Determination of reorder point aims to meet the safety stock. This is a central parameter of inventory control. This study aims to find reorder point based on goods classification and safe stock. This approach is implemented in retail information systems that have been running. The information system has about 15.000 active items with the number of sales transactions around 1.100 per day. The problem in determining the reorder point is the unavailability of the safe stock reference. Lack of safe stock information triggered the ordering goods error. This error causes over stock. It can increase the potential of expired goods. In this study the researcher classifies the goods and determines the amount of safe stock to control the inventory. We used ABC analysis method for goods classification. It divides the group of goods into A, B, C, and D. The amount of safe stock is determined based on the goods sale’s history using Min Max Analysis method. Classification result is used to determine the limits on the inventory of allowed items to be ordered. Limitation safety stock amount refers to the limits from the min max method result. While, testing is done by comparing cost before and after implementation of this method.

Abstrak

The study period of students who pass the time limit and high numbers of dropout in a college can affect the value of campus accreditation. The anticipation of that possibility, the college must make predictions about potential students don’t graduate on time. This study aims to build a system capable of predicting students who have the potential. If students with unpredictable graduation risks can be identified in the early stages, then the indication of dropout rates may be reduced by providing special appeals to students at risk. Prediction analysis applies the K-Nearest Neighbors method to dig up the trace data stack and look for the proximity of the data with the new data. The test data used student class of 2011 with 100 students as sample data. This method of classification is based on several attributes, namely the evaluation of the 1st semester to semester 6th, the number of GPA, credits that have been taken each semester, number of credits passed, and the number of credits that didn’t pass. The result of classification becomes the output of the system which is then entered into the testing phase. This stage compares the output with the original data with 70, 73% accuration result.

Puji syukur kami panjatkan ke hadirat Tuhan Yang Maha Esa, Ida Sang Hyang Widhi Wasa, karena telah melimpahkan rahmat-Nya berupa kesempatan dan pengetahuan sehingga buku Pemanfaatan Teknologi Informasi Untuk Bisnis UKM Sekolah Tinggi Manajemen Informatika & Komputer Stmik Indonesia Program Studi Teknik Informatika ini bisa selesai pada waktunya.Terima kasih juga kami ucapkan kepada rekan sejawat yang telah berkontribusi dengan memberikan ide-idenya sehingga buku ini bisa disusun dengan baik dan rapi.Kami berharap semoga buku ini bisa menambah pengetahuan para pembaca. Namun terlepas dari itu, kami memahami bahwa buku ini masih jauh dari kata sempurna, sehingga kami sangat mengharapkan kritik serta saran yang bersifat membangun demi terciptanya buku selanjutnya yang lebih baik lagi. Semoga buku ini dapat bermanfaat bagi kita semua, khususnya para UMKM yang akan mulai menjalankan bisnisnya.

Determination of rainfall is important to determine the intensity of rain that occurs in an area. Rain intensity that is too high will certainly have a bad impact. Forecasting or prediction techniques are used to determine the likelihood of intensity occurring in the following year. However, rainfall data are continuous numerical data. Measurement of accuracy becomes more difficult if the data type is like that. So, this study tests the accuracy of rainfall forecasting in the city of Denpasar from a different perspective. This test combines the Z-score method and the Fuzzy set theory to normalize and classify rainfall data. This combination determines the degree of rainfall membership divided into Upper, Middle, and Lower levels. Based on the results of rainfall accuracy testing starting in 2012-2016 obtained an average value of accuracy of 85% with training data that is data in 2007-2015. The normalization process greatly affects the value of the training data.