No abstract

The grading of fresh milk affects the quality classification in the dairy industry. This study aims to analyze and design a smart grading system using machine learning models to classify the grade of fresh milk. Business process analysis helped understand the capturing steps as the main elements, such as the smart grading system. The result of the requirement analysis showed how smart the grading system involved stakeholders. The machine learning model can help the Internet of Things system classify goods or services. Artificial Neural Network and K-means were designed to classify and group indicators of fresh milk quality. The variables used in this study consisted of pH, temperature, odour, turbidity, colour, fat, and taste values. The data were taken from the upstream dairy industry SAE Pujon. The classification result of fresh milk grades using ANN consisted of three low, medium, and high grades. The accuracy value of the classification obtained is 98.74%. The attributes used for grouping were temperature and colour. The best clusterization that used K-Means is the third cluster. Based on the data analysis, the smart grading system made users save time knowing the grade of fresh milk easier.

The agricultural sector is the leading sector of the Indonesian economy. Oyster mushrooms are one of the subsectors of agriculture. High opportunities for business sustainability and consumer demand make it necessary to analyze the financial viability of this venture. Financial feasibility analysis is required to assist CV. XYZ to see the feasibility of the development effort to be run. This research uses investment and production cost analysis method, cost of goods sold, Break Even Point (BEP), Net Present Value (NPV), Payback Period (PP), and Incremental Rate of Return and Ratio B/C. The result of the financial feasibility of CV. XYZ is BEP by selling product 50 baglog or Rp. 150,000 per production. NPV valued at Rp 253,181,432, Payback Period in year 1, IRR worth 40% and Ratio B/C 1.42 in the first year up to the fifth year.

Indonesia was ranked 46th out of 160 countries in the Logistic Performance Index 2018, below most ASEAN countries. Indonesia’s dependence on inter-island interconnections causes high container handling costs. Mango is one of Indonesia’s superior fruits. While almost every province in Indonesia produces mango, the major mango producers are mainly in Java Island and Eastern Indonesia. This production distribution leads to the demand to distribute mango from the production centers to other areas, which raises concerns about quality preservation. A well-designed logistics network configuration is imperative for a perishable food supply chain such as mango due to its influence on material flow balance, postharvest losses, costs, and response capacity. This study aims to review the problems of mango transportation in Indonesia and the thriving technology in transportation conditions monitoring and propose a formulation to develop a logistics network configuration. The presented paper discusses a multi-objective problem of customers maximization, postharvest losses rate minimization, and transportation cost minimization. We elaborate on the challenges with implementing multimodal transportation for perishable products, requirement analysis for the stakeholders, and transportation conditions monitoring. Ultimately, we develop a surplus model following the multi-objective problems considering the constraints of the harvest season, production volume, and distance from production centers to buyers’ locations. Field data and secondary information consist of material flow behaviour and market competition. The logistics network configuration models the material flow balance, postharvest losses, costs, and response capacity.

Soybean is one of the primary commodities of food after rice and corn. Soybean in Indonesia is one of the strategic food crops. The problem of local soybean supply chain compared to the important soybean chain is the supply of local soybeans involving many parties in soybeans. The number of distribution channels for the supply of local soybeans is also a trend for changes in price variations. More effective than efficient and efficient supply chains in local soybeans. The objectives of this study are (1) to identify conditions and protection in the soybean supply chain; (2) Analyzing the performance of the soybean agroindustry supply chain. To analyze the investment supply chain using the Food Supply Chain Network (FSCN), SCOR-AHP is used to analyze supply chain performance. The structure of soybean agroindustry supply chain in Cianjur Regency involves farmer, breeder, collector, Indonesian Tofu and Tempe Producers Cooperatives (KOPTI), agro-industry, local traders and interregional traders. The results of the calculation of the valuation of the market at the farmer level are 85,325%, traders are 92,926%, and agroindustry is 87,004%.