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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Techno.Com: Jurnal Teknologi Informasi Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics EMITTER International Journal of Engineering Technology KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Technomedia Journal Applied Technology and Computing Science Journal GUYUB: Journal of Community Engagement Proceeding ISETH (International Summit on Science, Technology, and Humanity) Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Jurnal Informatika: Jurnal Pengembangan IT
Tessy Badriyah, Tessy
Politeknik Elektronika Negeri Surabaya
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Journal : emitter international journal of engineering technology

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Arrhythmia Classification Using Long Short-Term Memory with Adaptive Learning Rate Assodiky, Hilmy; Syarif, Iwan; Badriyah, Tessy
EMITTER International Journal of Engineering Technology Vol 6 No 1 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.757 KB) | DOI: 10.24003/emitter.v6i1.265

Abstract

Arrhythmia is a heartbeat abnormality that can be harmless or harmful. It depends on what kind of arrhythmia that the patient suffers. People with arrhythmia usually feel the same physical symptoms but every arrhythmia requires different treatments. For arrhythmia detection, the cardiologist uses electrocardiogram that represents the cardiac electrical activity. And it is a kind of sequential data with high complexity. So the high performance classification method to help the arrhythmia detection is needed. In this paper, Long Short-Term Memory (LSTM) method was used to classify the arrhythmia. The performance was boosted by using AdaDelta as the adaptive learning rate method. As a comparison, it was compared to LSTM without adaptive learning rate. And the best result that showed high accuracy was obtained by using LSTM with AdaDelta. The correct classification rate was 98% for train data and 97% for test data.
Classification Algorithms of Maternal Risk Detection For Preeclampsia With Hypertension During Pregnancy Using Particle Swarm Optimization Tahir, Muhlis; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 6 No 2 (2018)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (565.13 KB) | DOI: 10.24003/emitter.v6i2.287

Abstract

Preeclampsia is a pregnancy abnormality that develops after 20 weeks of pregnancy characterized by hypertension and proteinuria.  The purpose of this research was to predict the risk of preeclampsia level in pregnant women during pregnancy process using Neural Network and Deep Learning algorithm, and compare the result of both algorithm. There are 17 parameters that taken from 1077 patient data in Haji General Hospital Surabaya and two hospitals in Makassar start on December 12th 2017 until February 12th 2018. We use particle swarm optimization (PSO) as the feature selection algorithm. This experiment shows that PSO can reduce the number of attributes from 17 to 7 attributes. Using LOO validation on the original data show that the result of Deep Learning has the accuracy of 95.12% and it give faster execution time by using the reduced dataset (eight-speed quicker than the original data performance). Beside that the accuracy of Deep Learning increased 0.56% become 95.68%. Generally, PSO gave the excellent result in the significantly lowering sum attribute as long as keep and improve method and precision although lowering computational period. Deep Learning enables end-to-end framework, and only need input and output without require for tweaking the attributes or features and does not require a long time and complex systems and understanding of the deep data on computing.
Hospital Length of Stay Prediction based on Patient Examination Using General features Rabiatul Adawiyah; Badriyah, Tessy; Syarif, Iwan
EMITTER International Journal of Engineering Technology Vol 9 No 1 (2021)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v9i1.609

Abstract

As of the year 2020, Indonesia has the fourth most populous country in the world. With Indonesia’s population expected to continuously grow, the increase in provision of healthcare needs to match its steady population growth. Hospitals are central in providing healthcare to the general masses, especially for patients requiring medical attention for an extended period of time. Length of Stay (LOS), or inpatient treatment, covers various treatments that are offered by hospitals, such as medical examination, diagnosis, treatment, and rehabilitation. Generally, hospitals determine the LOS by calculating the difference between the number of admissions and the number of discharges. However, this procedure is shown to be unproductive for some hospitals. A cost-effective way to improve the productivity of hospital is to utilize Information Technology (IT). In this paper, we create a system for predicting LOS using Neural Network (NN) using a sample of 3055 subjects, consisting of 30 input attributes and 1 output attribute. The NN default parameter experiment and parameter optimization with grid search as well as random search were carried out. Our results show that parameter optimization using the grid search technique give the highest performance results with an accuracy of 94.7403% on parameters with a value of Epoch 50, hidden unit 52, batch size 4000, Adam optimizer, and linear activation. Our designated system can be utilised by hospitals in improving their effectiveness and efficiency, owing to better prediction of LOS and better visualization of LOS done by web visualization.
Co-Authors Achmad Basuki Adhi P., Basik Afifah, Izza Nur Agung Muliawan Ali Ridho Barakbah Alwan Fauzi APRIANDY, KEVIN ILHAM Arna Fariza Assidiqi, Moh Hasbi Assodiky, Hilmy Assodiky, Hilmy Bima Sena Bayu Dewantara Binti Kholifah Dadet Pramadihanto Daisy Rahmania Syarif Deyana Kusuma Wardani Dimas Bagus Santoso Faradisa, Rosiyah Fitri Setyorini Hamida, Silfiana Nur Hardiyanti, Fitriani Rohmah Huda, Achmad Thorikul Idris Winarno Ika Restuningtyas Ira Prasetyaningrum iwan Syarif Kholifah, Binti Maulana, Hanan Ammar Maulana, Yufri Isnaini Rochmat Maulana, Yufri Isnaini Rochmat Muhlis Tahir Nur Sakinah Pahlevi, Muhammad Nur Riza Prasetyo Wibowo Rabiatul Adawiyah Rachmawati, Oktavia Citra Resmi Ria Rahmawati Rosiyah Faradisa Rudi Kurniawan Rulisiana Widodo Selvia Ferdiana Kusuma Sesulihatien, Wahjoe Tjatur Setiawardhana, Setiawardhana Solichul Huda Susanti, Puspasari Tahir, Muhlis Tri Harsono Wicaksono, Andri Permana Wicaksono, Andri Permana Willy Sandhika