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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Majalah Forum Teknik UGM Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Pendidikan Sains IPTEK Journal of Proceedings Series Proceedings of KNASTIK TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Seminar Nasional Informatika (SEMNASIF) ELINVO (Electronics, Informatics, and Vocational Education) Semesta Teknika Jurnal Ilmiah KOMPUTASI Journal of Electrical Technology UMY JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Paradigma Emerging Science Journal Communications in Science and Technology JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) IJITEE (International Journal of Information Technology and Electrical Engineering) Prosiding Seminar Nasional Program Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal FORTEI-JEERI Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal INFOTEL
Indah Soesanti
Universitas Gadjah Mada
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mechanical Engineering Medicine & Pharmacology Social Sciences

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Akuisisi Data dan Pengolahan Isyarat Elektrokardiograf Menggunakan Modul USB Dataq DI-148U Henry Sulistyo; Thomas Sri Widodo; Maesadji Tjokronagoro; Indah Soesanti
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 2 No 1: Februari 2013
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1073.153 KB)

Abstract

Heart signal data acquisition for the purpose of research is still an obstacle when using the standard ECG equipment from the manufacturer. The problem is the expensive equipment and the conversion of images into digital data. By using instrumentation amplifiers and electronic components in the market can be realized a simple ECG prototype by using interface Dataq DI-148U USB module for cardiac signals data acquisition.In this study, single channel ECG prototype realized with twelve leads by manually selected. Tests using five subjects that compared the results with standard hospital ECG equipment.From ECG prototype tests each sub section, the results are in accordance with the planning. The use of adhesive floating electrode type showed a smaller noise. IIR digital filter effectively reduces noise with SNR above of 20 dB and baseline wander at frequency cutoff 0.95 Hz. The threshold method is capable of detecting the peak R-R interval well to calculate the heart rate.
A Improving Feature Selection on Heart Disease Dataset With Boruta Approach Muhammad Arzanul Manhar; Indah Soesanti; Noor Akhmad Setiawan
Journal FORTEI-JEERI Vol. 1 No. 1 (2020): FORTEI-JEERI
Publisher : Forum Pendidikan Tinggi Teknik Elektro Indonesia (FORTEI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (274.681 KB) | DOI: 10.46962/forteijeeri.v1i1.6

Abstract

Coronary artery disease (CAD) is one of the deadliest diseases in the entire world, including in Indonesia. CAD occurs due to narrowing or blockage of coronary arteries which is usually caused by atherosclerosis. Various studies have been conducted with the aim to predict the nature and characteristics of this disease. Some researches uses the Z-Alizadeh Sani dataset which consists of 54 attributes with two results of classification, CAD and Normal to classify its data. Feature selection is one way to reduce the number of attributes that exist by leaving the attributes that have a high effect on the dataset. In this study, the Boruta method is used as a feature selection to minimize the attributes and leave the attributes with high relative with the dataset. By reducing the attributes in the dataset through the feature selection process, sets of 17 and 18 attributes are selected as attributes with high relative with the dataset. These attributes then used to calculate the accuracy value of the dataset using the several classification methods and 90,3% accuracy is obtained from this study.
Analysis of Segmentation Parameters Effect towards Parallel Processing Time on Fuzzy C Means Algorithm Cepi Ramdani; Indah Soesanti; Sunu Wibirama
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 1, No 4 (2017): December 2017
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1362.623 KB) | DOI: 10.22146/ijitee.35025

