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All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi 'ADALAH Bulletin of Electrical Engineering and Informatics Scan : Jurnal Teknologi Informasi dan Komunikasi Seminar Nasional Informatika (SEMNASIF) Jurnal Inspiration Bulletin of Electrical Engineering and Informatics Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik Melek IT: Information Technology Journal Journal Research of Social Science, Economics, and Management Sewagati

Handayani Tjandrasa

Departemen Teknik Informatika Institut Teknologi Sepuluh Nopember Surabaya

Author-ID : 404187

Aerospace Engineering Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Articles

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IMPROVED DEEP LEARNING ARCHITECTURE WITH BATCH NORMALIZATION FOR EEG SIGNAL PROCESSING Purnomo, Adenuar; Tjandrasa, Handayani
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 19, No. 1, Januari 2021
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v19i1.a1023

Deep learning is commonly used to solve problems such as biomedical problems and many other problems. The most common architecture used to solve those problems is Convolutional Neural Network (CNN) architecture. However, CNN may be prone to overfitting, and the convergence may be slow. One of the methods to overcome the overfitting is batch normalization (BN). BN is commonly used after the convolutional layer. In this study, we proposed a further usage of BN in CNN architecture. BN is not only used after the convolutional layer but also used after the fully connected layer. The proposed architecture is tested to detect types of seizures based on EEG signals. The data used are several sessions of recording signals from many patients. Each recording session produces a recorded EEG signal. EEG signal in each session is first passed through a bandpass filter. Then 26 relevant channels are taken, cut every 2 seconds to be labeled the type of epileptic seizure. The truncated signal is concatenated with the truncated signal from other sessions, divided into two datasets, a large dataset, and a small dataset. Each dataset has four types of seizures. Each dataset is equalized using the undersampling technique. Each dataset is then divided into test and train data to be tested using the proposed architecture. The results show the proposed architecture achieves 46.54% accuracy for the large dataset and 93.33% accuracy for the small dataset. In future studies, the batch normalization parameter will be further investigated to reduce overfitting.

Urban Road Materials Identification using Narrow Near Infrared Vision System Heru Purnomo Ipung; Handayani Tjandrasa
International Journal of Electrical and Computer Engineering (IJECE) Vol 7, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

An urban road materials vision system using narrow band near infrared imaging indexes were proposed. This proposed imaging indexes were enhancement for previous work on autonomous multispectral road sensing method. Each urban road material has different near infrared spectral patterns which is as the base of its spectral identification. The new proposed imaging indexes, which using similar formula of NDVI, was normalized with narrow band near infrared spectrum range of 720nm to 1000nm of wavelength, were used to identify concretes, aggregates/sands/rocks, clay, natural dry fibers and bitumen/asphalt that make up most of urban road materials. This paper proposes imaging indexes evaluation from experiment results to identify those urban road materials. There were seven narrow band optical filter sets with the center spectrum at 710nm, 730nm, 750nm, 800nm, 870nm, 905nm and 970nm. Normalization band used was 720nm using high pass optical filter. The proposed multi-spectral imaging indexes were able to show the potential to classify the selected urban road materials, another approach may need to clearly distinguish between concrete and aggregates. The comparison to the previous imaging indexes (NDVI, NDGR, NDBR) were presented that used for urban road materials identification.

New Lossless Compression Method using Cyclic Reversible Low Contrast Mapping (CRLCM) Hendra Mesra; Handayani Tjandrasa; Chastine Fatichah
International Journal of Electrical and Computer Engineering (IJECE) Vol 6, No 6: December 2016
Publisher : Institute of Advanced Engineering and Science

In general, the compression method is developed to reduce the redundancy of data. This study uses a different approach to embed some bits of datum in image data into other datum using a Reversible Low Contrast Mapping (RLCM) transformation. Besides using the RLCM for embedding, this method also applies the properties of RLCM to compress the datum before it is embedded. In its algorithm, the proposed method engages Queue and Recursive Indexing. The algorithm encodes the data in a cyclic manner. In contrast to RLCM, the proposed method is a coding method as Huffman coding. This research uses publicly available image data to examine the proposed method. For all testing images, the proposed method has higher compression ratio than the Huffman coding.

