Updating road network maps is essential for transportation services, as incomplete or inaccurate maps can lead to inefficiencies and diminish service quality. The online transportation industry generates vast amounts of GPS data as drivers navigate, which is valuable for mapping road networks and improving traffic management. However, since drivers do not cover all roads, satellite imagery plays a crucial role in identifying areas that are not mapped. By combining GPS data as labels with satellite imagery, the extraction of new road networks becomes more accurate. This research employs a deep learning Convolutional Neural Network (CNN) with the U-Net architecture for road segmentation, allowing for the identification of new paths. Two different encoders are tested in this research: Inception-ResNet-V2 and a pure U-Net encoder. The Inception-ResNet-V2 encoder achieves an accuracy of 91.3%, while the pure U-Net encoder achieves 90.7%. In terms of Dice Loss, the models record values of 0.051 and 0.08, respectively. The research highlights the effectiveness of different U-Net encoders in road network segmentation. With high accuracy and low Dice Loss, this approach provides a reliable method for automatically updating road maps. It has potential applications in navigation systems, urban planning, and AI-driven intelligent transportation systems.

The in-situ soil infiltration test using A Double Ring Infiltrometer (DRI) apparatus can be conducted in the field according to DIN 19682-7 standards and procedures. As required by these standards, the traditional paper-based measurement form can be replaced with a new application developed to meet standard requirements. The DRI apparatus consists of two concentric rings placed in the soil, filled with water, while the outer ring maintains a constant water level. The water level drop in the inner ring is observed and recorded at regular intervals. The infiltration rate can be calculated for each interval by measuring the change in water height over time. This new application facilitates the automatic calculation of both the actual soil infiltration rate and the Horton soil infiltration model. Comparison tests between the application results and Excel calculations have yielded similar outcomes. The goal of this research is to develop a mobile web-based application for recording data and calculating soil infiltration measurements using the DRI method. The research methodology involves transforming the measurement procedure into a concept, designing the application, and then implementing that design. By replacing the paper-based process, this application will enhance the efficiency, accuracy, and flexibility of soil infiltration measurement projects in various locations. Furthermore, the data will be stored in the cloud, allowing for crowdsourced infiltration data collection and monitoring from any location, including the office.

Waste, as a result of human activities, is a complex issue that requires appropriate solutions. With the increasing volume of waste, waste management in Indonesia has become a major challenge. The research examined the waste problem in Indonesia, focusing on analyzing and grouping 311 regencies/cities based on waste sources in 2022. The research also aimed to provide an in-depth understanding of waste characteristics in each region as a basis for designing more effective waste management policies at the regional level. The research applied hierarchical clustering, combining Ward’s method with Euclidean distance analysis. The analysis shows 14 significant clusters with different waste composition characteristics. Interpretation of the cluster results identifies areas with low to high levels of waste. Clusters 1 to 4 have relatively little waste composition, while clusters 5 to 14 have increasing waste levels, with cluster 14 being an area with very high waste levels. The research results are expected to serve as a basis for the government to formulate more targeted and adaptive policies for handling waste in the future. The implications include improving waste management systems, recycling programs, and community education. By understanding the waste composition of each region, the government can implement solutions that suit its needs. The research provides an overview of the waste problem at the regional level in Indonesia and can be the basis for developing more effective policies. In future research, it is recommended to use more accurate and complete waste data in each regency/city for more in-depth results.

Traditional datasets are often limited, which can affect the accuracy of analyses. Additionally, the use of students’ real data raises privacy concerns. Generative Adversarial Networks (GANs) offer a solution by generating synthetic data that closely mirrors real-world data without compromising sensitive information. The research explored the application of GANs to enhance student performance datasets by addressing challenges related to data scarcity and privacy in educational research. In the research, GANs were utilized to generate synthetic student performance data. The accuracy of the data was assessed using Mean Absolute Percentage Error (MAPE), with values ranging from 0.004% to 19.92% across various statistical measures and means. These results demonstrated the reliability of the synthetic data, making it suitable for further analysis. The synthetic datasets were then analyzed using the Apriori algorithm, a well-known method in data mining for discovering significant patterns and relationships. A lower bound minimum support of 0.1 (10%) and a minimum confidence threshold of 0.6 (60%) were applied, ensuring the identification of meaningful associations. The analysis reveals important patterns and relationships among student attributes and behaviors. The research highlights the potential of GANs to advance data-driven educational research. By generating high-quality synthetic data, GANs allow researchers to conduct comprehensive analyses while addressing privacy concerns. The research contributes to the methodological approach to data augmentation in education, offering new opportunities for ethical and robust research.

