developed a therapy forautism in the form of movement by humanoid robots such asa gymnastic movement. The movement is expected to be ableto be followed by the autism and has a positive impact ontherapy. One of the advantages of this study is being able toadd robot movement flexibly, so that the movements suggestedby psychiatrists should be able to be implemented and help tosupport the autism.Keywords: humanoid robots, RoboCup, robot's movements, therapy for children autisms.
Object Extraction Using Probabilistic Maps of Color, Depth, and Near-Infrared Information Muhammad Attamimi; Kelvin Liusiani; Astria Nur Irfansyah; Hendra Kusuma; Djoko Purwanto
JAREE (Journal on Advanced Research in Electrical Engineering) Vol 4, No 1 (2020): April
Publisher : Department of Electrical Engineering ITS and FORTEI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25796216.v4.i1.106

Abstract

Object extraction is one of the important and chal-lenging tasks in the computer vision and/or robotics ? elds.This task is to extract the object from the scene using anypossible cues. The scenario discussed in this study was the objectextraction which considering the Space of Interest (SOI), i.e.,the three dimensional area where the object probably existed.To complete such task, the object extraction method based onthe probabilistic maps of multiple cues was proposed. Thanksto the Kinect V2 sensor, multiple cues such as color, depth, andnear-infrared information can be acquired simultaneously. TheSOI was modeled by a simple probabilistic model by consideringthe geometry of the possible objects and the reachability of thesystem acquired from depth information. To model the color andnear-infrared information, a Gaussian mixture models (GMM)was used. All of the models were combined to generate theprobabilistic maps that were used to extract the object fromthe scene. To validate the proposed object extraction, severalexperiments were conducted to investigate the best combinationof the cues used in this study.Keywords: color information, depth information, near-infrared information, object extraction, probabilistic maps.
Sistem Segmentasi Jalan dan Objek untuk Kendaraan Otonom Menggunakan Kamera RGB-D Aldy Helnawan; Muhammad Attamimi; Astria Nur Irfansyah
Jurnal Teknik ITS Vol 12, No 1 (2023)
Publisher : Direktorat Riset dan Pengabdian Masyarakat (DRPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373539.v12i1.110848

Abstract

Kendaraan roda empat yang memiliki kemampuan untuk melakukan perjalanan antar titik tanpa adanya operator manusia yang dimana menggunakan kombinasi antar sensor, kamera, radar, dan kecerdasan buatan (AI). Penggunaan kamera RGBDNIR (Red Green Blue Depth Near Infrared) dengan alat yaitu kamera Intel RealSense D435i yang dapat digunakan baik didalam ataupun diluar ruangan dimana sensor modul depth dan NIR dapat digunakan ketika keadaan kurang pencahayaan atau lingkungan redup. Dilakukannya penelitian ini dikarenakan untuk mencari solusi dari tingginya tingkat kecelakaan di jalan serta mencari solusi atas kelemahannya kamera RGB dalam penangkapan citra untuk yang dipadukan dengan penggunaan machine learning untuk pengambilan keputusan dalam menentukan kelas objek yang terdeteksi dan diproses untuk menghasilkan solusi dalam melakukan segmentasi di lingkungan terbuka (luar ruangan). Untuk perangkat lunak pemprograman yang akan digunakan yaitu Python serta pustaka yang akan digunakan antara lain PyTorch, OpenCV, dan TensorFlow dengan alat komputasi berupa laptop yang memiliki GPU Nvidia RTX 3060 atau sejenisnya. Hasil dari penelitian ini berupa gambar segmentasi dan pengenalan kelas objek yang terdeteksi dengan tingkat keakuratan dengan beberapa model mulai dari 39.60% hingga 63.71% yang dapat digunakan untuk penentuan kelas yang terbaca. Dengan tingkat literasi beragam sampai nilai terkecil 2E-10 dan memiliki waktu pemprosesan untuk setiap citra dari 0.22 detik sampai 0.01 detik.
Co-Authors Ahmad Fathur Muhtadin Aldy Helnawan Alvin Daffa Kumara Zulfiansyah Astria Nur Irfansyah Devy Kuswidiastuti Djoko Purwanto Hasby Fahrudin Hendra Kusuma Kelvin Liusiani Kusuma, Hendra Siswanto Mufid Naufal Baay Muhammad Rivai Riky Tri Yunardi Riza Agung Ronny Mardiyanto, Ronny Rudy Dikairono Ruth Johana Hutagalung Syahri Muharom Takashi Omori Takayuki Nagai Takayuki Nagai Totok Mujiono, Totok Trulien Jeremiah Elmer