scholarly journals Analysis Comparative Method Shari’a Compliant Asset Pricing Model

SENTRALISASI ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 132
Author(s):  
Bekti Wiji Lestari ◽  
Erma Setiawati ◽  
Noer Sasongko
Keyword(s):  
Stock Returns ◽  
Mean Square Error ◽  
Comparative Method ◽  
Explanatory Power ◽  
Sampling Technique ◽  
Coefficient Of Determination ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Risk Free Rate

The researcher's view of the CAPM model is not in accordance with the Islamic economy because there is an Rf element as an instrument that contains an interest element, so a modified model of CAPM based on sharia is introduced, namely SCAPM. This study aims to analyze the differences in the SCAPM method according to Tomkins & Karim (1987) SCAPM non Rf, Ashker (1987) SCAPMZ, Shaikh (2010) SCAPM NGDP, and Hanif (2011) SCAPMI. The sampling technique used was purposive sampling and obtained 19 samples. Data analysis used is the calculation of Mean Absolute Deviation (MAD), Mean Square Error (MSE), and the coefficient of determination. The results of the calculation of Mean Absolute Deviation (MAD) and Mean Square Error (MSE) explain that there are differences from the SCAPM models without risk free rate, SCAPMZ, SCAPM NGDP, and SCAPMI. Meanwhile, SCAPMI has the best explanatory power than the other four SCAPM models. It is recommended that Islamic and conventional investors use SCAPMZ modeling in predicting stock returns.

ANALISIS PERENCANAAN PENJUALAN DENGAN METODE TIME SERIES (STUDI KASUS PADA PD. SUMBER JAYA ALUMINIUM)

2019 ◽  
Vol 3 (7) ◽  
pp. 780-789
Author(s):  
Lingga Yuliana
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation

Penjualan dan produksi adalah dua hal yang saling berkaitan dan tidak dapat terpisahkan didalam suatu pengoperasian perusahaan, didalam memproduksi suatu produk perusahaan harus melihat tersedia dalam gudang / penyimpanan serta beberapa jumlah yang akan dijual. Disebabkan dengan hal tersebut maka perusahaan perlu melakukan peramalan penjualan (sales forecasting). Tujuan dalam penelitian ini menentukan metode peramalan yang tepat berdasarkan tingkat kesalahan terkecil berdasarkan nilai Mean Absolute Deviation (MAD), Mean Square Error (MSE) dan Mean Absolute Procentage Error (MAPE). Penelitian ini menunjukkan bahwa plot data peramalan penjualan rak piring menunjukkan kecenderungan naik (trend). Metode peramalan yang dianggap terbaik terdapat pada metode winter multiplikatif, karena tiga nilai kesalahan (error) yang diuji yaitu MAD, MSE dan MAPE menunjukkan tingkat kesalahan yang paling kecil dengan metode tersebut. Nilai kesalahan yang ditunjukkan dalam penelitian ini, dimana nilai MAD sebesar 73,5, nilai MSE sebesar 10137,7 dannilai MAPE sebesar 4,9.

scholarly journals Peramalan Penerbitan Ijin Mendirikan Bangunan Dengan Single Moving Average Dan Exponential Smoothing

2019 ◽  
Vol 18 (2) ◽  
Author(s):  
Yogha Pramana ◽  
Rukmi Sari Hartati ◽  
Komang Oka Saputra
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Percent Error ◽  
Mean Absolute Percent Error ◽  
Mean Error ◽  
Absolute Percent Error

Ijin Mendirikan Bangunan adalah ijin yang diberikan oleh Kepala Daerah pada pemilik bangunan untuk mendirikan bangunan, mengubah, memperluas, mengurangi atau merawat bangunan sesuai dengan persyaratan administratif dan persyaratan teknis yang berlaku. Peramalan adalah merupakan perkiraan mengenai terjadinya suatu kejadian pada masa depan. Peramalan merupakan sebuah alat bantu yang penting dalam perencanaan yang efesien dan efektif. Prosesnya untuk mengetahui kebutuhan di masa datang antara lain kebutuhan ukuran kuantitas, kualitas, waktu dan lokasi untuk pemenuhan permintaan barang ataupun jasa. Peramalan merupakan bagian awal dari pengambilan suatu keputusan akhir. Data Ijin Mendirikan Bangunan (IMB) di hitung dengan metode Simple Moving Average dan Exponential Smoothing untuk mengetahui nilai dari Mean Error, Mean Absolute Deviation, Mean Square Error, Standar Error, Mean Absolute Percent Error.

scholarly journals Peramalan Perencanaan Produksi Batu Tela Dengan Metode Exponential Smoothing (Studi Kasus Pada CV. Sinar Sowi Kabupaten Manokwari)

2019 ◽  
Vol 2 (2) ◽  
pp. 54-59
Author(s):  
Suwoko ◽  
Dirarini Sudarwadi ◽  
Nurwidianto
Keyword(s):  
Mean Square Error ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Error ◽  
Alpha Models ◽  
The Mean ◽  
Exponential Smoothing Method

