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All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal INKOM TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal technoscientia Jurnal Mahasiswa TEUB Jurnal Teknologi Informasi dan Ilmu Komputer Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJID (International Journal on Informatics for Development) Indonesian Journal of Electrical Engineering and Computer Science JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Teknik Informatika (JUTIF) Mechanical Engineering for Society and Industry
Utaminingrum, Fitri
Fakultas Ilmu Komputer, Universitas Brawijaya
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation

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Deteksi Titik Api Terpusat Menggunakan Kamera Dengan Notifikasi Berbasis Sms Gateway Pada Raspberry Pi Syahrul Yoga Pradana; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 2 No 12 (2018): Desember 2018
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Fire accident is an event that produce flame, in which fire accident could make a significant loss and even casualty. Fire accident mostly caused by human error. This research conducted to create a system for flame detection utilizing camera (2 camera) attached in every room inside a home in order to facilitate user to detect flame as soon as possible when the fire started in each of the room, and also made an advantage from Gateway SMS as a notifier alert. This system utilized a Mini PC (Raspberry Pi 3 Model B) acted as a data processor and control system. And also, this system completed by using Logitech C525 Camera acted as an input to take flame image. As for the Buzzer, it rings the alarm dan Modul SIM900A acted as an output to notify by SMS when there is flame. This system used image processing method processed inside Raspberry Pi 3 Model B doing some controls over the output income which is by doing Morphological Image Filtering through OpenCV. Result obtained for flame detection accuracy was 90%, as for flame detection inter-camera in every room was 96.66%, and the computation mean time tests were 27.5ms, 22.2ms, and 36.25ms
Implementasi Background Subtraction Untuk Klasifikasi Keripik Kentang Berbasis Raspberry Pi Menggunakan Metode Naive Bayes Yongki Pratama; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Potatoes are a kind of vegetables that can be processed into various foods, one of which is the chips. Many big companies in Indonesia produce potato chips. One of them is Istana Factory located in Batu Tourism City. The potatoes processed have various sizes from the largest size (Super), medium size (AB), and then the smallest size (A). The process of sorting at the factory has been done by human manually, then it will produce less relative output. Therefore, it is needed a research about a tool that can sort the potato chips automatically. In this study, the system made is in the form of conveyor, which a webcam is installed as a censor to take pictures from potato chips, then those are processed in Raspberry Pi using image processing of Background Subtraction method. Potato chips will be classified based on size read by the system by using the w and h value parameters or the width and height of the bounding box of potato chips that are converted to actual size by millimeters. The value is used as a reference to be classified with the Naive Bayes method. Naive Bayes is used for the classification method because it is a method that has high performance and has excellent accuracy for classification. From the results of test conducted Background Subtraction can read the image of potatoes well. The reading of potato chip size from the system gets a small error of 3.73%. Then the accuracy obtained with Naive Bayes method in chips classification with 90 training data and 30 test data is worth 93.33 having an average processing time speed of 1.7 ms from 30 times of the test. Then it is performed a test of hardware servo that has been running based on the system.
Pengenalan Wajah dengan Pose Unik menggunakan Metode Learning Vector Quantization Achmad Dinda Basofi Sudirman; Yuita Arum Sari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

The face is one of the characteristics of human natural physiology that can be used for biometric identification for facial recognition. Face recognition is an alternative to systems such as presence and authentication. Nowadays there are so many companies or researchers to create a system that can recognize people's faces, but there is still a face recognition system that can be tricked by showing people who have been recognized by the system in the system's camera area, even though people who are actually recognized by the system are not in the area that. This research will utilize the LVQ method for classification or facial recognition because it is well proven in face recognition conducted by previous research. Feature extraction is used in the form of skin image taking with HSV color space because HSV color space is better at detecting skin images according to existing research. The unique face image or pose used consists of 3 different eye poses to improve the safety of face recognition. In 10 different test scenarios, the results of this study have an average accuracy of 81.3%. However, the system still cannot distinguish each pose from the existing data.