Abstract

Fuzzy C Means algorithm or FCM is one of many clustering algorithms that has better accuracy to solve problems related to segmentation. Its application is almost in every aspects of life and many disciplines of science. However, this algorithm has some shortcomings, one of them is the large amount of processing time consumption. This research conducted mainly to do an analysis about the effect of segmentation parameters towards processing time in sequential and parallel. The other goal is to reduce the processing time of segmentation process using parallel approach. Parallel processing applied on Nvidia GeForce GT540M GPU using CUDA v8.0 framework. The experiment conducted on natural RGB color image sized 256x256 and 512x512. The settings of segmentation parameter values were done as follows, weight in range (2-3), number of iteration (50-150), number of cluster (2-8), and error tolerance or epsilon (0.1 – 1e-06). The results obtained by this research as follows, parallel processing time is faster 4.5 times than sequential time with similarity level of image segmentations generated both of processing types is 100%. The influence of segmentation parameter values towards processing times in sequential and parallel can be concluded as follows, the greater value of weight parameter then the sequential processing time becomes short, however it has no effects on parallel processing time. For iteration and cluster parameters, the greater their values will make processing time consuming in sequential and parallel become large. Meanwhile the epsilon parameter has no effect or has an unpredictable tendency on both of processing time.
A Review of Feature Selection and Classification Approaches for Heart Disease Prediction Fathania Firwan Firdaus; Hanung Adi Nugroho; Indah Soesanti
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 4, No 3 (2020): September 2020 (in progress)
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.59193

Abstract

Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research.
ECG Signal Classification Review Muhammad Rausan Fikri; Indah Soesanti; Hanung Adi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.60295

Abstract

The heart is an important part of the human body, functioning to pump blood through the circulatory system. Heartbeats generate a signal called an ECG signal. ECG signals or electrocardiogram signals are basic raw signals to identify and classify heart function based on heart rate. Its main task is to analyze each signal in the heart, whether normal or abnormal. This paper discusses some of the classification methods which most frequently used to classify ECG signals. These methods include pre-processing, feature extraction, and classification methods such as MLP, K-NN, SVM, CNN, and RNN. There were two stages of ECG classification, the feature extraction stage and the classification stage. Before ECG features were extracted, raw ECG signal data first processed in the pre-processing stage because ECG signals were not necessarily free of noise. Noise will cause a decrease in accuracy during the classification process. After features were extracted, ECG signals were then classified with the classification method. Neural Network methods such as CNN and RNN are best to use since they can give better accuracy. For further research, the machine learning method needs to be improved to get high accuracy and high precision in the ECG signals classification.
Image Analysis for MRI-Based Brain Tumor Classification Using Deep Learning Krisna Nuresa Qodri; Indah Soesanti; Hanung Adi Nugroho
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 1 (2021): March 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.62663

Abstract

Tumors are cells that grow abnormally and uncontrollably, whereas brain tumors are abnormally growing cells growing in or near the brain. It is estimated that 23,890 adults (13,590 males and 10,300 females) in the United States and 3,540 children under the age of 15 would be diagnosed with a brain tumor. Meanwhile, there are over 250 cases in Indonesia of patients afflicted with brain tumors, both adults and infants. The doctor or medical personnel usually conducted a radiological test that commonly performed using magnetic resonance image (MRI) to identify the brain tumor. From several studies, each researcher claims that the results of their proposed method can detect brain tumors with high accuracy; however, there are still flaws in their methods. This paper will discuss the classification of MRI-based brain tumors using deep learning and transfer learning. Transfer learning allows for various domains, functions, and distributions used in training and research. This research used a public dataset. The dataset comprises 253 images, divided into 98 tumor-free brain images and 155 tumor images. Residual Network (ResNet), Neural Architecture Search Network (NASNet), Xception, DenseNet, and Visual Geometry Group (VGG) are the techniques that will use in this paper. The results got to show that the ResNet50 model gets 96% for the accuracy, and VGG16 gets 96% for the accuracy. The results obtained indicate that transfer learning can handle medical images.
Topic Modeling in the News Document on Sustainable Development Goals Hidayatul Fitri; Widyawan Widyawan; Indah Soesanti
IJITEE (International Journal of Information Technology and Electrical Engineering) Vol 5, No 3 (2021): September 2021
Publisher : Department of Electrical Engineering and Information Technology,Faculty of Engineering UGM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijitee.67467

