Makanan menjadi salah satu kebutuhan hidup utama dari individu. Makanan yang dikonsumsi terdiri dari berbagai macam menu yang kaya akan rasa dan nutrisi yang melimpah. Untuk menentukan menu makanan, maka diperlukan bahan makanan yang digunakan sebagai bahan dasar pembuatan menu tersebut. Bahan makanan yang digunakan dapat dihasilkan secara pribadi maupun disediakan oleh alam. Untuk langsung memasak menu makanan yang diolah dari bahan makanan yang dipilih, tentunya memberikan banyak manfaat seperti dapat mengkonsumsi makanan yang bersih dan higienis, serta dapat mengurangi biaya pengeluaran makanan instan. Namun, sebagian besar masyarakat masih memiliki rasa kebingungan dalam menentukan menu makanan yang tepat berdasarkan bahan makanan yang tersedia. Hal ini tak luput dari peranan teknologi informasi dalam mempermudah aktivitas dan kegiatan manusia, bahkan dalam menentukan menu makanan sehari-hari. Masyarakat dapat memilih bahan-bahan makanan yang disediakan dan dapat mengetahui menu makanan apa saja yang dapat diolah dari bahan-bahan makanan tesebut.
This study aims to do a prediction of demand goods at a factory for 1 day ahead using double moving average method and comparing the forecasting results. Data source come from two different types of data which are complete data and clean data. Clean data was an optimal data that has been cleaned from outlier using boxplot method. The data source used in the calculation is simulation data for 945 days. Based on the test results, Shows the results of forecasting using complete data that is equal to 4692 with MAPE 6.88 while the results of forecasting use clean data that is equal to 4876 with MAPE 3.84. From these results, it can be concluded that forecasting using clean data is more accurate than forecasting using complete data because the smaller the error rate (MAPE) produced, the better the accuracy.
News is information about facts or opinions that are interesting to know. News can be obtained from various media such as newspapers and the internet. As is well known, news has various topics, such as politics, sports and others. There is also the same story written with the addition of a little information. This causes it to take more time to get the headline of the news. Therefore we need a system for news clustering using the K-Means method and news summarizing using the Maximum Marginal Relevance (MMR) method in order to obtain information from news more easily and efficiently. News that is processed in the form of a collection of files (multi document) with the extension txt. The summarization process goes through the text preprocessing stage, which consists of sentence segmentation, case folding, tokenizing, filtering, stemming. The next step is TF-IDF calculation to calculate word weight then Cosine Similarity to calculate the similarity between documents. After that, enter the K-Means stage for clustering division and proceed with determining the summary with MMR. Based on the results testing that has been done, this application is running well, the results of clustering and summarizing news can make it easier for users to get news summaries from some similar news.
Developing a website to be used as a medium of learning mathematics. The material presented on the website discusses transformation geometry which includes translation, rotation, and dilation. In the process of making this website, it will be explained how the stages of development and how users respond to the website. The development process is carried out starting with (1) needs analysis (2) media design (3) expert validation (4) media testing (5) until new products are created in the form of learning media. The expert validation stage is carried out by lecturers and mathematics teachers. The questionnaire analysis is carried out in a descriptive qualitative manner using form filling.
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