This study aims to find out how much forecasting the production of concrete brick at CV. Sinar Sowi. The data analysis method used is the Exponential Smoothing method by using forecasting error measurements namely Mean Square Error (MSE) and Mean Absolute Deviation (MAD). From the data that has been analyzed, the writer can conclude that the use of alpha model 0.1 Exponential Smoothing method, the value of the Exponential Smoothing method, the value of Mean Square Error is 11,114,950 and the value of Mean Absolute Deviation is 962. The use of alpha 0.5 model Exponential Smoothing method, the value of Mean Square Error is 1,114,776 and the value of Mean Absolute Deviation is 305. While the use of the alpha 0.9 model is Exponential Smoothing, the Mean Square Error value is -9.374 and the Mean Absolute Deviation value is -28. Of the three existing alpha models, namely 0.1; 0.5 and 0.9, then what will be used in forecasting is alpha 0.9 because the error value is the lowest, namely the Mean Square Error of -9,374 and Mean Absolute Deviation is -28. From the calculation of concrete brick forecasting at CV. Sinar Sowi in Manokwari Regency, the forecasting results were 39,698 units.

scholarly journals Metode Peramalan moving Average, 1000 ANALISIS PERBANDINGAN PERAMALAN PERMINTAAN PELUMAS PT XYZ DENGAN METODE MOVING AVERAGE, EXPONENTIAL SMOOTHING DAN NAIVE METHOD

2021 ◽  
Vol 2020 (1) ◽  
pp. 1000-1010
Author(s):  
Destia Anisya Ramdani ◽  
Fahriza Nurul Azizah
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Exponential Smoothing ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Naive Method

Pelumas merupakan produk dari PT XYZ yang digunakan untuk kendaraan dan mesin-mesin industri. Peramalan umumnya dilakukan untuk meramalkan jumlah produksi di masa mendatang dengan menggunakan data historis atau data-data pada permintaan sebelumnya terhadap produk perusahaan. Penelitian ini dilakukan untuk menguji enam metode peramalan agar dapat mengetahui metode mana yang tepat untuk diterapkan pada PT XYZ. Peramalan pada PT XYZ ini menggunakan data historis permintaan tahun 2019 dari bulan januari hingga bulan desember yang telah merepresentasikan pola permintaan setiap tahun di PT XYZ. Data ini digunakan untuk meramalkan setahun kedepan.Penelitian kali ini akan membandingkan enam metode peramalan diantaranya metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, exponential smoothing dengan α=0,9 dan naive method. Untuk bahan perbandingan dari keenam metode yang telah disebutkan maka diberikan peramalan yaitu dengan metode penyimpangan Mean Absolute Deviation (MAD), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan Absolute Presentage Error (MAPE).Hasil penelitian ini menunjukkan metode peramalan exponential smoothing dengan α=0,9 dengan nilai penyimpangan MAD 2.364,50, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 dapat dikatakan metode yang lebih optimal untuk diterapkan di PT XYZ karena memiliki nilai penyimpangan paling rendah dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5 dan naive method.Sehingga PT XYZ untuk menentukan tingkat permintaan konsumen dapat menggunakan metode exponential smoothing dengan α=0,9, karena setelah dilakukan perbandingan dari hasil penyimpangan setiap metode dan telah terbukti bahwasannya metode exponential smoothing dengan α=0,9 memiliki nilai penyimpangan MAD 2.364,60, MSE 12.448.875,06, RMSE 3.528,30 dan MAPE 0,60 yang artinya merupakan nilai penyimpangan terkecil dari metode moving average 3 bulanan, moving average 5 bulanan, exponential smoothing dengan α=0,1, exponential smoothing dengan α=0,5, dan naive method.

scholarly journals ANALISA KOMPARASI NEURAL NETWORK BACKPROPAGATION DAN MULTIPLE LINEAR REGRESSION UNTUK PERAMALAN TINGKAT INFLASI

2019 ◽  
Author(s):  
Amrin Amrin
Keyword(s):  
Neural Network ◽  
Linear Regression ◽  
Root Mean Square Error ◽  
Multiple Linear Regression ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation

Tingkat inflasi tidak dapat dianggap remeh dalam sistem perekonomian suatu negara dan pelaku bisnis pada umumnya. Jika inflasi dapat diramalkan dengan akurasi yang tinggi, tentunya dapat dijadikan dasar pengambilan kebijakan pemerintah dalam mengantisipasi aktivitas ekonomi di masa depan. Pada penelitian ini akan digunakan metode prediksi neural network backpropagation dan multiple linear regression untuk memprediksi tingkat inflasi bulanan di indonesia, selanjutnya membandingkan manakah yang terbaik dari kedua metode tersebut. Data inflasi yang digunakan bersumber dari Badan Pusat Statistik dari tahun 2006-2015, dimana 80% sebagai data training dan 20% sebagai data testing. Dari hasil analisis data yang dilakukan disimpulkan bahwa Performa model multiple linear regression lebih baik dibandingkan dengan metode neural network backpropagation dengan nilai mean absolute deviation (MAD) sebesar 0.0380, mean square error (MSE) sebesar 0.0023, dan nilai Root Mean Square Error (RMSE) sebesar 0.0481

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).

