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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 Perfecting a Video Game with Game Metrics Bulletin of Electrical Engineering and Informatics Jurnal technoscientia 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
Utaminingrum, Fitri
Fakultas Ilmu Komputer, Universitas Brawijaya
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

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Helmet Monitoring System using Hough Circle and HOG based on KNN Rachmad Jibril Al Kautsar; Fitri Utaminingrum; Agung Setia Budi
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 12 No 1 (2021): Vol. 12, No. 01 April 2021
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2021.v12.i01.p02

Abstract

Indonesian citizens who use motorized vehicles are increasing every year. Every motorcyclist in Indonesia must wear a helmet when riding a motorcycle. Even though there are rules that require motorbike riders to wear helmets, there are still many motorists who disobey the rules. To overcome this, police officers have carried out various operations (such as traffic operation, warning, etc.). This is not effective because of the number of police officers available, and the probability of police officers make a mistake when detecting violations that might be caused due to fatigue. This study asks the system to detect motorcyclists who do not wear helmets through a surveillance camera. Referring to this reason, the Circular Hough Transform (CHT), Histogram of Oriented Gradient (HOG), and K-Nearest Neighbor (KNN) are used. Testing was done by using images taken from surveillance cameras divided into 200 training data and 40 testing data obtained an accuracy rate of 82.5%.
Three combination value of extraction features on GLCM for detecting pothole and asphalt road Yoke Kusuma Arbawa; Fitri Utaminingrum; Eko Setiawan
Jurnal Teknologi dan Sistem Komputer Volume 9, Issue 1, Year 2021 (January 2021)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2020.13828

Abstract

The rate of vehicle accidents in various regions is still high accidents caused by many factors, such as driver negligence, vehicle damage, and road damage. However, transportation technology developed very rapidly, for example, a smart car. The smart car is land transportation that does not use humans as drivers but uses machines automatically. However, vehicle accidents are still possible because automatic machines do not have the intelligence like humans to see all the vehicle's obstacles. Obstacles can take many forms, one of them is road potholes. We propose a method for detecting road potholes using the Gray-Level Cooccurrence Matrix with three features and using the Support Vector Machine as a classification method. We analyze the combination of GLCM Contrast, Correlation, and Dissimilarity features. The results showed that the combination of Contrast and Dissimilarity features had the best accuracy of 92.033 %, with a computing time of 0.0704 seconds per frame.
Early Detection of COVID-19 Patient’s Survavibility Based On The Image Of Lung X-Ray Image Using Deep Neural Networks Hilmy Bahy Hakim; Fitri Utaminingrum; Agung Setia Budi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vo. 6, No. 3, August 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i3.1265

Abstract

SARS-CoV-2 causes an infection called COVID-19, which is caused by a new coronavirus. One of the symptomps that dangerous to the patients is developing pneumonia in their lungs. To detect pneumonia symptoms, one of the newest methods is using CNN (Convolution Neural Networks). The problem is when able to detect pneumonia, the patient's survivability, which knowing this will be helpful to decide the priority for each patient, is still in question. The CNN used in this research to classify the patient’s future condition, but met some major problems that the dataset is very few and unbalance. The image augmentation was used to multiply the dataset, and class weight was applied to prevent miscalculation on minority class. 6 CNN architectures used to find the best model. The result VGG19 architecture has the best overall accuracy, in training, it has 80% accuracy, 89% accuracy invalidation, and 82% f1 score accuracy on classifying the testing dataset means the best model if looking for accuracy on prediction, but this cost a prediction time that longest compared to other CNN architectures. MobileNet is the fastest, but it cost much worse on prediction accuracy, only 55%. The ResNet50 model has balanced prediction accuracy/time, it got 77% f1 accuracy, and also 8.49 seconds of prediction time, 9 seconds less than VGG19.
Retinal blood vessel segmentation using multiple line operator-based methods Randy Cahya Wihandika; Putra Pandu Adikara; Sigit Adinugroho; Yuita Arum Sari; Fitri Utaminingrum
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3026

