ANALISIS RUNTUN WAKTU FUZZI UNTUK PREDIKSI BANJIR SECARA WAKTU NYATA

Kejadian bencana banjir di Indonesia sering menimbulkan banyak korban, baik jiwamaupun materi. Secara umum 34% dari seluruh kejadian bencana yang terjadi di seluruhIndonesia didominasi oleh bencana banjir. Untuk mencegah bertambahnya jumlah korban,maka dari segi pengetahuan dapat dilakukan pendekatan secara struktural dan nonstruktural, salah satu pendekatan non struktural adalah dengan mengembangkan sistemperingatan dini. Tujuan penelitian ini adalah mengimplementasikan metode runtun waktu fuzzi dalam aplikasi yang dapat memprediksi banjir secara waktu nyata dan membangunaplikasi sistem informasi berbasis web untuk memberikan informasi hasil prediksi banjirsecara waktu nyata melalui metode runtun waktu fuzzi. Runtun waktu fuzzi (FTS) merupakan metode prediksi data yang menggunakan prinsip prinsip fuzzi sebagai dasarnya. Sistem prediksi dengan runtun waktu fuzzi menangkap pola dari data yang telah lalu kemudian digunakan untuk memproyeksikan data yang akandatang. Metode ini sering digunakan oleh para peneliti untuk menyelesaikan masalahprediksi. Tahapan yang dilakukan dengan menggunakan runtun waktu fuzzi didasarkanpada deret waktu historis, yaitu : menentukan semesta pembicaraan, pemisahan semestapembicaraan, membangun fuzzi set, fuzzifikasi data history, menentukan fuzzy logicalrelationships (FLR), menentukan fuzzy logical relationships group (FLRG), menghitunghasil prediksi per menit. Dari hasil penelitian ini, dapat disimpulkan bahwa penerapan metode runtun waktu fuzzi dalam prediksi banjir dapat menghasilkan prediksi yang baik, sehingga dapat dipergunakanuntuk acuan memprediksi bencana banjir secara real time pada sebuah ketinggian level airdi suatu tempat. Penerapan metode runtun waktu fuzzi dalam memprediksi bencana banjirsecara waktu nyata diperoleh melalui percentage error yang diukur dengan menggunakanMean Absolute Percentage Error (MAPE) diperoleh error rata-rata sebesar 0,44%, dandiperoleh juga nilai Mean Standart Error (MSE) sebesar 0,67 hal ini artinya membuktikanbahwa prediksi yang dihasilkan dapat mendekati data aktual. Kata-kunci : runtun waktu fuzzi, prediksi, banjir, waktu nyata Existance Flood in Indonesia often cause many casualties, both mental and material. In general 34% of all disaster events that occurred in Indonesia is dominated by the flood disaster. To prevent the increasing number of victims, then in terms of knowledge can be approached structural and non-structural, non-structural one approach is to develop an early warning system. The purpose of this research is to implement a method of fuzzy time series in applications that can predict the Flood in real-time and build a web-based informationsystem application to provide information which results in real-time Flood prediction based on time series methods Fuzzi. Fuzzy time series is a method that uses the fuzzy principles as the basis for predicting the data. Fuzzy Time series prediction system capture patterns in the data that has been and is then used to project the data to come. This method is often used by researchers to solve the prediction. Problem the research steps of using fuzzy time series is based on a time series of historical, i.e. : defining the universe of discourse, splitting the universe of discourse, building fuzzy sets, fuzzification history data, determining the fuzzy logical relationships (FLR), determining the fuzzy logical relationships of group (FLRG), counting predicted results per minute. From results of this research the application of the method of fuzzy time and series in flood predictions can yield good predictions, so it can be used to predict floods reference in real time at a height of water level somewhere. Application of the method of fuzzy time series in predicting floods in real time obtained by percentage error is measured using the Mean Absolute Percentage Error (MAPE) obtained an average error of 0.44%, and also the value obtained Mean Standard Error (MSE) by 0.67 it means proving that the predictions generated can be closer to the actual data. Keywords: fuzzy time series, prediction, flood, real-tim

HIGH ORDER FUZZY TIME SERIES MODEL AND ITS APLICATION TO IMKB

The observations of some real time series such as temperature and stock market can take different values in a day. Instead of representing the observations of these time series by real numbers, employing linguistic values or fuzzy sets can be more appropriate. In recent years, many approaches have been introduced to analyze time series consisting of observations which are fuzzy sets and such time series are called fuzzy time series. In this study, a novel approach is proposed to analyze high order fuzzy time series model. The proposed method is applied to IMKB data and the obtained results are discussed. IMKB data is also analyzed by using some other fuzzy time series methods available in the literature and obtained results are compared to results obtained from the proposed method. As a result of the comparison, it is seen that the proposed method produce accurate forecasts

