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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Masyarakat Informatika Journal on Mathematics Education (JME) Journal on Mathematics Education (JME) CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Register: Jurnal Ilmiah Teknologi Sistem Informasi Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics Jurnal Sistem Cerdas MATHunesa: Jurnal Ilmiah Matematika Edukasia: Jurnal Pendidikan dan Pembelajaran Jurnal Sistem Informasi Journal on Mathematics Education Limits: Journal of Mathematics and Its Applications

Elly Matul Imah

Universitas Negeri Surabaya

Author-ID : 564583

Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Studi Komparasi Ekstraksi Fitur pada Pengenalan Wajah Menggunakan Principal Component Analysis (PCA) dan Wavelet Daubechies Intan P, Riskyana Dewi; Imah, Elly Matul
Jurnal Masyarakat Informatika Vol 6, No 12 (2015): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Paper ini membahas perbandingan ekstraksi fitur untuk pengenalan wajah menggunakan metode Principal Component Analysis (PCA) dan Wavelet Daubechies untuk pengenalan wajah . Basis wavelet daubechies yang digunakan adalah wavelet db2, db4, dan db8. Setiap dekomposisi dilakukan hingga Â levelÂ ke-3 yang kemudian diambil fitur aproksimasi wavelet dan fitur statistik wavelet. Variasi nilai komponen utama dimulai dari nilai komponen ke-1 hingga nilai komponen ke-100 dari 4096 nilai eigen. Nilai komponen ke-1 memiliki presentase sebesar 62% sedangkan nilai komponen ke-100 memiliki presentase sebesar 99% dari total nilai eigen,. Pengujian sistem menggunakan 216 citra wajah yang diambil dari dataset The Japanese Female Facial Expression (JAFFE) yang terdiri dari 10 individu dengan masing-masing sekitar 20 wajah per- individu. Pemilihan data train dan data tes menggunakan cross validation Â dengan rata-rata akurasi 94.42%. Â Dari hasil percobaan menggunakan Random Forest Classifier diperoleh tingkat pengenalan tertinggi untuk ekstraksi menggunakan PCA sebesar 100% pada variasi data 95% ,sedangkan tingkat pengenalan tertinggi untuk ekstraksi menggunakan Wavelet Daubechies sebesar Â 98.611% pada wavelet db2 menggunakan fitur aproksimasi wavelet.

STUDENTSâ€™ COGNITIVE PROCESSES IN SOLVING PROBLEM RELATED TO THE CONCEPT OF AREA CONSERVATION Ekawati, Rooselyna; Kohar, Ahmad Wachidul; Imah, Elly Matul; Amin, Siti Maghfirotun; Fiangga, Shofan
Journal on Mathematics Education Vol 10, No 1 (2019)
Publisher : Department of Doctoral Program on Mathematics Education, Sriwijaya University

This study aimed to determineÂ the cognitive processÂ employed in problem-solving related to the concept of area conservation for seventh graders.Â Two students with different mathematical ability were chosen to be the subjectsÂ of this research. Each of them was the representative of high achievers and low achievers based on a set of area conservation test.Â Results indicate that both samples performedÂ more cyclic processesÂ on formulating solution planning, regulating solution part and detecting and correcting error during the problem-solving. However, itÂ wasÂ found that theÂ highÂ achiever student performed some processes than those of low achiever. Also, while the high achiever student did not predict any outcomes of his formulated strategies, the low achiever did not carry out the thought process after detecting errors of the initial solution gained. About the concept of area conservation, the finding also reveals that within the samplesâ€™ cognitive processes, the use of area formula come first before students decided to look for another strategyÂ such as doingÂ â€˜cut-rotate-pasteâ€™ for the curved planes, which do not have any direct formula. The possible causes of the results were discussed to derive some recommendation for future studies.

