Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R² values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data.

This research focuses on the load and stability of unmanned aerial vehicles that are as light as possible but with optimal stability functions and increasing the capacity of liquid fertilizer lifted by unmanned aerial vehicles. The increase in lift is related to the payload and stability to be carried by the unmanned aerial vehicle. By optimizing the arm using the arm carbon method, so without reducing the strength and function of the drone, the gross weight of the components becomes much lighter, so that the weight can be focused on increasing the capacity of liquid fertilizer. With the increase in liquid fertilizer capacity, of course, the weight also increases, this raises a new problem, which is to make the balance value when the drone take-off has a large magnitude, thus affecting stability which causes the potential to fail to fly, therefore this research was carried out so that the drone has stability. maximum and can minimize the effects of shocks from increasing capacity after using arm-carbon. The measurements taken were the success rate of the system in flying over agricultural land, power testing and current measurements. After testing, it was found that by using a split tank and carbon arm system, current and power tend to be more stable, so that battery life is longer, liquid fertilizer capacity can be increased and the potential for flight failure can be minimized with a split tank system with 90% accuracy, carried out optimizing weight with carbon arms and increasing the capacity of liquid fertilizer by 30%, using a split tank system increases the stability of the drone at take-off by up to 80%.

This study aims to develop a transporter robot as a learning tool for elementary and secondary school students to introduce them to robotics. The robot features a differential drive system and a lifting arm akin to a forklift system. The robot is controlled using an ESP32 microcontroller and connected to an Android application through a Firebase cloud database. Utilizing an IoT platform, the training can be conducted online, allowing students to control the robot remotely while it remains at the training location. The transporter robot is designed to provide an interactive and hands-on learning experience in robotics, enabling students to grasp fundamental concepts in robotics, programming, and control systems. The differential drive system offers excellent maneuverability, while the lifting arm allows the robot to perform tasks such as lifting and moving objects, adding functional aspects to the learning process. Test results indicate that the robot performs well under online control, with fast and accurate responses to commands from the Android application. The use of Firebase ensures stable and real-time communication between the robot and the application. This implementation not only enhances the accessibility of robotics training but also enables the development of students' STEM skills in a more flexible and affordable manner. The study concludes that an ESP32-based transporter robot with online control via an Android application and Firebase cloud database can be an effective and innovative learning tool. This tool aims to enhance students’ STEM skills -especially in programming, control systems, and mechanical design- through interactive, real-world applications.

Penerapan teknologi Internet of Things (IoT) di sektor pertanian memberikan solusi inovatif dalam pengelolaan hama tanaman cabai. Penelitian ini bertujuan untuk merancang alat penyemprotan pestisida berbasis IoT dengan kontrol otomatis menggunakan Arduino, ESP32, dan sumber energi panel surya. Metode Research and Development (R&D) digunakan, meliputi identifikasi masalah, perencanaan, pengembangan produk, uji coba, evaluasi, dan validasi lapangan. Hasil pengujian menunjukkan alat ini mampu menyemprotkan air atau pestisida hingga jarak 5 meter dengan cakupan area penyemprotan seluas 10 m² per siklus. Dengan kapasitas semprotan mencapai 12 liter per siklus, alat ini berhasil menurunkan populasi hama kutu kebul hingga 70% serta meningkatkan produktivitas tanaman cabai sebesar 30% dibandingkan metode manual. Analisis penggunaan energi menunjukkan efisiensi alat melalui pemanfaatan panel surya 120 Wp dan baterai 12V 100Ah yang mendukung operasional pompa selama 1 jam 48 menit. Penelitian ini memberikan solusi berkelanjutan untuk pengelolaan hama tanaman dan peningkatan hasil panen secara efektif dengan memanfaatkan energi terbarukan.

Battery management systems are essential in electric vehicles and renewable energy applications, especially in ensuring optimal battery health and performance. Especially regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of battery depend on the accuracy of the SOC parameter estimation. Moreover, the system that applies active balancing technology will move cells that have high SOC data to cells that have low SOC. Many methods have been developed, but the long execution time makes it less than optimal when applied. The speed of SOC estimation is also required in active balancing technology in addition to the accuracy factor. Therefore, this study proposes to estimate SOC parameters using a statistical and metaheuristic approach method from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R2 values compared to the polynomial and linear regression method.

Fokus penelitian adalah mengembangkan mekanika dan dinamika pada robot edukasi berbasis IoT dan ESP32. Selain juga untuk memperkenalkan robotika sebagai media yang efektif dalam pembelajaran. Robot ini memiliki sistem penggerak diferensial dan lengan pengangkat yang mirip dengan sistem forklift. Robot ini dikontrol menggunakan mikrokontroler ESP32 dan terhubung ke aplikasi Android melalui basis data cloud Firebase. Dengan memanfaatkan platform IoT, pelatihan dapat dilakukan secara daring, yang memungkinkan siswa untuk mengendalikan robot dari jarak jauh saat robot berada di lokasi pelatihan. Robot pengangkut dirancang untuk memberikan pengalaman belajar robotika yang interaktif dan langsung, yang memungkinkan siswa untuk memahami konsep dasar dalam robotika, pemrograman, dan sistem kontrol. Sistem penggerak diferensial menawarkan kemampuan manuver yang sangat baik, sementara lengan pengangkat memungkinkan robot untuk melakukan tugas-tugas seperti mengangkat dan memindahkan objek, yang menambahkan aspek fungsional pada proses pembelajaran. Hasil pengujian menunjukkan bahwa robot bekerja dengan baik di bawah kendali daring, dengan respons yang cepat dan akurat terhadap perintah dari aplikasi Android. Penggunaan Firebase memastikan komunikasi yang stabil dan waktu nyata antara robot dan aplikasi. Implementasi ini tidak hanya meningkatkan aksesibilitas pelatihan robotika tetapi juga memungkinkan pengembangan keterampilan dengan cara yang lebih fleksibel dan terjangkau. Studi ini menyimpulkan bahwa robot transporter berbasis ESP32 dengan kontrol daring melalui aplikasi Android dan basis data cloud Firebase dapat menjadi alat pembelajaran yang efektif dan inovatif. Ini mendukung pengembangan keterampilan teknis dan pemahaman praktis siswa tentang robotika, menyediakan landasan yang kuat untuk studi lebih lanjut dalam teknologi dan teknik. Kinerja robot yang menjanjikan dalam pengaturan kontrol daring menyoroti potensinya sebagai sumber daya pendidikan yang berharga dalam lingkungan pembelajaran modern