Text Mining for Pest and Disease Identification on Rice Farming with Interactive Text Messaging Edio da Costa; Handayani Tjandrasa; Supeno Djanali
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

To overcome pests and diseases of rice farming, farmers always rely on information and knowledge from agricultural experts for decision making. The problem is that experts are not always available when the farmers need and the cost is quite high. Pests and diseases elimination is hard to be done individually since the farmers are lack of knowledge about the pest types that attack the rice fields. The objective of this study is to build a knowledge-based system that can identify pests and diseases interactively based on the information that has been told by the farmers using SMS communication services. The system can provide a convenience way to the farmers in delivering pests and disease problem information using a natural language. The text mining method performs tokenizing, filtering and porter stemming that used to extract important information sent by a SMS service. The method of Jaccard Similarity Coefficient (JSC) was used to calculate similarities of each pest and disease based on symptoms that are sent by the farmers through SMS. The corpus database usedin this study consists of 28.526 root words, 1.309 stop wordsand 180 words list. Pest and disease database reference in this study was obtained from the Ministry of Agriculture and Fisher (MAF) Timor-Leste. The result of the experiment shows that the system is able to identify the symptoms based on the keywords identified with the accuracy of 81%. The result of pest and disease identification has the accuracy of 86%.

Hybrid Speckle Noise Reduction Method for Abdominal Circumference Segmentation of Fetal Ultrasound Images Fajar Astuti Hermawati; Handayani Tjandrasa; Nanik Suciati
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

Fetal biometric size such as abdominal circumference (AC) is used to predict fetal weight or gestational age in ultrasound images. The automatic biometric measurement can improve efficiency in the ultrasonography examination workflow. The unclear boundaries of the abdomen image and the speckle noise presence are the challenges for the automated AC measurement techniques. The main problem to improve the accuracy of the automatic AC segmentation is how to remove noise while retaining the boundary features of objects. In this paper, we proposed a hybrid ultrasound image denoising framework which was a combination of spatial-based filtering method and multiresolution based method. In this technique, an ultrasound image was decomposed into subbands using wavelet transform. A thresholding technique and the anisotropic diffusion method were applied to the detail subbands, at the same time the bilateral filtering modified the approximation subband. The proposed denoising approach had the best performance in the edge preservation level and could improve the accuracy of the abdominal circumference segmentation.

Automatic Leukemia Cell Counting using Iterative Distance Transform for Convex Sets Nenden Siti Fatonah; Handayani Tjandrasa; Chastine Fatichah
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 3: June 2018
Publisher : Institute of Advanced Engineering and Science

The calculation of white blood cells on the acute leukemia microscopic images is one of the stages in the diagnosis of Leukemia disease. The main constraint on calculating the number of white blood cells is the precision in the area of overlapping white blood cells. The research on the calculation of the number of white blood cells overlapping generally based on geometry. However, there was still a calculation error due to over segment or under segment. This paper proposed an Iterative Distance Transform for Convex Sets (IDTCS) method to determine the markers and calculate the number of overlapping white blood cells. Determination of marker was performed on every cell both in single and overlapping white blood cell area. In this study, there were tree stages: segmentation of white blood cells, marker detection and white blood cell count, and contour estimation of every white blood cell. The used data testing was microscopic acute leukemia image data of Acute Lymphoblastic Leukemia (ALL) and Acute Myeloblastic Leukemia (AML). Based on the test results, Iterative Distance Transform for Convex Sets IDTCS method performs better than Distance Transform (DT) and Ultimate Erosion for Convex Sets (UECS) method.

Ovarian Cancer Identification using One-Pass Clustering and k-Nearest Neighbors Isye Arieshanti; Yudhi Purwananto; Handayani Tjandrasa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 4: December 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i4.1203

The identification of ovarian cancer using protein expression profile (SELDI-TOF-MS) is important to assists early detection of ovarian cancer. The chance to save patient’s life is greater when ovarian cancer is detected at an early stage. However, the analysis of protein expression profile is challenging because it has very high dimensional features and noisy characteristic. In order to tackle those difficulties, a novel ovarian cancer identification model is proposed in this study. The model comprises of One-Pass Clustering and k-Nearest Neighbors Classifier. With simple and efficient computation, the performance of the model achieves Accuracy about 97%. This result shows that the model is promising for Ovarian Cancer identification.

