Malaysians generate 15,000 tons of food waste per day and dispose of it in the landfill, contributing to greenhouse gas emissions. As a solution for the stated problem, this research aims to produce an excellent quality bokashi compost from household organic waste using a smart composting bin. The bokashi composting method is conducted, whereby banana peels are composted with three types of bokashi brans prepared using 12, 22, and 32 mL of EM-1 mother cultured. During the 14 days composting process, the smart composting bin collected the temperature, air humidity, and moisture content produced by the bokashi-composting process. With the ATmega328 microcontroller, these data were uploaded and synchronized to Google Sheet via WIFI. After the bokashi-composting process was completed, three of each bokashi compost and a control sample were buried in separate black soil for three weeks to determine each compost’s effectiveness. NPK values and the C/N ratio were analyzed on the soil compost. From the research, 12 mL of EM-1 shows the most effective ratio to the bokashi composting, as it resulted in a faster decomposition rate and has an optimum C/N ratio. Bokashi composting can help to reduce household food wastes. An optimum amount of the EM-1 used during the bokashi-composting process will produce good quality soil without contributing to environmental issues.

Water is a critical component of life on this planet, as humans, animals, and plants all rely on water supplies for survival. Regrettably, human activity has contaminated water sources. As a means of remedying this situation, each river should have an intelligent system that continuously monitors the water quality index at each water source. However, testing and establishing a realistic acceptability level for all water quality and control parameters takes time. Some of the data on the parameter’s values were also not immediately available. To address this problem, an estimation based on past samples and readings was applied. The process also implemented an exemplary computer system-based method known as an artificial neural network (ANN) Which has evolved into a promising and versatile tool for data prediction over the last few years. This article analyses and compares a host of ANN techniques that have been researched or used in the past to determine water quality. According to this study’s review of available literature, there are two types of ANN computation engines: Feedforward network systems and recurrent network systems that can be used for the purposes of determining water quality. Each method has advantages and disadvantages which were studied and this study hypothesises that with right techniques will be able to accurately predict and account for river water quality.

Water quality is one of the most highly debated issues worldwide at the moment. Inadequate water supplies affect human health, hinder food production, and degrade the environment. Using contemporary technology to analyze pollution statistics can help solve pollution issues. One option is to take advantage of advancements in intelligent data processing to conduct hydrological parameter analysis. To perform conclusive water quality studies, a lot of data is necessary. Unfilled data (information gaps) in the long-term hydrological data set may be due to equipment faults, collection schedule delays, or the data collection officer’s absence. The lack of hydrological data skews its interpretation. Therefore, interpolation is used to recreate and fill missing hydrological data. From 2012 to 2017, the Klang River’s biochemical oxygen demand (BOD) in Selangor, Malaysia, was sampled. This study examined three methods of interpolation for their effectiveness using the MATLAB software: piecewise cubic hermite interpolating polynomial (PCHIP), cubic Spline data interpolation (Spline), and modified Akima partitioned cubic hermite interpolation (Makima). The accuracy is assessed using root mean square error (RMSE). All interpolation algorithms offer excellent results with low RMSE. However, PCHIP delivers the best match between interpolated and original data.

Autonomous quadrocopter recently has shown a significant usages and potentials to be used widely in the commercial market. This caused the aircraft to become a well-liked subject among the researchers and research organizations. With good controllability and stability on its motions will make the usability of the quadrocopter to perform a task reliably. For this to happen, clean signals from accelerometer and gyroscope sensors appreciably play an essential role. However, the sensor's signals are routinely encounter problems with noise and drift. Hence, a filter is required to correct this imperfectness. In this paper, a simplified algorithm for Kalman's filter appropriates to be used by a low-cost microcontroller is presented. The research mainly focuses on filtering noisy outputs from the accelerometer so that the output is necessarily clean to be used for real-time quadrocopter pitch and roll angles estimation. Experimental results on assessment of the algorithm by varying the process and measurement noises are provided. The results reveal with correct tuning on the noise parameters; the filter algorithm is satisfying to give stable angles values with fine accuracy.

Water retention ponds developed in UiTM Pasir Gudang is used to accommodate the overflowed water from the campus facilities. Overflowed water is accumulated into this water retention pond and will flow to other water sources. Water from the water retention pond has potential to contaminate other water sources as well as the environment. To maintain the water retention pond, a sustainable water monitoring system with an integration of internet of things (IoT) technology is designed and implemented. A fully developed integrated IoT monitoring system is presented in this research paper. The developed system will assess the water parameter such as pH and water temperature as well as water quality to serve as the water health indicator. This system includes a pH sensor, temperature sensor, rain sensor, sustainable power supply, wireless frequency data transmission module, and IoT data acquisition system in the base station. After the water data process has been performed and verified, the authorities will be notified of the unhealthy water condition. The developed system also allows any person or authority to monitor the water quality in that environment from anywhere. As a result of the collected data for water data parameters, the developed system provides an efficient water parameter monitoring system. Thus, early actions can be taken to reduce any harmful effects to the environment due to poor intervention.

Water quality plays a major role in issues related to public health and marine life. Hence, monitoring river for contaminations is vital for ensuring safe and sustainable water resources. Conventional method for assessing water quality index is costly as it requires considerable amount of time and laboratory resources. Therefore, this study proposes a water quality index model based on artificial neural network. A six-year data for Air Busuk River is obtained from the Department of Environment. Dissolved oxygen, biological oxygen demand, and ammoniacal nitrogen has shown high correlation with water quality index. The water quality index model is then developed based on these parameters, employing the non-linear autoregressive with exogeneous input structure. Generally, the model which is based on three chemical parameters has shown satisfactory performance with overall regression of 0.8767 and passed the correlation function tests. The model offers a potentially efficient method for assessing water quality with cost-saving benefits for government agencies and monitoring authorities.