The increasing demand for electricity has driven the development of hybrid renewable energy systems (HRES) in developing countries and remote areas. HRES combines various renewable energy sources to overcome the limitations of each source by utilizing their respective strengths. Although HRES has advantages such as flexibility and low pollution, the development of renewable energy needs to consider environmental impacts and principles of sustainable development. This study aims to find the optimal configuration of a hybrid renewable energy system in the new capital region of Indonesia, namely IKN Nusantara in East Kalimantan province. We conducted a geospatial approach and literature review to gather relevant information, such as solar radiation data, hydropower potential, and biomass potential. We utilized the hybrid optimization model for electric renewables (HOMER) software to design the HRES, and we optimized the results using the multi-criteria decision-making (MCDM) method. The experimental results show that the combination of PV and hydro is the optimal configuration in terms of energy and environmental aspects, with a relative closeness value of 0.675 compared to the ideal solution. Therefore, the implementation of MCDM to evaluate and determine the best configuration of HRES in the IKN Nusantara region is worth considering for further research and development.

The expansion of artificial intelligence (AI) technologies has signaled an entirely new era in which the creation and sharing of information, both correct and misleading, are becoming increasingly automated. This research of the literature explores the landscape of AI-generated misinformation, including its various manifestations, underlying technology, societal impact, and detection tools. This paper reviews articles from the Google Scholar database related to AI-Generated Misinformation focusing on the following research questions: the types, content distribution, detector variations, differences among the various tools, and strategies for developing AI-based tools. The result is to provide an absolute comprehension of this topic, underlining the importance of interdisciplinary collaboration, robust detection methods, and media literacy with the intention to solve the ethical and societal issues it poses in the age of digital technology.

Street lighting is a significant component of safe driving during the afternoon and nighttime when sunlight is unavailable, as it enhances drivers’ visibility of the road, other vehicles, and surrounding areas. Lately, the integration of solar panels as the primary power source for streetlights has emerged as a promising, environmentally sustainable alternative for reducing energy consumption. However, because solar energy output is strongly affected by environmental conditions, a real-time monitoring system is essential for ensuring effective operation. This study presents the design of a monitoring system for solar-powered street lighting that integrates sensors to measure temperature, humidity, sunlight intensity, the voltage and current of solar panels, batteries, and lamps, from which the power output of each component is calculated. The measured data are displayed locally on a Thin Film Transistor (TFT) Liquid Crystal Display (LCD) and simultaneously transmitted to a cloud for remote access through a web interface. Performance evaluations confirm the system’s accuracy, with average measurement errors of 1.7% for temperature and humidity, 0.8% for voltage, and 1.5% for current. These results demonstrate that the system provides reliable real-time monitoring, thereby enhancing the efficiency, reliability, and maintainability of solar-powered street lighting systems.

Bird pests pose a significant threat to agriculture, causing extensive crop damage and economic losses. Traditional bird repellent methods, such as scarecrows and loud noises, often lose their effectiveness over time as birds adapt. This paper reviews the development and effectiveness of an automated bird repellent system, integrating Internet of Things (IoT) and Artificial Intelligence (AI) technologies. The study used a systematic literature review (SLR) methodology, analyzing 20 articles published between 2015 and 2024. Key findings show that automated systems, utilizing sensors and AI algorithms such as YOLO, DenseNet, and Mask R-CNN, significantly improve bird detection and repellent accuracy. The DenseNet model, in particular, achieved a detection accuracy of 99.65%. The review highlights the need for further research to optimize sensor placement and assess the long-term impacts of this technology on bird behavior and agricultural ecosystems. This comprehensive review underscores the potential of automated bird repellent systems to improve crop protection and sustainability in agriculture.

Pada era perkembangan teknologi yang semakin pesat, pelacakan dan keamanan menjadi aspek yang sangat penting dalam berbagai bidang industri. Hal ini merupakan sebuah respons terhadap tantangan global yang semakin kompleks. Salah satu teknologi yang dapat menjawab tantangan ini adalah teknologi RFID. Dengan RFID, pengelola industri dapat melakukan pelacakan inventaris yang akurat secara real-time, melakukan pemantauan yang jauh lebih akurat terhadap aliran barang, serta mengidentifikasi potensi kerugian akibat hilangnya barang atau kerusakan dalam rantai pasokan. Artikel ini mengulas berbagai artikel dari database scienceDirect yang berhubungan dengan penggunaan RFID pada sistem keamanan dan pelacakan dengan pertanyaan penelitian: bidang yang menggunakan RFID, reader yang digunakan, tag yang digunakan, dampak, dan komunikasi data. Metode yang digunakan pada penulisan ini adalah Systematic Literature Review (SLR). Hasil artikel review ini dapat memberi gambaran bahwa menurut mayoritas artikel, bidang yang paling banyak memanfaatkan RFID untuk sistem keamanan dan pelacakan adalah bidang manufaktur yang menggunakan tag dan reader RFID pasif melalui sistem komunikasi nirkabel dengan memasang tag pada objek yang digunakan dan menempatkan reader pada akses-akses tertentu. Penggunaan RFID ini juga memiliki berbagai dampak yang positif menurut mayoritas artikel seperti dapat mempersingkat waktu operasional, melacak objek kecil, meminimalisir kesalahan pelacakan, serta memperkecil biaya yang dibutuhkan.

Sentry Gun is a weapon designed to monitor national borders automatically without requiring an operator. In making the Sentry Gun system, mathematical modelling is carried out using the black box method to determine the transfer function of the plan or system to be controlled. Sentry Gun is in the form of a 1:3 model compared to the original made of Aluminum and Steel. Image processing is used as an object sensor tracking Sentry Gun. Open CV is used in the Image Processing process. A DC motor is used as an actuator in the movement of the elevation and azimuth axes (2 Dof). As feedback, a rotary encoder is used to detect the rotation of the DC motor. Due to limitations in the manufacture of mechanical systems so that at the azimuth angle using helical gear there is a backless which makes the system unstable and takes a long time to reach the settling time. The motor movement control used is PID which is based on intelligent control of the genetic algorithm so that the movement of the tool is precise, smooth and on target. System modelling was carried out using the MATLAB Identification System feature. Iterated 100 times in determining the value of Kp, Ki, and Kd. so that the values of Kp 245, Ki 180 and Kd 215 are obtained. PID tuning based on intelligent control of genetic algorithms can produce a more optimal step response system. Namely Rise Time Produced 0.96s, Overshoot 5%, Settling Time 5.29s and steady state error 0%.