scholarly journals Application of Artificial Intelligence in Prediction of Road Freight Transportation

2017 ◽  
Vol 29 (4) ◽  
pp. 363-370 ◽  
Author(s):  
Bogna Mrowczynska ◽  
Maria Ciesla ◽  
Aleksander Krol ◽  
Aleksander Sladkowski
Keyword(s):  
Artificial Intelligence ◽  
Historical Data ◽  
Percentage Error ◽  
Double Exponential ◽  
Road Freight ◽  
The Mean ◽  
Double Exponential Smoothing ◽  
Trucking Companies ◽  
Road Freight Transportation

Road freight transport often requires the prediction of volume. Such knowledge is necessary to capture trends in the industry and support decision making by large and small trucking companies. The aim of the presented work is to demonstrate that application of some artificial intelligence methods can improve the accuracy of the forecasts. The first method employed was double exponential smoothing. The modification of this method has been proposed. Not only the parameters but also the initial values were set in order to minimize the mean absolute percentage error (MAPE) using the artificial immune system. This change resulted in a marked improvement in the effects of minimization, and suggests that the variability of the initial value of S2 has an impact on this result. Then, the forecasting Bayesian networks method was applied. The Bayesian network approach is able to take into account not only the historical data concerning the volume of freight, but also the data related to the overall state of the national economy. This significantly improves the quality of forecasting. The application of this approach can also help in predicting the trend changes caused by overall state of economy, which is rather impossible when analysing only the historical data.

Application of Several Models for the Forecasting of the Container Throughput of the Abidjan Port in Ivory Coast

2017 ◽  
Vol 28 ◽  
pp. 157-168 ◽  
Author(s):  
Pascal Kany Prud’ome Gamassa ◽  
Yan Chen
Keyword(s):  
Ivory Coast ◽  
Research Work ◽  
Forecast Model ◽  
Exponential Smoothing ◽  
Percentage Error ◽  
Forecasting Model ◽  
Combination Forecast ◽  
Double Exponential ◽  
The Mean ◽  
Double Exponential Smoothing

The Abidjan Port in Ivory Coast has the second highest volume of Container Throughput in West Africa and aims to become a hub Port for the region and one of the most developed Port in Africa in the coming years. In this article, several Forecasting Models are applied to accurately forecast the Abidjan Port Container Throughput. These models are the Grey model, the Linear Regression model, the Double Exponential Smoothing model and a Combination Forecast model. After the application of each model, their results have been compared with the mean absolute percentage error. From their results, the Double Exponential Smoothing model has got the smallest error and been found to have the best data available on the research work, becoming at the same time, the best forecasting model for the Abidjan Port Container Throughput. A forecast of the Abidjan Port Container Throughput was finally made for the period covering the year 2015 up to the year 2020.

scholarly journals Analisis Air Buangan Kantin di Kampus II Universitas Bhayangkara Jakarta Raya

2020 ◽  
Vol 2 (1) ◽  
pp. 13-24
Author(s):  
Lisa Adhani ◽  
Wahyu Kartika ◽  
Dovina Navanti
Keyword(s):  
Waste Water ◽  
Liquid Waste ◽  
Quality Standard ◽  
Absolute Deviation ◽  
Double Exponential ◽  
Wastewater Quality ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Potential Pollution

Canteen is a producer of domestic liquid waste that has the potential to cause pollution. Likewise with the student canteen, it has the potential to produce waste that causes environmental pollution if it is not treated properly. The use of Montecarlo software in this study is to support quantitative analysis in predicting potential pollution from Ubhara canteen waste with Crystall ball prediction. The results of laboratory analysis in the form COD, BOD dan TSS, showed that the quality of the canteen waste water did not meet the requirements for wastewater quality standard based on Ministry of Environment Decree No. 112 of 2003. Supported by the results of CB Predictor simulations showing the potential of pollution of the Ubhara canteen waste water to the environment continues to increase significantly, also seen from the Double Exponential Smoothing Method, producing MAD (Mean Absolute Deviation) 170.82, Theil's U 0.9951, and Confidence Interval Lower 5% and Upper 99.5%.