STUDY COMPARISON OF SVM-, K-NN- AND BACKPROPAGATION-BASED CLASSIFIER FOR IMAGE RETRIEVAL Muhammad Athoillah; M. Isa Irawan; Elly Matul Imah
Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Classification is a method for compiling data systematically according to the rules that have been set previously. In recent years classification method has been proven to help many peopleâ€™s work, such as image classification, medical biology, traffic light, text classification etc. There are many methods to solve classification problem. This variation method makes the researchers find it difficult to determine which method is best for a problem. This framework is aimed to compare the ability of classification methods, such as Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Backpropagation, especially in study cases of image retrieval with five category of image dataset. The result shows that K-NN has the best average result in accuracy with 82%. It is also the fastest in average computation time with 17,99 second during retrieve session for all categories class. The Backpropagation, however, is the slowest among three of them. In average it needed 883 second for training session and 41,7 second for retrieve session.

STUDY COMPARISON BACKPROPOGATION, SUPPORT VECTOR MACHINE, AND EXTREME LEARNING MACHINE FOR BIOINFORMATICS DATA umi mahdiyah; M. Isa Irawan; Elly Matul Imah
Jurnal Ilmu Komputer dan Informasi Vol 8, No 1 (2015): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

A successful understanding on how to make computers learn would open up many new uses of computers and new levels of competence and customization. A detailed understanding on information- processing algorithms for machine learning might lead to a better understanding of human learning abilities and disabilities. There are many type of machine learning that we know, which includes Backpropagation (BP), Extreme Learning Machine (ELM), and Support Vector Machine (SVM). This research uses five data that have several characteristics. The result of this research is all the three investigated models offer comparable classification accuracies. This research has three type conclusions, the best performance in accuracy is BP, the best performance in stability is SVM and the best performance in CPU time is ELM for bioinformatics data.

AUTOMATIC ARRHYTHMIAS DETECTION USING VARIOUS TYPES OF ARTIFICIAL NEURAL NETWORK BASED LEARNING VECTOR QUANTIZATION (LVQ) Diane Fitria; Muhammad Anwar Ma'sum; Elly Matul Imah; Alexander Agung Gunawan
Jurnal Ilmu Komputer dan Informasi Vol 7, No 2 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Abstract An automatic Arrythmias detection system is urgently required due to small number of cardiologits in Indonesia. This paper discusses only about the study and implementation of the system. We use several kinds of signal processing methods to recognize arrythmias from ecg signal. The core of the system is classification. Our LVQ based artificial neural network classifiers based on LVQ, which includes LVQ1, LVQ2, LVQ2.1, FNLVQ, FNLVQ MSA, FNLVQ-PSO, GLVQ and FNGLVQ. Experiment result show that for non round robin dataset, the system could reach accuracy of 94.07%, 92.54%, 88.09% , 86.55% , 83.66%, 82.29 %, 82.25%, and 74.62% respectively for FNGLVQ, FNLVQ-PSO, GLVQ, LVQ2.1, FNLVQ-MSA, LVQ2, FNLVQ and LVQ1. Whereas for round robin dataset, system reached accuracy of 98.12%, 98.04%, 94.31%, 90.43%, 86.75%, 86.12 %, 84.50%, and 74.78% respectively for GLVQ, LVQ2.1, FNGLVQ, FNLVQ-PSO, LVQ2, FNLVQ-MSA, FNLVQ and LVQ1.