Sistem Deteksi Posisi Objek Acak Berbasis Image Processing Pada Platform MyRIO Alrynto Alrynto; Dahnial Syauqy; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 1 (2019): Januari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Object recognition in this case position of random object is one of the main concern for development of computer science. Object recognition is very useful in another major implementation like maaping, industrial manufacturing, medical, security and many more. For implementation to another major, output of the system need to caliberate for that major. System output on this research are distance and slope value that have been caliberated into international value of length. This research try to develop object recognition using microcontroler by National Instrument MyRIO 1900 as image processor and webcam camera as optical sensor. The camera take object image from above of the object. System using image processing with Geometric Match Pattern method that match object image with template image geometrically and then will take data position and slope of the object. Output from image processing will show in the computer. In the testing will use one type of object in this case using reactangle and placed randomly. The testing will measure the function of the system for read object position, slope and manualization accuracy. Position test have percentage success 99.63% and slope test have percentage success 96.8%. Accuracy test using pixel tolerance and the highest accuracy 100% on 5 pixel tolerance.
Deteksi Gerakan Kepala Berdasarkan Analisis Bounding Box Pada Citra Digital Berbasis Raspberry Pi Muzammilatul Jamiilah; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

In general, navigation is done by using the hands and feet. However, as we know in Indonesia there are some people who have limitations or do not have hands and feet. So that users who have disabilities in their hands and feet will have difficulty for navigating or controlling the tools. Therefore, this study proposes that head movement as a control can replace the function of hands and feet by utilizing bounding box analysis. This study uses a Logitech C310 camera. The process used in this research is image processing. The output generated in this system is LED Control. Test accuracy results on good skin color detection to detect the face skin color is a distance of 50 cm, 75 cm, 100 cm, and 125 cm at morning and afternoon and also a distance of 50 cm, 75 cm at night. The accuracy of the results of testing the detection of head movement distance and the best time for doing it is at a distance of 50 cm in the morning, afternoon and night with an overall percentage of 90.62%. The average computation time of each movement for the right side is 59.87 ms, the left side is 57.64 ms, upright is 55.72ms, and the last look down is 44.62ms.The accuracy of system integration with hardware or LED is 100%.
Klasifikasi Jenis Buah Apel Lokal Berdasarkan Penciri Warna, Aspectratio dan GLCM Menggunakan Belt Konveyor Berbasis Raspberry Pi Lita Nur Fitriani; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Batu City has a variety kind of produce. One of the abundant product of plantations is apples. There are four types of apples, such as Anna Apple, Manalagi Apple, Wanglin Apple, and Rome Beauty Apple. From the four types of apples, when crop is done, and then sorting will be done based on its type, this process still uses human power. Certainly, this process is often inaccurate because the process of selection done can be different for each person. Based on these problems, a sorting system is made by utilizing a classification that can separate the four types of apples based on shape, color, and texture. In this system, it uses a Webcam as a censor to capture images of apples, and then it is processed on Rapberry Pi 3. The process of sorting uses three servos as an actuator to push the apples into its classification. The image that has been captured by the webcam will be processed on Raspberry Pi, and then the image will be done with image processing method to get the Hue, Aspectratio and GLCM Contrast values. If the value has been obtained, Raspberry Pi 3 and Arduino Uno communicate by using I2C serial communication, so that the servo will move based on the result of classification. From the study that has been done, it is obtained the result of the accuracy of aspectratio value as much as 80%. For testing, the accuracy between software and hardware is as much as 80%. While the average time of computation is as much as 159972 ms or 15 seconds.
