Inflation is one indicator to measure the development of a nation. If inflation is not controlled, it will have a lot of negative impacts on people in a country. There are many ways to control inflation, one of them is forecasting. Forecasting is an activity to find out future events based on past data. There are various kinds of artificial intelligence methods for forecasting, one of which is the extreme learning machine (ELM). ELM has weaknesses in determining initial weights using trial and error methods. So, the authors propose an optimization method to overcome the problem of determining initial weights. Based on the testing carried out the purposed method gets an error value of 0.020202758 with computation time of 5 seconds.

Abstract-House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.

AbstrakInflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.Kata kunci: inflasi, extreme learning machine, backpropagation, jaringan syaraf tiruan. AbstractInflation is one indicator to measure the development of a nation. If inflation is not controlled will give many negative impacts to the people in a country. There are many ways to control inflation, one with forecasting. Forecasting is an activity to know future events based on past data. In this research using artificial intelligence method is extreme learning machine (ELM). The advantages of ELM are fast in the learning process. Based on ELM testing gets obtained an error value of 0.0202008, smaller than the backpropagation method of 1.16035821. It proves that ELM method is very suitable for forecasting.

<span lang="EN-US">Distribution is an important aspect of industrial activity to serve customers on time with minimal operational cost. Therefore, it is necessary to design a quick and accurate distribution route. One of them can be design travel distribution route using the k-means method and genetic algorithms. This research will combine k-means method and genetic algorithm to solve VRPTW problem. K-means can do clustering properly and genetic algorithms can optimize the route. The proposed genetic algorithm employs initialize chromosome from the result of k-means and using replacement method of selection. Based on the comparison between genetic algorithm and hybrid k-means genetic algorithm proves that k-means genetic algorithm is a suitable combination method with relative low computation time, are the comparison between 2700 and 3900 seconds.</span>

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