Abstract

Indonesia is a developing country and supports the program of the Sustainable Development Goals (SDGs) which consist of 17 goals. SDGs is not only the government’s duty, but a shared duty from any elements. Online media has a crucial role in implementing goals of Indonesia’s SDG. Information published in online news related to the SDGs is an important consideration for the government, society, and all elements. Categorizing news manually to find out news topics is very time-consuming and done by the ability of news editors. News presented by online media on the news site can be used as topic modeling, where hidden topics can be found in the news on online media. Topic modeling will classify data based on a particular topic and determine the relationship between text. Latent Dirichlet allocation (LDA) is one of the methods on topic modeling to find out the trend of topics of SDGs news. Based on the result of this research, the implementation of LDA is the right choice for finding topics in a document. The result of topic modeling with k = 17 obtained the highest coherence score of 0.5405 on topic 8. Topic 8 discussed news related to the eighth SDGs goals, namely decent work and economic growth. This categorization was based on words formed after the LDA process. Then, topic 5 discussed the news on the 17th SDGs goals, namely partnerships for the goals. Topic 6 discussed the news of the first SDGs, namely no poverty.
A review of convolutional neural network-based computer-aided lung nodule detection system Sekar Sari; Tole Sutikno; Indah Soesanti; Noor Akhmad Setiawan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1044-1061

Abstract

Worldwide, lung cancer is the major cause of death and rapidly spreads. Lung tissue that is benign does not grow significantly, but lung tissue that is malignant grows rapidly and attacks the body, posing a grave threat to one's health. This paper provides a literature review of computer-aided detection (CAD) systems for lung cancer diagnosis. Preprocessing, segmentation, detection, and classification are the stages of the CAD system. This review divides the preprocessing into three stages: image smoothing, edge sharpening, and noise removal. Additionally, lung segmentation is divided into three stages: histogram-based thresholding, linked component analysis, and lung extraction. The detecting phase aids in decreasing the workload. Several techniques are briefly described, including random forest, naïve bayes, k-nearest neighbor (k-NN), support vector machine (SVM), and convolutional neural network (CNN). Classification is the final stage; the image is then identified as containing or not possessing nodules. The prospect of incorporating CNN-based deep learning techniques into the CAD system is discussed. This paper is superior to other review studies on this topic due to its comprehensive examination of pertinent literature and structured presentation. We hope that our research may help professional researchers and radiologists design more effective CAD systems for lung cancer detection.
A Brief Study of The Use of Pattern Recognition in Online Learning: Recommendation for Assessing Teaching Skills Automatically Online Based Pipit Utami; Rudy Hartanto; Indah Soesanti
Elinvo (Electronics, Informatics, and Vocational Education) Vol 7, No 1 (2022): Mei 2022
Publisher : Department of Electronic and Informatic Engineering Education, Faculty of Engineering, UNY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (583.925 KB) | DOI: 10.21831/elinvo.v7i1.51354

Abstract

Online learning has become a trend for the current generation of students who have been exposed to advanced information and communication technology. Smart education can use pattern recognition. Manual assessments are subjective and inconsistent. To overcome these problems, pattern recognition can be used in the non-verbal aspect assessment system. This study describes pattern recognition in online learning about the functions, modalities, and algorithms and specifically related to the recognition system of non-verbal aspects of teaching skills. The literature study was carried out through the stages of planning, selection, extraction, and selection. There are 86 articles reviewed. The first result is the functions of implementing pattern recognition in online learning are engagement recognition, attention detection, emotion recognition, learning behavior, learning activity recognition, authentication, teaching training, etc. using four classifications of modality: visual, audio, biosignal, behavioral, and CNN as the most widely used learning algorithm. Secondly, all modalities (except behavioral) and CNN algorithm can be used for assessing teaching skills. Early development of the non-verbal aspect assessment system can use Facial Expression Recognition (FER) and Hand Gesture Recognition (HGR). The future analysis needs to focus on technology characteristics, the meaningfulness of the content, and the proper teaching mode. In the end, hopefully, prospective teachers will acquire technology that can make it easier for them to practice teaching and get objective assessments.
Perancangan Analog Front End (AFE) untuk Akuisisi Isyarat EKG Enas Dhuhri Kusuma; Fikri Zaini Baridwan; Indah Soesanti
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2023: SNESTIK III
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2023.4134