scholarly journals Daily Forecasting for Antam's Certified Gold Bullion Prices in 2018-2020 using Polynomial Regression and Double Exponential Smoothing

2021 ◽  
Vol 3 (4) ◽  
pp. 45-53
Author(s):  
Tresna Maulana Fahrudin ◽  
Prismahardi Aji Riyantoko ◽  
Kartika Maulida Hindrayani ◽  
I Gede Susrama Mas Diyasa
Keyword(s):  
Time Series Data ◽  
Polynomial Regression ◽  
Exponential Smoothing ◽  
Series Data ◽  
Percentage Error ◽  
Training Set ◽  
Double Exponential ◽  
Forecasting Performance ◽  
One Step ◽  
Double Exponential Smoothing

Gold investment is currently a trend in society, especially the millennial generation. Gold investment for the younger generation is an advantage for the future. Gold bullion is often used as a promising investment, on other hand, the digital gold is available which it is stored online on the gold trading platform. However, any investment certainly has risks, and the price of gold bullion fluctuates from day to day. People who invest in gold hopes to benefit from the initial purchase price even if they must wait up to five years. The problem is how they can notice the best time to sell and buy gold. Therefore, this research proposes a forecasting approach based on time series data and the selling of gold bullion prices per gram in Indonesia. The experiment reported that Holt’s double exponential smoothing provided better forecasting performance than polynomial regression. Holt’s double exponential smoothing reached the minimum of Mean Absolute Percentage Error (MAPE) 0.056% in the training set, 0.047% in one-step testing, and 0.898% in multi-step testing.

Implementasi Metode Double Exponential Smoothing dalam Memprediksi Pertambahan Jumlah Penduduk di Wilayah Kabupaten Karawang

2020 ◽  
Vol 12 (2) ◽  
pp. 95-103
Author(s):  
Andini Diyah Pramesti ◽  
Mohamad Jajuli ◽  
Betha Nurina Sari
Keyword(s):  
Population Distribution ◽  
Exponential Smoothing ◽  
Smoothing Method ◽  
Percentage Error ◽  
Population Census ◽  
Double Exponential ◽  
Double Exponential Smoothing ◽  
Exponential Smoothing Method ◽  
Better Than ◽  
Population Numbers

The density and uneven distribution of the population in each area must be considered because it will cause problems such as the emergence of uninhabitable slums, environmental degradation, security disturbances, and other population problems. In the data obtained from the 2010 population census based on the level of population distribution in Karawang District, the area of West Karawang, East Karawang, Rengasdengklok, Telukjambe Timur, Klari, Cikampek and Kotabaru are zone 1 regions which are the densest zone with a population of 76,337 people up to 155,471 inhabitants. This research predicts / forecasting population growth in the 7 most populated areas for the next 1 year using Double Exponential Smoothing Brown and Holt methods. This study uses Mean Absolute Percentage Error (MAPE) to evaluate the performance of the double exponential smoothing method in predicting per-additional population numbers. Forecasting results from the two methods place the Districts of East Telukjambe, Cikampek, Kotabaru, East Karawang, and Rengasdengklok in 2020 to remain in zone 1 with a range of 76,337 people to 155,471 inhabitants. Whereas in the Districts of Klari and West Karawang are outside the range in zone 1 because both districts have more population than the range in zone 1. From the results of MAPE both methods are found that 6 out of 7 districts in the method Holt's double exponential smoothing produces a smaller MAPE value compared to the MAPE value generated from Brown's double exponential smoothing method. It was concluded that in this study the Holt double exponential smoothing method was better than Brown's double exponential smoothing method.