Klasifikasi Minyak Goreng Berdasarkan Frekuensi Penggorengan Menggunakan Metode K-Nearest Neighbor Berbasis Raspberry Pi Linda Silvya Putri; Fitri Utaminingrum; Tibyani Tibyani
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Cooking oil is often used by the people as stapel for frying food ingredients. There are several types of oil, one of which is vegetable oil. Vegetable oil contains essential fatty acid which has function to prevent constriction of blood vessel that will effect accumulation of cholesterol. The cooking oil used repetitively can cause various diseases. The cooking oil used repetitively will make the double bonds of oxidized oil, and form peroxide groups and cyclic monomers, and will contain trans fatty acid. From these problems, it is necessary to have a system that can classify frequency of the use of cooking oil. In this study, the parameters studied in cooking oil are from color and turbidity. To determine classification of the frying frequency in cooking oil, for color detection of R (Red), G (Green), B (Blue) is obtained from the results of raspberry pi camera readings, and for turbidity is obtained from LDR (Light Emitting Diode) readings by Raspberry Pi 3 by using the KNN (K-Nearest Neighbor) method. From the results of study, it is known that the percentage of accuracy from R (Red), G (Green), B (Blue) readings on a raspberry pi camera with TCS3200 censor is R = 98.102%, G = 98.072%, B = 96.732%. In study of system using the KNN (K-Nearest Neighbor) method with 72 training data and 30 test data, is obtained an accuracy K=1, K=3, K=5 73.33% with an average time computing system of 3.9 ms.
Implementasi Connected Component Labeling untuk Deteksi Objek Penghalang Bagi Penyandang Tunanetra Berbasis Raspberry Pi Ida Yusnilawati; Fitri Utaminingrum; Mochammad Hannats Hanafi Ichsan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Blind is a condition where the both senses of sight do not work to receive information like the alert person, that's why it needs auxiliary tool like stick to carry out daily activities. However, the stick still has a deficiency too that can only be used to touch objects or obstacles with a limited range. One of technologies that enables blind people in carrying out daily activities is to use computer vision for processing of digital image that can detect a barrier object when a blind person walks in the room. This system uses a webcam camera as a censor attached in front of the user's chest at a height of 110cm and a camera tilt of 41áµ’, so that it can take the image in front of the user up to 125cm. The detection process of this barrier object is done in several steps, such as resizing the image, cropping, then thresholding. This thresholding process utilizes values from the RGB image of floor. To get a blob in the image uses connected component labeling 4 connectify used to label pixels. Pixels that have been labeled will be analyzed to be able to detect barrier object. From the study that has been done by the system, it can detect barrier object with accuracy of 91,66%. The result of study for accuracy of system integration with hardware is 98.33%, and the average time of system computing is 166.15 ms.
Deteksi dan Pengenalan Wajah sebagai Pendukung Keamanan Menggunakan Algoritme Haar-Classifier dan Eigenface Berbasis Raspberry Pi Hernanda Agung Saputra; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

One of the things that is inseparable from the progress of technology is security. If we only rely on a security system using human power, it is also not so effective because people also have a sense of tired. Therefore a security support system was created such as barcode, rfid card, PIN, password and etc. However, the use of media that has some security flaws namely can be lost, stolen or damaged, and abused by people who are not responsible. One of the alternatives that can be performed i.e. utilize face as data security. On the research of this system are made using Raspberries Pi 3 were integrated with the Logitech webcam C525 as input, as well as the mikrokontroller Arduino Uno as ultrasonic and light sensor processing. For LCD, buzzer, and module SIM800L is used as the output of the system to provide notification in the form of a alarm,visual text, and SMS. This system uses Haar-Classifier to detect face objects in the image captured by the webcam. Next, Eigenface method is used to get weight of face image. After weight of face image obtained, search the smallest difference in weight of face image of new faces with the image of the face on the database where the results determine how the output from the system. From the results of testing the accuracy of face detection, best accuracy is obtained at a distance of 40 cm with 100% accuracy. Overall accuracy of testing the accuracy of face recognition at a distance of 40 cm is 75%. From system integration testing software with hardware obtained percentage error of 0%. The average time of computation in recognizing a face is 0.11536 seconds.
