SGSC Framework: Smart Government in Supply Chain Based on FODA

Smart System has implemented in government sector. There are varies Implementation that was utilized by research activities for numerous domains is very broad. Besides that, the Industry, transportation and health, also where such a system is incredibly beneficial. This study discuss supply chain and governmental link issue, coordination of all stakeholder in supply chain has to reflect the government role. It support with the condition in Indonesian government environment is unique. It is a challenge to construct smart system based on Feature Oriented Domain Analysis (FODA) approach. It can produce software product line (SPL). We proposed framework for develop software product line for smart supply chain in government sector. It is used to enhance and improve the development of software systems by multiple software system developers. It will be a guidance for construct smart government, and more specificity in supply chain for government system area environment. It is called SGSC Framework. It consists of four layers, such as optimization layer, integration layer, supply chain layer and data layer

User Experience Analysis of the Users Babacucu.Com

The Internet has made sharing information easier. By extension, it has also made sharing things easier. The problem is gauging the user experience of free E-commerce websites such as Babacucu.com to see whether people are interested to visit it or not. One of the elements of user experience is usability, that will be measured in this research. The methods used to measure the usability are questionnaires and usability testing with the users of Babacucu as subjects. The results of this study are the level of usability of Babacucu.com and recommendations on how to improve the site's usability

Controlling The Patient Services Mechanism in Hospital Using Soft System Methodology (Case Study ABC Hospital in Karawang)

Controlling The Patient Services Mechanism in Hospital Using Soft System Methodology (Case Study ABC Hospital in Karawang

Intelligent decision support model for recommending restaurant

People’s lifestyles began to change, now they tend to be interested in trying various types of culinary practically. The number of restaurants does not mean someone will visit each restaurant, so it is going to depend on various consideration. Here, an intelligent decision support model was developed to help people to get a restaurant suggestion that suitable for them. Seven parameters were adopted scientifically, i.e. customer interest, price/budget, distance between customer and restaurant, taste rating, cleanliness rating, facilities rating, and halal or nonhalal status. Through using the methods fuzzy logic, cosine similarity distance, selection, and optimization (i.e. hybrid Latin hyper-cube-hill-climbing), model is able to provide restaurant recommendation for individual user or group. In this study, the experiment involved 75 restaurants in Jakarta and eight customers

Real-Time Oil Palm Fruit Grading System Using Smartphone and Modified YOLOv4

The classification of the ripeness degree of oil palm fruit has attracted the attention of numerous researchers. However, there are still many challenges due to constraints in the dataset, methodologies used, and variations in the use of data categories. Detecting oil palm fruit bunches accurately is crucial, given their complex shape and characteristics, particularly when different ripeness categories are present in a pile of oil palm. Most studies utilize oil palm images or the color spectrum of oil palm fruit to classify the level of ripeness. However, these methods are not real-time and lack efficiency. This study proposes a real-time model for determining the ripeness degree of oil palm using a smartphone and video data as input, incorporating modifications to the object detection approach. The research process involves collecting videos of palm oil piles using smartphones in the grading area of the palm oil industry. The videos are then pre-processed and labelled for the object detection and classification process. A detection and classification model is developed using the YOLOv4 approach with several performance improvements, enabling implementation on smartphones. The best-performing model is tested for detecting and classifying the ripeness of fresh fruit bunches using an android-based smartphone. The testing results, based on the mAP value, demonstrate that the YOLOv4 model with 16 quantization performs 12% better than YOLOv4 Tiny. Based on the test results at the grading location, this model can efficiently detect fruit bunches that do not meet the quality standards