Judul: Sistem real time berbasis jaringan syaraf tiruan untuk prediksi masa kadaluarsa biskuit dengan sensor dielektrik
Abstrak: Biscuit is type of dried food which found frequently on expired condition in market, therefore prediction method should be implemented in avoidance of this condition. Recently, prediction of shelf-life of biscuit has been done by laboratory test, but this test has some disadvantages, i.e.: time-consuming, expensive, also required trained panelists, complex equipment and suitable ambience. One alternative method is the dielectric sensor integrated with Artificial Neural Networks (ANN) in an intelligent real-time system. The aim of this research is to select the measurement attribute dielectric (dielectric parameters, the condition of the circuit and frequency), to formulate design of sensors can measure the value of selected parameters, to design an ANN model, and to integrate sensor and ANN dielectric to predict the shelf-life of biscuits in real time. Selection dielectric parameters and conditions of the circuit (CC/Constant current or CV/Constant Voltage) performed with Linear Regression, Feature Selection (Relief) and Artificial Neural Networks. Dielectric parameters for determining the best shelf life of biscuits are parallel capacitance (Cp). Cp constantly in the top five at three selection method, especially in ANN. Whereas the best condition of the circuit's for determining the shelf life of biscuits are CC, because always at the first sequence of three methods of selection. Selection of the frequency range 42 Hz - 5 MHz done graphically. Frequency range is able to differentiate a good shelf-life is a biscuit with a low frequency range from 42 to 351.65 Hz and the high frequency of 4721.13 to 50054.56 Hz. Selection results of the previous stage for dielectric parameter, the condition of the circuit and frequency is used for sensor design formulation basic. Approach the design tools in accordance with the results of the selection is a RC circuit. Capacitance (C) be the parameters of the sensor dielectric designed. The frequency of this sensor set 5-6 kHz, because the results are more sensitive reading capacitance values of different shelf-life and type biscuits. Designing neural network model is the next stage. This model is designed by using a data input capacitance (C), frequency (f) and Dielectric Constanta (k) from the sensor readings. Moreover, the input data on ANN is a type of packaging, type of biscuit and actual shelf-life (in packaging). ANN learning algorithm is Back propagation. For training process, trial and error is processed in the activation function, learning function, amount of nodes per hidden layer variation, and number of hidden layer. The best training architecture (lowest MSE and highest R) is ANN with the 5 hidden layer, 10 nodes per hidden layer, the hidden layer activation function tansig, the output layer activation function purelin, learning function trainlm and 86 epoch. ANN combined with dielectric parameters has lowest MSE (9,8648 x 10-5) and R highest (99.80%) in the training performance. ANN models are integrated with dielectric sensor using microcontroller AVR ATMega 8535. This integration to measure in real time the dielectric value (variable input) of an unknown product shelf life and readable on computer. To facilitate interaction with the user, an interface is built with MATLAB GUI toolbox. So, integration of ANN and capacitance
Keyword: Artificial Neural Network, biscuit, dielectric sensor, intelligent real time system, shelf-life

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: Trichomonas Gallinae Penyebab Trichomonosis Pada Unggas
Abstrak: Trichomonas gallinae adalah protozoa yang bergerak aktof dengan keempat flagella yang mempunyai layar dan pada salah satu sisinya terdapat membran seperti sirip ikan yang berombak. Spesies dari genus Trichomonas ini mempunyai arti penting pada unggas, karena dapat menyebabkan infeksi pada alat pencernaan bagian atas.
Keyword: