Along with technological advances, the increase in the number of motorized vehicles that have occurred in recent decades has led to an increasingly pressing problem of parking availability. This is especially the case in densely populated urban areas, where drivers often spend a long time looking for an empty parking space. The purpose of this research is to facilitate the process of finding a parking space for users. The Smart Parking System aims to make it easier for parking lot users to find a place to park without spending a lot of time. Parking monitoring in the Smart Parking System is carried out by using the website as a display media on the monitor. This system consists of an ultrasonic sensor as a vehicle detector and NodeMCU as a microcontroller. The design of this system shows that it has not worked perfectly because it still has problems and deficiencies in the prototype system. Due to differences in the NodeMCU type, it is difficult to connect to the access point. For the solution, NodeMCU must be connected one by one to the Arduino IDE serial monitor so that it can be connected to the access point. The results of this research trial, the sensor can detect parked vehicles and can send data to NodeMCU. It takes 1 second to send data from each sensor to NodeMCU and it takes 4 seconds to loop from 4 sensors. Meanwhile, sending data from NodeMCU to the database takes 15-20 seconds from each NodeMCU, this can happen because it is influenced by the signal.

Research on the development of data integration system design and web-based system in the calculation of Marine Ecological Carrying Capacity (MECC) for managing marine and coastal resource management has been carried out. In this study, Nunukan Regency, North Kalimantan was chosen as the study area. The process of integrating data in managing marine and coastal resources in a sustainable manner using the carrying capacity concept approach is carried out by designing an information system development system in processing and displaying the results of MECC calculations. This can make it easier for decision makers to develop the utilization of blue economic potential in accordance with geographic conditions as well as efforts that can be made to increase the productivity of the blue economy in Nunukan Regency, North Kalimantan.

Seasonal characteristics of wave height in Southern Bali waters (SBW) in 2014 was simulated using SWAN (Simulating Wave Nearshore) model with the resolution of 1/216° (0.51 km). The model was forced using Cross Calibrated Multi Purposed (CCMP) wind data with resolution 1/4° (27.75 km) and 1/600° (0.18 km) bathymetry data derived from Batimetri Nasional (BATNAS) provided by Geospatial Information Agency (BIG). The result shows that the highest (lowest) seasonal average of Significant Wave Height (SWH) in the SBW in 2014 during east (west) monsoon or JJA (DJF) in June-July-August (December-January-February) months was about 2.2 m (1.4 m). Meanwhile, SWH during the first (second) transitional monsoon or MAM (SON) in March-April-March (September-October-November) months was about 1.7 m (2.1 m). The 2D spectrum analysis exhibits that seasonal wave characteristic in the study region was dominated by swell propagation from the Indian Ocean (IO) associated with the Gallian Cyclone. Corresponds to the SWH, seasonal average of wave energy spectrum during east monsoon (JJA) shows the highest value up to 0.0038 m2s/deg compared to the other seasons.

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Hydrometeorological hazard early warning system has been developed in order to support public society in anticipating the extreme weather in Indonesia. Meanwhile, the early warning system in Indonesia still produced locally and need arduous human resources to process the enormous size data to have a good quality early warning. Therefore, in order to increase the effectivity, efficiency, and efficacy of the early warning system to produce the desired result, the automatic process was needed. This study will explain further the mechanism of process automation from the weather forecast to the output of warning displayed in an information system. The automation process combines several scripting in Python and batch script for GIS processing, as well as process scheduling using task scheduler. The output of extreme weather warning is in form of JSON files stored in organized folder directory. The automated process proven to be more useful in increasing the effectivity, efficiency, and efficacy of the early warning system to produce the desired result and later the automated process could be enhanced further to boost the lead time and reducing the arduous task in producing the early warning system.