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All Journal EDUMATIC: Jurnal Pendidikan Informatika J-Com (Journal of Computer)
Hidayanti, Deni Andria
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Computer Science & IT Education

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Penerapan Metode Weighted Moving Average pada Sistem Peramalan Stok Bahan Laundry Hidayanti, Deni Andria; Syafwan, Havid; Akmal, Akmal
Jurnal Pendidikan Informatika (EDUMATIC) Vol 8 No 1 (2024): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v8i1.25636

Abstract

Information systems are created in stores so that they can easily make data processing well, and can make predictions or forecasts about stocks that will be prepared in future periods. The purpose of this study is to create and produce a stock forecasting system for laundry materials by applying the Weighted Moving Average (WMA) method. This research is a type of development using a waterfall model by conducting stages of needs analysis, design, implementation and testing. The Analysis phase is carried out to identify the needs used to be applied to this forecasting system. The design stages consist of interface design, flowcharts, use cases and entity relationship diagrams. Implementation and testing are carried out directly using black box testing to see the extent of the functionality of the system that has been created. Our findings show that the system we developed is in the form of a web-based laundry material stock forecasting system. This system is also successful in testing using black box testing, all system components are functioning properly. This system is also calculated accordingly, where the results of the Mean Absolute Percentage Error (MAPE) in detergent stocks get a percentage of 10%, or an accuracy rate of 90%. Meanwhile, the fragrance stock obtained a MAPE yield of 7%, with an accuracy rate of 93%.
KLASTERISASI TINGKAT PENJUALAN OBAT PADA APOTEK JAKA WIJAYA DENGAN MENGGUNAKAN METODE K-MEANS hidayanti, deni andria; kurnia, fitri; rahmawati, rahmawati
J-Com (Journal of Computer) Vol. 4 No. 1 (2024): Maret 2024
Publisher : STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/j-com.v4i1.3048

Abstract

Abstrak: A pharmacy is a health service facility to help improve the health of the community, a pharmacy is also a place for professional pharmacists to practice their work. To determine the amount of stock inventory, Jaka Wijaya Pharmacy requires a clusterization of sales stock data. The method that can be used is the K-Means algorithm. This algorithm is based on a simple idea. K-Means is a distance-based clustering method that divides data into a number of clusters and this algorithm only works on numeric attributes. The data processed in this research is a sample taken from the Jaka Wijaya Pharmacy data in 2022. The Jaka Wijaya Pharmacy dataset consists of the attributes No, Drug Item, Type, Packaging, Initial Stock, Cost Price, Unit Conversion, Selling Price, Number of Transactions, Ending Stock, Shelf, Warehouse-Office Codes. With the K-Means Clustering method, it is possible to group drug sales data with stock that is not selling well as cluster 0, stock that is selling well as cluster 1, and stock that is selling very well as cluster 2. The sample data to be tested consists of 170 data from the Jaka Wijaya Pharmacy. Where the cluster results show that there are several results, namely cluster 0 totaling 102, cluster 1 totaling 34, and cluster 2 totaling 34 decisions, where the decisions include very in demand, in demand, not in demand.Keywords: Data Mining; Jaka Wijaya Pharmacy; K-means ClusterAbstrak: Apotek  merupakan  sarana  pelayanan  kesehatan  untuk  membantu meningkatkan kesehatan  bagi  masyarakat, apotek juga sebagai tempat praktik tenaga profesi apoteker dalam melakukan  pekerjaan. Untuk menentukan jumlah persediaan stok, Apotek Jaka Wijaya membutuhkan suatu clusterisasi data stok penjualan. Metode yang dapat digunakan yaitu algoritma K-Means. Algoritma ini didasarkan pada ide sederhana. K-Means adalah  metode Clustering berbasis jarak yang membagi data ke dalam sejumlah cluster dan algoritma ini hanya bekerja pada atribut numeric. Data yang diolah dalam penelitian ini merupakan sampel yang diambil dari data Apotek Jaka Wijaya pada tahun 2022. Dataset Apotek Jaka Wijaya terdiri dari atribut No, Item Obat, Jenis, Kemasan, Stok Awal, Harga Pokok, Konversi Satuan, Harga Jual, Jumlah Transaksi, Stok Akhir, Rak, Kode Gudang-Kantor. Dengan metode K-Means Clustering maka dapat mengelompokkan data penjualan obat dengan stok kurang laris sebagai cluster 0, stok laris sebagai cluster 1, dan stok sangat laris sebagai cluster 2. Data sampel yang akan diuji terdiri dari 170 data dari Apotek Jaka Wijaya. Yang dimana hasil cluster menunjukkan terdapat beberapa hasil yaitu cluster 0 berjumlah 102, cluster 1 berjumlah 34, dan cluster 2 berjumlah 34 keputusan yang dimana keputusan itu meliputi sangat laris, laris, kurang laris. Kata kunci: Apotek Jaka Wijaya; Data Mining;  K-means Cluster
Co-Authors Akmal Akmal Fitri Kurnia Havid Syafwan Rahmawati, Rahmawati