Judul: A Design of Portable Smart Surveillance Device Based on IoT for Perishable Goods Logistics
Abstrak: During transportation, restricted surveillance processes have triggered discrepancies, especially for perishable goods highly vulnerable to environmental factors. Conventional fleets are unable to monitor real-time delivery and goods conditions. Late in capturing and informing abnormal circumstances, causing product spoilage is arduous to prevent. The establishment of technology 4.0, such as the emergence of the internet of things (IoT) and artificial intelligence, offers solutions. This study presented a design of a smart surveillance device for perishable goods logistics based on IoT. Firstly, a requirements analysis is the basis for developing a prototype of the IoT system to monitor the transportation process in real-time. Then, one machine learning technique, specifically artificial neural network (ANN) predicted the remaining shelf life and the engine state during the shipment process. Finally, a developed warning mechanism establish to deal with abnormal conditions, such as sudden coolant failure or fraud in refrigerator settings. The prototype development deployed DHT sensors, SW-420 sensors, a GPS module, and a microcontroller to display all smart surveillance features proposed in this study, specifically, temperature, humidity, current fleet location, shelf life, engine state, and early warning in real-time. ANN model predicted the quality of goods, represented by the remaining shelf life and engine state, on or off, well with an accuracy of 97,96% and 92,93%, respectively. It is necessary to add other relevant environmental parameters to the predictive modeling of the quality of goods. This work was helpful for logistics managers in ensuring the quality of goods to the customers., Proses pemantauan yang terbatas selama transportasi memicu beberapa ketidaksesuaian, terutama untuk barang yang mudah rusak dimana sangat rentan terhadap faktor lingkungan. Armada konvensional tidak mampu memantau pengiriman dan kondisi barang secara real-time. Keterlambatan dalam menangkap dan menginformasikan kondisi abnormal menyebabkan kerusakan produk sulit untuk dicegah. Kehadiran teknologi 4.0, seperti internet of things (IoT) dan kecerdasan buatan, menawarkan solusi untuk permasalahan ini. Penelitian ini menghadirkan rancangan perangkat pemantauan cerdas untuk logistik barang yang mudah rusak berbasis IoT. Pertama-tama, analisis kebutuhan dilakukan untuk mengembangkan prototipe berbasis sistem IoT yang mampu memantau proses transportasi secara real-time. Salah satu teknik pembelajaran mesin, khususnya jaringan saraf tiruan (JST), digunakan untuk memprediksi sisa umur simpan dan status mesin selama proses pengiriman. Untuk mengatasi kondisi abnormal, seperti kerusakan pendingin mendadak atau penipuan dalam pengaturan pendingin, mekanisme peringatan dikembangkan. Prototipe yang dikembangkan dengan menggunakan sensor DHT, sensor SW-420, modul GPS, dan mikrokontroler telah mampu menampilkan semua fitur pemantauan cerdas yang diusulkan dalam penelitian ini yaitu temperatur, kelembaban, lokasi armada saat ini, umur simpan, status mesin, dan peringatan dini secara real-time. Model JST memprediksi kualitas barang, yang direpresentasikan oleh sisa umur simpan, dan kondisi mesin, hidup atau mati, dengan akurasi masing-masing 97,96% dan 92,93%. Untuk memperkaya efikasi penelitian ini, perlu ditambahkan parameter lingkungan lain yang relevan dengan pemodelan prediktif kualitas barang. Penelitian ini bermanfaat bagi manajer logistik untuk memastikan kualitas barang kepada pelanggan.
Keyword: ANN, IoT, perishable goods, smart surveillance

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: Distribution and Diversity of Ectoparasite Mites on Geckos in Indonesia
Abstrak: Data on the diversity and dispersal of parasitic mites on house geckos in Indonesia are very scarce. In this work, the distribution and diversity of mites living on three species of house geckos, namely Cosymbotus platyurus, Hemidactylus frenatus, and H. garnotii collected throughout Indonesia, has been elaborated. Geckos and mites were captured and immediately preserved in 70% ethanol. Whole mount of the mites was prepared by clearing in lactophenol followed by mounting on polyvinyl lactophenol solutions. The SEM preparation was conducted to examine the detail morphological characters of the mites. The results showed that among 448 individuals of geckos, 221 geckos were infected by Geckobia mites. Prevalences of C. platyurus, H. frenatus, and H. garnotii infested by mites were 14.8%, 50.69%, and 79.6%, respectively. Three different spesies of Geckobia (G1, G2, and G3) could be differentiated; and based on similarities of their morphological characters to ones described in published literatures, Geckobia G2 is Geckobia glebosum and Geckobia G3 is Geckobia bataviensis while G1 could not be identified to the species level. The highest mean intensity of Geckobia G1 infestation was found on H. garnotii (I=7.0), G. glebosum infestation on H. frenatus (I=3.5), and G. bataviensis infestation on H. garnotii (I=11.8). In general, C. platyurus was infested by the least number of mites. Geckobia G1 were found living on the skin folds on the claws, G. glebosum were found mainly on the body and thigh, and G. bataviensis were found on almost all parts of the host’s body. Geckobia mites are distributed randomly throughout Indonesian Archipelago, following the pattern of distribution of their hosts. So it is concluded that Geckobia G1, G. glebosum, and G. bataviensis are sympatric.
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