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: Synthesis of Biphasic Calcium Phosphate Made of Eggshells Through A Hydrothermal Method
Abstrak: Biphasic Calcium Phosphate mixed of hydroxyapatite and tricalcium phosphate. A hydrothermal method of synthesizing by mixing CaO as calcium source and (NH4)2HPO4 as phosphate source with distilled water. CaO got from calcination process made of eggshells. All samples were synthesized using hydrothermal reactor then followed by sintering process. The molar ratio of Ca and P for all samples was 0.5 M : 0.3 M. Hydrothermal temperature and sintering temperature were variated they are 250 ºC, 300 ºC for hydrothermal and 800 ºC, 900 ºC, 1000 ºC for sintering, higher treatment temperature, tricalcium phosphate appears. Final results of synthesis were characterized by using X-ray Diffraction and Fourier Transform Infrared. X-ray Diffraction patterns was ecompared with database Join Committee on Powder Diffraction Standards and found tricalcium phosphate and hydoxyapatite.
Keyword: biphasic calcium phosphate, hydrothermal, hydroxyapatite, tricalcium phosphate