scholarly journals IMPLEENTASI MODEL HYBRID DALAM MEMPREDIKSI PENYEBARAN COVID-19 DI WILAYAH JAWA BARAT

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 124-137
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
World Health Organization ◽  
Error Rate ◽  
World Health ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Data Set ◽  
Absolute Percentage Error ◽  
Hybrid Data ◽  
Health Organization

Di awal tahun 2020, dunia dikagetkan dengan kejadian infeksi berat dengan penyebab yang belum diketahui, yang berawal dari laporan dari Cina kepada World Health Organization (WHO) terdapatnya 44 pasien pneumonia yang berat di suatu wilayah yaitu Kota Wuhan, Provinsi Hubei, China, tepatnya di akhir tahun 2019. Pada perkembangannya, wabah ini kemudian diidentifikasi sebagai wabah Virus Covid-19. Penambahan jumlah kasus COVID-19 berlangsung sangat cepat, sampai dengan 16 Februari 2020, secara global dilaporkan 51.857 kasus terkonfirmasi di 25 negara dengan 1.669 kematian (CFR 3,2%). Di Indonesia ada lebih dari 2 ribu kasus ditemukan dan hampir 200 orang telah meninggal. Di wilayah Jawa barat, kasus positif Covid-19 juga terus bertambah. Data penyebaran virus Corona Covid-19 di Jawa Barat mengalami perubahan, Rabu (1/4/2020). Terpantau melalui situs resmi Pusat Informasi dan Koordinasi Covid-19 Provinsi Jawa Barat (Pikobar), jumlah orang terpapar positif Corona mencapai 198 orang. Data yang diakses dari pikobar.jabarprov.go.id tersebut juga merilis sudah ada 11 pasien yang dinyatakan sembuh dan diperbolehkan pulang dari rumah sakit. Sementara jumlah pasien yang meninggal dunia berjumlah 21 orang. Sedangkan untuk pasien dalam pengawasan (PDP), jumlah yang telah diproses dalam pengawasan mencapai 727 orang. Sedangkan yang telah selesai menjalani pengawasan mencapai 242 orang. Total PDP di Jabar berjumlah 969 orang. Berbekal dari data Pusat Informasi dan Koordinasi Covid-19 Jawa Barat, penulis tertarik melakukan penelitian untuk memprediksi penyebaran kasus positif Covid-19 di Jawa Barat. Dalam penelitian ini, model yang digunakan adalah Hybrid. Data set yang digunakan adalah data pasien positif Covid-19 mulai bulan April 2020 sampai dengan bulan Februari 2021. Berdasarkan hasil penelitian yang sudah dilakukan, model Hybrid mampu memprediksi jumlah penyebaran kasus Covid-19 di Jawa Barat. Hal ini dibuktikan dengan hasil training teknik Hybrid memiliki error rate sebesar 0,4615, yang dilanjutkan dengan analisa akurasi prediksi selama tiga bulan, yaitu bulan Desember 2020, Januari, dan Febrauri 2021. Dari hasil prediksi, model Hybrid memiliki nilai rata – rata Mean Absolute Deviation (MAD) sebesar 351. Sedangkan berdasarkan analisa prediksi dengan teknik Mean Absolute Percentage Error (MAPE) memiliki rata rata tingkat kesalahan sebesar 0,2061 atau 20,6%.

scholarly journals Kajian Adaptive Neuro-Fuzzy Inference System (ANFIS) Dalam Memprediksi Penerimaan Mahasiswa Baru Pada Universitas Buana Perjuangan Karawang

2021 ◽  
Vol 6 (1) ◽  
pp. 44-54
Author(s):  
Tatang Rohana Cucu
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

Abstract - The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang.   Keywords: ANFIS, Backpropagation, Hybrid, Prediction

scholarly journals Compliance of clinical trial registries with the World Health Organization minimum data set: a survey

Trials ◽  
2009 ◽  
Vol 10 (1) ◽  
Author(s):  
Lorenzo P Moja ◽  
Ivan Moschetti ◽  
Munira Nurbhai ◽  
Anna Compagnoni ◽  
Alessandro Liberati ◽  
...  
Keyword(s):  
Clinical Trial ◽  
World Health Organization ◽  
World Health ◽  
Minimum Data Set ◽  
Data Set ◽  
Clinical Trial Registries ◽  
Minimum Data ◽  
The World ◽  
Health Organization

scholarly journals Parents’ Assessments of Disability in Their Children Using World Health Organization International Classification of Functioning, Disability and Health, Child and Youth Version Joined Body Functions and Activity Codes Related to Everyday Life