Testing an Ectoin Containing Emollient for Atopic Dermatitis

2019 ◽  
Vol 15 (3) ◽  
pp. 191-195
Author(s):  
Kam Lun Hon ◽  
Jeng Sum Kung ◽  
Wing Gi Gigi Ng ◽  
Ting Fan Leung
Keyword(s):  
Quality Of Life ◽  
Atopic Dermatitis ◽  
Water Loss ◽  
Historical Data ◽  
Transepidermal Water Loss ◽  
Patient Acceptability ◽  
The Mean ◽  
Topical Medications ◽  
Colonization Status

Aim: To describe the methodology in studying patient’s acceptability and efficacy of an ectoin containing emollient for atopic dermatitis (AD). Methods: We described the methodology that we used in studying emollients and moisturisers, and patient acceptability of a group of AD patients before and following usage of an ectoin-containing proprietary emollient. These data were also compared with other brand emollients that we previously reported, namely Restoradom®, Ezerra® and Ezerra plus®. Results: 30 subjects (50% Male, Mean (SD) age: 9.8 (3.6) years with AD used the trial emollient W for four weeks. AD severity of subjects (by objective SCORAD) was moderate (n=22) and severe (n=8). Compliance was good and patients generally managed to use the moisturisers daily, with individual reports of a ‘tingly’ sensation by some subjects when applied to inflamed wounds. 63% reported “very good” or “good”, whereas 37% reported “fair” or “poor” acceptability of the moisturisers. Following use of the trial emollient, area affected, disease intensity and severity significantly improved, as demonstrated in objective SCORAD (p=0.002). There were also significant improvements in POEM (p=0.035), and PADQLQ scores (p=0.017). For skin measurements, only transepidermal water loss had improved (p=0.035) after the treatment. There was no significant improvement of itch or sleep scores, skin hydration, pH, S. aureus colonization status, or need for use of topical medications. When compared with historical data of other emollients, the mean age of patients on emollient W was younger; efficacy and acceptability among these emollients were similar. Conclusions: Methodology of emollient research is described. Doctors should provide evidencebased information about the efficacy of emollients. The ectoin-containing proprietary emollient improves disease and quality of life following its use in 4 weeks. Efficacy and acceptability are similar among 4 proprietary emollients.

scholarly journals PERBANDINGAN METODE FUZZY TIME SERIES DAN HOLT DOUBLE EXPONENTIAL SMOOTHING PADA PERAMALAN JUMLAH MAHASISWA BARU INSTITUT PERTANIAN BOGOR

2013 ◽  
Vol 12 (2) ◽  
pp. 25
Author(s):  
S. STEVEN ◽  
S. NURDIATI ◽  
F. BUKHARI
Keyword(s):  
Time Series ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Peramalan merupakan kegiatan memprediksi nilai suatu variabel di masa yang akan datang. Tujuan penelitian ini adalah memprediksi jumlah mahasiswa baru Institut Pertanian Bogor dengan menggunakan metode fuzzy time series dan metode pemulusan eksponensial ganda dari Holt serta membandingkan kedua metode tersebut dengan cara melihat tingkat ketepatan peramalan Mean Absolute Percentage Error (MAPE). Metode fuzzy time series menggunakan himpunan fuzzy dalam proses peramalannya sedangkan metode pemulusan eksponensial ganda dari Holt menggunakan pemulusan nilai dari serentetan data dengan cara menguranginya secara eksponensial. Dalam meramalkan jumlah mahasiswa baru Institut Pertanian Bogor, metode fuzzy time series menghasilkan tingkat ketepatan peramalan yang lebih baik dengan nilai MAPE sebesar 6.41 % dibandingkan dengan metode pemulusan eksponensial ganda dari Holt dengan nilai MAPE sebesar 7.75 %. Setelah dilakukan studi kasus, metode pemulusan eksponensial ganda dari Holt akan lebih akurat hasil peramalannya jika data yang digunakan lebih banyak.

scholarly journals Penerapan Metode Double Exponential Smoothing dan Moving Average pada Peramalan Permintaan Produk Gasket Cap di PT. Nesinak Industries