Implementasi Fuzzy Time Series pada Prediksi Jumlah Penjualan Rumah

Rumah adalah bangunan gedung yang berfungsi sebagai tempat tinggal yang layak huni, sarana pembinaan keluarga, cerminan harkat dan martabat penghuninya, serta aset bagi pemiliknya. Menurut Direktur Jenderal Pembiayaan Perumahan Kementerian Pekerjaan Umum Perumahan Rakyat, perumahan di Indonesia masih mengalami defisit pasokan sebesar 7,6 juta pada 2015 dan menargetkan akan mengurangi angka kebutuhan rumah atau backlog perumahan di 2019 mendatang menjadi 5,4 juta agar masyarakat Indonesia bisa mempunyai rumah sendiri. Tingginya kebutuhan rumah yang harus dipenuhi memerlukan kerjasama seluruh stakeholder di bidang perumahan seperti developer properti. Dalam rangka menghadapi para pesaing dan mempertahankan kelangsungan perusahaan, dibutuhkan manajemen yang baik di bidang perencanaan. Tujuan penelitian ini adalah untuk memprediksi jumlah penjualan rumah sehingga dapat dijadikan acuan oleh developer properti dalam menyusun perencanaan. Memprediksi adalah suatu teknik analisa perhitungan untuk memperkirakan kejadian dimasa depan dengan menggunakan referensi data-data di masa lalu. Metode yang digunakan dalam memprediksi jumlah penjualan rumah adalah metode fuzzy time series. Metode Fuzzy Time Series (FTS) merupakan suatu konsep yang digunakan untuk meramalkan masalah di mana data historis diubah ke dalam nilai linguistik. Adapun hasil pengujian Mean Absolute Percentage Error (MAPE) yang sudah dilakukan menunjukkan tingkat akurasi prediksi sebesar 85,79% dengan menggunakan margin sebesar 5% sehingga perancangan ini dapat diterapkan oleh developer properti

A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

A Fuzzy Logic Approach to Prove Bullwhip Effect in Supply Chains

The bullwhip effect in nowadays Supply Chains has become a major source of problems and has attracted supply chain scientists attentions. This paper explores the concept of bullwhip effect in supply chains throughout a completely new approach. Assuming all demands are fuzzy in supply chain, fuzzy If-Then rules are used to show the bullwhip effect. Application of fuzzy logic is due to the fuzzy nature of supply chain problems. The new approach can be the source of inspiration for new solutions to the bullwhip effect in supply chains base on fuzzy logic and fuzzy If-Then rules. Fuzzy time series are widely used in this paper. First for data generation, we apply a modified version of Hwang fuzzy time series with a neural network for defuzzification and finally to show the bullwhip effect, we use Lee fuzzy time series which is based on Fuzzy If-Then rules, Genetic Algorithm and Simulated Annealing

Fuzzy Local Trend Transform based Fuzzy Time Series Forecasting Model

A fuzzy local trend transform based fuzzy time series forecasting model is proposed to improve practicability and forecast accuracy by providing forecast of local trend variation based on the linguistic representation of ratios between any two consecutive points in original time series. Local trend variation satisfies a wide range of real applications for the forecast, the practicability is thereby improved. Specific values based on the forecasted local trend variations that reflect fluctuations in historical data are calculated accordingly to enhance the forecast accuracy. Compared with conventional models, the proposed model is validated by about 50% and 60% average improvement in terms of MLTE (mean local trend error) and RMSE (root mean squared error), respectively, for three typical forecasting applications. The MLTE results indicate that the proposed model outperforms conventional models significantly in reflecting fluctuations in historical data, and the improved RMSE results confirm an inherent enhancement of reflection of fluctuations in historical data and hence a better forecast accuracy. The potential applications of the proposed fuzzy local trend transform include time series clustering, classification, and indexing

Constructing Fuzzy Time Series Model Using Combination of Table Lookup and Singular Value Decomposition Methods and Its Application to Forecasting Inflation Rate

Fuzzy time series is a dynamic process with linguistic values as its observations. Modelling fuzzy time series data developed by some researchers used discrete membership functions and table lookup method from training data. This paper presents a new method to modelling fuzzy time series data combining table lookup and singular value decomposition methods using continuous membership functions. Table lookup method is used to construct fuzzy relations from training data. Singular value decomposition of firing strength matrix and QR factorization are used to reduce fuzzy relations. Furthermore, this method is applied to forecast inflation rate in Indonesia based on six-factors one-order fuzzy time series. This result is compared with neural network method and the proposed method gets a higher forecasting accuracy rate than the neural network method

Forecasting Inflation Rates with High Order Fuzzy Time Series Approach

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir.Öngörülerin daha doğru elde edilmesi daha doğru kararlara neden olacaktır. T.C.Merkez bankası her yılın belirli dönemlerinde enflasyon raporları yayınlamaktadır.Raporlarda enflasyon beklentisi anketi sonuçları yer almaktadır. Bu çalışmada tüketicifiyat endeksi yüksek dereceli bulanık zaman serisi yaklaşımı ile öngörülmüştür. Yüksekdereceli bulanık zaman serisi modelinde ilişkilerin belirlenmesi yapay sinir ağları ileyapılmaktadır. Tüketici fiyat endeksi zaman serisi, ayrıca literatürde yer alan bazıbulanık zaman serisi yaklaşımları ile tahmin edilerek, öngörü doğruluğu açısından T.C.Merkez Bankası enflasyon beklentisi anketi sonuçları ile karşılaştırılmıştır. To obtain inflation forecasts is an important economic issue. The moreaccurate forecasts we get implies the more precise decisions we make. The central Bank reports inflation rates in certain periods of every year. In this reports the results ofinflation expectation survey are presented. In this study we use an approach in whichrelationship is determined by artificial neural network in high order fuzzy time seriesmodel. Time series of consumer price index is estimated by both the artificial neuralnetwork based method and some fuzzy approaches which is common in the literature.The results are compared to the results of inflation expectation survey analysisconducted by Central Bank of the Republic of Turkey in the aspect of forecastsaccuracy