Ganjar Alfian

A relocation simulation model for one-way carsharing service

An improved service of carsharing, one-way service enables customers to use the vehicles from one station and return to other station. The common issue in one-way carsharing service is that the vehicle stock of each station become imbalance, thus will lead to the less customer satisfaction and less utilization of vehicles. Consequently, the relocation is used by the system to move the appropriate vehicle to a high demand station in order to elevate customer satisfaction. This paper will demonstrate the periodically relocation model for a one-way carsharing system and tested on simulation model. Computational simulation result on commercially operational data of carsharing in South Korea is involved to solve the relocation problem in one-way system. The results we have obtained in this study provide a clear insight into the impact of model on low relocation cost compared to the traditional relocation.

Application of simulation method and regression analysis to optimize car operations in carsharing services: A case study in South Korea

A carsharing service is a form of public transportation that enables a group of people to share vehicles based at certain stations by making reservations in advance. One of the common problems of carsharing is that companies can have difficulty optimizing the number of vehicles in operation. This paper reports on investigations of the relationship between the number of cars and the number of reservations per day with either the acceptance ratio or utilization ratio based on the commerciallyoperational dataset of a carsharing company in Korea. A discrete event simulation is run to analyze a round-trip service for every possible number of cars and number of reservations with the output acceptance ratio and utilization ratio. The simulation data revealed that increasing the number of reservations with respect to a certain number of cars will decrease the acceptance ratio, thus increasing the percentage of the utilization ratio. Based on the simulation data results, a rational regression model can achieve high precision when predicting the acceptance ratio or the utilization ratio compared to other prediction algorithms such as the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) models. K-means clustering was used to understand the pattern and provide additional policies for carsharing companies. Consequently, opening a carsharing business is very promising in terms of profit, escalating the level of customer satisfaction. In addition, a small reduction in the utilization ratio by operators will create a large increase in the acceptance ratio.

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

Current technology provides an efficient way of monitoring the personal health of individuals. Bluetooth Low Energy (BLE)-based sensors can be considered as a solution for monitoring personal vital signs data. In this study, we propose a personalized healthcare monitoring system by utilizing a BLE-based sensor device, real-time data processing, and machine learning-based algorithms to help diabetic patients to better self-manage their chronic condition. BLEs were used to gather users’ vital signs data such as blood pressure, heart rate, weight, and blood glucose (BG) from sensor nodes to smartphones, while real-time data processing was utilized to manage the large amount of continuously generated sensor data. The proposed real-time data processing utilized Apache Kafka as a streaming platform and MongoDB to store the sensor data from the patient. The results show that commercial versions of the BLE-based sensors and the proposed real-time data processing are sufficiently efficient to monitor the vital signs data of diabetic patients. Furthermore, machine learning–based classification methods were tested on a diabetes dataset and showed that a Multilayer Perceptron can provide early prediction of diabetes given the user’s sensor data as input. The results also reveal that Long Short-Term Memory can accurately predict the future BG level based on the current sensor data. In addition, the proposed diabetes classification and BG prediction could be combined with personalized diet and physical activity suggestions in order to improve the health quality of patients and to avoid critical conditions in the future.

Efficient Digital Signage Layout as a Replacement to Virtual Store Layout

Now a days, the printed message can be displayed in digital format with the help of Digital Signage which is used as replacement to virtual store in this paper. Digital Signage is very effective tool for advertisement, merchandising and entertainment that catches customers attention as well. The aim of this paper is to demystify the design of Digital Signage layout in order to attract customers attention. To improve the Customer Relationship Management (CRM), the Digital Signage can be placed at subway stations, shopping malls, bus stops, airports etc and the payment can be done with the help of Smartphone application. The advantages of traditional store layouts, such as grid, freeform and racetrack, are discussed and based upon these, a model layout for Digital Signage has been proposed.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

With the increase in the amount of data captured during the manufacturing process, monitoring systems are becoming important factors in decision making for management. Current technologies such as Internet of Things (IoT)-based sensors can be considered a solution to provide efficient monitoring of the manufacturing process. In this study, a real-time monitoring system that utilizes IoT-based sensors, big data processing, and a hybrid prediction model is proposed. Firstly, an IoT-based sensor that collects temperature, humidity, accelerometer, and gyroscope data was developed. The characteristics of IoT-generated sensor data from the manufacturing process are: real-time, large amounts, and unstructured type. The proposed big data processing platform utilizes Apache Kafka as a message queue, Apache Storm as a real-time processing engine and MongoDB to store the sensor data from the manufacturing process. Secondly, for the proposed hybrid prediction model, Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection and Random Forest classification were used to remove outlier sensor data and provide fault detection during the manufacturing process, respectively. The proposed model was evaluated and tested at an automotive manufacturing assembly line in Korea. The results showed that IoT-based sensors and the proposed big data processing system are sufficiently efficient to monitor the manufacturing process. Furthermore, the proposed hybrid prediction model has better fault prediction accuracy than other models given the sensor data as input. The proposed system is expected to support management by improving decision-making and will help prevent unexpected losses caused by faults during the manufacturing process.

Application of RFID and Computer Vision for the Inventory Management System

The RFID technology can be used for item tracking and inventory control. However, the problem such as miss reading and ghost reading usually occur in RFID implementation and has impact on low accuracy of inventory management system. In this study, the computer vision is used to solve the problem of miss reading and ghost reading in RFID. The RFID and computer vision can act as ears and eyes respectively, thus by combining both technologies; it is expected to increase the accuracy of system. The result of experiment has showed that the combination of RFID and computer vision has increased the system accuracy, as the computer vision can help the RFID system to detect the miss reading and ghost reading.

Intelligent Food Distribution Monitoring System

Food quality and safety has gained main attention, due to increasing health awareness of customer, improved economic standards and lifestyle of modern societies. Thus, it is important for consumers to purchase good quality products in order to keep the customer satisfaction level. In this study, we propose traceability system for food by monitoring the location as well as temperature and humidity. The RFID technology and wireless sensor network are utilized in this study to perform the experiment. The real testbed implementation has been performed in one of the Korean Kimchi Supply Chain. The result showed that our proposed system gave the benefit to the manager as well as customer by providing real time location as well as temperature-humidity history. It will help manager to optimize the food distribution while for the customer it will increase the satisfaction by maintaining the freshness of product.