KKN-PPM: Improvement of Creativity Processing Purple Uwi into Purple Uwi Chips

Based on observations by DPL and KKN students of Madura University, Blaban Village is located on a plateau of rocks that make it difficult to grow plants. The soil condition of Blaban village is barren and dry during the dry season, during the rainy season the soil is cracked so that it needs water absorption to collect rainwater in the soil and improve the structure of the dry soil during the dry season. With a combination of science from soil structure (Geophysical Science), water supply in the soil (Geophysical Science), liquid fertilizer (Agricultural Science), product processing (Industrial Engineering), quality and quantity management (Industrial Engineering and Management Science), financial management (Economics), marketing management (Industrial and Economic Engineering) even taking care of business licenses (Legal and Administrative Sciences), and online sales using applications (Informatics Engineering). In order to maximize the processing of purple uwi into purple uwi chips in Blaban Village, the initial planting of purple uwi to processing as well as selling purple uwi chips are done through offline and online methods. The results obtained are optimizing the sale of purple uwi crops into purple uwi chips, which has more demand by all consumers in various regions

Estimated Profits of Rengginang Lorjuk Madura by Used Comparison of Holt-Winter and Moving Average

Rengginang Lorjuk is a typical Madura food that is ordered more by SMEs and is found in Sumenep Regency and several other areas in Madura. This product is made for supplies and orders, where demand will surge at certain times. Therefore, SMEs of Rengginang Lorjuk is required to have good planning in determining the selling price in accordance with the revenue target obtained. Considering that the main raw materials used are sticky rice and ensis leei (lorjuk) are raw materials that have fluctuating prices, this studio compares forecasting methods namely Holt Winter (HW) and Moving Average (MA), supported by MSE and MAPE, in order to obtain accurate forecasting results. These forecasting results show that HW has better accuracy than the MA, which is then used to calculate the cost of production with an Activity-Based Costing system, which requires charging costs for all activities carried out in production, namely the cost of raw materials, direct labor costs, and overhead factory fee. Using MAPE values, this study yields 4 estimates of production costs in accordance with changes in raw material costs

Optimasi Rancangan Jaringan Distribusi Pada Rantai Pasok Bahan Pangan di Jawa Timur

Desain jaringan supply chain merupakan keputusan strategis, yaitu penentuan jumlah, lokasi dan kapasitas pada setiap bagian supply chain. Jaringan distribusi supply chain yang efisien dapat meminimumkan biaya operasi pada semua bagian supply chain dan dapat bergerak dengan cepat dalam memenuhi kebutuhan konsumen. Penelitian ini dilakukan pada tiga tingkatan jaringan distribusi supply chain bahan pangan (pemasok, distribution center, dan konsumen) di Jawa Timur dengan menggunakan multi produk, multi periode, dan ketidakpastian pada pasokan bahan pangan. Tujuan dari penelitian ini adalah (1) menghasilkan model desain jaringan distribusi bahan pangan, (2) menentukan jumlah dan lokasi distribution center (DC) bahan pangan yang dibutuhkan agar dapat meminimumkan total biaya logistik. Masalah dalam penelitian ini dirumuskan dalam model Mixed Integer Linear Programming (MILP) dan diselesaikan menggunakan teknik standar branch-and-bound pada software Lingo 11. Hasil dari penelitian ini menunjukkan bahwa pada skenario permintaan 10% total biaya logistik yang paling rendah pada skenario 1 dengan membuka dua DC dengan kapasitas 100.000 Ton, yaitu pada DC (5 dan 6). Pada skenario permintaan 20% total biaya logistik yang paling rendah pada skenario 5 dengan membuka dua DC dengan kapasitas 200.000 Ton, yaitu pada DC (5 dan 6). Dan pada skenario permintaan 30% total biaya logistik yang paling rendah pada skenario 9 dengan membuka dua DC dengan kapasitas 300.000 Ton, yaitu pada DC (5 dan 6). Peningkatan jumlah permintaan dapat mempengaruhi kapasitas DC yang akan dibuka, yaitu pada permintaan 10% menggunakan kapasitas sebesar 100.000 Ton, pada permintaan 20% menggunakan kapasitas DC sebesar 200.000 Ton, dan pada permintaan 30% menggunakan kapasitas DC sebesar 300.000 Ton. ========================================================================================= Supply chain network design is a strategic decision, namely the determination of the number, location and capacity on any the supply chain part. The efficient supply chain distribution network can minimize the operation cost in all the supply chain parts and be able to move quickly to meet the consumer needs. The aim of this study were (1) generate the model of food distribution network design, (2) determine the number and location of foodstuffs distribution centers (DC) thats needed in order to minimize the total logistics cost. This study conducted at three levels of the food supply chain distribution network (suppliers, distribution centers, and consumers) in East Java by using a multi-product, multi-period, and uncertainty in food supply. The problem formulated as a Mixed Integer Linear Programming model (MILP) and solved using standard branch-and-bound techniques on software of Lingo 11. The results indicated that the lowest total logistics costs on the demand scenario 10% was in scenario 1 with opening of two DC (DC5 & DC6) by 100.000 Tons capacity. On the demand scenario 20% was in scenario 5 with opening of two DC (DC5 & DC6) by 200.000 Tons capacity. And the demand scenario 30% was in scenario 9 with opening of two DC (DC5 & DC6) by 300.000 Tons capacity. The increase in the demand number influenced the opening DC capacity, at 10% demand used DC by 100.000 Tons capacity, for 20% demand used DC by 200.000 Tons capacity, and for 30% demand used DC by 300.000 Tons capacity

Application of K-Means Clustering for Detection Downy Mildew at Madura Corn Plant Using Digital Image Processing

The development and cultivation of corn is necessary in line with the increasing consumption of food ingredients and industrial needs, especially food products made from corn. In the development of maize in Indonesia, the main obstacle is the disturbance of Plant Pest Organisms (OPT), especially diseases, one of which is downy mildew. This disease can be identified by a change in color, so we need a way to find out the difference between the color of healthy leaves and the color of leaves that have changed due to downy mildew. One solution that can be used is image processing. Therefore the aim of this study was to detect downy mildew based on leaf color in corn plants based on digital image processing, to produce precise and objective results. The algorithm used is the K-Means Clustering algorithm. This study uses 50 images of training data and 25 images of test data. Based on the simulation of downy mildew disease identification using K-Means Clustering it achieves an accuracy rate of 85%.