Abstract

The morphological alterations of the retinal blood vessels are important indicators that can be utilized to diagnose and track the progression of a number of disorders. Diabetic retinopathy (DR) is a condition that destroys the retina and is the major cause of visual loss caused by high blood glucose levels. One of the retinal objects impacted by DR is the blood vessel. By regularly monitoring changes in the retinal blood vessels, severe DR or even vision loss can be avoided. The condition of the blood vessel can be examined by segmenting the blood vessel area from a digital fundus image. Segmenting retinal blood vessels manually, on the other hand, is time-consuming and tedious, and especially when dealing with a high number of photographs. As a result, a system for segmenting retinal blood vessels automatically is crucial. Furthermore, methods for automatically segmenting retinal blood vessels are useful for person authentication systems based on the retina. Blood vessel segmentation can be accomplished in a number of ways. Based on the prior line operator method, an improved version of the line operator method is proposed in this paper. The proposed method demonstrates an improvement in accuracy over the previous method, with an accuracy of 94.61%.
Perbandingan Pretrained Model Transformer pada Deteksi Ulasan Palsu Aisyah Awalina; Fitra Abdurrachman Bachtiar; Fitri Utaminingrum
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9, No 3: Juni 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2022935696

Abstract

Kemudahan untuk memperoleh informasi saat ini, telah sedikit membantu hidup kita. Seperti mencari ulasan untuk menimbang tempat atau barang yang akan dipilih. Beberapa orang memanfaatkan hal tersebut dengan membuat ulasan palsu untuk kepentingan mereka sendiri. Sehingga deteksi ulasan palsu sangat dibutuhkan. Model Transformer saat ini banyak diterapkan pada pemrosesan bahasa alami karena kinerja yang diperoleh nya sangat baik. Ada dua pendekatan yang dapat dilakukan dalam model Transformer yaitu pre-training dan fine-tuning. Penelitian sebelumnya telah banyak menggunakan fine-tuning dari model Transformer dikarenakan adanya kemudahan dalam pelatihan, waktu yang lebih sedikit, biaya dan kebutuhan lingkungan yang lebih rendah dibanding proses pre-training. Akan tetapi penelitian sebelumnya masih sedikit yang membandingkan model deep learning dengan fine-tuning yang khusus diterapkan pada deteksi ulasan palsu. Penelitian ini melakukan perbandingan model Transformer menggunakan pendekatan fine-tuning dengan metode deep learning yaitu CNN dengan berbagai pretrained word embedding untuk mengatasi deteksi ulasan palsu pada dataset Ott. Model RoBERTa mengungguli model Transformer dan deep learning dimana nilai akurasi 90,8%; precision 90%; recall 91,8% dan f1-score 90,8%. Namun dari segi waktu komputasi model pelatihan, DistilBERT memperoleh waktu komputasi terkecil yaitu dengan nilai 200,5 detik. Meskipun begitu, hasil yang diperoleh model Transformer maupun deep learning memiliki kinerja yang baik untuk deteksi ulasan palsu pada dataset Ott.AbstractThe ease of obtaining information today has helped our lives, like looking for reviews to weigh the place or item to choose. Some people take advantage of this by creating spam reviews for their benefit. So the detection of spam reviews is needed. Transformer models are currently widely applied to natural language processing because they have outstanding performance. Two approaches in the Transformer model is pre-training and fine-tuning. Previous studies have used a lot of fine-tuning due to the ease of training, less time, costs, and lower environmental requirements than the pre-training process. However, a few previous studies compare deep learning models with fine-tuning applied explicitly for detecting spam reviews. This study compares the Transformer model using a fine-tuning approach with a deep learning method, namely CNN, which uses various pre-trained word embedding to overcome the detection of false reviews in the Ott dataset. The result is RoBERTa model outperforms between Transformer and deep learning models, where the accuracy is 90.8%, precision is 90%, recall is 91.8%, and f1-score is 90.8%. Afterward, DistilBERT models obtained the shortest computation time with 200.5 seconds. However, the results obtained by both Transformer and deep learning models perform well to detect spam reviews in the Ott dataset.
Building Segmentation of Satellite Image based on Area and Perimeter using Region Growing Ervin Yohannes; Fitri Utaminingrum
Indonesian Journal of Electrical Engineering and Computer Science Vol 3, No 3: September 2016
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v3.i3.pp579-585

Abstract

A building can be known by look shape, color, and texture. Building can be detected by using many method. Region growing is one simple segmentation method because only use seed point. Before segmentation, the image must be preprocessing include sharpening, binerization by otsu method. Sharpening for clarify image and otsu method changed image valued 0 and 1. Next step is post-preprocessing include segmentation using region growing and opening closing operation. And the last process is detection building where building of detection will be signed. In this research, we present region growing for building segmentation by using both area and perimeter as a important variable in the region growing. Value of area more than 10 and perimeter is more than 50 are produced most of building.
Handwriting Arabic Character Recognition Using Features Combination Fitriyatul Qomariyah; Fitri Utaminingrum; Muchlas Muchlas
IJID (International Journal on Informatics for Development) Vol. 10 No. 2 (2021): IJID December
Publisher : Faculty of Science and Technology, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2021.2360

