Low Cost Green Car (LCGC) merupakan terobosan baru yang dibuat oleh pemerintah dalam dunia otomotif khususnya di Indonesia. Dengan dukungan konsep ramah lingkungan jenis mobil LCGC ini menjadi alternatif bagi konsumen yang menginginkan mobil khususnya di Indonesia. Untuk memperkirakan mobil LCGC sesuai dengan kebutuhan dapat menggunakan sistem pendukung keputusan dengan metode simple additive weighting (SAW). Data didapat dari observasi dan interview yang dilakukan melalui sales dan montir mobil atau pakar yang lebih mengetahui tentang kondisi pasar dan kebutuhan masyarakat. Hasil yang dicapai adalah merancang aplikasi sistem pendukung keputusan memprediksi mobil LCGC yang sesuai keinginan konsumen berdasarkan harga, kapasitas mesin, fitur keselamatan, hemat bahan bakar, akomodasi kabin. Dengan menentukan nilai preferensi setiap alternatif dibuat perangkingan sehingga dari alternatif yang ada mendapatkan alternatif terbaik.

Arabica coffee beans are one of the main varieties of coffee beans developed in Indonesia. In making decisions to determine quality Arabica coffee beans, an appropriate system is needed to analyze problems in solving and efficient and accurate data presentation. Therefore, a computer-based system or method is needed to facilitate the selection of the best Arabica coffee beans. This study uses the Simple Multi Attribute Rating Technique (SMART) method. The SMART method is a decision-making method to solve the problem of choosing a multi-objective choice among several criteria, so that later it will be able to produce an effective and efficient analysis. The input criteria that are the priority in selecting the best Arabica coffee beans are aroma with a weight of 25, color with a weight of 25, taste with a weight of 25, dirt content with a weight of 15, and price with a weight of 10. Of the 25 alternatives tested in this system, Gayo Avatara Natural Arabica coffee beans were the best first alternative, followed by Aceh Gayo Wet Hull, Java Ijen Natural, Java Ijen Honey, and Kintamani Natural. This decision support system for selecting the best Arabica coffee beans provides speed, accuracy, and data accuracy in selecting the best Arabica coffee beans which will be used by coffee lovers to provide coffee with a delicious taste. So the results of the decision from 25 types of Arabica coffee, there are 11 types of Arabica coffee with a rating of "Very Good", 10 types of Arabica coffee with a rating of "Good", and 4 types of Arabica coffee with a rating of "Quite Good".