Optic Nerve Head Segmentation Using Hough Transform and Active Contours Handayani Tjandrasa; Ari Wijayanti; Nanik Suciati
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 10, No 3: September 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v10i3.833

Optic nerve head is part of the retina where ganglion cell axons exit the eye to form the optic nerve. Glaucomatous changes related to loss of the nerve fibers decrease the neuroretinal rim and expand the area and volume of the cup. Therefore optic nerve head evaluation is important for early diagnosis of glaucoma. This study implements the detection of the optic nerve head in retinal fundus images based on the Hough Transform and Active Contour Models. The process starts with the image enhancement using homomorphic filtering for illumination correction, then proceeds with the removal of blood vessels on the image to facilitate the subsequent segmentation process. The result of the Hough Transform fitting circle becomes the initial level set for the active contour model. The experimental results show that the implemented segmentation algorithms are capable of segmenting optic nerve head with the average accuracy of 75.56% using 30 retinal images from the DRIVE database.Optic nerve head segmentation using hough transform and active contours

Robot Motion Control Using the Emotiv EPOC EEG System Sandy Akbar Dewangga; Handayani Tjandrasa; Darlis Herumurti
Bulletin of Electrical Engineering and Informatics Vol 7, No 2: June 2018
Publisher : Institute of Advanced Engineering and Science

Brain-computer interfaces have been explored for years with the intent of using human thoughts to control mechanical system. By capturing the transmission of signals directly from the human brain or electroencephalogram (EEG), human thoughts can be made as motion commands to the robot. This paper presents a prototype for an electroencephalogram (EEG) based brain-actuated robot control system using mental commands. In this study, Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) method were combined to establish the best model. Dataset containing features of EEG signals were obtained from the subject non-invasively using Emotiv EPOC headset. The best model was then used by Brain-Computer Interface (BCI) to classify the EEG signals into robot motion commands to control the robot directly. The result of the classification gave the average accuracy of 69.06%.

Co-Authors Aditya Bagusmulya, Aditya Agus Zainal Arifin Agus Zainal Arifin Ahmad Hayam Brilian, Ahmad Hayam Ahmad Hifdhul Abror, Ahmad Hifdhul Ahmad Hifdu Abror Ahmad Mustofa Ahmad Reza Musthafa, Ahmad Reza Ahmad Syauqi Ahmad Syauqi Akwila Feliciano Akwila Feliciano Anny Yuniarti Ari Wijayanti Azwar Rizal Alfarisi Bilqis Amaliah Chastine Fatichah Claudia Primasiwi Daniel Sugianto Darlis Herumurti Diana Purwitasari Dini A. Navastara Dini Adni Navastara, Dini Adni Edio da Costa Elvira, Ade Irma Evelyn Sierra Fajar Astuti Hermawati Fandy Kuncoro Adianto Fandy Kuncoro Adianto Fiqey Indriati Eka Sari Gama Wisnu Fajarianto Gigih P. Cahyono Hendra Mesra Heru Purnomo Ipung Isye Arieshanti Kevin Christian Hadinata Kevin Christian Hadinata Muhammad Fikri Sunandar Nadya Anisa Syafa Nanik Suciati Ndung'u, Michael Nenden Siti Fatonah Nenden Siti Fatonah Nurseno Bayu Aji, Nurseno Bayu Purnomo, Adenuar Qomariah, Dinial Utami Nurul Rangga Kusuma Dinata Rangga Kusuma Dinata Ricky Eka Putra Safhira Maharani Safhira Maharani Saiful Bahri Musa Saiful Nur Budiman Salim Bin Usman Salim Bin Usman Sandy Akbar Dewangga Sherly Rosa Anggraeni Sherly Rosa Anggraeni Siprianus Septian Manek Stendy B. Sakur, Stendy B. Supeno Djanali Susanti, Martini Dwi Endah Whardana, Adithya Kusuma Wiwik Anggraeni Yudhi Purwananto Yulia Niza Yulia Niza Yunan Helmi Mahendra, Yunan Helmi Yunita Ardilla Zakiya Azizah Cahyaningtyas Zakiya Azizah Cahyaningtyas

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