Article Per Year (5 Year)
p-Index From 2019 - 2024
0.408
P-Index
This Author published in this journals
All Journal Jurnal Informatika Jurnal Teknik Elektro Jurnal Teknik Industri Jurnal Titra Record and Library Journal Journal of Documentation and Information Science International Journal of Industrial Optimization (IJIO)
Siana Halim
Fakulty of Industrial Technology,Industrial Engineering Department, Petra Christian University, Jl. Siwalankerto 121-131, Surabaya 60236
Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Statistical Learning for Predicting Dengue Fever Rate in Surabaya Halim, Siana; Felecia, Felecia; Octavia, Tanti
Jurnal Teknik Industri Vol 22, No 1 (2020): JTI June 2020
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (503.901 KB) | DOI: 10.9744/jti.22.1.37-46

Abstract

Dengue fever happening most in tropical countries and considered as the fastest spreading mosquito-borne disease which is endemic and estimated to have 96 million cases annually. It is transmitted by Aedes mosquito which infected with a dengue virus. Therefore, predicting the dengue fever rate as become the subject of researches in many tropical countries. Some of them use statistical and machine learning approach to predict the rate of the disease so that the government can prevent that incident. In this study, we explore many models in the statistical learning approaches for predicting the dengue fever rate. We applied several methods in the predictive statistics such as regression, spatial regression, geographically weighted regression and robust geographically weighted regression to predict the dengue fever rate in Surabaya. We then analyse the results, compare them based on the mean square error. Those four models are chosen, to show the global estimator’s approaches, e.g. regression, and the local ones, e.g. geographically weighted regression. The model with the minimum mean square error is regarded as the most suitable model in the statistical learning area for solving the problem. Here, we look at the estimates of the dengue fever rate in the year 2012, to 2017, area, poverty percen­tage, precipitation, number of rainy days for predicting the dengue fever outbreak in the year 2018. In this study, the pattern of the predicted model can follow the pattern of the true dataset.
Currency movement forecasting using time series analysis and long short-term memory Putri, Kristina Sanjaya; Halim, Siana
International Journal of Industrial Optimization Vol 1, No 2 (2020)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (214.208 KB) | DOI: 10.12928/ijio.v1i2.2490

Abstract

Foreign exchange is one type of investment, which its goal is to minimize losses that could occur. Forecasting is a technique to minimize losses when investing. The purpose of this study is to make foreign exchange predictions using a time series analysis called Auto-Regressive Integrated Moving Average (ARIMA) and Long Short-term memory methods. This study uses the daily EUR / USD exchange rates from 2014 to March 2020. The data are used as the model to predict the value of the foreign exchange market in April 2020. The model obtained will be used for predictions in April 2020, where the RMSE values obtained from time series analysis (ARIMA) with a window size of 100 days and LSTM sequentially as follows 0.00527 and 0.00509. LSTM produces lower RMSE values than ARIMA. LSTM has better prediction results; this is because the LSTM has the ability to learn so that it can utilize a large amount of data while ARIMA cannot use it. ARIMA does not have the ability to learn even though given a large amount of data it gives poor forecasting results. The ARIMA prediction is the same as the values of the previous day.
PETA KENDALI X DENGAN UKURAN SAMPEL DAN INTERVAL PENGAMBILAN SAMPEL YANG BERVARIASI Singgih, Pauline Astari; Halim, Siana; Octavia, Tanti
Jurnal Teknik Industri Vol 2, No 2 (2000): DESEMBER 2000
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.2.2.pp. 72-83

Abstract

Shewhart X chart is widely used in statistical process control for monitoring variable data and has shown good performance in detecting large mean shift but less sensitive in detecting moderate to small process shift. X chart with variable sample size and sampling interval (VSSI X chart) is proposed to enhance the ability of detecting moderate to small process shift. The performance of VSSI X chart is compared with those of Shewhart X chart, VSS X chart (Variable Sample Size X chart) and VSI X chart (Variable Sampling Interval X chart). Performance of these control charts is presented in the form of ATS (Average Time to Signal) which is obtained from computer simulation and markov chain approach. The VSSI X chart shows better performance in detecting moderate mean shift. The simulation is then continued for VSSI X chart and VSS X chart with minimum sample size n 1=1 and n 1=2. Abstract in Bahasa Indonesia : Peta kendali X Shewhart telah umum digunakan dalam pengendalian proses statistis untuk data variabel dan terbukti berfungsi dengan baik untuk mendeteksi pergeseran rerata yang besar, namun kurang cepat dalam mendeteksi pergeseran rerata yang sedang hingga kecil. Untuk mengatasi kelemahan ini, diusulkan penggunaan peta kendali X dengan ukuran sampel dan interval pengambilan sampel yang bervariasi (peta kendali VSSI). Kinerja peta kendali X VSSI dibandingkan dengan kinerja peta kendali Shewhart, peta kendali X VSS (peta kendali X dengan ukuran sampel yang bervariasi), dan peta kendali X VSI (peta kendali X dengan interval waktu pengambilan sampel yang bervariasi). Kinerja peta kendali dinyatakan dalam nilai ATS (Average Time to Signal) yang didapatkan dari hasil simulasi program komputer maupun perhitungan Rantai Markov. Peta kendali X VSSI terbukti mempunyai kinerja yang lebih baik dalam mendeteksi pergeseran rerata yang sedang. Selain itu juga disimulasikan penggunaan peta kendali X VSSI dan peta kendali X VSS dengan ukuran sampel minimum n1=1 dan n1=2. Kata kunci: peta kendali X, variable sample size, variable sampling interval, ATS.
Co-Authors Adrian Michael Wibisono Agnes Eval Aida Fitriani Tobing Amelia Agnes Sunjono, Amelia Agnes Arif Chandra Cicely Elviani David Christian, David David Soebiantoro Demmy Kasih, Demmy Dian Wulandari Felecia - Felecia . Felecia Felecia Felicia Tedja Widjaja, Felicia Tedja Genesius Chandra Hendrik Sumawi Herman Mallian Indriati Bisono Indriati N Bisono Inggrid -, Inggrid Inggrid Inggrid Jani Rahardjo Johan Widjaja Njo Anastasia Olivia Sigit, Olivia Oviliani Yenty Yuliana Pauline Astari Singgih Putri, Kristina Sanjaya Shirley Adelia Stefanie Hartanto Tan Didik Wardoyo Tanusi Tanti Octavia Valencia Suseno veronica - Yohan Wahyudi Yuliana Vina Humira