2017 ◽  
Vol 11 ◽  
pp. 117955651771503 ◽  
Author(s):  
Niels Ove Illum ◽  
Kim Oren Gradel
Keyword(s):  
World Health Organization ◽  
Children With Disabilities ◽  
Muscular Atrophy ◽  
International Classification ◽  
World Health ◽  
International Classification Of Functioning ◽  
Disability And Health ◽  
Data Set ◽  
Health Organization

Aim: To help parents assess disability in their own children using World Health Organization (WHO) International Classification of Functioning, Disability and Health, Child and Youth Version (ICF-CY) code qualifier scoring and to assess the validity and reliability of the data sets obtained. Method: Parents of 162 children with spina bifida, spinal muscular atrophy, muscular disorders, cerebral palsy, visual impairment, hearing impairment, mental disability, or disability following brain tumours performed scoring for 26 body functions qualifiers (b codes) and activities and participation qualifiers (d codes). Scoring was repeated after 6 months. Psychometric and Rasch data analysis was undertaken. Results: The initial and repeated data had Cronbach α of 0.96 and 0.97, respectively. Inter-code correlation was 0.54 (range: 0.23-0.91) and 0.76 (range: 0.20-0.92). The corrected code-total correlations were 0.72 (range: 0.49-0.83) and 0.75 (range: 0.50-0.87). When repeated, the ICF-CY code qualifier scoring showed a correlation R of 0.90. Rasch analysis of the selected ICF-CY code data demonstrated a mean measure of 0.00 and 0.00, respectively. Code qualifier infit mean square (MNSQ) had a mean of 1.01 and 1.00. The mean corresponding outfit MNSQ was 1.05 and 1.01. The ICF-CY code τ thresholds and category measures were continuous when assessed and reassessed by parents. Participating children had a mean of 56 codes scores (range: 26-130) before and a mean of 55.9 scores (range: 25-125) after repeat. Corresponding measures were −1.10 (range: −5.31 to 5.25) and −1.11 (range: −5.42 to 5.36), respectively. Based on measures obtained at the 2 occasions, the correlation coefficient R was 0.84. The child code map showed coherence of ICF-CY codes at each level. There was continuity in covering the range across disabilities. And, first and foremost, the distribution of codes reflexed a true continuity in disability with codes for motor functions activated first, then codes for cognitive functions, and, finally, codes for more complex functions. Conclusions: Parents can assess their own children in a valid and reliable way, and if the WHO ICF-CY second-level code data set is functioning in a clinically sound way, it can be employed as a tool for identifying the severity of disabilities and for monitoring changes in those disabilities over time. The ICF-CY codes selected in this study might be one cornerstone in forming a national or even international generic set of ICF-CY codes for the benefit of children with disabilities, their parents, and caregivers and for the whole community supporting with children with disabilities on a daily and perpetual basis.

scholarly journals PENERAPAN METODE EXPONENTIAL MOVING AVERAGE PADA PERAMALAN PENGGUNAAN AIR DI PDAM KOTA GORONTALO

2020 ◽  
Vol 1 (2) ◽  
pp. 69-77
Author(s):  
WA SALMI ◽  
ISMAIL DJAKARIA ◽  
RESMAWAN RESMAWAN
Keyword(s):  
Water Use ◽  
Average Method ◽  
Moving Average ◽  
Dry Season ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Facing the dry season, it is probable that there is a lack of water or excess distribution at one point during distribution to every house that uses PDAM water every day. This will result in community instability in using water and inaccurate users. Therefore, forecasting of the amount of water used in PDAM Kota Gorontalo for the next period. The method used to forecast is the Exponential Moving Average method. Criteria in determining the best method is based on the value of Mean Absolute Deviation and Mean Absolute Percentage Error. After forecasting each smoothing constant is compared, the best model. in predicting the amount of water use in PDAM Kota Gorontalo is an Exponential Moving Average with a smoothing constant of 0.15 because it has the smallest MAD and MAPE values.

scholarly journals Aplikasi Triple Exponential Smoothing Untuk Forecasting Jumlah Penduduk Miskin

2020 ◽  
Vol 3 (2) ◽  
pp. 76-82
Author(s):  
Padrul Jana
Keyword(s):  
Poverty Reduction ◽  
Mean Squared Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Poor People ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Absolute Percentage Error ◽  
The Government

