Article Per Year (5 Year)
p-Index From 2021 - 2026
2.036
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Jurnal Teknik ITS Jurnal Sains dan Seni ITS IPTEK Journal of Proceedings Series IPTEK The Journal for Technology and Science Gamatika Jurnal Matematika & Sains Prosiding Seminar Matematika dan Pendidikan Matematik Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Purifikasi InPrime: Indonesian Journal Of Pure And Applied Mathematics Science Nature Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI) CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS Indonesian Journal of Applied Mathematics Limits: Journal of Mathematics and Its Applications International Journal of Computing Science and Applied Mathematics-IJCSAM
Erna Apriliani
Departemen Matematika Institut Teknologi Sepuluh Nopember
Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Physics Other

Published : 39 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 39 Documents
Search

 1   2   3   4 
ESTIMASI GERAK PROYEKTIL DENGAN METODE EXTENDED KALMAN FILTER (EKF) PADA INISIAL KONDISI PESAWAT UDARA BERGERAK Riska Aprilia; Erna Apriliani; Hendro Nurhadi
Jurnal Nasional Aplikasi Mekatronika, Otomasi dan Robot Industri (AMORI) Vol 1, No 1 (2020)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (977.479 KB) | DOI: 10.12962/j27213560.v1i1.6646

Abstract

Abstrak — Salah satu usaha untuk mempertahankan negara adalah pengembangan dalam bidang kedirgantaraan. Contohnya pada jalur udara dengan pesawat yang dilengkapi dengan teknologi senjata api. Pada bidang kemiliteran, proyektil senjata api yang digunakan adalah proyektil kaliber 12.7 × 99 mm karena memiliki kecepatan yang sangat tinggi. Oleh karena itu diperlukan sebuah estimator untuk memprediksi lintasan proyektil yang ditembak dari pesawat udara. Salah satu estimator yang dapat digunakan adalah Extended Kalman Filter. Metode ini merupakan pengembangan dari metode Kalman Filter. Pada penelitian ini Extended Kalman Filter dibandingkan dengan Kalman Filter untuk mengetahui hasil estimasi yang optimal dengan data pengukuran diasumsikan linier. Hasil estimasi menunjukkan bahwa Extended Kalman Filter memiliki hasil yang optimal untuk memprediksi lintasan proyektil yang ditembak dari pesawat udara bergerak. Hal ini ditunjukkan dengan tingkat keakurasian sebesar 97.81% pada posisi  x, 64.34% pada posisi y, dan 98.13% pada posisi z.
METODE ESTIMASI PENYEBARAN POLUTAN DI UDARA Erna Apriliani
Purifikasi Vol 12 No 2 (2011): Jurnal Purifikasi
Publisher : Department of Environmental Engineering-Faculty of Civil, Environmental and Geo Engineering. Institut Teknologi Sepuluh Nopember, Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j25983806.v12.i2.204

Abstract

Air pollution is the real problem in the metropolitan city and industrial area. Estimation of air pollution distribution is important for recommending emission minimization. Three estimation methods for air pollution distribution, namely numerical method (Euler and Runge-Kutta method), Recursive Least Square method and data assimilation (Kalman Filter) method were applied in this research. The algorithms and the simulations were described, the accuracy of each method was not compared, but the advantages and disadvantages of these methods were described. Distribution of carbon monoxide in Surabaya was estimated using these methods. This research showed that numerical method could not be applied in real condition. The RLS method needed a lot of time series data of concentration of pollution. The data assimilation method could be applied in real condition with a few time series pollutant data, and for estimating pollutant concentrations in some locations.
H_∞ state feedback for linear systems with decentralized control inputs Helisyah Nur Fadhilah; Guisheng Zhai; Dieky Adzkiya; Erna Apriliani
SCIENCE NATURE Vol 2 No 4 (2019): SCIENCE NATURE
Publisher : Faculty of Mathematics and Natural Sciences, University of Pattimura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/SNvol2iss4pp219-231year2019

