Peramalan Jumlah Tersangka Penyalahgunaan Narkoba Menggunakan Metode Multilayer Perceptron

Pratiwi, Putu Githa; Putra, I Ketut Gede Darma; Putri, Desy Purnami Singgih

doi:10.24843/jim.2019.v07.i02.p06

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…Peramalan merupakan kegiatan memprediksi nilai, kejadian, peristiwa, waktu, lokasi, kuantitas, atau kualitas di masa mendatang yang didasarkan pada pertimbangan subjektif terhadap data masa lalu sehingga informasi yang dihasilkan dari peramalan dapat menjadi referensi penting bagi masyarakat [13]. Metode peramalan kuantitatif adalah metode peramalan dengan memanfaatkan informasi di masa lalu dan metode kualitatif adalah metode peramalan dengan memanfaatkan pendapat dari ahli atau pakar untuk pengambilan keputusan dari hasil peramalan yang dilakukan [14].…”

Section: Peramalanunclassified

Peramalan Kualitas Udara di Kota Jakarta Pusat dengan Metode Long Short-Term Memory dan Support-Vector Regression

Wicaksono

Sasmita²,

Pratama³

2023

jitter

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Kualitas udara merupakan masalah serius di kota-kota besar di seluruh dunia, karena memiliki dampak yang signifikan terhadap kesehatan masyarakat dan lingkungan. Penelitian ini membahas mengenai peramalan kualitas udara di Kota Jakarta Pusat menggunakan Metode Long Short-Term Memory (LSTM) dan Support-Vector Regression (SVR). Tujuan dari penelitian ini untuk membantu para pembuat kebijakan dalam membuat keputusan tentang pengelolaan dan penanggulangan kualitas udara serta dalam mengambil tindakan yang sesuai ke depannya untuk melindungi kesehatan masyarakat dan lingkungan. Parameter yang diramalkan dengan Metode LSTM dan SVR adalah nilai konsentrasi polutan Particulate Matter 10 (PM10), Sulfur Dioksida (SO2), Karbon Monoksida (CO), Ozon Permukaan (O3), dan Nitrogen Dioksida (NO2) dengan evaluasi menggunakan metrik Mean Absolute Percentage Error (MAPE) dan Root-Mean-Square Error (RMSE). Hasil yang didapat dari Metode LSTM adalah rata-rata nilai MAPE sebesar 12,15% dan rata-rata nilai RMSE sebesar 0,0941, sedangkan Metode SVR memiliki hasil rata-rata nilai MAPE sebesar 16,19% dan rata-rata nilai RMSE sebesar 0,1666.

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Section: Peramalanunclassified

Peramalan Kualitas Udara di Kota Jakarta Pusat dengan Metode Long Short-Term Memory dan Support-Vector Regression

Wicaksono

Sasmita²,

Pratama³

2023

jitter

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“…(2) Denormalisasi dilakukan pada hasil peramalan dari model paling baik dan bertujuan mengubah kembali data hasil prediksi ke awal untuk menjaga kestabilan dan menghalangi kerancuan data, seperti dinyatakan pada Persamaan 5 [13]. Parameter Xi menyatakan nilai denormalisasi, y nilai keluaran, Xmax data nilai maksimum, dan Xmin data nilai minimum.…”

Section: Metode Penelitianunclassified

Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

Tarigan

Bayupati

Putri

2021

J. Teknol. dan Sist. Komput

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Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.

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“…Important decision making for society, one part of decision making is forecasting. Forecasting is one part of Data Mining [13].…”

Section: Data Miningmentioning

confidence: 99%

Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm

Dewi

Jaya

Sanjaya

2021

JIM

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COVID-19 (coronavirus disease 2019) is a large family of viruses that cause mild to severe illness, such as the common cold or colds and serious illnesses such as MERS and SARS. COVID-19 has become a pandemic, meaning that there has been an increase in cases of the disease which is quite fast and there has been spread between countries and caused enormous losses in various countries. The increasing number of COVID-19 cases every day in Indonesia, including in Bali Province and the resulting losses underlie the forecasting of the number of COVID-19 in Bali Province. Forecasting is carried out using the Neural Network algorithm for time series data on the number of COVID-19 in Bali Province. The data used is data on the number of COVID-19 in the Bali Province in the form of time series data sourced from the Bali Provincial Health Office. The entire forecasting process uses the Rapidminer Studio tools starting from preprocessing, modeling, testing and validation. The results of the RMSE (Root Mean Square Error) evaluation value based on testing for the positive patients were 18.956, the patients recovered were 15.413, the patients under treatment were 5.066 and the patients who died was 0.233.

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Peramalan Jumlah Tersangka Penyalahgunaan Narkoba Menggunakan Metode Multilayer Perceptron

Cited by 6 publications

References 7 publications

Peramalan Kualitas Udara di Kota Jakarta Pusat dengan Metode Long Short-Term Memory dan Support-Vector Regression

Peramalan Kualitas Udara di Kota Jakarta Pusat dengan Metode Long Short-Term Memory dan Support-Vector Regression

Comparison of support vector machine and backpropagation models in forecasting the number of foreign tourists in Bali province

Forecasting Number of COVID-19 in Bali Province Using Neural Network Algorithm

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