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Muhammad Hafidh Kurniawan ◽  
Dene Herwanto
Keyword(s):  
Moving Average ◽  
Smoothing Method ◽  
Percentage Error ◽  
Product Demand ◽  
Demand Prediction ◽  
Automotive Components ◽  
Double Exponential ◽  
Application Data ◽  
Double Smoothing ◽  
Double Exponential Smoothing

PT. Nesinak Industries is a company which focuses on the manufacturing process of an electronic component as well as automotive components (vehicle). In business activities, such as production, a strategy is required to survive in competition. Planning and forecasting are a strategy that can be implemented to accomplish these goals. In this study, the data used are previous sealing application data from January 2019 to March 2021. The objective of this study is to forecast product demand over the next period in order to be able to respond to customer demand. Data processing in this study utilize the Brown exponential  double smoothing method  and the moving average is then determined with the lowest MAPE (Mean Absolute Percentage Error) value to be used for the company’s product demand prediction calculations. The value of taken from Brown's exponential dual smoothing method is the value of with the two lowest error values from 0.1 to 0.9, whose value with the least error value is = 0.8 and = 0.9. In terms of the moving average method, the researchers tested a period of three months and a period of four months. In the MAPE calculation, the results of exponential double smoothing = 0.8 of 26.92 %, exponential double smoothing = 0.9 of 26.22 %, moving average n = 3 of 32.46%, and moving average n = 4 of 34.77%.

scholarly journals Pengembangan Business Intelligence Dashboard untuk Monitoring Aktivitas Pariwisata (Studi Kasus: Dinas Pariwisata Provinsi Bali)

2021 ◽  
Vol 8 (6) ◽  
pp. 1159
Author(s):  
Evan Himawan Saragih ◽  
I Putu Agung Bayupati ◽  
Gusti Agung Ayu Putri
Keyword(s):  
Business Intelligence ◽  
System Development ◽  
Black Box ◽  
Percentage Error ◽  
Double Exponential ◽  
Black Box Testing ◽  
On Line ◽  
Analytical Processing ◽  
Double Exponential Smoothing ◽  
Model View

<p class="Judul2">Bali merupakan satu dari beberapa destinasi wisata yang mendatangkan wisatawan nusantara dan mancanegara di Indonesia. Pada tahun 2017, wisatawan mancanegara yang berkunjung ke Bali adalah sebesar 5,6 juta orang dan didominasi oleh wisatawan dari negara Cina. Jumlah kunjungan pada seluruh objek wisata yang ada di Bali tahun 2017 mencapai 17,8 juta kunjungan. Berdasarkan hal tersebut, pemerintah daerah membutuhkan strategi dan keputusan dalam pembangunan sarana dan prasarana yang dapat mendukung pengembangan dan kemajuan pariwisata di Bali. Pemanfaatan teknologi <em>business intelligence</em> dalam menganalisa data dalam jumlah yang besar akan membantu. Penelitian ini mengembangkan sistem informasi memakai pendekatan BI dalam menganalisis data pariwisata Bali dan manajemen data dengan menggantikan pemakaian kertas menjadi media komputer sehingga data tidak hilang begitu saja, namun digunakan sebagai acuan saat menentukan keputusan. Dalam pengembangan sistem digunakan beberapa metode diantaranya <em>framework </em>Codeigniter dengan arsitektur <em>web</em> MVC (<em>Model, View, </em>Controller), OLAP (<em>On-line Analytical Processing</em>) untuk menampilkan visualisasi data, dan <em>double exponential smoothing</em> menampilkan hasil peramalan data pada periode berikutnya. Nilai <em>error </em>dari metode peramalan tersebut dapat dihitung menggunakan algoritma <em>Mean Absolute Percentage Error</em>. Agar dapat mengetahui tingkat pemanfaatan terhadap pengembangan sistem ini, maka digunakan metode <em>black box testing</em>, <em>usability testing</em>, dan <em>user acceptance test</em> untuk mengetahui kualitas dan fungsionalitas sistem dari segi input, output, dan penilaian oleh pengguna sistem. Penelitian ini memperlihatkan bahwa pemakaian teknologi BI tidak hanya mendukung pada perusahaan namun juga mendukung pada bidang pariwisata, pemerintahan dan layanan. Sistem yang dikembangkan dapat membantu proses pemantauan pariwisata dan pendukung dalam pengambilan keputusan.</p><p class="Judul2"> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"><em><em>Bali is one of several tourist destinations that bring domestic and foreign tourists to Indonesia. In 2017, there were 5.6 million foreign tourists visiting Bali and dominated by tourists from China. The number of visits to all tourist objects in Bali in 2017 reached 17.8 million visits. Based on this, local governments need strategies and decisions in the development of facilities and infrastructure that can support the development and progress of tourism in Bali. The use of business intelligence technology in analyzing large amounts of data will help. This study develops an information system using the BI approach in analyzing Bali tourism data and data management by replacing paper use as computer media so that data does not just disappear, but is used as a reference when making decisions. In system development, several methods are used including the Codeigniter framework with the MVC web architecture (Model, View, Controller), OLAP (On-line Analytical Processing) to display data visualization, and double exponential smoothing to display the results of forecasting data in the next period. The error value of this forecasting method can be calculated using the Mean Absolute Percentage Error algorithm. In order to determine the level of utilization of this system development, black box testing, usability testing and user acceptance tests are used to determine the quality and functionality of the system in terms of input, output, and assessment by system users. This study shows that the use of BI technology is not only supportive of companies but also supports tourism, government and services. The system developed can assist the tourism monitoring process and support decision making.</em></em></p>