KLASIFIKASI BEAT ARITMIA PADA SINYAL EKG MENGGUNAKAN FUZZY WAVELET LEARNING VECTOR QUANTIZATION Elly Matul Imah; T. Basaruddin
Jurnal Ilmu Komputer dan Informasi Vol 4, No 1 (2011): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Pengenalan pola beat dalam analisa rekaman elektrokardiogram (EKG) menjadi bagian yang penting dalam deteksi penyakit jantung terutama aritmia. Banyak metode yang dikembangkan terkait dengan pengenalan pola beat, namun sebagian besar masih mengunakan algoritma klasifikasi klasik di mana masih belum mampu mengenali outlier klasifikasi. Fuzzy Learning Vector Quantization (FLVQ) merupakan salah satu algoritma yang mampu untuk mengenali outlier klasifikasi tetapi juga memiliki kelemahan untuk sistem uji yang bukan data berkelompok. Dalam tulisan ini peneliti mengusulkan Fuzzy Wavelet LearningVector Quantization (FWLVQ), yaitu modifikasi FLVQ sehingga mampu mengatasi data crisp maupun data fuzzy dan juga memodifikasi inferensi sistemnya sebagai perpaduan model fuzzy Takagi Sugeno Kang dengan wavelet. Sinyal EKG diperoleh dari database MIT-BIH. Sistem pengenalan pola beat secara keseluruhan terbagi atas dua bagian yaitu data pra proses dan klasifikasi. Hasil percobaan diperoleh bahwa FWLVQ memiliki akurasi sebesar 90.20% untuk data yang tidak mengandung outlier klasifikasi dan 87.19% untuk data yang melibatkan outlier klasifikasi dengan rasio data uji outlier klasifikasi dengan data non-outlier sebesar 1:1. The recognition of beat pattern in analysis of recording an electrocardiogram (ECG) becomes an important detection of heart disease, especially arrhythmias. Many methods are developed related to the recognition of beat patterns, but most still use the classical classification algorithms which are still not able to identify outlier classification. Fuzzy Learning Vector Quantization (FLVQ) is one of the algorithms that can identify outlier classification but also has a weakness for test systems that are not grouped data. In this paper we propose a Fuzzy Wavelet Quantization Learning Vector (FWLVQ), which is modified so as to overcome FLVQ crisp data and fuzzy data and also modify the inference system as a combination of Takagi Sugeno Kang fuzzy model with the wavelet. ECG signal obtained from the MIT-BIH database. Beat pattern recognition system as a whole is divided into two parts: data pre-processing and classification. The experimental results obtained that FWLVQ has an accuracy 90.20% for data that does not contain outlier classification and 87.19% for the classification of data involving outlier ratio outlier test data classification with non-outlier data of 1:1.

EARLY DETECTION AND MONITORING SYSTEM OF HEART DISEASE BASED ON ELECTROCARDIOGRAM SIGNAL Muhammad Anwar Ma'sum; Elly Matul Imah; Alexander Agung Gunawan
Jurnal Ilmu Komputer dan Informasi Vol 7, No 1 (2014): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information)
Publisher : Faculty of Computer Science - Universitas Indonesia

Abstract Heart disease is the number one deadly disease in Indonesia. One of the main causes of fatality is the late detection of the disease. To avoid escalation of mortality caused by heart disease, we need early detection and monitoring system of heart disease. Therefore, in this research we propose an early detection and monitoring system of heart disease based on ECG signal. The proposed system has three main components: ECG hardware, smartphone, and server. Since the proposed system is designed to classify heartbeat signal, heart disease symptom can be detected as early as possible. We use FLVQ-PSO algorithm to classify heartbeat signal. Experiment result shows that classification accuracy of the system can reach 91.63%. Moreover, the proposed system can be used to verify patients heartbeat by cardiologists from distant area (telehealth). Experiment result shows that responsiveness of the system for the telehealth system is less than 0.6 seconds.

Face Recognition Using Complex Valued Backpropagation Zumrotun Nafisah; Febrian Rachmadi; Elly Matul Imah
Jurnal Ilmu Komputer dan Informasi Vol 11, No 2 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition. Complex-Valued Backpropagation is an algorithm modified from Real-Valued Backpropagation algorithm where the weights and activation functions used are complex. The dataset used in this study consist of 250 images that is classified in 5 classes. The performance of face recognition using Complex-Valued Backpropagation is also compared with Real-Valued Backpropagation algorithm. Experimental results have shown that Complex-Valued Backpropagation performance is better than Real-Valued Backpropagation.