Deteksi Kendaraan Roda Empat untuk Mendukung Keamanan Berkendara Menggunakan Histogram Of Oriented Gradients dan Support Vector Machine Berbasis Raspberry Pi Intan Fatmawati; Fitri Utaminingrum; Wijaya Kurniawan
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 3 No 2 (2019): Februari 2019
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Abstract

Driving safety is the most important thing in driving on the highway to avoid accidents. Accidents occur due to several factors including lack of concentration while driving, drowsiness, and so forth. One effort to avoid or reduce the risk of accidents when driving is to maintain the distance of the vehicle with the vehicle in front of it. In this study the theme of image processing is used to achieve the goal of maintaining distance between vehicle by utilizing the Histogram of Oriented Gradients (HOG) method as a way of extracting vehicle features which in this case are cars, then classified by the Vector Support Machine (SVM) method to distinguish between car classes and not cars, the two methods are implemented uses a Raspberry Pi camera mounted on the dashboard to detect the vehicle in front of it. When the distance of the vehicle with the vehicle in front is less than or equal to 15 meters (≤15m), the buzzer will sound as a sign that the vehicle is too close. The accuracy of the system in detecting cars using Support Vector Machine (SVM) that based on the Histogram of Oriented Gradients (HOG) feature by testing at a distance of 10 m, 15 m, 20 m, dan 30 m is 81.3% and the testing accuracy of Hardware and Software integration is 87.5%.
Co-Authors Abadi, Dendy Satria Abiyyu Herwanto Achmad Dinda Basofi Sudirman Achmad Jafar Al Kadafi Adam Ibrahim, Muhammad Adharul Muttaqin Adinugroho, Sigit Aditia Reza Nugraha Afdy Clinton Afrizal Rivaldi, Afrizal Agung Setia Budi Agung Setia Budi Agung Setia Budi, Agung Setia Agus Wahyu Widodo Ahmad Wali Satria Bahari Johan Ahmad Wildan Farras Mumtaz Ainandafiq Muhammad Alqadri Akbar Dicky Purwanto Akbar Wira Bramantya Akbar, Muhammad Danar Al Amin, Nisrina Fairuz Hafizhah Al Huda, Fais Alfan Rafi'uddin Ardhani Alfianto Palebangan Alhamdi, Achmad Fahri Aliffandi Purnama Putra Alrynto Alrynto Alvin Evaldo Darmawan Amalia Septi Mulyani Amaliah, Ichlasuning Diah 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 Awalina, Aisyah Bagas Nur Rahman Bagus Septian Aditya Wijayanto Barlian Henryranu Prasetio Beryl Labique Ahmadie Blessius Sheldo Putra Laksono Budi Atmoko Burhan, M.Shochibul Cahyo, Muhammad Pandu Dwi Candra, Alvin Choirul Huda Constantius Leonardo Pratama Dahnial Syauqy Danudoro, Kevin Daris Muhammad Yafi Desy Marinda Oktavia Sitinjak Dewi Amalia Dharmatirta, Brian Aditya Dimas Rizqi Firmansyah Dony Satrio Wibowo Duwi Purnama Sidik Dzakwan Daffa Ramdhana Eko Sakti Pramukantoro, Eko Sakti Eko Setiawan Eko Setiawan Enny Trisnawati, Enny Ervin Yohannes Ester Nadya Fiorentina Lumban Gaol Faris Chandra Febrianto Farrassy, Muhtady Fatwa Ramdani, Fatwa Fernando, Leo