This study aims to predict the number of poor in Indonesia for the next few years using a triple exponential smoothing method.The purpose of this research is the result of the forecast number of poor people in Indonesia accurate forecast results are used as an alternative data the government for consideration of government to determine the direction of national poverty reduction policies. This research includes the study of literature research, by applying the theory of forecasting to generate predictions of poor people for coming year. Furthermore, analyzing the mistakes of the methods used in terms of the count: Mean Absolute Deviation (MAD), Mean Square Error (MSE), Mean absolute percentage error (MAPE) and Mean Percentage Error (MPE). The function of this error analysis is to measure the accuracy of forecasting results that have been conducted.These results indicate that the number of poor people in 2017 amounted to 24,741,871 inhabitants, in 2018 amounted to 24,702,928 inhabitants, in 2019 amounted to 24,638,022 inhabitants and in 2020 amounted to 24,547,155 people. The forecasting results show an average reduction in the number of poor people in Indonesia last five years (2016-2020 years) ranges from 0.16 million. Analysis forecasting model obtained an mean absolute deviation (MAD) obtained by 0.246047. Mean squared error (MSE) of forecasting results with the original data by 1.693277. Mean absolute percentage error (MAPE) of 3.040307% and the final Mean percentage error (MPE) of 0.888134%.Kata Kunci: Forecasting, Triple Exponential Smoothing

scholarly journals SISTEM PENDUKUNG KEPUTUSAN DALAM PEMILIHAN MODEL DENGAN MENGGUNAKAN METODE TIME SERIES

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Noer Chamid ◽  
Muhammad Ainul Yaqin ◽  
Nailul Izzah
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Trend Line ◽  
Absolute Percentage Error

Analisis time series antara lain memahami dan menjelaskan mekanisme tertentu, meramalkan suatu nilai di masa depan dan mengoptimalkan sistem kendali. Dalam pengambilan keputusan yang menggunakan analisis time series tersebut perlu menggunakan software yang prabayar seperti Minitab, SPSS dan SAS sehingga perlu pembuatan sistem informasi yang mendukung keputusan dalam analisis tersebut. Sistem informasi yang dibuat tersebut akan dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah atau data lainnya. Model yang digunakan dalam menduga adalah dengan menggunakan 4 (empat) metode, yaitu : Metode Moving Average, Metode Eksponential Smooting, Metode Linier Trend Line dan Seasonal Adjusment. Dari 4 (empat) metode tersebut, dapat dipilih model yang terbaik dengan menggunakan kriteria menentukan nilai Mean Absolute Deviation (MAD) dan Mean Absolute Percentage Error (MAPE) yang terkecil. Sistem informasi yang dibuat tersebut sudah dilakukan uji coba terhadap kehandalan dan diimplementasikan dalam pengambilan keputusan untuk menentukan penyusunan target pendapatan asli daerah di pemerintah daerah. Sistem Pendukung Keputusan ini dapat dijadikan sebagai tool dalam membuat rekomendasi sebuah keputusan.Kata Kunci: Time Series, Sistem Pendukung Keputusan, Pendapatan Asli Daerah                                                                       

scholarly journals ARDL METHOD: Forecasting Data Curah Hujan Harian NTB

Jurnal Varian ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 73-82
Author(s):  
Ulul Azmi ◽  
Zilullah Nazir Hadi ◽  
Siti Soraya
Keyword(s):  
Mean Squared Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Root Mean Squared Error ◽  
Absolute Percentage Error ◽  
Distributed Lag ◽  
Autoregressive Distributed Lag ◽  
Squared Error ◽  
Parameter Error

Penelitian ini berisi tentang prediksi atau forecasting data iklim di Nusa Tenggara Barat (NTB) tahun 2011, yakni jumlah hari terjadinya hujan dengan menggunakan metode Autoregressive Distributed Lag (ARDL). Data yang digunakan yaitu data iklim di Nusa Tenggara Barat (NTB) dari tahun 2006 -2010, dengan menggunakan beberapa parameter error seperti Mean Absolute Deviation (MAD), Mean Squared Error (MSE), Root Mean Squared Error (RMSE) dan Mean Absolute Percentage Error (MAPE). Berdasarkan hasil simulasi data iklim di Nusa Tenggara Barat (NTB) tersebut, diperoleh prediksi jumlah hari terjadinya curah hujan pada tahun 2011 sebesar 226 hari dengan nilai MAD 20,8069, MSE 3,5569, RMSE 1,88597, dan MAPE 11,9297 . Dan prediksi jumlah hari terjadinya hujan pada tahun 2011 sebanyak 225,928 hari atau jika di bulatkan menjadi 226 hari dengan nilai parameter error MAD sebesar 20,8069, sehingga dapat disimpulkan pada tahun 2011 terjadi peningkatan jumlah hari terjadinya hujan di Nusa Tenggara Barat (NTB).

scholarly journals PENERAPAN METODE HOLT WINTER DAN SEASONAL ARIMA PADA PERAMALAN PERKEMBANGAN WISATAWAN MANCANEGARA YANG DATANG KE INDONESIA.