Abstract

This paper considers state feedback with decentralized structure for interconnected systems. The connection between subsystems is described by a directed graph. To design a decentralized controller, we use the information from its own subsystem and other subsystems based on the interconnection. Decentralized controller is defined as a solution of bilinear matrix inequality (BMI) problem, which is then solved by using the homotopy approach. Two numerical examples are performed to show validity of the design procedure
The Reduced Rank of Ensemble Kalman Filter to Estimate the Temperature of Non Isothermal Continue Stirred Tank Reactor Erna Apriliani; Dieky Adzkiya; Arief Baihaqi
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 13 No. 2 (2011): DECEMBER 2011
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (296.561 KB) | DOI: 10.9744/jti.13.2.107-112

Abstract

Kalman filter is an algorithm to estimate the state variable of dynamical stochastic system. The square root ensemble Kalman filter is an modification of Kalman filter. The square root ensemble Kalman filter is proposed to keep the computational stability and reduce the computational time. In this paper we study the efficiency of the reduced rank ensemble Kalman filter. We apply this algorithm to the non isothermal continue stirred tank reactor problem. We decompose the covariance of the ensemble estimation by using the singular value decomposition (the SVD), and then we reduced the rank of the diagonal matrix of those singular values. We make a simulation by using Matlab program. We took some the number of ensemble such as 100, 200 and 500. We compared the computational time and the accuracy between the square root ensemble Kalman filter and the ensemble Kalman filter. The reduced rank ensemble Kalman filter can’t be applied in this problem because the dimension of state variable is too less.
Desain Kontrol pada Model Gerak Lateral-Direksional Unmanned Aerial Vehicle (UAV) Menggunakan Output Feedback Control Wiwit Ratnasari; Erna Apriliani; Mardlijah Mardlijah
Jurnal Sains dan Seni ITS Vol 11, No 2 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v11i2.75502

Abstract

Pengembangan teknologi di berbagai bidang dari tahun ke tahun semakin pesat, begitu pula pengembangan teknologi di bidang kedirgantaraan. Salah satu pengembangannya adalah pesawat tanpa awak atau Unmanned Aerial Vehicle (UAV). Dalam penelitian ini, dilakukan analisis sistem dan desain kontrol pada model gerak lateral-direksional UAV menggunakan Output Feedback Control. Sistem bersifat tidak stabil, terkontrol, dan teramati. Agar UAV menjadi stabil, maka dilakukan penerapan Pole Placement pada desain kontrol dengan 5 skenario feedback gain (K_1,K_2,K_3,K_4,K_5,) untuk memindahkan nilai eigen yang menyebabkan sistem UAV tidak stabil. Skenario pada sudut selip (β) paling baik adalah desain kontrol dengan K_4 yang waktu stabilnya paling cepat yaitu 322 s, begitu pula pada sudut roll (ϕ) skenario terbaik adalah desain kontrol dengan K_4 dimana pada waktu 172 s sudah stabil.
Estimasi Parameter Model Inflasi untuk Menganalisa Pengaruh Covid-19 Menggunakan GSTAR-Filter Kalman Miftakhul Janah Seftia Agustina; Sentot Didik Surjanto; Erna Apriliani
Jurnal Sains dan Seni ITS Vol 11, No 2 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v11i2.75776