Abstract

The recognition of Arabic handwriting is a challenging problem to solve. The similarity among the fonts appears as a problem in the recognition processing. Various styles, shapes, and sizes which are personal and different across individuals make the Arabic handwriting recognition process even harder. In this paper, the data used are Arabic handwritten images with 101 sample characters, each of which is written by 15 different handwritten characters (total sample 101x15) with the same size (81x81 pixels). A well-chosen feature is crucial for making good recognition results. In this study, the researcher proposed a method of new features extraction to recognize Arabic handwriting. The features extraction was done by grabbing the value of similar features among various types of font writing, to be used as a new feature of the font. Then, City Block was used to compare the obtained feature to other features of the sample for classification. The Average accuracy value obtained in this study was up to 82%.
Feature extraction comparison for facial expression recognition using adaptive extreme learning machine Muhammad Wafi; Fitra A. Bachtiar; Fitri Utaminingrum
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i1.pp1113-1122

Abstract

Facial expression recognition is an important part in the field of affective computing. Automatic analysis of human facial expression is a challenging problem with many applications. Most of the existing automated systems for facial expression analysis attempt to recognize a few prototypes emotional expressions such as anger, contempt, disgust, fear, happiness, neutral, sadness, and surprise. This paper aims to compare feature extraction methods that are used to detect human facial expression. The study compares the gray level co-occurrence matrix, local binary pattern, and facial landmark (FL) with two types of facial expression datasets, namely Japanese female facial expression (JFFE), and extended Cohn-Kanade (CK+). In addition, we also propose an enhancement of extreme learning machine (ELM) method that can adaptively select best number of hidden neurons adaptive ELM (aELM) to reach its maximum performance. The result from this paper is our proposed method can slightly improve the performance of basic ELM method using some feature extractions mentioned before. Our proposed method can obtain maximum mean accuracy score of 88.07% on CK+ dataset, and 83.12% on JFFE dataset with FL feature extraction.
Implementasi Sistem Kontrol dan Monitoring pH pada Tanaman Kentang Aeroponik secara Wireless Andrika Wahyu Wicaksono; Edita Rosana Widasari; Fitri Utaminingrum
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 5 (2017): Mei 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1815.675 KB)

Abstract

The needs of potato each year has increased, but not offset by increased production and land area for commodity crops of potatoes. To boost production in an increasingly limited land, aeroponics techniques into one solution for farmers who have no land availability. Aeroponics potato production techniques have yields more good than conventional techniques and with the land. PH is one of the elements that greatly affect the growth of aeroponic plant. The ideal pH range for an aeroponics system ranges between 5.5-6.5. Then the system control and monitoring is required in an aeroponics techniques. In this research for controlling and monitoring the State of a pH using wireless transmission. There are six nodes that is two nodes, one node sensor Coordinator, and three nodes of the actuators. From the test results obtained by the sensor data reading of pH value of 1% error within an error reading of 0.08 degree pH. Sensor data transmission using wireless data on delivery without hitch has the accuracy of data delivery of 99.98% with one node of the sensors and 96.13% with two sensor nodes. On delivery with the hitch has the level of accuracy of the data delivery of 99.93% with one sensor nodes and of 92.99% with two sensor nodes
Sistem Monitoring Cairan Infus Terpusat Menggunakan Pengolahan Citra Digital Ringga Aulia Primahayu; Fitri Utaminingrum; Dahnial Syauqy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 8 (2017): Agustus 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (740.318 KB)

Abstract

Imbalances between patient and medical personnel, especially nurses on duty 24 hours monitoring the condition of inpatients result in negligence. For example in terms of monitoring the condition of intravenous fluids. Based on these case examples, a system that is able to reduce the level of negligence and help the performance of medical personnel to improve hospital services. So to overcome this, a system designed to monitor intravenous fluids centrally using digital image processing. Some digital image processing methods used are thresholding to separate object image with the background, morphology to improve threshold image results by using dilation and erosion operation, moment invariant to describe characteristic shape and infusion fluid condition seen from a number of area and position. By using Raspberry Pi as a processing unit and sending information the infusion fluid condition is controlled centrally on the local network using TCP / IP socket as the communication medium to the server. The results of this study indicate that the system can detect infusion fluid conditions using several methods of processing digital images and send detection results to the server.
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 Fitriyah, Hurriyatul 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 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