Forecasting Infant Mortality Rate for China: A Comparison Between α-Sutte Indicator, ARIMA, and Holt-Winters

Kurniasih, Nuning; Ahmar, Ansari Saleh; Hidayat, Dadang Rahmat; Agustin, Herlina; Rizal, Edwin

doi:10.1088/1742-6596/1028/1/012195

Cited by 13 publications

(9 citation statements)

References 5 publications

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“…This interactive nature is intended to facilitate the integration between the various components in the decision-making process such as procedures, policies, analytical techniques, and experience and managerial insight to establish a flexible decision framework [23], [24]. Decision Support System is a computer-based information system that generates various decision alternatives to assist management in handling various structured or unstructured problems using data and models [25]- [33]. Computer-based word is a keyword, because it is almost impossible to build a DSS without using a computer as a tool, especially for storing data and managing models.…”

Section: Methodsmentioning

confidence: 99%

Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section

Siahaan¹,

Djanggih²,

Aswari³

2018

Preprint

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Employees are the backbone of corporate activities and the giving of bonuses, job titles and allowances to employees to motivate the work of employees is very necessary, salesman on the company very much and to find the best salesman cannot be done manually and for that required the implementation of a system in this decision support system by applying the TOPSIS method, it is expected with the implementation of TOPSIS method the expected results of top management can be fulfilled.

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Section: Methodsmentioning

confidence: 99%

Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section

Siahaan¹,

Djanggih²,

Aswari³

2018

Preprint

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“…α-Sutte Indicator is a novel technical analysis based on the Sutte Indicator method that was initially proposed in 2017 by Ahmar [26]. As known, the stock movements failed to be always consistent, sometimes suddenly descended and sometimes suddenly ascended, which caused less accuracy in the domain of stock forecasting using the common technical indicators such as simple moving average (SMA), moving average convergence/divergence (MACD), relative strength index (RSI), stochastic and Bolinger Band [23,26,39]. Hence, the development of α-Sutte Indicator is expected to overcome the weakness of the mentioned methods in predicting the stock movements by considering five elements of the stock movements, namely price at the time of opening, closing, highest and lowest, along with the volume of transactions [24].…”

Section: α-Sutte Indicatormentioning

confidence: 99%

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Wang

Yao

et al. 2020

Epidemiol. Infect.

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Forecasting the epidemics of the diseases is very valuable in planning and supplying resources effectively. This study aims to estimate the epidemiological trends of the coronavirus disease 2019 (COVID-19) prevalence and mortality using the advanced α-Sutte Indicator, and its prediction accuracy level was compared with the most frequently adopted autoregressive integrated moving average (ARIMA) method. Time-series analysis was performed based on the total confirmed cases and deaths of COVID-19 in the world, Brazil, Peru, Canada and Chile between 27 February 2020 and 30 June 2020. By comparing the prediction reliability indices, including the root mean square error, mean absolute error, mean error rate, mean absolute percentage error and root mean square percentage error, the α-Sutte Indicator was found to produce lower forecasting error rates than the ARIMA model in all data apart from the prevalence testing set globally. The α-Sutte Indicator can be recommended as a useful tool to nowcast and forecast the COVID-19 prevalence and mortality of these regions except for the prevalence around the globe in the near future, which will help policymakers to plan and prepare health resources effectively. Also, the findings of our study may have managerial implications for the outbreak in other countries.

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“…In this research was conducted, the results text file compression of compressed text file encryption is performed into a text file in the form Ciphertext [14]- [17], then the Ciphertext text file pasted into the image file and retrieved stego images [18], [19]. To find out the quality of the stego images, then performed the calculation value of the Mean Squared Error (MSE) [20]- [23] and Peak Signal to Noise Ratio (PSNR) [2], [24], [25] stego file from the image. Next do the extraction of text files in the files of the form Ciphertext stego images and is done in order to obtain decryption Plaintext as well as do decompression in order to return to the text [26]- [28].…”

Section: Introductionmentioning

confidence: 99%

Increase the PSNR Of Image Using LZW and AES Algorithm With MLSB on Steganography

Wijayanto¹,

Zarlis²,

Situmorang³

2018

IJET

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There are many research has done a hybridization approach of text message insertion that has been compressed with Lempel-Ziv-Welch (LZW) algorithm and has also been encrypted. The text messages in ciphertext form are inserted into the image file using LSB (Least Significant Bit) method. The results of this study indicate that the value of Peak Signal to Noise Ratio (PSNR) lower than the LSB method of 0.94 times with a ratio of 20.33%, with Kekre's method of 10.04%. To improve the value of PSNR stego image of insertion, in this research is inserted audio samples using 5 bits to reduce the amount of data insertion, so it can get the value of MSE stego image low. Prior to insertion, the text file is compressed with the Lempel-Ziv-Welch (LZW) algorithm and encrypted with the Advanced Encryption Standard (AES) algorithm. Then, the insertion of compression and encrypted text files is done with the Modified Least Significant Bit (MLSB) algorithm. To performa test reliability of steganography, the image stego image is done by calculating Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR). At extraction process with MLSB algorithm, decryption with AES algorithm and decompression with LZW algorithm. The experimental results show that the MSE values obtained are lower and the proposed PSNR method is better with (α) 1,044 times than the Kaur method, et al. The result of embed text file extraction from the stego image works well resulting in encrypted and uncompressed text files.

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Forecasting Infant Mortality Rate for China: A Comparison Between α-Sutte Indicator, ARIMA, and Holt-Winters

Cited by 13 publications

References 5 publications

Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section

Technique for Order Preference by Similarity to Ideal Solution as Decision Support Method for Determining Employee Performance of Sales Section

Forecasting the epidemiological trends of COVID-19 prevalence and mortality using the advanced α-Sutte Indicator

Increase the PSNR Of Image Using LZW and AES Algorithm With MLSB on Steganography

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