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All Journal International Journal of Electrical and Computer Engineering JUITA : Jurnal Informatika Jurnal Informatika
Tati Latifah Erawati Rajab
Bandung Institute of Technology
Computer Science & IT Electrical & Electronics Engineering

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Data prediction for cases of incorrect data in multi-node electrocardiogram monitoring Sugondo Hadiyoso; Heru Nugroho; Tati Latifah Erawati Rajab; Kridanto Surendro
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1540-1547

Abstract

The development of a mesh topology in multi-node electrocardiogram (ECG) monitoring based on the ZigBee protocol still has limitations. When more than one active ECG node sends a data stream, there will be incorrect data or damage due to a failure of synchronization. The incorrect data will affect signal interpretation. Therefore, a mechanism is needed to correct or predict the damaged data. In this study, the method of expectation-maximization (EM) and regression imputation (RI) was proposed to overcome these problems. Real data from previous studies are the main modalities used in this study. The ECG signal data that has been predicted is then compared with the actual ECG data stored in the main controller memory. Root mean square error (RMSE) is calculated to measure system performance. The simulation was performed on 13 ECG waves, each of them has 1000 samples. The simulation results show that the EM method has a lower predictive error value than the RI method. The average RMSE for the EM and RI methods is 4.77 and 6.63, respectively. The proposed method is expected to be used in the case of multi-node ECG monitoring, especially in the ZigBee application to minimize errors.
Kajian Penelitian Pemrosesan Bunyi dan Aplikasinya pada Teknologi Informasi Ranny Ranny; Iping Supriana Suwardi; Tati Latifah Erawati Rajab; Dessi Puji Lestari
JUITA : Jurnal Informatika JUITA VoL. 7 Nomor 1, Mei 2019
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (278.673 KB) | DOI: 10.30595/juita.v7i1.3491

Abstract

Hasil dari peneitian banyak digunakan dan dikembangkan pada aplikasi yang telah banyak dimanfaatkan pada kehidupan sehari-hari. Proses identifikasi bunyi menjadi salah satu penelitian yang banyak dilakukan. Identifikasi bunyi yang dilakukan oleh manusia berbeda satu sama lain. Misal pada suara detak jantung, pada pendengar umum, suara detak jantung tidak memiliki informasi apa pun terkait kesehatan, tapi jika suara detak jantung diperdengarkan pada ahli medik atau dokter, maka informasi yang diperoleh akan berbeda, dokter dapat mengidentifikasikan suara detak jantung dikaitkan dengan kondisi kesehatan jantung. Selain dalam bidang medis, bunyi juga dimanfaatkan pada aplikasi berbasis bunyi dan suara pada Smart Homes. Namun, sebelum mengkaji tentang aplikasi pada Smart Homes dan aplikasi lain maka akan dibahas beberapa teori dasar tentang bunyi dan suara, seperti: teori suara dan bunyi, noise pada data suara, serta ekstraksi ciri suara bunyi yang secara spesifik akan menjelaskan tentang Mel Frequency Cepstrum Coefficients (MFCC). Berdasarkan hasil kajian dapat dibuat kerangka kerja aplikasi yang dibuat. Kerangka kerja yang disusun merupakan kerangka kerja yang umum dilakukan pada aplikasi dan penelitian tentang penggunaan data suara dan bunyi. Selain itu kajian ini akan menjabarkan tentang lingkup penelitian bunyi dan suara yang telah banyak dilakukan. Melalui penjabaran tentang lingkup penelitian didapatkan peluang penelitian yang dapat dilakukan pada data bunyi dan suara serta tantangannya.
Avoiding Machine Learning Becoming Pseudoscience in Biomedical Research Meredita Susanty; Ira Puspasari; Nilam Fitriah; Dimitri Mahayana; Tati Erawati Latifah Rajab; Hasballah Zakaria; Agung Wahyu Setiawan; Rukman Hertadi
Jurnal Informatika Vol 10, No 1 (2023): April 2023
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v10i1.12787

Abstract

The use of machine learning harbours the promise of more accurate, unbiased future predictions than human beings on their own can ever be capable of. However, because existing data sets are always utilized, these calculations are extrapolations of the past and serve to reproduce prejudices embedded in the data. In turn, machine learning prediction result raises ethical and moral dilemmas. As mirrors of society, algorithms show the status quo, reinforce errors, and are subject to targeted influences – for good and the bad. This phenomenon makes machine learning viewed as pseudoscience. Besides the limitations, injustices, and oracle-like nature of these technologies, there are also questions about the nature of the opportunities and possibilities they offer. This article aims to discuss whether machine learning in biomedical research falls into pseudoscience based on Popper and Kuhn's perspective and four theories of truth using three study cases. The discussion result explains several conditions that must be fulfilled so that machine learning in biomedical does not fall into pseudoscience
Co-Authors Agung Wahyu Setiawan Dessi Puji Lestari Dimitri Mahayana Heru Nugroho Iping Supriana Suwardi Ira Puspasari Kridanto Surendro Meredita Susanty Nilam Fitriah Ranny Ranny RUKMAN HERTADI Sugondo Hadiyoso Zakaria, Hasballah