scholarly journals Sistem Informasi Peramalan Penjualan Barang Dengan Metode Double Exponential Smoothing (Studi Kasus Istana Sayur)

2020 ◽  
Vol 6 (3) ◽  
pp. 9-14
Author(s):  
Yuri Ariyanto ◽  
Ahmadi Yuli Ananta ◽  
Muhammad Robbi Darwis Darwis
Keyword(s):  
Forecast Error ◽  
Exponential Smoothing ◽  
Mean Absolute Percentage Error ◽  
Percentage Error ◽  
Absolute Percentage Error ◽  
Double Exponential ◽  
Double Exponential Smoothing

Abstrak—Istana Sayur merupakan salah satu toko yang menjual beberapa macam sayuran, buah buahan dan bahan makanan yang selalu berusaha meningkatkan dan menjaga kualitas layanan, mencoba mengurangi kerugian dari pengendalian persediaan stok barang secara manual yang kurang baik akibat kelebihan dan kekurangan stok yang dialami saat ini, maka diperlukan fitur sebagai sistem informasi kasir dan peramalan stok barang. Tujuan dari pembuatan sistem informasi ini adalah analisa Forecasting secara manual ke dalam sebuah sistem informasi agar lebih praktis, dengan pemrograman PHP berframework CodeIgniter dan MySQL sebagai databasenya. Dengan menggunakan metode Double Exponential Smoothing Holt untuk pengambilan keputusan dalam jangka waktu tertentu dan pemanfaatkan pergerakan data pada masa lalu yang bersifat trend dimana datanya bersifat linier. Setelah dilakukan observasi pada Istana Sayur, Malang, didapat data transaksi penjualan dan barang pada tahun 2016-2018. Dari hasil perhitungan metode yang dipakai pada sistem ini kemudian dihitung Forecast Error-nya dengan menggunakan metode Mean Absolute Percentage Error. Dari analisa yang telah dilakukan, didapatkan hasil bahwa dengan menggunakan Mean Absolute Percentage Error didapat nilai untuk Sawi Caisim Manis dengan nilai 15.05%, Telor Ayam dengan nilai 15.78%, Cabe Hijau dengan nilai 12.45%, Buncis dengan nilai 22.22%, Cengkeh dengan nilai 34.69%, Bawang Putih dengan nilai 19.53%, Tempe dengan nilai 20.60% dan Kentang dengan nilai 17.58%. Sehingga Sawi Caisim Manis, Telor Ayam, Cabe Hijau, Bawang Putih dan Kentang tergolong kedalam kategori baik karena memiliki nilai diantara 10%-20%. Sedangkan untuk Buncis, Cengkeh dan Tempe tergolong kedalam kategori cukup karena memiliki nilai diantara 20%-50%. Saran untuk pengembangan aplikasi ini adalah perlunya penambahan metode lain sebagai pembanding tingkat keakuratan.