scholarly journals PERBANDINGAN METODE FTS DAN MA PADA PERAMALAN PERSEDIAAN BERAS

2020 ◽  
Vol 6 (3) ◽  
pp. 29-36
Author(s):  
Deddy Kusbianto ◽  
Agung Pramudhita ◽  
Nurhalimah
Keyword(s):  
Time Series ◽  
Mean Square Error ◽  
Moving Average ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Absolute Percentage Error

Dalam memenuhi kebutuhan masyarakat Kabupaten Malang dan menjaga stabilitas ketersediaan beras pemerintah setempat perlu melakukan proses peramalan. Dimana dalam melakukan proses peramalan menggunakan metode peramalan, salah satunya dengan menggunakan metode Fuzzy Time Series dan Moving Average yaitu dengan menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akan da¬¬tang. Dari hasil implementasi dua metode tersebut menghasilkan perbandingan jumlah persediaan beras. hasil perbandingan tersebut akan dipakai untuk mengukur tingkat error dari masing – masing metode dengan menggunakan MAD (Mean Absolute Deviation), MSE (Mean Square Error), RMSE ( Root Square Error ) dan MAPE (Mean Absolute Percentage Error). Kesimpulannya adalah metode fuzzy time series cocok digunakan untuk studi kasus peramalan persediaan beras dibandingkan menggunakan metode moving average. Sehingga untuk proses peramalan selanjutnya dan untuk mendapatkan hasil dengan tingkat error sedikit dapat menggunakan metode fuzzy time series

PENERAPAN METODE SINGLE MOVING AVERAGE DAN EXSPONENTIAL SMOOTHING PADA USAHA ASRIE MODESTA

2020 ◽  
Vol 3 (1) ◽  
pp. 547
Author(s):  
Dirarini Sudarwadi Sudarwadi ◽  
Mila Fitriani ◽  
Nurlaela Nurlaela
Keyword(s):  
Mean Square Error ◽  
Moving Average ◽  
Secondary Data ◽  
Primary Data ◽  
Moving Averages ◽  
Mean Absolute Deviation ◽  
Mean Square ◽  
Absolute Deviation ◽  
Forecasting Method ◽  
Second Period

This study  aims to (1) analyze the number of demands for batik products in the second period of 2018. (2) To analyze the most appropriate forecasting method. (3) To analyze the forecasting of the first period in 2019 using the selected forecasting method. This reseach uses primary data and secondary data with data collection techniques using interviews, observation, and documentation. The analysis used is Single Moving Averages and Exsponential Smoothing. The results of research in forecasting demand for batik products in 2019 with the Single Moving Average method are 3,936 units with Mean Absolute Deviation (MAD) of 632.5 units and Mean Square Error (MSE) of 693,718 units. And the Exsponential Smoothing Alpha 0.05 method is 2,788,879 units, with Mean Absolute Deviation (MAD) of 694,318 units and Mean Square Error (MSE) of 960,665 units. The method suggested to company in making forecast predictions is to use the Single Moving Averages method because it has the smallest error rate that compared to the Exsponential Smoothing method with an Alpha value of 0.05.

Modelling response of infiltration loss toward water table depth using RBFN, RNN, ANFIS techniques

2021 ◽  
Vol 25 (2) ◽  
pp. 227-234
Author(s):  
Sandeep Samantaray ◽  
Abinash Sahoo
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Water Table ◽  
Environmental Sustainability ◽  
Model Development ◽  
Water Table Depth ◽  
Water Levels ◽  
Coefficient Of Determination ◽  
Mean Square ◽  
Inference System

Accurate prediction of water table depth over long-term in arid agricultural areas are very much important for maintaining environmental sustainability. Because of intricate and diverse hydrogeological features, boundary conditions, and human activities researchers face enormous difficulties for predicting water table depth. A virtual study on forecast of water table depth using various neural networks is employed in this paper. Hybrid neural network approach like Adaptive Neuro Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN), Radial Basis Function Neural Network (RBFN) is employed here to appraisal water levels as a function of average temperature, precipitation, humidity, evapotranspiration and infiltration loss data. Coefficient of determination (R2), Root mean square error (RMSE), and Mean square error (MSE) are used to evaluate performance of model development. While ANFIS algorithm is used, Gbell function gives best value of performance for model development. Whole outcomes establish that, ANFIS accomplishes finest as related to RNN and RBFN for predicting water table depth in watershed.

scholarly journals IndoorPlant: A Model for Intelligent Services in Indoor Agriculture Based on Context Histories

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1631
Author(s):  
Bruno Guilherme Martini ◽  
Gilson Augusto Helfer ◽  
Jorge Luis Victória Barbosa ◽  
Regina Célia Espinosa Modolo ◽  
Marcio Rosa da Silva ◽  
...  
Keyword(s):  
Technology Acceptance ◽  
Mean Square Error ◽  
Ease Of Use ◽  
Coefficient Of Determination ◽  
Square Root ◽  
Mean Square ◽  
Use Context ◽  
The Mean ◽  
Acceptance Model ◽  
The Internet Of Things

The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.

Sign in / Sign up

Export Citation Format

Share Document