Indoor tag localization technology is one area of research applied to positioning, tracking, and navigation. UWB is a signal that is widely used for indoor tag localization with the consideration of having higher accuracy, wide bandwidth, and low power consumption. The diversity and breadth of research in the field of indoor tag localization can be confusing for new researchers to find out how this research has progressed until this day. The use of other sensors besides UWB in indoor tag localization is an alternative solution offered, thus obscuring the trend of UWB research on indoor tag localization. The way to find out whether indoor tag localization research using UWB still has a good trend and has research opportunities is to read paper reviews from previous researchers. However, until now no one has reviewed the previous paper review using the SMS and SLR methods. The SMS method is quantitative which directs the paper collection to the intended research area so it can be mapped properly. SMS is the process of identifying, categorizing, and analyzing existing literature that is relevant to a certain research topic. Meanwhile, the SLR method is qualitative which makes paper discussions sharper and more focused. SLR is the process of collecting and critically analyzing multiple research studies or papers through a systematic process. It aims to synthesize the findings of the studies and conclude the state of the art on the topic. This paper describes the literacy results of the paper using the SMS and SLR methods to thoroughly discuss research developments on UWB indoor tag localization. The discussion includes research developments and topic areas of indoor tag localization from 2017-2022, trends in the use of UWB in indoor tag localization research, issues and metrics that are carried out in indoor tag localization research, and opportunities for future research. Literacy results show that machine learning, filtering, and sensor fusion are topic areas that are currently being researched on UWB indoor tag localization. In general, every research optimizes performance metrics, namely accuracy, scalability, energy efficiency, latency, cost, and complexity. Each optimized metric related to different issues will be represented in the form of a taxonomy for easy understanding and explained in detail in this paper. Several future promising research opportunities are described in this paper to provide insight to other researchers to dig deeper into this research field.

The HydroFarm project presents an innovative IoT-based control and monitoring system for hydroponic plant growth, integrating advanced image processing techniques and mobile applications to enhance urban farming practices. This system addresses critical challenges faced by urban farmers, such as limited space and the need for precise environmental management. By employing a comprehensive approach that combines various sensors (DHT22, DS18B20, pH, TDS) with an ESP32 microcontroller, HydroFarm enables real-time monitoring of essential parameters like temperature, humidity, pH, and nutrient levels. A significant novelty of this project lies in its use of a convolutional neural network (CNN) for plant health assessment through image processing. This technique allows for accurate detection of plant conditions, categorizing leaves as healthy or unhealthy based on visual data captured via a mobile application. The application, developed in Kotlin, not only facilitates user interaction but also provides automated and manual control over nutrient delivery systems based on real-time sensor data. Testing results indicate that the HydroFarm system achieves a high accuracy rate of 96% in detecting plant health conditions, with the sensors providing accurate and consistent data to maintain effective control over hydroponic parameters. The system usability scale (SUS) evaluation yielded an impressive score of 81.875, categorizing the application as excellent and user-friendly. Overall, HydroFarm represents a significant advancement in hydroponic farming technology by integrating IoT capabilities with deep learning for enhanced decision-making and operational efficiency in urban agriculture. The findings underscore the potential for scaling this model to improve food security and promote sustainable agricultural practices in densely populated areas.

