For rapid deployments of various IoT application systems, we have developed Smart Environmental Monitoring and Analytical in Real-Time (SEMAR) as an integrated server platform. It is equipped with rich functions for collecting, analyzing, and visualizing various data. Unfortunately, the proper configuration of SEMAR with a variety of IoT devices can be complex and challenging for novice users, since it often requires technical expertise. The assistance of Generative AI can be helpful to solve this drawback. In this paper, we present an implementation of a sensor input setup assistance service for SEMAR using prompt engineering techniques and Generative AI. A user needs to define the requirement specifications and environments of the IoT application system for sensor inputs, and give them to the service. Then, the service provides step-by-step guidance on sensor connections, communicating board configurations, network connections, and communication protocols to the user, which can help the user easily set up the configuration to connect the relevant devices to SEMAR. For evaluations, we applied the proposal to the input sensor setup processes of three practical IoT application systems with SEMAR, namely, a smart light, water heater, and room temperature monitoring system. In addition, we applied it to the setup process of an IoT application system for a course for undergraduate students at the Insitut Bisnis dan Teknologi (INSTIKI), Indonesia. The results demonstrate the effectiveness of the proposed service for SEMAR.

Technology revolution in computer and information made Learning Man-agement System has widely been used to assist learning process. On the other side the advancement of technology in multimedia made many re-searchers have positive result in game based learning as factor that increase motivation and interest to deliver educational information. We try to pro-pose a concept to implement a multiplayer matching quiz in Moodle LMS to enrich game based learning using Moodle future research about game-based learning implementation in Moodle. The proposed model implements websocket as its multiplayer backend and game questions using Moodle Quiz plugin as a question bank.

Nowadays, a pedestrian navigation system using a smartphone has become popular as a useful tool to reach an unknown destination. When the destination is the office of a person, a detailed map information is necessary on the target area such as the room number and location inside the building. The information can be collected from various sources including Google maps, websites for the building, and images of signs. In this paper, we propose a map information collection tool for a pedestrian navigation system. To improve the accuracy and completeness of information, it works with the four steps: (1) a user captures building and room images manually, (2) an OCR software using Google ML Kit v2 processes them to extract the sign information from images, (3) web scraping using Scrapy (v2.11.0) and crawling with Apache Nutch (v1.19) software collects additional details such as room numbers, facilities, and occupants from relevant websites, and (4) the collected data is stored in the database to be integrated with a pedestrian navigation system. For evaluations of the proposed tool, the map information was collected for 10 buildings at Okayama University, Japan, a representative environment combining complex indoor layouts (e.g., interconnected corridors, multi-floor facilities) and high pedestrian traffic, which are critical for testing real-world navigation challenges. The collected data is assessed in completeness and effectiveness. A university campus was selected as it presents a complex indoor and outdoor environment that can be ideal for testing pedestrian navigations in real-world scenarios. With the obtained map information, 10 users used the navigation system to successfully reach destinations. The System Usability Scale (SUS) results through a questionnaire confirms the high usability.

Nowadays, dried Moringa leaves (M. oleifera) are increasingly in demand due to their health benefits. High-quality ones have shown remarkable positive effects as antioxidants, antidiabetics, and anti-inflammatory agents. However, in the industry, the quality classification process into six categories is performed manually by farmers, which is time-consuming and error-prone. Particularly, the two highest categories of Class A and Class B are hard to distinguish, since they are visually similar. In this paper, to automate the classification process, we introduce a new high-resolution dataset, extract color and texture features using the Gray-Level Co-occurrence Matrix (GLCM) method, and present a two-stage classification method using the Light Gradient Boosting Machine (LightGBM) algorithm with them. The experimental results show that the proposal improved classification accuracy from 82% by the baseline algorithm to 90% while maintaining high processing efficiency, demonstrating its potential for real-time and scalable industrial applications in dried Moringa leaves quality grading.

Nowadays, Internet of Things (IoT) application systems play an essential role in smart cities, industry, healthcare, agriculture, and smart homes. For non-expert users, designing and implementing IoT application systems remains challenging, especially when configuring sensors, edge devices, and server platforms. To support configuration tasks of IoT application systems, we have developed an AI-based setup assistance tool. However, AI models still fail to reliably support newly released or previously unseen devices, sometimes producing incomplete or erroneous outputs that may lead to configuration failures. Incorporating their technical-document information into Retrieval-Augmented Generation (RAG) is an effective way to supplement AI knowledge and improve reliability. In this paper, we propose a generative AI-based technical data extraction tool to address the challenges. It extracts essential technical information using the schema-based extraction from given PDF or HTML datasheets and converts it into a structured format suitable for AI-supported configurations. A local vector database is used to enable semantic similarity retrieval and provide document-grounded evidence for RAG-based answering, ensuring consistent support for previously unseen IoT devices. For evaluations, we applied the proposal to several sensor and device datasheets and compared extracted specifications with ground-truth values to measure accuracy and completeness. Then, we compared end-to-end configuration QA reliability against a commercial baseline (ChatPDF) using the golden benchmark. The results show that the proposed tool reliably acquires key specifications and significantly improves end-to-end configuration QA reliability. Across 960 golden QA pairs, the proposed method improves Recall from 0.636 to 0.926 and Accuracy from 0.595 to 0.807 compared with ChatPDF.

The Internet of Things (IoT) technology has grown rapidly over the past decade, resulting in deployments of thousands of IoT devices around the world. Then, managing firmware updates for these numerous devices poses significant challenges. Firmware updates face issues such as version rollback, modified firmware files, and potential man-in-the-middle (MITM) attacks, highlighting the need for a secure over-the-air (OTA) firmware update mechanism. In this paper, we propose an automated OTA firmware update mechanism, integrated with continuous integration (CI) and continuous delivery (CD) to ensure trusted sources for firmware origins. It offers security, error handling during firmware updates, and monitoring of the update process. For evaluations, we implemented the proposal with the SEMAR IoT application server that has been implemented in our previous studies. Then, we verified the integrity and authentication, measured the performance and resource utilization, and performed benchmarking tests to assess the efficiency. The results demonstrate that the proposal is sufficiently reliable and efficient.