STUDENTS’ COGNITIVE PROCESSES IN SOLVING PROBLEM RELATED TO THE CONCEPT OF AREA CONSERVATION Rooselyna Ekawati; Ahmad Wachidul Kohar; Elly Matul Imah; Siti Maghfirotun Amin; Shofan Fiangga
Journal on Mathematics Education Vol 10, No 1 (2019)
Publisher : Department of Doctoral Program on Mathematics Education, Sriwijaya University

This study aimed to determine the cognitive process employed in problem-solving related to the concept of area conservation for seventh graders. Two students with different mathematical ability were chosen to be the subjects of this research. Each of them was the representative of high achievers and low achievers based on a set of area conservation test. Results indicate that both samples performed more cyclic processes on formulating solution planning, regulating solution part and detecting and correcting error during the problem-solving. However, it was found that the high achiever student performed some processes than those of low achiever. Also, while the high achiever student did not predict any outcomes of his formulated strategies, the low achiever did not carry out the thought process after detecting errors of the initial solution gained. About the concept of area conservation, the finding also reveals that within the samples’ cognitive processes, the use of area formula come first before students decided to look for another strategy such as doing ‘cut-rotate-paste’ for the curved planes, which do not have any direct formula. The possible causes of the results were discussed to derive some recommendation for future studies.

ONLINE KERNEL AMGLVQ FOR ARRHYTHMIA HEARBEATS CLASSIFICATION Elly Matul Imah; R. Sulaiman
Jurnal Ilmiah Kursor Vol 8 No 4 (2016)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28961/kursor.v8i4.108

This study proposes Online Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) for handling imbalanced data sets. KAMGLVQ is extended version of AMGLVQ that used kernel function to handling non-linear classification problems. Basically AMGLVQ is vector quantization based learning. The vector quantization based learning is very simple algorithm that can be applied to the multiclass problem and the complexity of LVQ can be controlled during training process. KAMGLVQ works at online kernel learning system that integrating feature extraction and classification. The architecture network of KAMGLVQ consists of three layers, input layer, hidden layer, and an output layer. The hidden layer of KAMGLVQ is adaptive; this algorithm will generate a number of hidden layer nodes. The algorithm implement on real ECG signals from the MIT-BIH arrhythmias database and synthetic data. The experiments showed that KAMGLVQ able improve the accuracy of classification better than SVM or back-propagation NN; also able to reduce the time computational cost.

Co-Authors A. Fitrananda A. Fitrananda A. Fitrananda A. Marshadiany A. Marshadiany A. Marshadiany A.R. Hutomo A.R. Hutomo A.R. Hutomo Abi Nizar Sutranggono Ahmad Wachidul Kohar Ainiya Aziza AINIYA FAHRUROSI AZIZA Alexander Agung Gunawan ALFATARA NURUS SAADAH ANDY RIZKI WIYONO Atik Wintarti Awalah, Ervi Anisatul Budiarto, Mega T. Dessy Kusumaningrum Diane Fitria Durrotun Nabilah Febrian Rachmadi Fiangga, Shofan Fitrananda, A. G.P. Prikarti G.P. Prikarti G.P. Prikarti Hasanuddin Al-Habib Hutomo, A.R. Ismail Ivan Kurnia Laksono Karisma Karisma KURNIA LAKSONO, IVAN Mahdiyah, Umi Marshadiany, A. Mohammad Isa Irawan Muhammad Anwar Ma'sum Muhammad Athoillah, Muhammad Nia Amelia NURUS SAADAH, ALFATARA Prikarti, G.P. R. Sulaiman Riskyana Dewi Intan P, Riskyana Dewi Riskyana Dewi Intan Puspitasari RIZKI WIYONO, ANDY Rooselyna Ekawati Siti Maghfirotun Amin T. Basaruddin Zumrotun Nafisah

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