NILAI TAMBAH SNACK SAYURAN ORGANIK UD JIBARIZ DI KECAMATAN GETASAN KABUPATEN SEMARANG

Home industry Jibariz is one of the SMEs that processes organic vegetable products into sticks and dumplings. The purpose of the study was to determine the amount of added value generated, analyze costs, revenues, profits from processing, and business developments from the trend of selling Jibariz organic vegetable snacks 2 years before (2018, 2019) and during the Covid-19 pandemic (2020). This research was conducted in Jampelan hamlet, Getasan sub-district, Semarang district. Sampling was done intentionally (purposive sampling). The data was processed using quantitative descriptive methods. Analysis of the data used in this study is the analysis of added value using the Hayami method. The result of the research shows that the added value of Jibariz organic vegetable snack is Rp. 61,800 per kilogram or 68.66% of the product value. The average cost incurred each month is Rp. 3,557,556 with a total production of 600 packs per month and able to provide a monthly income of Rp. 9,000,000 and the profit earned is Rp. 3,181,000. The value of the R/C ratio obtained is 1.5 so it is feasible to continue. The development of Jibariz's organic vegetable snack business in the 2 years before Covid-19 the sales trend fell, while in 2020 during the Covid-19 pandemic the sales trend increased.

Pendeteksian Citra Pengunjung Menggunakan Single Shot Detector untuk Analisis dan Prediksi Seasonality

This study discusses the analysis of retail store with time series method to obtain information about sales trend and seasonality by looking at visitor data and total transaction data at a time period. Data in the form of the number of customers who visit are obtained through CCTV video camera recordings placed at retail store X and the total transaction occurred at retail store X. The visitor counting uses the deep learning method with SSD (Single Shot Detector) object detection framework and MobileNet architecture. The library used to count the number of customers visiting the store is OpenCV, Pandas, Numpy, Dlib, and Imutils. The number of customers visiting the store will then be compared to the number of transactions that occur at the same time so that a conversion rate can be obtained. From here, we can see sales trend that occur at any time. Time series analysis is also carried out to determine and analyze the pattern of data obtained based on certain time to predict the things that need to be done in the future. Through this research, information has been successfully obtained related to seasonality patterns, value and interpretation of retail conversion rates, models for predicting the number of visitors and transactions, and answering the hypothesis with the Wilcoxon test method obtained a p-value of 0,014 which states that the data pattern of the number of consumers is not the same as the transaction data pattern.

Peramalan Tren Penjualan Menu Restoran Menggunakan Metode Single Moving Average

<p>Penelitian ini bertujuan untuk meramalkan tren penjualan menu pada restoran guna membantu pihak pengelola restoran dalam menentukan dan memberikan rekomendasi pengelolaan stok menu. Peramalan dilakukan dengan mengimplementasikan metode <em>single moving average</em> pada data transaksi penjualan selama periode 15 bulan, yakni bulan Januari-Desember 2018 dan Januari-Maret 2019 untuk menghasilkan ramalan bulanan dan harian. Total sampel data latih yang diolah sebanyak 10.515 record yang merupakan data transaksi penjualan pada bulan Januari-Desember tahun 2018, serta 2.246 record data bulan Januari-Maret 2019 sebagai data uji (untuk menguji akurasi ramalan). Hasil pengujian hasil ramalan bulanan untuk Top-10 menu menghasilkan perhitungan MAPE <em>(Mean Absolut Percentage Error) sebesar </em>4% yang berarti tingkat akurasi sangat baik, yakni sebesar 96%. Sedangkan pengujian hasil ramalan harian menghasilkan MAPE yang cukup tinggi yaitu sebesar 39.2%, mengindikasikan nilai akurasi yang cukup rendah, yakni 60.8%. Meskipun akurasi untuk ramalan harian, masih rendah namun hasil penelitian ini dapat memberikan gambaran kepada pengelola hotel tentang rentang minimum-maksimal stok yang perlu disiapkan untuk menu tertentu pada hari-hari tertentu. Untuk memperoleh akurasi prediksi harian yang lebih akurat, penelitian ini akan dilanjutkan dengan mencoba metode lain serta menambah jumlah data latih.</p><p> </p><p class="Judul2"><strong><em>Abstract</em></strong></p><p class="Judul2"><em>This research aims to forecast sales trend of a restaurant menus to help the restaurant management in determaining and providing recommendations for managing stocks. Forecasting was performed by applying the single moving average towards fifteen months recorded data transaction, namely January to December 2018, and Januari to March 2019 to establish monthly and daily forecast. Total data training was 10.515 recods data transaction obtained from Januari to December 2018, while data testing was 2.246 record data transaction within Januari to March 2019. Result for montly forecast shows, that the average accuracy reached 96% (MAPE 4%) indicating the forecast is almost perfect. While, for daily forecast the average accuracy is only 60.8% (MAPE 39,2%) indicating that the forecast is less accurate. Although, accuracy of the daily forecast is considered less accurate, the result still can be used by the restaurant management to figure-out minimum and maximum amount of stock to be prepared for certain menus in certain days. </em></p>