Traditional assessment methods often face a trade-off between accessibility and in-depth evaluation. While multiple-choice exams offer easy grading, they may limit the ability to assess critical thinking and analytical skills. On the other hand, essay exams provide a valuable tool to gauge these skills, but their manual evaluation has several drawbacks. First, grading essays is a time-consuming process. Each student’s response requires individual attention, leading to a significant workload for educators. Second, subjectivity is a major concern. Factors like the evaluator’s mental state, fatigue, or even background can influence their judgment, leading to inconsistencies and potential biases in grading. This paper proposes solving these challenges using artificial intelligence (AI) for essay assessment. We present a novel approach utilizing a One-dimensional Convolutional Neural Network (1D CNN) deep learning model. This model is specifically designed to analyze image-based student answer sheets, automatically classifying them according to the scores allocated for each question. The dataset used consists of answer sheets from 30 students in a coding class, each provided with a pre-annotated template. Our development process divided the available data into a 60/40 split, with 60% dedicated to testing the model’s performance and 40% used for training. The model achieved an average validation accuracy of 81.18% through this training. These results suggest that the proposed 1D CNN model offers a promising avenue for mitigating the limitations of manual essay grading. By automating the process and reducing subjectivity, this approach has the potential to streamline the assessment workload for educators while promoting consistency and fairness in evaluating student learning outcomes.

In 2021, colon cancer is the second most common cause of death for this type of cancer. Therefore, in this study, a colon cancer classification system was developed to help medical staff classify 2 types of cancer colon adenocarcinomas and benign colonic tissues. The classification method uses the Convolution Neural Network (CNN) with the architecture VGG16, VGG19, ResNet101, ResNet152, MobileNetV2, DenseNet201 and InceptionV3. We used 10.000 image datasets that divided into 7200 training data, 1800 validation data and 1000 test data. Pre-trained models are used to extract new features and training data. The best performance parameter based on accuracy, precision, recall and f1-score and confusion matrix are obtained in 3 architectures, namely VGG19, ResNet101 and ResNet152. These architectures can identify and classify both types of colon cancer with 100% accuracy.

System reliability is essential for improving operational efficiency and increasing revenue in the telecommunications sector. Recently, Machine Learning (ML) methodologies have shown significant promise in addressing system failures, network interruptions, and overall reliability challenges. Despite growing interest in this area, the broad scope of existing research can be overwhelming, making it difficult for novice researchers to identify key patterns, techniques, and gaps in the literature. This paper presents a comprehensive Systematic Mapping Study (SMS), covering 1,647 retrieved articles, revealed that research is heavily concentrated on fiber-based technologies (FTTH, PON, broadband) with limited focus on linking ML to enhance system reliability and to business outcomes such as churn and revenue. The Systematic Literature Review (SLR) applied a PRISMA-guided selection, resulting in 14 high-quality studies that were analyzed in depth. This review covers the period from 2019 to 2025, with particular attention to ML applications in network churn prediction, fault diagnosis, FTTH (Fiber to the Home) demand forecasting, system dependability, network optimization, and predictive maintenance. From the results of the SMS and SLR, we proceeded to the Latent Dirichlet Allocation (LDA) stage, where we categorized the findings into 10 emerging topics. Among these, four topics—Topic 2, Topic 3, Topic 6, and Topic 10—show notable upward trends. The paper concludes focus on High-Performance PON Systems and User-Centric Optical Networks. The taxonomy from this paper identified methodologies and an exploration of promising future research directions in ML for system dependability and network development, providing valuable insights for both scholars and practitioners.

The hydroponic planting method is one solution for supplying vegetable needs where agricultural land is limited. Hydroponics allows the growing of vegetables in stages in a limited area by utilizing water as a growing medium. Water quality greatly determines plant fertility, so monitoring must be carried out regularly. Currently, the agricultural sector in Sukabumi has a large potential for the economy of the community. Farmers develop hydroponic farming but monitoring of water quality is still done traditionally. Therefore, in this study, a water quality monitoring system is proposed including pH, turbidity, and temperature. Another parameter that is observed is the water level in the reservoir which is useful for maintaining water circulation. This system works online through the internet network, both the sensing process, data transmission, and data display using the Internet of Things (IoT) platform. The measured parameters can be observed via a web application. Performance evaluation of sensor devices is carried out by comparing the measurement values of standard devices. The test results on the system that has been implemented show that the system has high accuracy, and all parameters are successfully displayed on the web page. The applied systems can increase the fertility of vegetables on hydroponic land so that it can improve the quality of production.