The Indonesian seaweed trade is still dominated by dry seaweed, even though Indonesia's seaweed still has high prospects for development. The purpose of this study is to analyze and provide strategies for supporting the prospects of Indonesia's seaweed trade. In achieving this goal, we use qualitative desk study methods and SWOT analysis. The results showed that Indonesia's seaweed trade still shows the opportunity to increase high added value by prioritizing the trade of semi-finished products rather than dried seaweed. This is supported by the strength of Indonesian seaweed which has a large potential for cultivation, a high number of laborer, high consumption of domestic and foreign seaweed derivative products and the availability of sufficient technology and expertise. Those strengths used to seize high import market opportunities. The existence of a national seaweed development road map also helps create a better prospect for seaweed trade.
Online culinary businesses are in charge of preventive service failure to avoid greater loss and keeping service performance. The losses occurred due to protracted customer disappointment with service failures which lead to worse churning customers. We propose a prevention system to identify and allocate the failure position. It also sets the action rules to failure. This requires to analyze the current failure structure and cause. This work used decision tree classification (ID3) to design the rule. The failure structure was denied by the component of delivery time and food quality. The parameters to allocate the failure were delivery time, traffic jam, rush hour ordering, weekend/weekday order, rain/not rain day, giving GPS/not, suitable order, food complement, and packaging quality. It had four failure types: none, low, medium, and high. The model had 73% of accuracy to classify failure. The action for low failure was in charge by the delivery man in the form to speed the delivery time not exceed 85 minutes and by kitchen staff in the form to check the order before it sent. The medium failure was in charge by the delivery man in the form to speed the delivery time not exceed 55 minutes in not rush hour order.
Dispatching is a critical part in current online shopping. It relates to how the delivery man assignment should minimize cost along with the service from a source to an end customer with an appropriate scheduled time. The problem arises as neither enough products to deliver nor delivery men are available for dispatch, resulting in suboptimal service and a waste of money. The study aimed to formulate the cost of restaurant dispatching for inducing a deep learning-based solution with the gated recurrent unit recurrent neural network to receive hourly order data and to engage the result for near feature delivery man schedule with minimum cost. The result showed that cost formulation minimized the number of delivery men times the wage per hour with the constraints of each delivery man carrying a maximum of five orders in one way and 11 work hours/day. The deep learning input model used 1078 historical data which were filtered using the Savitzky-Golay method. The root mean square errors of training and testing were 2.35 and 2.41, respectively. Moreover, the number of delivery men every hour was found in a range from one to four people. Furthermore, the deep learning approach saved costs of up to 43.8%. AbstrakPrediksi Masalah Penugasan Kurir dengan Pembelajaran Mendalam. Pengiriman merupakan salah satu hal yang penting dalam model belanja online. Hal itu berhubungan dengan bagaimana penugasan kurir untuk mengantar pesanan hingga ke konsumen dengan cepat dan biaya yang ditimbulkan sesedikit mungkin. Permasalahan yang terjadi adalah jika ada ketidakseimbangan antara jumlah kurir dan jumlah pesanan yang harus diantarkan sehingga menyebabkan pelayanan tidak optimal dan berakhir pemborosan. Tujuan dari penelitian ini adalah memformulasi model biaya pengiriman yang terdapat di restoran, menyusun solusi deep learning gated recurrent Unit (GRU) recurrent neural network (RNN) untuk mendapatkan data pesanan setiap jam, dan menggunakan hasil yang didapat untuk menyusun jadwal penugasan kurir yang menghasilkan biaya minimum. Hasil yang didapat adalah formulasi dari biaya penugasan adalah meminimumkan jumlah kurir dikali dengan upah tiap jamnya, dengan batasan masing-masing kurir maksimum membawa 5 pesanan dalam sekali jalan dan 11 jam kerja/hari. Input dalam model deep learning adalah 1078 data historis pesanan online yang sudah disaring menggunakan metode Savitzky-Golay. RMSE dari data pelatihan dan data percobaan masing-masing 2.35 dan 2.41. Jumlah kurir yang didapat dari metode ini adalah 1-4 orang dari yang sebelumnya 3-4 orang. Metode ini mampu menghemat biaya hingga 43.8%.
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