The fundamental challenges related to the inability of traditional metering infrastructure to provide accurate and fast data and the lack of visibility to manage electricity usage information have driven the development of smart metering solutions. Smart metering, which is part of the smart grid architecture, has evolved over the years along with the needs of the electric power system infrastructure that requires efficient energy management initiatives. Advanced Metering Infrastructure (AMI) is one of the technologies being developed as a smart metering infrastructure. AMI consists of systems and networks, which are responsible for collecting and analyzing data received from smart meters. In addition, AMI also manages various electricity-related applications and services based on data collected from smart meters. The implementation of AMI has been proven to provide various positive results for both energy service providers and consumers. AMI is able to increase the accuracy of energy consumption recording by up to ±0.5% and reduce billing errors by up to 95%. Therefore, AMI plays an important role in the smooth functioning of the smart grid. In developing AMI technology, of course, there are challenges. Therefore, this paper provides an overview of smart metering technology, its design requirements, protocols and challenges, and policy issues.

Wastewater management is a critical component of environmental sustainability, particularly in regions undergoing rapid industrial development such as Bali. The Wastewater Treatment Plant (IPAL) in Suwung plays an essential role in ensuring that effluents meet environmental quality standards mandated by regional and national regulations. However, current monitoring practices at the facility are conducted manually, resulting in potential delays in pollution detection and timely decision-making. This study proposes the design and implementation of an Internet of Things (IoT)-based prototype for real time wastewater quality monitoring. The system integrates multiple sensors-including pH, temperature, turbidity, and water level sensors-controlled by an arduino UNO microcontroller and ESP 32 module. Measurement data are transmitted to Blynk and ThingSpeak platforms in real time, stored in a data logger for further analysis, and made accessible via a web-based monitoring interface. The results demonstrate that the system is capable of delivering accurate and real time data, which can support more efficient monitoring and facilitate timely environmental management decisions. This prototype offers a reliable and scalable solution for digitizing wastewater monitoring processes in treatment facilities

Photoplethysmography (PPG) is a non-invasive technique for measuring various physiological parameters, including blood glucose levels. However, PPG signals are often affected by noise and artefacts that reduce the accuracy of analysis and prediction. Therefore, an effective noise filtering method is needed to make the signal quality and ready for feature extraction for blood glucose estimation. This study offers a solution to the problem of noise in PPG signals through the application of appropriate pre-processing methods. This study aims to select quality PPG signals through three pre-processing methods: detrend, smoothing, and 0.5-5 Hz bandpass filter. The effectiveness of the three methods was evaluated through ADF test to measure the stationarity of the signal, frequency spectrum analysis to observe the distribution of frequency components, and SNR test to assess the signal to noise ratio. Based on the analysis of 67 data samples, the p-value <0.05 was obtained, indicating that the signal has reached a stationary condition. In addition, the average test statistic of men is higher than that of women, indicating that men's signals are more stationary after detrend. Meanwhile, 36 samples (54%) had SNR ≥ 20 dB indicating that more than half of the data were of good enough quality for further analysis. The results show that multi-stage pre-processing improves the quality of PPG signals, validated through quantitative tests of stationarity and SNR values. Thus, the preprocessed and improved quality PPG signals are considered feasible for use in the development of estimation models for various physiological parameters, including blood glucose levels.

Micro Hydro Power (MHP) is a small power plant that uses water to generate electricity. It can be found in irrigation canals, rivers, or waterfalls. The amount of water and head height are important factors in determining the power output. The irrigation canal in Medewi Village is being used as a research location for MHP. Researchers will use the data to perform calculations and design a prototype. They will also conduct an economic analysis of the MHP to determine its feasibility. The discharge at the research location is 0.23 m3/s, and the waterfall is 2.24 m high. Through the calculation results, the researchers determined that the turbine is a crossflow turbine. The turbine power is 3.9 kW, the generator power is 4.375 kVA, and the turbine power in the homer software is 4.195 kW. The cost of building the MHP in the irrigation canals in Medewi Village is IDR20.391.899,26. With a 6% discount rate, the BCR is 1,51, the NPV value is IDR30.827.069, the IRR is 12,3%, and the BEP result per unit The payback period is 1 year 5 months and 2 weeks. Selling 46.585,056 kWh results in an LCC of IDR44.598.314. Using MHP throughout its economic life results in a cost that is less than the price paid to PLN.

FTTH is a technology that is important in providing high-speed internet services to customers. However, interruptions or anomalies in the FTTH network may cause service interruptions that impact user experience. Anomaly data from FTTH telecommunications networks are collected and processed using preprocessing techniques to prepare data before being used in SVM model training. The training process is carried out by using training data to classify data as normal or anomaly. After the training, an evaluation of the performance of the SVM model was carried out using test data that had never been seen before. The results of the analysis show the ability of the SVM model to detect anomalies in FTTH telecommunication networks with high accuracy and good performance. The conclusion of this study is that machine learning techniques with the SVM method have great potential in anomaly detection in FTTH telecommunication networks.

