Penelitian ini membangun sebuah Smart Home berbasis IoT menggunakan suara pada Google Assistant. Hal ini dibutuhkan sebagai solusi untuk orang sakit yang berada di kursi roda/tempat tidur atau orang disabilitas tetapi dapat berbicara atau orang lanjut usia yang tidak dapat mencapai saklar agar dapat menghidupkan/mematikan perangkat rumah. Selain itu, agar dapat mengontrol perangkat rumah dari jarak yang sangat jauh. Sistem yang dibangun menggunakan perintah suara pada aplikasi Google Assistant di android. Google Assistant mengubah perintah suara menjadi teks. Teks tersebut kemudian diteruskan dari Google Assistant ke Webhooks oleh IFTTT. Webhooks akan melakukan request ke HTTP RESTful API. Dengan library phpMQTT yang terdapat di HTTP RESTful API, perintah di publish ke MQTT Broker. ESP32 Dev Kit sebagai microcontroller yang terhubung dengan internet menerima perintah dari MQTT Broker untuk menyalakan atau mematikan lampu yang ada di rumah. Pada pengujian sistem telah berhasil menyalakan dan mematikan lampu dengan perintah suara menggunakan Google Assistant.
Plagiarisme merupakan tindakan mengambil gagasan, mengambil hasil riset, mengakuisisi hasil riset, dan meringkas suatu tulisan tanpa menyebutkan sumbernya. Metode cosine similarity merupakan salah satu metode yang dapat digunakan untuk menghitung nilai kemiripan antar dokumen. Tahapan yang dilakukan sistem untuk menghasilkan nilai kemiripan antar dokumen yaitu dengan membandingkan dokumen jurnal yang di upload dengan dokumen repository yang diperoleh dari hasil grabbing data DOAJ dan tersimpan di database. Dalam perhitungan metode yang dilakukan akan diperoleh presentase nilai kemiripan antar dokumen. Setelah itu akan dihitung kembali untuk mencari nilai kemiripan dokumen jurnal antar publisher yang ada di dokumen repository. Berdasarkan skenario uji coba yang dilakukan dengan menghitung jumlah dokumen relevan terambil dibagi dengan jumlah dokumen yang ada dalam database kemudian dikali 100%, maka diperoleh nilai recall pada Aplikasi Deteksi Plagiarisme Menggunakan Metode Cosine Similarity yaitu 13%. Sedangkan untuk memperoleh nilai precision dilakukan skenario pengujian dengan menghitung jumlah dokumen relevan terambil dibagi dengan jumlah dokumen relevan dalam pencarian kemudian dikali 100% diperoleh hasil 8%.
I-Device (Intelligent Devices) is one of the fastest growing devices since the beginning of this decade. Some of its major problems are accuracy and performance. This study aims to present an improvement in the performance of those devices. We used a simulation application for I-Devices to conduct the experiment. The simulation was built based on classifying results using Logarithmic learning for Generalized Classifier Neural Networks (L-GCNN). The output was a simulation that will be implemented on a smart mosque system. L-GCNN itself was a modification method of GCNN to improve the processing speed and have high accuracy as a classifier method. This method will take a role when the given parameters meet the conditions of the devices to take an action. To simplify the understanding of the simulation models, we used a game application to make an interactive simulation for our project in an environment that represents the real-world condition of the mosque. The result of this study shows that the devices could make a decision by themselves accurately. Additionally, using LGCNN models, we could reduce the processing iteration compared to other models. The experiment results show that LGCNN has an average value of 90% in accuracy, precision, recall, and f1. Keywords: Automation, Classifier, L-GCNN, Neural Network, Decision.
Ma'had Sunan Ampel Al'aly is an educational institution under the auspices of the State Islamic University of Maulana Malik Ibrahim Malang which aimsto create an academic culture to improve the religious knowledge of new students at the Maulana Malik Ibrahim State Islamic University of Malang. So far, there is no blueprint at MSAA, so it is necessary to make a blueprint as a guideline for designing the ma'Ahad system design. With the bueprint, ma'had will have system design guidelines. So that when there is a new policy it will be easier to re-plan. To create a blueprint you can use the enterprise architecture framework with the Zachman Frameworkmethod. Zachman Framework is one of the best known and used enterprise architecture frameworks. In this study the Zachman Framework was used to design the blueprint in Ma'had Sunan Ampel Al'aly.
Piles of waste increase in line with population growth and consumption patterns. The concept of bioconversion using black soldier fly larvae can solve the problem of organic waste management. From these problems, an application of Internet of Things technology is needed. The system implemented aims to allow the system to find out how much accuracy, precision, and recall are in making decisions on media quality values using the Naive Bayes method. The main feature of this Naive Bayes Classifier is the very strong assumption of the independence of each condition or event. From the research results, the system has been successfully built according to the research design, as well as the goals that have been fulfilled in completing the development of the smart maggot. Several sensors used in this study were tested so that sensor performance could be determined by finding the average error value. Three parameters are measured; namely, the temperature obtained an average error of 1.6%, air humidity obtained an average error of 2.03%, and soil moisture obtained an average error of 2.7%. By measuring using Python, the Confusion Matrix is obtained so that the test results from the calculation of the Naive Bayes method can find the data in the form of accuracy, precision, and recall. Accuracy percentage results obtained 92%, precision percentage average results obtained 93%, and recall percentage average results obtained 92%. The conclusion shows the results of the system's accuracy obtained have worked well.
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