The development of economy is closely related with the inflation rate of the country. Generally the inflation can be measured by the Consumer Price Index (CPl). The aim of this paper is using overlapping measure and cumulative measure to measure the inflation. In overall, these two methods are adequate for measuring the inflation. As a result, monthly measure is useful because it believe can give a perfect measure (since its sample is large) and can get rid of or smaller the error. Besides that, the value of the weighted CPl and the unweighted CPl are almost same. As the result, the unweighted of CPl is used to measure the inflation. The result of this two methods are satisfactory.
[Perkembangan sesuatu ekonomi adalah berkait rapat dengan kadar inflasi dalam sesebuah negara. Secara amnya, inflasi dapat diukur dengan Indeks Harga Pengguna (IHP). Tujuan kertas kerja ini adalah mengukur inflasi dengan menggunakan kaedah data bertindih dan kaedah kumulatif. Pada keseluruhannya kedua-dua kaedah tersebut adalah sesuai digunakan sebagai pengukuran inflasi. Oleh hal yang demikian, pengukuran secara bulanan digunakan kerana dipercayai bahawa ia dapat memberi ukuran yang lebih tepat (saiz sampel yang besar) dan dapat mengelakkan atau mengecilkan ralat berlaku. Selain itu, penggunaan IHP berpemberat dan IHP tanpa pemberat didapati mempunyai nilai yang hampir sama. Oleh itu, nilai IHP tanpa pemberat digunakan dalam pengukuran inflasi. Keputusan yang didapati daripada kedua-dua kaedah tersebut adalah memuaskan]
The Analytic Hierarchy Process (AHP) is a recognised modern approach to solve decision making problems. Initially introduced by Saaty in 1971 as a tool for handling individual decision making situation, the method has since been extended to incorporate groups. In this paper, a new method for AHP group decision making is proposed. The method integrates AHP with a Data Envelopment Analysis (DEA)-based preferential aggregation method. It manipulates the preferential weights and ranking aspect of each decision maker in coming up with an optimisation model that determines the best efficiency score of each alternatives. These efficiency scores are then used to rank the alternatives and determine the group decision weights. A comparative analysis of the method with another AHP group decision making method indicates a similar ‘satisfactory index’ level.