2017 ◽  
Vol 6 (4) ◽  
pp. 29
Author(s):  
Dila Mulya ◽  
Yudiantri Asdi ◽  
Ferra Yanuar
Keyword(s):  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Abstrak. Pada tugas akhir ini akan dirumuskan pemodelan peramalan perkembanganwisatawan mancanegara yang datang ke Indonesia dengan metode Holt Winter dan Sea-sonal ARIMA. Kemudian hasil peramalan perkembangan wisatawan dengan menggu-nakan kedua metode tersebut akan dibandingkan berdasarkan nilai Mean Squared Devi-ation (MSD), Mean Absolute Percentage Error (MAPE) serta Mean Absolute Deviation(MAD). Berdasarkan hasil yang diperoleh, model terbaik untuk peramalan perkem-bangan wisatawan mancanegara yang datang ke Indonesia adalah model SARIMA(0; 1; 1)(1; 1; 0)12 , karena nilai MAPE, MAD dan MSD yang diperoleh lebih kecil dari-pada model Holt Winter.Kata Kunci: Holt Winter, Seasonal Arima, Trend, Musiman

Performance Evaluation of Adaptive Neuro-Fuzzy Inference System (ANFIS) In Predicting New Students (Case Study : UBP Karawang)

2021 ◽  
Vol 2 (2) ◽  
pp. 31-36
Author(s):  
Tatang Rohana ◽  
Bayu Priyatna
Keyword(s):  
Error Rate ◽  
Fuzzy Inference ◽  
Percentage Error ◽  
Hybrid Technique ◽  
Mean Absolute Deviation ◽  
Absolute Deviation ◽  
Inference System ◽  
Neuro Fuzzy ◽  
New Students ◽  
The Mean

The process of admitting new students is an annual routine activity that occurs in a university. This activity is the starting point of the process of searching for prospective new students who meet the criteria expected by the college. One of the colleges that holds new student admissions every year is Buana Perjuangan University, Karawang. There have been several studies that have been conducted on predictions of new students by other researchers, but the results have not been very satisfying, especially problems with the level of accuracy and error. Research on ANFIS studies to predict new students as a solution to the problem of accuracy. This study uses two ANFIS models, namely Backpropagation and Hybrid techniques. The application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) model in the predictions of new students at Buana Perjuangan University, Karawang was successful. Based on the results of training, the Backpropagation technique has an error rate of 0.0394 and the Hybrid technique has an error rate of 0.0662. Based on the predictive accuracy value that has been done, the Backpropagation technique has an accuracy of 4.8 for the value of Mean Absolute Deviation (MAD) and 0.156364623 for the value of Mean Absolute Percentage Error (MAPE). Meanwhile, based on the Mean Absolute Deviation (MAD) value, the Backpropagation technique has a value of 0.5 and 0.09516671 for the Mean Absolute Percentage Error (MAPE) value. So it can be concluded that the Hybrid technique has a better level of accuracy than the Backpropation technique in predicting the number of new students at the University of Buana Perjuangan Karawang

scholarly journals COMPARISON OF SOME STATISTICAL FORECASTING TECHNIQUES WITH GMDH PREDICTOR: A CASE STUDY

2018 ◽  
Vol 47 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Syed Misbah Uddin ◽  
Aminur Rahman ◽  
Emtiaz Uddin Ansari
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Demand forecasts are extremely important for manufacturing industry and also needed for all type of business and business suppliers for distribution of finish products to the consumer on time. This study is concerned with the determination of accurate models for forecasting cement demand. In this connection this paper presents results obtained by using a self-organizing model and compares them with those obtained by usual statistical techniques. For this purpose, Monthly sales data of a typical cement ranging from January, 2007 to February, 2016 were collected. A nonlinear modelling technique based on Group Method of Data Handling (GMDH) is considered here to derive forecasts. Forecast were also made by using various time series smoothing techniques such as exponential smoothing, double exponential smoothing, moving average, weightage moving average and regression method. The actual data were compared to the forecast generated by the time series model and GMDH model. The mean absolute deviation (MAD, mean absolute percentage error (MAPE) and mean square error (MSE) were also calculated for comparing the forecasting accuracy. The comparison of modelling results shows that the GMDH model perform better than other statistical models based on terms of mean absolute deviation (MAD), mean absolute percentage error (MAPE) and mean square error (MSE).

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