Abstract

Electrocardiogram or ECG is a curve representing electrical activity of the heart. ECG recorder generally consists of analog front end (AFE) and microcontroller for analog signal acquisition, signal processing and data transmission. As a signal conditioner, AFE is the most important part of an ECG recorder. With AFE, ECG signal with 1mV magnitude and interfered, will be conditioned to a microcontroller readable signal by its analog input. Proposed AFE is an electronic circuit with two main functions: amplifier and filtering. The filter uses second order Sallen-Key configuration. Proposed AFE uses two main components: operational amplifier and instrumentation amplifier. The device is able to amplify ECG signals with 66 dB gain or 1800V/V so a signal with 1mV magnitude will be able to be conditioned into a microcontroller compatible signal. CMRR of the proposed device is 79.9 dB with SNR 30 dB.
Co-Authors Adha Imam Cahyadi Adhi Soesanto, Adhi Adhi Susanto Adhistya Erna Permanasari Afrisal, Hadha Agus Eko Minarno Agus Jamal Al-Fahsi, Resha Dwika Hefni Andrey Nino Kurniawan Andrey Nino Kurniawan Nino Kurniawan Andrey Nino Kurniawan, Andrey Nino Anna Nur Nazilah Chamim Aqil Aqthobirrobbany Aqthobirrobbany, Aqil Arief Rachma Wibowo Bambang Sutopo Bana Handaga Beta Estri Adiana Cepi Ramdani Chamim, Anna Nur Nazilah Danny Kurnianto Desyandri Desyandri Dewi Purnamasar Diah Priyawati Dian Nova Kusuma Hardani Domy Kristomo Dwi Rochmayanti Dwi Rochmayanti Dwi Rochmayanti Eka Firmansyah Elfrida Ratnawati Faaris Mujaahid Fathania Firwan Firdaus Fikri Zaini Baridwan Hanifah Rahmi Fajrin Hanung Adi Nugroho Hedi Purwanto Hendriyawan A., M. S. Henry Sulistyo Hidayatul Fitri Hotama, Christianus Frederick Husnul Rahmawati Sakinnah I Made Agus Wirahadi Putra Ikhwan Mustiadi Indriana Hidayah Isbadi Urifan Karisma Trinanda Putra, Karisma Trinanda Krisna Nuresa Qodri Litasari Litasari Litasari M.S. Hendriyawan Achmad Maesadji Tjokronagoro Maesadji Tjokronagoro Maesadji Tjokronegoro Medycha Emhandyksa Meirista Wulandari Muhamad Yusvin Mustar Muhammad Arzanul Manhar Muhammad Rausan Fikri Noor Akhmad Setiawan Nurokhim Nurokhim Oki Iwan Pambudi Oktoeberza, Widhia KZ Oyas Wahyunggoro Paulus Tofan Rapiyanta Pipit Utami Ramadoni Syahputra Ratnasari Nur Rohmah Rina Susilowati Risanuri Hidayat Rudy Hartanto Sekar Sari Siti Helmyati Soesanto, Adhi Sulistyo, Henry Sunu Wibirama Syahfitra, Febrian Dhimas Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Thomas Sri Widodo Tole Sutikno Warsun Najib Widyawan Widyawati Prima, Widyawati Wijaya, Nur Hudha Wijaya, Nur Hudha Wiyagi, Rama Okta Yudhi Agussationo Yundari, Yundari