Judul: Rancang Bangun Sistem Smart grading Susu Segar Berbasis Internet of Things
Abstrak: Kualitas susu segar merupakan faktor utama dalam menentukan harga susu. Peternak susu menjual susu segar dengan kualitas yang berbeda-beda. Hal yang mempengaruhi kualitas ini adalah kondisi hewan dan lingkungan. Faktor lain seperti pemantauan suhu penyimpanan susu segar masih menggunakan metode konvensional oleh karena itu, tujuan dari penelitian ini adalah menentukan grade susu segar dan monitoring suhu dan kelembaban secara real time. Salah satu alternatif baru yang dapat digunakan untuk mengatasi permasalahan ini adalah memanfaatkan teknologi terbaru, seperti Internet of Things (IoT). Pertama, analisis kebutuhan sistem yang berbasis IoT untuk mengembangkan prototipe smart grading susu segar. Pemodelan sistem smart grading susu segar berbasis rekayasa sistem. Kedua, menerapkan teknik pembelajaran mesin yaitu Jaringan Saraf Tiruan (JST), digunakan untuk klasifikasi grade susu segar berdasarkan kualitas. Prototipe sistem dikembangkan menggunakan alat sensor DHT22 dan Node-MCU ESP-8266, yang mampu menampilkan fitur secara real time. Alat cerdas ini menampilkan suhu dan kelembaban untuk menjaga kualitas susu segar selama proses pre-produksi. Atribut yang menentukan sistem smart grading adalah suhu, kelembaban, jumlah mikroba, berat jenis, dan total lemak. Model JST mampu mengklasifikasi sistem smart grading dengan akurasi 92.59%. Penelitian ini menghasilkan model yang mampu monitoring suhu dan kelembaban secara real time dan menampilkan grade susu segar tiap peternak. Rancang bangun sistem smart grading susu segar membantu dalam penentuan harga jual susu segar tiap peternak.
Keyword: Internet of Things, Jaringan Saraf Tiruan, Smart Grading, Susu Segar, Artificial Neural Network, Fresh Milk, Internet of Things, Smart Grading System

Judul: Sistem Intelijen Penilaian dan Prediksi Kualitas Susu Pasteurisasi (Studi Kasus pada PT . Industri Susu Alam Murni Bandung)
Abstrak: The aim of this research is developing an intelligent system for assessment and prediction of pasteurized milk quality. The system is implemented by integrated expert systems and Artificial Neural Network (ANN). This system is designed and developed by Matlab 6.5 and called SINKUAL-SP. The logical strategy model used is “Forward Chaining” and the tracing method used is “Best First Search” method. The method of Certainty Factor (CF) is used for handling uncertainty. Model Based Management system consist of five model : quality of fresh milk model, quality of process model, quality packaging and storage model, statistical process control model and prediction model by neural network. Multilayer system is used for the neural network architecture with one input layer, one hidden layer dan one output layer. The activation function is Sigmoid Bipolar activation function which gives the best performance network with learning rate 0.005 and momentum 0.9 together with RMSE, MSE and SSE as criterion error. The validation for neural network indicates the conformity between output of neural network and output of goal with value of RMSE 0.009959, MSE 0.00999 and SSE 0.009950. Decision for acceptance fresh milk and pasteurized of milk is implemented by acceptance grade which gives control quality process is easier than previously system. Based on the grade acceptance, the management of industry can take decision and action allow with a significant output of grade. In the research implementation stage, the data, gained in the company of “PT. ISAM” in April 2005, brings a significant output of research regarding quality of fresh milk such as the grade B, grade A for quality of process, and grade B for quality packaging and storage. This system gives a suggestion for user to improve the quality of pasteurized milk. Based on the outputs from the systems used, highly priority strategy quality system reconstruction, with 0.236 as weighting value. The quality improvement system upon the results of research is seemed to be a way of improving the quality of milk in process of pasteurization.
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Judul: Asymptotic Distribution of Estimator for the Intensity Function of a Doubly Periodic Poisson Process
Abstrak: In this manuscript, estimation of the intensity function of a doubly periodic Poisson process is discussed. It is assumed that only a single realization of the Poisson process is observed in a bounded window [0,n]. It is also assumed that the period of the intensity function is known. The rate of consistency and asymptotic normality of the estimator are formulated. Finally, a confidence interval for the local intensity function is also formulated.
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