Abstract

Pandemi Covid-19 selain mengganggu kesehatan manusia juga dapat mengganggu kesehatan ekonomi di seluruh dunia termasuk Indonesia. Dengan keadaan ekonomi yang tidak stabil akhir-akhir ini, permasalahan inflasi menjadi salah satu fokus penting bagi pemerintah. Inflasi merupakan salah satu indikator penting dalam stabilitas perekonomian bagi suatu negara. Oleh karena itu, perlu adanya pemodelan matematika yang sesuai yang dapat memprediksi inflasi di masa mendatang. Pengaruh Covid-19 terhadap inflasi dapat diamati dengan memperhatikan pergerakan inflasi terhadap Covid-19 berdasarkan plot data inflasi. Selanjutnya data inflasi dimodelkan menggunakan model Generalized Space Time Autoregressive (GSTAR) dengan menggunakan pembobotan invers jarak antar lokasi dan pembobotan normalisasi korelasi silang untuk mendapatkan model inflasi yang sesuai. Selanjutnya dilakukan estimasi pada parameter model menggunakan metode Filter Kalman (FK). Hasil akhir menunjukkan bahwa Filter Kalman mampu memperbaiki hasil estimasi pada model GSTAR sehingga didapatkan hasil prediksi yang mendekati data aktual. Hal ini ditunjukkan dengan hasil simulasi dan nilai MAPE yang lebih kecil dari pada nilai MAPE model GSTAR-OLS dan GSTAR-GLS sebesar 0.14302%.
Estimasi Tingkat Inflasi Nasional Menggunakan ARCH-GARCH Filter Kalman Radisha Fanni Sianti; Sentot Didik Surjanto; Erna Apriliani
Jurnal Sains dan Seni ITS Vol 11, No 2 (2022)
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM), ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j23373520.v11i2.75827

Abstract

Tingkat inflasi nasional merupakan salah satu indikator yang penting dalam menganalisis pertumubuhan perekonomian suatu negara. Tingkat inflasi yang tidak dikelola dengan baik dapat menyebabkan perekonomian suatu negara mengalami kemunduran. Pada data tingkat inflasi nasional digunakan model ARIMA (Autoregressive Integrated Moving Average) dan terdeteksi terdapat adanya heteroskedastisitas, sehingga digunakan model time series ARCH-GARCH (Autoregressive Conditional Heteroskedasticity-Generalized Conditional Heteroskedasticity). Model yang sesuai yaitu ARCH(1) dengan nilai MAPE (Mean Absolute Percentage Error) yang masih sangat besar yaitu 34,662%. Oleh karena itu, untuk mendapatkan nilai error yang lebih kecil dilakukan perbaikan error dengan menggunakan Filter Kalman. Hasil akhir menunjukkan bahwa Filter Kalman mampu memperbaiki hasil estimasi yang ditandai dengan nilai MAPE ARCH-Filter Kalman lebih kecil dibandingkan dengan model ARCH. Hasil estimasi terbaik pada data tingkat inflasi nasional adalah Filter Kalman polinomial derajat 2 dengan nilai Q=R=0,01 yang memiliki nilai MAPE terkecil yaitu 1,0035%.
Perbandingan Metode Extended Kalman Filter dan Ensamble Kalman Filter dalam Mengestimasi Pertumbuhan Sel Kanker dengan Pengobatan Virus Oncolytic Rifki Ilham Baihaki; Didik Khusnul Arif; Erna Apriliani
CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS Vol 4, No 1 (2023): CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS
Publisher : jcgant

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Virus is a microorganism that can spread and infect living cells, such as humans, animals, and plants. Not all viruses have negative effects, as in the case of oncolytic viruses. This type of virus is modified to infect and kill cancer cells. The success of cancer therapy using this virus depends on the pattern of interaction between the virus population and cancer cells, which can be described by a mathematical model. This research uses two methods to estimate the growth of cancer cells with oncolytic virus therapy, namely the Extended Kalman Filter (EKF) and the Ensemble Kalman Filter (EnKF). The results show that EKF has a faster computation time compared to EnKF. However, the EKF estimation results are still inferior to those of EnKF.
Perbandingan Metode Extended Kalman Filter dan Ensamble Kalman Filter dalam Mengestimasi Pertumbuhan Sel Kanker dengan Pengobatan Virus Oncolytic Rifki Ilham Baihaki; Didik Khusnul Arif; Erna Apriliani
CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS Vol 4, No 1 (2023): CGANT JOURNAL OF MATHEMATICS AND APPLICATIONS
Publisher : jcgant