Luis Figo Ramadhan Hendri Fikri, Aqil Dzakwanul Fitra Abdurrachman Bachtiar Fitrahadi Surya Dharma Fitria Indriani Fitriyah, Hurriyatul Fitriyani, Rahma Nur Gabe Siringoringo Gagana Ghifary Ilham Gembong Edhi Setyawan Guruh Adi Purnomo Haikal, M. Fikri Hassadiqin, Hasbi Hendry Y. Nanlohy Herman Tolle Hernanda Agung Saputra Hilman Syihan Ghifari Hilmy Bahy Hakim Hisdianton, Oktavian Huda Ahmad Hidayatullah Hurmuzi, Abdan Idza Hurriyatul Fitriyah Ichsan Ali Rachimi Ida Yusnilawati Ikhsan Rahmad Ilham Imam Cholissodin Imam Faris Intan Fatmawati Irnayanti Dwi Kusuma Irsal, Riyandi Banovbi Putera Issa Arwani Jawahir, Asma Kamilah Nur Joan Chandra Kustijono Juniman Arief Kabisat, Aldiansyah Satrio Kelvin Himawan Eka Maulana Kezia Amelia Putri Kirana Sekar Ayu Kohichi Ogata, Kohichi Krisna Pinasthika Lailil Muflikhah Laksono Trisnantoro Laksono, Blessius Sheldo Putra Larasati, Anindya Zulva Leina Alimi Zain Lilo Nofrizal Akbar Linda Silvya Putri Lita Nur Fitriani LUTHFATUN NISA M. Ali Fauzi M. Fiqhi Hidayatulah M.Shochibul Burhan Marianingsih, susi Marsha Nur Shafira Masyita Lionirahmada Maulana Yusuf 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 Mufita, Aulia Riza Muhadzdzib, Naufal Muhamad Fauzan Alfiandi Muhammad Amin Nurdin Muhammad Arga Farrel Arkaan Muhammad Fadhel Haidar Muhammad Hafid Khoirul Muhammad Ibrahim Kumail Muhammad Nazrenda Ramadhan Muhammad Rafi Zaman Muhammad Raihan Wardana Budiarto Muhammad Rizky Rais 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 Paulus Ojak Parasian Permana, Frihandhika Pratama, Aimar Abimayu Pratama, Wildan Bagus Priyanpadma, Sulthon Purboningrum, Fadhila Putera, Muhammad Reza Dahri Putra Pandu Adikara Putra, Firnanda Al Islama Achyunda Putra, Reza Qonita Luthfiyani Qurrotul A'yun Rachmad Jibril Al Kautsar Rahma Tiara Puteri Rahmatul Bijak Nur Kholis Rahmawati, Athirah Naura Rakhmadina Noviyanti, Rakhmadina Ramadhani, Roihaan Randy Cahya Wihandika Randy Cahya Wihandika Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renaldi Primaswara Praetya Renita Leluxy Sofiana Rhaka Gemilang Sentosa Ringga Aulia Primahayu Riyandi Banovbi Putera Irsal Rizal Maulana Rizal Maulana, Rizal Rizdania, Rizdania Rizka Husnun Zakiyyah Rizky Haris Risaldi Rizky Teguh Nursetyawan Rizky Yuztiawan, Fachrie Robbani, Ihwanudien Hasan Rochmawanti, Ovy Samuel Andika Sasongko, Listyawan Dwi Shaleh, Achmad Rizqi Ilham Shih, Timothy K. Sigit Adinugroho Simangunsong, Bryan Nicholas Josephin Hotlando Siswanti Slamet Arifmawan Sri Mayena Surga, Itsar Irsyada Syahrul Yoga Pradana Syaifuddin, Tio Tiara Sri Mulati Tibyani Tibyani Tibyani Tobias Sion Julian Tsani, Farid Nafis Versa Christian Wijaya Vikorian, Eldad Virza Audy Ervanda Wahyu Adi Prijono Wayan Firdaus Mahmudy Widasari, Edita Rosana Wijaya Kurniawan Wijaya, Waskitha William Hutamaputra Willy Andika Putra Wisik Dewa Maulana Yazid Basthomi Yoke Kusuma Arbawa Yongki Pratama Yuita Arum Sari Yuita Arum Sari Yuita Arum Sari Zamaliq Zamaliq Zhuliand Rachman Zulfina Kharisma Frimananda