Ultima Computing : Jurnal Sistem Komputer

Solar Radiation Intensity Imputation in Pyranometer of Automatic Weather Station Based on Long Short Term Memory

2024-01-05T09:22:35+00:00

Automatic Weather Station (AWS) experienced problems in the form of component damage and communication system failure, resulting in incomplete parameter data. Component damage also occurs in pyranometers. Decreased pyranometer performance results in deviations, uncertainty in measuring solar radiation intensity, and data gaps. Data imputation is one solution to minimize measurement deviations and the occurrence of missing AWS pyranometer data. This research aims to design and analyze the accuracy performance of the multisite AWS pyranometer solar radiation intensity data imputation model when a data gap occurs. This research attempts to utilize the spatio-temporal relationship of multisite AWS solar radiation intensity in the imputation model. Long-Short Term Memory (LSTM) algorithm is used as an estimator in the multisite AWS pyranometer network. Data imputation modeling stage includes data collection, data pre-processing, creating missing data scenarios, LSTM design and model testing. Overall, LSTM-based imputation model has ability of filling gap data on AWS Cikancung pyranometer with maximum missing sequence of 12 hours. Imputation model has MAPE 1.76% - 5.26% for missing duration 30 minutes-12 hours. It still it meet WMO requirement for solar radiation intensity measurement with MAPE<8%.

Predictive Maintenance Automatic Weather Station Sensor Error Detection using Long Short-Term Memory

2024-01-05T09:25:57+00:00

Weather information plays a crucial role in various sectors due to Indonesia's wide range of weather and extreme climate phenomena. Automatic Weather Stations (AWS) are automated equipment designed to measure and collect meteorological parameters such as atmospheric pressure, rainfall, relative humidity, atmospheric temperature, wind speed, and wind direction. Occasionally, AWS sensors may produce erroneous values without the technicians' awareness. This study aims to develop sensors error detection system for predictive maintenance on AWS using the Long Short-Term Memory (LSTM) model. The AWS dataset from Jatiwangi, West Java, covering the period from 2017 to 2021, will be utilized in the study. The study revolves around developing and testing four distinct LSTM models dedicated to each sensor: RR, TT, RH, and PP. The research methodology involves a phased approach, encompassing model training on 70% of the available dataset, subsequent validation using 25% of the data, and finally, testing on 5% of the dataset alongside the calibration dataset. Research outcomes demonstrate notably high accuracy, exceeding 90% for the RR, TT, and PP models, while the RH model achieves above 85%. Moreover, the research highlights that Probability of Detection (POD) values generally trend high, surpassing 0.8, with a low False Alarm Rate (FAR), typically below 0.1, except for the RH model. Sensor condition requirements will adhere to the rules set by World Meteorological Organization (WMO) and adhere to the permitted tolerance limits for each weather parameter.

An Automatic Internet of Things-Based System for Rabbit Cage

2024-01-05T09:23:13+00:00

Rabbits, low-maintenance mammals in terms of cost and space requirements, require meticulous care, encompassing disease control, feeding, and cage maintenance. To address these concerns, an automated system for feeding, drinking, temperature control, and monitoring rabbit manure gas levels within the cage was developed, all remotely accessible. The system comprises ultrasonic sensors, DHT11 sensors, MQ-135 gas sensors, a real-time clock (RTC), an Arduino Mega 2560 with built-in Wi-Fi, relays, servo motors, mini water pumps, mini fans, and a heat lamp. The feeding and drinking functions are automated, triggered by RTC sensor data or can be manually controlled through the Arduino IoT Cloud dashboard. Temperature regulation is managed based on data from the DHT11 sensor, and gas levels in the rabbit manure are monitored using the MQ-135 gas sensor. Conducting 30 tests for each primary function, including automatic and manual feeding/drinking, temperature control, and disinfectant spraying, these functions performed as designed. An exception occurred three times when the DHT11 microcontroller sensors lost connection, rendering the input from these sensors unusable. To address this issue, the addition of an extra voltage supply to the Arduino Mega 2560 microcontroller is proposed, mitigating this vulnerability.

Automatic Mass Waste Sorting System Using Inductive Proximity Sensor, Water Level Sensor and Image Processing using MobileNet Algorithm

2024-01-05T09:23:23+00:00

The global municipal solid waste is predicted to increase by threefold in 2050. Indonesia’s most wastes are unsorted and only end up in landfill and the waste management is less than ideal. An automatic mass waste sorting system is proposed to answer such problems. The automatic mass waste sorting system is designed to be able to identify and separate metal, plastic and organic waste using electrical sensors and image processing. The electrical sensors was able to identify waste types with 65% accuracy and the image processing system was able to identify waste types with 86.67% accuracy. The result doesn’t offer much advantage compared to other research on waste management system, however it is hoped that this research may inspire other researches on mass waste sorting systems.

Trajectory Planning of Spherical Pendulum Pattern for Application in Creating Batik Patterns

2024-01-05T09:23:35+00:00

Batik Pendulum is a new batik pattern created by Rumah Batik Komar using a single-string pendulum filled with wax. However, current production is still manual, so it is impossible to re-manufacture in large quantities. This research is part of a machine and software development project to produce Batik Pendulum, where this research will only focus on software development. The designed software will have a spherical pendulum trajectory planning feature through parameter changes. The spherical pendulum path was chosen because it has the same pattern as the currently produced Batik Pendulum. In planning the spherical pendulum trajectory, an algorithm that receives input in the form of parameters to produce a spherical pendulum pattern has been designed. From these inputs, it is proven that the proposed parameters can provide a variety of spherical pendulum patterns. Implementing the spherical pendulum trajectory planning in the software shows that the time required to change parameters until the output trajectory is generated is 1 – 2 seconds. So, there is no need for any feedback to the user.

EEG-Based Depression Detection in the Prefrontal Cortex Lobe using mRMR Feature Selection and Bidirectional LSTM

2024-01-05T09:23:54+00:00

Depression can induce significant anguish and impair one's ability to perform effectively in professional, academic, and familial settings. This condition has the potential to result in suicide. Annually, the number of deaths resulting from suicide exceeds 700,000. Among individuals aged 15-29, suicide has emerged as the fourth most prevalent cause of mortality. Challenges in treating depression include limited accessibility to mental health care in rural regions and misdiagnosis resulting from subjective evaluations, wherein insufficient expertise can contribute to inaccurate diagnoses. Electroencephalography (EEG) has gained popularity as a tool for the identification and study of a number of mental illnesses in the past several years. Therefore, an automated technique is required to precisely distinguish between normal EEG signals and depression signals. This research focused on developing an EEG-based depression detection system in the prefrontal cortex lobe area (Fp1, Fpz, and Fp2). One of the developments carried out in this research is the implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) as the model classification and minimum redundancy maximum relevance (mRMR) feature selection. Results suggest that the combination of mRMR feature selection with 25 features and the Bidirectional LSTM obtained 92.83% for the accuracy.