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25037/cgantjma.v4i1.93

Abstract

Virus is a microorganism that can spread and infect living cells, suchas humans, animals, and plants. Not all viruses have negative effects, as in thecase of oncolytic viruses. This type of virus is modified to infect and kill cancercells. The success of cancer therapy using this virus depends on the pattern ofinteraction between the virus population and cancer cells, which can bedescribed by a mathematical model. This research uses two methods to estimatethe growth of cancer cells with oncolytic virus therapy, namely the ExtendedKalman Filter (EKF) and the Ensamble Kalman Filter (EnKF). The results showthat EKF has a faster computation time compared to EnKF. However, the EKFestimation results are still inferior to those of EnKF.
Prediksi Harga Saham Menggunakan Geometric Brownian Motion Termodifikasi Kalman Filter dengan Konstrain Vivien Maulidya; Erna Apriliani; Endah Rokhmati Merdika Putri
Indonesian Journal of Applied Mathematics Vol 1 No 1 (2020): Indonesian Journal of Applied Mathematics Vol. 1 No. 1 October Chapter
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat (LPPM), Institut Teknologi Sumatera, Lampung Selatan, Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

An attractive profit is one of the attractions offered by stock investment. Changes in stock prices that are difficult to predict will result in uncertain value of profits, so it is necessary to predict the stock price using forecasting method. The model used is Geometric Brownian Motion (GBM). This model can predict future stock price movements based oh historical stock data. Forecasting results with the Geometric Brownian Motion model produce significant errors due to constant parameters. To reduce the values of error, it is necessary to add a filtering method that is Kalman Filter (KF) by limiting the state variables using norm. Historical data was taken from 3 different closing price stock data, namely shares of Bank BRI, PT. Telekomunikasi Indonesia Tbk, and Unilever Indonesia with period of January 1 – December 31, 2019. Based on the results obtained, the addition of contraints on the GBM-KF model does not significantly influence the MAPE value. At the forecasting stage using testing data with GBM-KF model without constraints, the average MAPE value for BBRI was 0.1122%, TLKM 0.0899%, and UNVR 0.0678%. While forcasting using GBM-KF model with constrains, the average MAPE value for BBRI was 0.0958%, TLKM 0.0808%, and UNVR 0.0674%. The values of MAPE obtained are included in the high accuracy forecasting category.
Co-Authors Abduh Riski, Abduh Adam Adam Adam Adam Agus N.A. Syarifuddin Agustin, Diah Ali Masduqi Amelia Margaretha Santoso Annisa Rahmita Soemarsono Arief Baihaqi Arief Baihaqi Aulia Siti Aisjah Bambang Sugandi bandung Arry Sanjaya Chairul Imron, Chairul Devi Yolanda Diah Agustin Dian A Merdekawati Didik Khusnul Arif Dieky Adzkiya Endah Rokhmati Merdika Putri Evita Purnaningrum Evita Purnaningrum Faridatul Masruroh Guisheng Zhai Hartatiati, Sri Suprapti Helisyah Nur Fadhilah Hendro Nurhadi Hendro Nurhadi I Gst Ngr Rai Usadha Lukman Hanafi Mahmud Yunus Mardlijah - Miftakhul Janah Seftia Agustina Mohamad Ilham Dwi Firmansyah Mohammad Isa Irawan Muhammad Fahmi Muzaki Nathanael Leon Gozali Ngatini Ngatini Ngatini, Ngatini Nuri Wahyuningsih Radisha Fanni Sianti Reni Sundari Resi Arumin Sani Rifki Ilham Baihaki Riska Aprilia Riska Aprilia, Riska Ronando, Elsen Sadjidon Sadjidon Sanjaya, Bandung Arry Sentot Didik Surjanto Shaffiani Nurul Fajar Soleha Soleha Teguh Herlambang Tomy Kurniawan Tri Juliana Permatasari Vivien Maulidya Wiwit Ratnasari Wiwit Sofiyanti Budiono Yoga Faisal Aminnudin Yunus, Mahmud