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ANALISA PERBANDINGAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) DAN ANALYTICAL HIERARCHY PROCESS (AHP) UNTUK SISTEM PENUNJANG KEPUTUSAN MASYARAKAT MISKIN PADA DESA CILOTO Darmin; Rizki Maulana; Alim Hardiansyah
IONTech Journal Vol. 2 No. 1 (2021): Februari 2021
Publisher : Institut Sains dan Teknologi Al-Kamal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62702/ion.v2i1.34

Abstract

Abstrak Sistem Pendukung Keputusan merupakan sistem informasi interakif yang menyediakan informasi, pemodelan, dan manipulasi data dan untuk meningkatkan efektifitas yang memungkinkan pengambilan keputusan lebih objektif. Tim Penanggulangan Kemiskinan (TPK) Desa Ciloto dalam melaksanakan tugasnya untuk menyeleksi warga yang tergolong miskin, saat ini masih sangat kesulitan dalam mendistribusikan bantuan yang akan diberikan kepada warga yang membutuhkan karena masih menggunakan cara manual dan bersifat objektif. Penilaian tingkat kemiskinan yang bersifat objektif mengakibatkan penerima bantuan tidak tepat sasaran, sehingga membutuhkan suatu sistem informasi untuk mendukung pengambilan keputusan yang lebih objektif. Agar perhitungan dan pengolahan data lebih akurat, maka dilakukan perhitungan menggunakan perbandingan pada sebuah metode Simple Additive Weighting (SAW) dan Analytical Hierarchy Process (AHP). Dalam pengembangan sebuah sistem metodologi yang digunakan adalah System Development Life Cycle (SDLC) dan untuk perancangan aplikasi menggunakan UML (Unified Modelling Language). Setelah dilakukan pengujian terhadap sistem, maka metode Simple Additive Weighting (SAW) lebih efektif dalam menentukan penerimaan bantuan, sehingga dalam implementasi sistem diharapkan dapat membantu pihak desa dalam memperoleh informasi tingkat kemiskinan di desa tersebut, sehingga pemberian bantuan yang diberikan pemerintah bisa lebih tepat sasaran. Abstract Decision support systems are interactive information systems that provide information, modeling, and manipulation of data and to increase effectiveness that enables more objective decision making. The ciloto village poverty reduction team (TPK) in carrying out its task of selecting residents classified as poor, is currently still very difficult in distributing aid to be given to people in need because it still uses manual and objective methods. an objective assessment of poverty levels results in recipients of aid that are not on target, so that information systems are needed to support more objective decision making. To make the calculation and processing of data more accurate, the calculation is done by using a comparison on simple additive weighting (SAW) and the Analytical Hierarchy Process (AHP) method in developing the system methodology used is the System Life Cycle (SDLC) and for application design using UML (Unified Modeling Language) after testing the system, the Simple Additive Weighting (SAW) method is more effective in determining the receipt of assistance so that the implementation of the system is expected to help villages in obtaining information about poverty levels in the village, so that assistance provided by the government can be more targeted.
PENERAPAN FORECASTING MENGGUNAKAN METODE SINGLE EXPONENTIAL SMOOTHING DAN TREND MOMENT DALAM MEMPREDIKSI PENJUALAN ALAT-ALAT OUTDOOR PADA EIGHTVENTURE Darmin
IONTech Journal Vol. 5 No. 2 (2024): Agustus 2024
Publisher : Institut Sains dan Teknologi Al-Kamal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62702/ion.v5i2.113

Abstract

Eightventure is one of Tokopedia's partners that sells a variety of outdoor equipment. In 3 years running, sales of outdoor equipment have fluctuated, which of course the increase and decrease in sales every month is difficult to predict. The problem that often arises in eigthventure is the occurrence of stock mismatches, this is because there are products that are not sold out and there are some products that many consumers ask for but are not fulfilled. With this problem, Eightventure must be able to predict how many products will be sold and how many products must be provided. On the other hand, the sales report data generated in Tokopedia has never been used because of the limitations of the Eightventure in processing the data, so that it is difficult to predict sales. Therefore, a research was carried out using the Single Exponential Smoothing and Trend Moment methods and to measure the level of prediction accuracy, the mean absolute deviation, mean squared error, root mean squared error and mean absolute percentage error were used. With these two methods, the results of research on sales predictions for the double layer dome tent product with a capacity of 6 BN 021 shows that the single exponential smoothing method results in mean absolute deviation 1.31, mean squared error 3.28, root mean squared error 1.81, and mean absolute percentage error of 35%. While the trend moment method obtained the mean absolute deviation 1.28, mean squared error 2.35, root mean squared error 1.53, and mean absolute percentage error 39%. Therefore, it can be noticed that the trend moment method is the most effective in calculating the eightventure sales forecast. Keywords : Sales, Single Exponential Smoohting, Trend Moment.