scholarly journals Peramalan Nilai Tukar Petani Subsektor Peternakan Menggunakan Fuzzy Time Series Lee

2021 ◽  
Vol 3 (1) ◽  
pp. 1-15
Author(s):  
Mahadi Muhammad ◽  
Sri Wahyuningsih ◽  
Meiliyani Siringoringo
Keyword(s):  
Time Series ◽  
Fuzzy Sets ◽  
Historical Data ◽  
Mean Absolute Percentage Error ◽  
Fuzzy Time Series ◽  
Percentage Error ◽  
Logical Relationship ◽  
Absolute Percentage Error ◽  
The Mean ◽  
Fuzzy Logical

ABSTRAKFuzzy time series (FTS) Lee adalah suatu metode peramalan yang digunakan ketika jumlah data historis yang tersedia sedikit, serta tidak mensyaratkan asumsi-asumsi tertentu yang harus terpenuhi. Metode ini menggunakan data historis berupa himpunan fuzzy yang berasal dari bilangan real atas himpunan semesta pada data aktual. FTS Lee adalah perkembangan dari FTS Song dan Chissom, FTS Cheng, serta FTS Chen. Pada penelitian ini dibahas penerapan FTS Lee pada data Nilai Tukar Petani Subsektor Peternakan (NTPT) di Kalimantan Timur. Tujuan penelitian ini adalah memperoleh hasil peramalan NTPT di Kalimantan Timur pada bulan Januari 2020 dengan menggunakan FTS Lee. Langkah awal dalam penelitian ini yaitu menentukan himpunan semesta pembicaraan, langkah kedua menentukan banyaknya himpunan fuzzy, langkah ketiga mendefinisikan derajat keanggotaan himpunan fuzzy terhadap  dan melakukan fuzzyfikasi pada data aktual, langkah keempat membuat fuzzy logical relationship, langkah kelima membuat fuzzy logical relationship group, langkah keenam melakukan defuzzyfikasi sehingga diperoleh hasil peramalan, serta dilanjutkan dengan menghitung nilai mean absolute percentage error. Hasil penelitian menunjukkan bahwa peramalan menggunakan FTS Lee pada bulan Januari 2020 adalah 110,25. Nilai mean absolute percentage error pada  hasil peramalan dengan menggunakan FTS Lee adalah sangat baik.  ABSTRACTLee’s Fuzzy time series (FTS) is a forecasting method that is used when the number of historical data that available was small and does not require certain assumptions to be fulfilled. This method uses historical data in the form of fuzzy sets derived from real numbers over the set of universes in the actual data. FTS Lee is a development of FTS Song and Chissom, FTS Cheng, and FTS Chen. This research discusses the application of FTS Lee to the Exchange Rate of Farmers Subsectors Farm (ERFSF) in Kalimantan Timur. The purpose of this study was to obtain the results of ERFSF forecasting in Kalimantan Timur in January 2020 using FTS Lee. The first step during research is to determine the set of speech universes, the second step is to determine the number of fuzzy sets, the third step is to define the degree of fuzzy association membership and fuzzification on the actual data, the fourth step is to create a fuzzy logical relationship, the fifth step is to create a fuzzy logical relationship group, the sixth step is to perform defuzzification in order to obtain forecasting results, and continue by calculating the mean absolute percentage error value. The results showed that forecasting using FTS Lee in January 2020 was 110,25. The mean absolute percentage error value in forecasting results using FTS Lee is very good.

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