Dalam memberikan suatu keputusan untuk kelayakan penerima bantuan rumah layak huni di Kelurahan Air Jamban masih bersifat manual sehingga proses pengambilan keputusan menjadi tidak akurat, lama dan bersifat objektif. Oleh karena itu dibutuhkan solusi berupa sistem pendukung pengambilan keputusan agar dapat memproses bantuan rumah layak huni lebih cepat dan akurat menggunakan kriteria yang ada. Metode yang digunakan adalah Fuzzy Attribute Decision Making (FMADM) untuk menentukan hasil seleksi setiap alternatif dan perhitungan pada penelitian ini menggunakan Simple Additive Weighting (SAW). Dari 10 data alternatif yang diuji coba maka terdapat hasil bahwa Alternatif 1 =28,5, alternatif 2=27,5, alternatif 3=31,5, alternatif 4 =30,25, alternatif 5 = 25,5, alternatif 6=17,9, alternatif 7 =24,4, alternatif 8 =22,9, alternatif 9 =27,75 dan alternatif 10 =31,5. Ada 8 kriteria yang digunakan yaitu bahan bakar untuk memasak, status rumah, jumlah anak, pendapatan, jenis lantai rumah, jenis atap rumah, jenis dinding rumah dan luar rumah (bangunan). Hasil akurasi dari penelitian ini adalah tingkat akurasi sebesar 95,44% untuk metode SAW dan 94,24% untuk FMADM.
As the population growth rate in Duri increases, the need for clean water also increases as needed. In Indonesia, PDAM is an institution that regulates and manages the provision of clean water for the community. So the amount of water produced and distributed should be adjusted to the demand for water. However, the problem arises in the form of waste of water at PT. PDAM Duri. Purpose of this study is to predict the amount of water consumption at PT. PDAM Duri by implementing Backpropagation Artificial Neural Network method. Variables of data taken from customer data were social, general social, household 1, household 2, household 3, commerce 1, commerce 2 and commerce 3. Data used in the prediction process was training data in 2016 and data testing in 2017. Actual amount of data at PT. PDAM Duri City 2016 until 2017 was 2.840.165 when the prediction result using artificial neural network back propagation method was 2.843.388. The number of training epochs was 4595 and the achievement of MSE (Mean Squared Error) on the test was 0,001 and the result of accuracy was 99,99900000%. Final result of this research was artificial neural network using back propagation method could predict the using of water consumption at PT. PDAM Duri for next year.
In the world of higher education, lecturers are one of the main components in building quality and quantity. Good quality will give good results as well, to improve the quality of each lecturer, it is necessary to have an award given to lecturers from the campus so that it becomes a motivation for lecturers to improve the quality given to students and the community. Amik Mitra Gama is a private campus located in the Duri Riau area, in this case to improve the quality of education one of the steps taken is to give awards and appreciation to the best lecturers who will be selected every year. To realize this, we need an easy calculation system that is carried out in the form of ranking according to the final value, therefore a decision support system using the ARAS method is chosen because it is very appropriate for the selection process and provides convenience in the calculations which are determined based on ranking. The decision support system using the ARAS method uses 8 criteria that are set as a reference in determining the best lecturers, namely Recent Education, Lecturer Functional Position, Lecturer Certification, Number of Journal Publications, Roles in Research, Journal Publication History, Research Grants, and Community Service. There are 10 lecturers in the field of computers who will be used as alternative data with lecturer codes D01, D02, D03, D04, D05, D06, D07, D08, D09, D10. The results obtained from this study are the lecturer code D04 = 0.0974, D06 = 0.0965, D09 = 0.0932, D07 = 0.0903, D03 = 0.0901 was selected as the best lecturer in 2021/2022. So that the results of this study can help the campus to determine the best lecturers every year fairly and be selected based on rankings.
Weak security systems in a house provide opportunities and opportunities for other people who are not entitled to take and steal valuables for homeowners. Based on these problems, research was carried out by making a home security system using a passive infrared receiver sensor and an Arduino-based sms gateway. The system is designed using electronic devices such as Arduino Microcontroller as data processing, PIR sensor to detect movement in the house, buzzer as an alarm and GSM 900 A module as a communication medium in the form of SMS which is connected to the homeowner's cell phone. This system works when the PIR sensor (Passive InfraRed Receiver) detects a human entering the house and then Arduino sends data in the form of an alarm from the buzzer and sends a danger sign in the form of SMS (Short Message Service). The results of the research conducted on the PIR sensor with a distance of 30-150 cm on the object movement caused an alarm to sound originating from the buzzer and SMS notifications were also successfully sent to the homeowner every time there was movement or an open door detected by the PIR sensor. This research shows that technology can be a solution to prevent crime, especially at home through a home security system using PIR sensors and SMS Gateway
The latest evolution of technology lately had triggered a lot of creativity in our world,especially in robotic in industry. The use of a robot aims to save money and produces product with the same quality.The robot movers and goods is one of the implementation of technology in the field of robotics has the ability to help people to move goods from a place to place that has been made on the color of a grouping of the goods. In this research, robot was designed based on microcontroller 328. Also it is equipped with one DC motors as wheel driver, four servo motors as arm driver, one IR (Obstacle) sensor as an item detector and one TCS3200 sensors as colour.
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