Judul: Integrasi Internet of Things dan Machine Learning untuk Logistik 4.0 pada Agroindustri Mangga Segar
Abstrak: Agroindustry serves as a vital economic driver by connecting farming, fresh handling and processing industries, and end consumers through an interconnected value chain. The advancements in agroindustrial engineering have expanded markets, thereby amplifying the logistics activities. However, maintaining the quality of perishable goods, like fresh fruit, during long-distance transportation remains a significant challenge in Indonesia due to inadequate postharvest handling and transportation conditions. Mangoes, a key horticultural product in Indonesia, are traded both domestically and internationally. Enhancing the quality assurance of these mangoes is crucial for increasing their market competitiveness. Real-time quality monitoring using Information and Communication Technology (ICT) and Internet of Things (IoT) has been identified as a solution to this issue. Moreover, machine learning can predict product quality and provide critical data for stakeholders to make informed logistics and quality-related decisions. The integration of IoT and machine learning can develop an accurate real-time quality monitoring system, boosting the competitiveness of Indonesia’s mango industry and reducing postharvest losses. This study aims to integrate IoT architecture with machine learning techniques to develop an intelligent system for the long-distance transportation of fresh mangoes, supporting the implementation of Logistics 4.0. The specific objectives are: (1) to investigate the respiration pattern and quality characteristics of mangoes during storage, (2) to model and classify mango maturity based on quality characteristics affected by storage conditions and duration, (3) to develop machine learning model for mango maturity prediction, (4) to create IoT architecture for real-time monitoring of long-distance mango transportation, and (5) to design an IoT-based information system for mango transportation. The proposed solutions include developing real-time monitoring of transportation conditions and real-time maturity prediction. This involves creating an information system, machine learning algorithms, IoT architecture and device, along with a model for maturity changes. Machine learning algorithm, IoT device, and information system’s performances were measured to ensure scalability. The research begins with system identification, followed by simultaneous modeling of maturity levels, IoT device engineering, and application development. A Deep Learning model is trained using datasets from laboratory observations of mango quality changes during storage and IoT-monitored environmental conditions. Transportation simulations validate the system's performance in both land and inter-island transportation. Findings indicate that mangoes with 85% maturity are optimal for long-distance transportation. A classification model for mango maturity based on quality attributes is developed. This model, utilizing changes in physicochemical parameters during storage, achieves over 95% accuracy in predicting maturity level changes during transportation. IoT devices successfully collect, record, and transfer transportation data to the database and information system. The information system provides accessible transportation information and maturity predictions to stakeholders via a web-based application. This dissertation exemplifies a compelling Artificial Intelligence of Things (AIoT) use case by integrating IoT architecture with machine learning techniques to tackle the logistics and transportation challenges of perishable goods. The IoT component involves sensors and devices that monitor environmental conditions, collecting real-time data transmitted to a central database. Machine learning models then analyze this data to predict mango maturity and quality changes during transportation, providing timely alerts to stakeholders for informed decision-making. This AIoT integration not only enhances quality monitoring accuracy
Keyword: Internet of Things (IoT), machine learning, Mangga, Agroindustri, logistik

Judul: Design of a Real-Time Temperature and Meat Quality Monitoring System Based on Internet of Things during Distribution at PT XYZ
Abstrak: Agroindustri memiliki peran penting dalam mengolah hasil produk pertanian, termasuk daging sapi yang memiliki nilai ekonomi tinggi namun mudah rusak. Untuk menjaga kualitas daging sapi selama distribusi, PT XYZ menghadapi tantangan dalam pengawasan suhu dan kualitas produk. Penelitian ini bertujuan merancang sistem monitoring suhu dan kualitas daging secara real-time menggunakan teknologi Internet of Things (IoT) untuk mengatasi permasalahan tersebut. Metode penelitian mencakup eksplorasi masalah, pendefinisian masalah, ideasi solusi, pengembangan prototipe, serta uji dan validasi sistem. Pada tahap eksplorasi, ditemukan bahwa fluktuasi suhu selama distribusi menyebabkan kerusakan produk. Oleh karena itu, solusi yang diusulkan adalah implementasi IoT untuk pemantauan suhu dan kualitas secara real-time. Prototipe yang dirancang terdiri dari perangkat keras seperti mikrokontroler, sensor suhu, dan modul Wi-Fi, serta perangkat lunak berbasis web untuk menampilkan data. Pengujian perangkat keras menunjukkan bahwa sensor memiliki tingkat kesalahan yang dapat diterima, sementara perangkat lunak berhasil menampilkan data suhu dan kualitas (TVB) serta grafik selama distribusi. Hasil penelitian menunjukkan bahwa sistem monitoring suhu dan kualitas daging berbasis IoT dapat meningkatkan efektivitas dan efisiensi rantai pasok dingin di PT XYZ. Sistem ini mampu mengurangi kerusakan produk dengan pemantauan real-time, meningkatkan kepuasan pelanggan dan daya saing perusahaan., The agro-industrial sector plays an important role in processing agricultural products, including beef, which has high economic value but is highly perishable. To maintain beef quality during distribution, PT XYZ faces challenges in monitoring temperature and product quality. This study aims to design a real-time temperature and quality monitoring system for beef using Internet of Things (IoT) technology to address these challenges. The research methodology includes problem exploration, problem definition, solution ideation, prototype development, and system testing and validation. During the exploration phase, it was found that temperature fluctuations during distribution cause product damage. Therefore, the proposed solution is the implementation of IoT for real-time temperature and quality monitoring. The designed prototype consists of hardware such as a microcontroller, temperature sensor, and Wi-Fi module, as well as web-based software to display the data. Hardware testing showed that the sensors have an acceptable error rate, while the software successfully displayed temperature and quality (TVB) data and graphs during distribution. The research results indicate that the IoT-based temperature and quality monitoring system can improve the effectiveness and efficiency of the cold supply chain at PT XYZ. This system can reduce product damage with real-time monitoring, increasing customer satisfaction and the company's competitiveness
Keyword: Internet of Things (IoT), sistem monitoring, Rantai Pasok Dingin, Distribusi Daging Beku

Judul: Separation of Neodymium from U3Si2/Al Fuel Plate for Burn-Up Determination
Abstrak: Neodymium is a common fission product produced in nuclear fission reactions. Neodymium in this study was chosen as a burn-up indicator for U3Si2/Al fuel element plates because the neodymium isotope is a stable fission product with a fairly high yield. Some procedures were carried out to separate neodymium, starting with the separation of cesium isotopes (137Cs, 134Cs) through the precipitation method, then separation of uranium isotopes (235U, 238U) and plutonium isotopes (239Pu, 238Pu) with Dowex 1x8 ion exchange resin, and separation of neodymium (Nd), samarium (Sm), as well as cerium (Ce) with Dowex 1x4 ion exchange resin. The Cs recovery reached 99.95%. The U and Pu isotopes resulting from the separation are subjected to an electrodeposition process and the weight of each isotope can be measured with an alpha spectrometer. The separation results of Nd from Sm and Ce have been proven using an XRF spectrometer. The neodymium isotopes separation was corrected using the standard addition method, and an average total Nd content of 2.4 ppm was obtained in the PEB U3Si2/Al bottom solution. It is hoped that this separation method can be applied in determining burn-up of nuclear fuel.
Keyword: neodymium, burn-up indicator, U3Si2/Al fuel plate