Visual Analysis of Merchandise Sales Trend Based on Online Transaction Log

Online transaction log records the relevant information of the users, commodities and transactions, as well as changes over time, which can help analysts understand commodities’ sales. The existing visualization methods mainly analyze the purchase behavior from the perspective of users, while analyzing the sales trend of commodities can better help merchants to make business decisions. Based on the transaction log, this paper puts forward the visual analysis framework of commodity sales trend and the corresponding data processing algorithm. The concepts of volatility and dynamic performance of sales trend are proposed, through which the multi-dimensional sales data of time-oriented are displayed in two-dimensional space. The “Feature Ring” is designed to display the detailed sales information of the products. Based on the above methods, a visual analysis system is designed and implemented. The usability and validity of the visualization methods are verified by using JD online transaction data. The visualization methods enable manufacturers to formulate production plans and carry out product research and develop better.

Retail Giant Sales Forecasting using Machine Learning

Sales forecasting is widely recognized and plays a major role in an organization’s decision making. It is an integral part in business execution of retail giants, so that they can change their strategy to improve sales in the near future. This helps in better management of their resources like machine, money and manpower. Forecasting the sales will help in managing the revenue and inventory accordingly. This paper proposes a model that can forecast most profitable segments at granular level. As most retail giants have many branches in different locations, consolidation of sales are hard using data mining. Instead using machine learning model helps in getting reliable and accurate results. This paper helps in understanding the sales trend to monitor or predict future applicable on different types of sales patterns and products to produce accurate prediction results.

PERANAN BIAYA DISTRIBUSI DAN BIAYA PERSONAL SELLING TERHADAP PENJUALAN PADA TOKO ERA KERAMIKA PEMATANGSIANTAR

ABSTRACTThe purpose of this study is to examine and analyze 1. The Description of distribution costs, personal selling costs, and sales at Keramika Pematangsiantar Era Store. 2. The role of distribution cost and personal selling costs on sales the Pematangsiantar Era Keramika Store both simultaneously and partially.Based on the results of the analysis through the trend formlula for 1. Distribution cost, it can be interpreted that the distribution cost are interpreted that the distribution costs are positively proportional to sales, where when X is raised one unit is predicted to increase sales. The trend for 2. Personal selling costs, it means that the costs of personal selling is positively proportional to sales, where when X is raised one unit is predicted to increase sales. 3. Sales trend, it means that sales experience a monthly increase of 197.368,42. Based on the results of the analysis and evaluation of distribution cost and personal selling cost, it has a role in increasing sales at Keramika Pematangsiantar Era Store. Keyword: Distribution Cost, Personal Selling Cost, and Sales

PERANAN BIAYA DISTRIBUSI DAN BIAYA PERSONAL SELLING TERHADAP PENJUALAN PADA TOKO ERA KERAMIKA PEMATANGSIANTAR

The purpose of this study is to examine and analyze 1. The Description of distribution costs, personal selling costs, and sales at Keramika Pematangsiantar Era Store. 2. The role of distribution cost and personal selling costs on sales the Pematangsiantar Era Keramika Store both simultaneously and partially.Based on the results of the analysis through the trend formlula for 1. Distribution cost, it can be interpreted that the distribution cost are interpreted that the distribution costs are positively proportional to sales, where when X is raised one unit is predicted to increase sales. The trend for 2. Personal selling costs, it means that the costs of personal selling is positively proportional to sales, where when X is raised one unit is predicted to increase sales. 3. Sales trend, it means that sales experience a monthly increase of 197.368,42. Based on the results of the analysis and evaluation of distribution cost and personal selling cost, it has a role in increasing sales at Keramika Pematangsiantar Era Store. Keyword: Distribution Cost, Personal Selling Cost, and Sales