Prediksi Pemakaian Listrik Menggunakan Jaringan Syaraf Tiruan dan ARIMA di Wilayah Sulluttenggo

Background this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly differen

Prediksi Pemakaian Listrik Menggunakan Jaringan Syaraf Tiruan dan ARIMA Di Wilayah Sulluttenggo

Background this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly differen

Prediksi Pemakaian Listrik Kelompok Tarif Menggunakan Jaringan Syaraf Tiruan dan ARIMA

AbstrakLatar Belakang penelitian ini dibuat dimana semakin meningkatnya kebutuhan listrik di setiap kelompok tarif. Yang dimaksud dengan kelompok tarif dalam penelitian ini adalah kelompok tarif sosial, kelompok tarif rumah tangga, kelompok tarif bisnis, kelompok tarif industri dan kelompok tarif pemerintah. Prediksi merupakan kebutuhan penting bagi penyedia tenaga listrik dalam mengambil keputusan berkaitan dengan ketersediaan energi listik. Dalam melakukan prediksi dapat dilakukan dengan metode statistik maupun kecerdasan buatan.            ARIMA merupakan salah satu metode statistik yang banyak digunakan untuk prediksi dimana ARIMA mengikuti model autoregressive (AR) moving average (MA). Syarat dari ARIMA adalah data harus stasioner, data yang tidak stasioner harus distasionerkan dengan differencing. Selain metode statistik, prediksi juga dapat dilakukan dengan teknik kecerdasan buatan, dimana dalam penelitian ini jaringan syaraf tiruan backpropagation dipilih untuk melakukan prediksi. Dari hasil pengujian yang dilakukan selisih MSE ARIMA, JST dan penggabungan ARIMA, jaringan syaraf tiruan tidak berbeda secara signifikan. Kata Kunci— ARIMA, jaringan syaraf tiruan, kelompok tarif.  AbstractBackground this research was made where the increasing demand for electricity in each group. The meaning this group is social, the household, business, industry groups and the government fare. Prediction is an important requirement for electricity providers in making decisions related to the availability of electric energy. In doing predictions can be made by statistical methods and artificial intelligence.            ARIMA is a statistical method that is widely used to predict where the ARIMA modeled autoregressive (AR) moving average (MA). Terms of ARIMA is the data must be stationary, the data is not stationary should be stationary  use differencing. In addition to the statistical method, predictions can also be done by artificial intelligence techniques, which in this study selected Backpropagation neural network to predict. From the results of tests made the difference in MSE ARIMA, ANN and merging ARIMA, artificial neural networks are not significantly different. Keyword—ARIMA, neural network, tarif group

APPLICATION OF K-NEAREST NEIGHBORS MODEL IN ELECTRICAL POWER NEEDS CLASSIFICATION FOR EACH REGION IN LHOKSEUMAWE CITY

Klasifikasi kebutuhan daya listrik untuk masing-masing daerah sangat diperlukan agar dapat menggambarkan kondisi daya yang dibutuhkan. Hal ini sangat penting untuk pelanggan baru yang ingin mengetahui daya yang diberikan, sebaliknya pelanggan lama juga dapat melihat dan  menurunkan daya atau menambah daya sesuai dengan kebutuhan. Adapun variable yang di gunakan pada penelitian ini adalah luas rumah, besaran daya listrik yang akan digunakan dan telah digunakan, pendapatan gabungan orang tua (kotor) / bulan, jumlah daya lampu yang ada dirumah, kemudian dilanjutkan dengan klasifikasi perkiraan daya listrik yang berikan. Klasifikasi yang digunakan adalah penentuan golongan Tarif/Daya  R-1/450 VA subsidi, R-1/900 VA subsidi, R-1/900 VA-RTM (Rumah Tangga mampu) non subsidi, R-1/1300 VA non subsidi, dan Tarif/Daya  R-1/2200 VA non subsidi. Selanjutnya untuk pengujian menggunakan data training sampel sebanyak 20 data sampel dari masing-masing pelanggan yang akan dilihat pengujiannya dengan tetangga yang paling dekat. Untuk sampel daya terdiri dari variable pengujian dan klasifikasi jenis pengelompokan. Pengujian K-Nearest Neighbors (KNN) untuk luas rumah nilai nya 3, besaran daya 3, pendapatan bernilai 2, jumlah daya keseluruhan, 3 dan konsumsi energi yang digunakan adalah 4. Hasil dari penelitian ini adanya aplikasi teknologi dalam model KNN dalam pengelompokan penentuan kebutuhan daya untuk masing-masing daerah di Kota Lhokseumawe.Classification for electric power requirements for each region is very necessary in order to describe the power conditions needed This is very important for new customers to want to know the power provided, otherwise old customers can also see and reduce power or add power according to needs. The variable used is the area of ​​the house, the amount of electric power that will be used and has been used, the combined income of parents (dirty) / month, the amount of power of the lights at home, then continued with the classification of the estimated electrical power provided. Furthermore, the classification used is the determination of the class of Tariff / Power R-1/450 VA subsidies, R-1/900 VA subsidies, R-1/900 VA-RTM (capable Household) non-subsidized, R-1/1300 VA non-subsidized, and Tariff / Power class R-1/2200 VA non-subsidized. Furthermore, for testing using training data samples as many as 20 sample data from each customer that will be seen with the closest neighbors. For power samples consist of testing variables and classification types. K-Nearest Neighbors (KNN) test for house area is 3, power 3, income 2, total power, 3 and energy consumption used is 4. Results from this research is the application of technology in the KNN in the determination of the power requirements for each region at Lhokseumawe City