As Internet of Things (IoT) technology develops, water quality monitoring systems for Vannamei shrimp farms have become more inventive and straightforward. The prototype IoT system monitors and controls the pool using sensors that can measure water quality parameters, such as temperature, pH, and salinity. The research aimed to design an automated water quality monitoring system for Vannamei shrimp aquaculture. The research used the E-4052C sensor, DS18B20 sensor, and DFRobot V1.0 sensor as data transmitting hardware (transmitter) and the receiving hardware microcontroller NodeMCU ESP32 as data processing, management, and control system tools. Then, the system used a Wi-Fi network to transfer data from the microcontroller to the Message Queue Telemetry Transport (MQTT) server as a data cloud. Several software programs, including Telegram, Node-Red, and ThingSpeak, help Android devices display real-time data. Test results for the accuracy of the sensor’s reading on water pH are 99.71, with an error rate of 0.29%. Meanwhile, the accuracy of the temperature sensor is 98.03 with an error rate of 1.7%. On the other hand, the accuracy of the salinity sensor is 99.49, with an error rate of 0.41%. The results indicate that all sensors have excellent performance. The real-time monitoring display and Android Telegram notification functions are good, and the automatic water quality monitoring tool is successfully operating in the Vannamei shrimp pool in Pamandati, South Konawe District, Southeast Sulawesi ssProvince, Indonesia.

In a public facility or private office where many people can get together, a fire detection device is a mandatory tool as an emergency alarm in the facility. However, the expense of the installation of the device is a troublesome matter. So, optimization is needed to minimize the number of these devices. The way to implement is to select the appropriate position to place the devices in public facilities. The research discussed the placement of the sensors in multi-story buildings. The multi-story buildings could be represented as cube composition graphs with the number of rooms, and the connectivity between the floor and its rooms was equal. The concept of this multi-story building was modeled into a generalized Petersen graph where a vertex represented a room, and an edge was the connectivity of rooms. The basis obtained on that metric dimension was represented as a sensor placed on the building. Then, the optimization of device placement was seen as determining the metric dimensions of the Petersen graph. In the research, the alternative sensor placements were computed using the graph metric dimension approach implemented in Python. The research successfully implements the metric dimension of to using Python code to obtain the alternative of its basis. A basic alternative indicates the location of the device placement like fire detectors, network access points, or other sensors inside a building.

The current low success level of Enterprise Resource Planning (ERP) implementation stimulates the rise of research to find the critical success factor of it. One of the challenges of ERP implementation is to find a ‘fit’ between business and system requirements. It is claimed that standardizing business processes to follow the ERP system will positively impact organizations to follow the best practice process. However, there is another challenge that organization should not reengineer their business process to fit the ERP system, but rather modify or customize the ERP system to match their business process. This belief argues that standardizing business processes is not the right solution that applies to every organization since it can degrade an organization’s competitive advantages. Based on the background, the research aimed to construct a comprehensive review to succeed in implementing an ERP system, particularly on ERP modification, using a systematic literature review method. It was done by collecting and analyzing scientific publications related to ERP critical success factors with special attention to system modification. The literature review results in a comprehensive explanation of ERP modification. It includes elaborating on different types of misfit and customization to cultivate the understanding of ERP modification, a flowchart to analyze misfit to help the organization to evaluate modification requests, and critical success factors of modified ERP implementation.

In line with the unstable market conditions in Indonesia, especially the plastic manufacturing industry, the companies need to be well prepared to survive. There were two research goals. First, it was to find out why the Overall Equipment Effectiveness (OEE) rate at the company did not meet the expected standard. Second, it was to determine what improvement should be made to increase the OEE for the machine. The research applied a case study in one of the plastic manufacturing industries located in Tangerang, Indonesia. It focused on the performance of Toshiba injection molding machine number 42/IS 450 GSW. The analysis was performed by applying Failure Mode and Effect Analysis (FMEA) with support from other tools such as Pareto chart, Cause and Effect Diagram (CED), and six big losses. By following the whole framework of the research, it finds causes of why the OEE does not meet the target. Then, from the results, it can be concluded that the implementation of the methods has a positive impact on the company. The OEE rate increases from an average of 26,43% to 78,87%. It means the OEE rate has achieved the company target of 75%.

This research discussed the properties of Spatially Corrected Blundell-Bond (SCBB) in overcoming the problem of endogeneity and spatial dependence that occur in dynamic Spatial Durbin Model (SDM) panels. The properties of the estimator tested were unbiased and normality. The properties test of the estimator was carried out using the Monte Carlo simulation approach. From the results of this research, it finds that the SCBB estimator has unbiased properties and follows a normal distribution. Based on the property of the estimator obtained, the SCBB parameter estimation method in the dynamic SDM panel model works well in overcoming endogeneity and spatial dependence problems.