A smart city is a concept that integrates information and communication technology to improve the efficiency of public services, the quality of life of citizens and environmental sustainability. Bali, as one of the largest tourism centers in Indonesia, has started implementing the smart city concept to support economic development and overcome the challenges of urbanization. This research aims to evaluate the implementation of smart cities in Bali through a literature review approach, focusing on six main components of smart cities. Analysis was carried out on various national and international studies and reports related to smart city implementation in Bali, especially in the Denpasar and Badung areas to identify the stages of development and implementation of each component. The main focus is to examine how technology has been applied in the public service, transportation, security and tourism sectors, as well as the challenges faced in implementing this technology. The evaluation results show that the implementation of the smart city concept in Bali still faces several challenges, including limited technological infrastructure and low community digital literacy. This research provides recommendations for increasing the effectiveness and maturity of smart city implementation in the future. It is hoped that the results of this research can become a reference for the government and stakeholders in planning a more integrated and sustainable smart city development strategy in Bali.

Abstract— Photoplethysmography (PPG) is a non-invasive optical technique for cardiovascular health monitoring, such as blood pressure estimation and arterial stiffness analysis. However, detecting fiducial points in PPG signals such as the onset, systolic peak, dicrotic notch, and diastolic peak is often hindered by noise, baseline wander, and physiological variability. Although various methods have been proposed, such as time-frequency domain analysis and machine learning algorithms, these approaches still have limitations, including high computational complexity and susceptibility to noise. This study proposes a gradient-based analysis approach to improve the accuracy of fiducial point detection in PPG signals. The gradient method is used to detect local maxima and minima in the PPG signal. By incorporating validation and correction modules based on temporal order and amplitude ratios, the approach achieves 100% detection accuracy after initial error correction (initial error rate: 58% for the dicrotic notch). The results demonstrate that this method effectively identifies all fiducial points (onset, systolic peak, dicrotic notch, diastolic peak) in 50 out of 50 datasets, with robust performance against noise and physiological variability. This study confirms that the gradient-based method is suitable for cost-efficient, portable diagnostic applications.

The rapid growth of population and urbanization poses significant challenges to global food security, particularly in urban areas. The conversion of agricultural land into residential and infrastructure zones reduces local food production capacity, while climate change exacerbates uncertainties in crop yields. To address these challenges, IoT-based vertical farming has emerged as an innovative solution to enhance efficiency and sustainability in food production systems. IoT technology enables vertical farming systems to monitor and control environmental variables such as temperature, humidity, lighting, and nutrient levels in real-time through sensors connected to artificial intelligence. The collected data is analyzed to optimize plant growth, minimize resource waste, and maximize crop yields while reducing energy consumption. Additionally, integrating IoT with automated irrigation systems and energy-efficient LED lighting further enhances water and electricity efficiency. From a sustainability perspective, IoT-based vertical farming allows for year-round food production without relying on vast land areas or favorable weather conditions. This research further explores how IoT contributes to improving resource efficiency, environmental sustainability, and the economic and social impacts of vertical farming. . Based on the research findings, the implementation of IoT in vertical farming has proven to be highly beneficial in enhancing sustainability and resource efficiency in urban food production. Through real-time monitoring and automated control systems, IoT enables precise regulation of key environmental factors such as temperature, humidity, lighting, and nutrient levels, ensuring optimal plant growth with minimal resource wastage.

As Industry 4.0 technologies evolve, the application of Artificial Intelligence (AI) in the manufacturing sector has become a major factor in improving operational efficiency, optimizing production processes, and reducing costs, enabling predictive analytics, data-driven maintenance, and automation of tasks that previously required human intervention. This study conducts a systematic literature review (SLR) on various AI methods applied in industrial automation, evaluates the effectiveness of their implementation, and identifies key challenges in their adoption. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Population, Intervention, Comparison, Outcome, Context (PICOC) approaches are adopted. The sources used to search the literature included four electronic databases, comprising ScienceDirect, Taylor & Francis, Scopus, and Emerald Insight, resulting in 33 selected articles. The result shows that AI contributes significantly to improving production efficiency, but it still faces challenges in system integration, implementation costs, and workforce readiness. This study provides a comprehensive overview of the effectiveness of AI implementation in industrial automation and the challenges that need to be overcome to optimize competitiveness and production efficiency

The increasing number of vehicles has led to several complex challenges, such as traffic congestion, air pollution, and safety driving issues. These challenges also result in economic losses. Fortunately, with the advance in technologies today, we can utilize Intelligent Transportation Systems (ITS) to establish a system that is more efficient, safer, and environmentally friendly. Specifically, the concept of Vehicle-to-Everything (V2X) has emerged as a potential solution to address various road-related challenges. However, the real-world implementation of V2X still faces numerous obstacles, particularly in terms of cost and safety concerns. Deploying V2X on a large scale requires significant investments due to the need for communication infrastructure and hardware development. Therefore, simulation can serve as an initial solution to tackle these challenges. Simulations make it possible to set up different communication network protocols and architectures and to model different traffic conditions without the risks that come with testing in the real world. In this review paper, we aim to give a clearer insight into the trends, challenges, and opportunities of V2X simulations in various traffic environments.