Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction

Purnawansyah, Purnawansyah; Haviluddin, Haviluddin; Darwis, Herdianti; Azis, Huzain; Salim, Yulita

doi:10.17977/um018v4i12021p14-28

Cited by 7 publications

(6 citation statements)

References 17 publications

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“…Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) is the error detection used. MAPE is used for error detection, which represents accuracy [48], while RMSE is used for error detection based on outliers [49]. MAPE and RMSE values getting smaller and closer to 0 indicate a more accurate prediction result.…”

Section: Discussionmentioning

confidence: 99%

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Maharani

2023

J. Appl. Data Sci.

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Financial systems depend on financial institution stability. This study used an ANFIS-ANN model to forecast bank insolvency probability. ANFIS, an adaptive network that combines fuzzy logic with ANN, is a promising bankruptcy prediction tool. This study compared the predictive performance of ANFIS against Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) using financial statements data from 42 banking issuers listed on the Indonesia Stock Exchange (IDX) from 2010 to 2021. This work aimed to build a robust bankruptcy prediction model, compared ANFIS to LSTM and CNN, and assessed each method's accuracy and applicability. MAPE and RMSE metrics were used for a thorough review. The study collected, preprocessed, and trained ANFIS, LSTM, and CNN models. ANFIS had 243 rules with triangular membership functions for input variables and constant output membership. Random search optimized LSTM and CNN hyperparameters. This study's novelty lies in its innovative comparison of ANFIS and deep learning for bank bankruptcy prediction used real-world financial data. ANFIS beat LSTM and CNN in MAPE, showing its predictive model capability. ANFIS has the lowest MAPE of 0.140335507 when trained and tested on 80%:20% data. In conclusion, ANFIS outperformed deep learning in bank soundness prediction. It provided vital insights into the banking sector's early warning and risk assessment. The results showed that ANFIS can help financial decision-makers to improve risk management.

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Section: Discussionmentioning

confidence: 99%

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Maharani

2023

J. Appl. Data Sci.

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“…Prediksi harga bawang putih memungkinkan kita untuk memproyeksikan harga berdasarkan pola historis, di mana neural network telah terbukti menjadi teknik prediksi yang andal dan efektif dalam banyak penelitian terdahulu [5]. Algoritma jaringan saraf seringkali diterapkan dalam menganalisis klasifikasi dan prediksi di berbagai bidang, seperti prediksi harga saham [6], harga bahan pangan [7], harga sembako [8], pandemi COVID-19 [9], lalu lintas jaringan [10], dan sebagainya.…”

Section: Pendahuluanunclassified

Penerapan Metode Neural Network untuk Prediksi Harga Bawang Putih di Kota Singkawang

Hamdi,

Budiantoro,

Sani

et al. 2024

Voteteknika

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Bawang putih adalah komoditas penting dalam perekonomian Kota Singkawang. Penelitian ini bertujuan untuk menerapkan metode Neural Network dalam meramalkan harga bawang putih di kota tersebut. Data harga bawang putih dari Badan Pusat Statistik Kota Singkawang untuk periode tahun 2016-2023 digunakan dalam penelitian ini. Setelah melalui proses analisis dan pengolahan data, model Neural Network dilatih menggunakan data historis untuk memprediksi harga bawang putih di masa mendatang. Hasil prediksi menunjukkan bahwa harga bawang putih cenderung stabil selama dua tahun ke depan, dengan nilai tetap pada angka 30,701 untuk bulan 1 tahun 2024, 30,303 untuk bulan 2 tahun 2024, dan seterusnya hingga tahun 2025. Penelitian ini memberikan wawasan penting bagi para pelaku pasar dalam mengantisipasi perilaku pasar dan pengambilan keputusan di sektor bawang putih di Kota Singkawang.

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“…The BPNN approximates data patterns and makes accurate predictions by repeating this procedure [24]. Pattern identification [25], image processing [26], time-series forecasting [27], and financial prediction [28] This work attempts to solve this research gap by presenting an innovative method that combines mean and median smoothing approaches to preprocess data and strengthen the model's robustness to boost BPNN's performance, increasing the accuracy of unique visitor predictions. This study was carried out in order to fulfill these goals.…”

Section: Literature Reviewmentioning

confidence: 99%

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

Wibawa

2023

J. Appl. Data Sci.

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Unique visitors are first-time IP address visitors in a certain time window, a significant indicator of an electronic journal's performance and accreditation. This study uses a backpropagation neural network (BPNN) to improve visitor prediction. From January 1, 2018, to December 31, 2018, the KEDS.csv file on the page contained page views, sessions, visitors, and new visitors. The data is preprocessed using mean and median smoothing. MSE and RMSE are examined and compared to the BPNN model without smoothing and the original data. The BPNN model with mean smoothing, MSE 0.00129, and RMSE 0.03518 with a learning rate of 0.4 on 1-2-1 architecture has the lowest error. Integrating mean and median smoothing techniques considerably enhances the BPNN forecasting model. Mean, and median smoothing lowers data volatility and noise, making predictions more accurate. The BPNN model with smoothing has lower MSE and RMSE than the model without smoothing and the original data. This work is unusual in combining mean and median smoothing with a BPNN model to predict unique visits to electronic journals. This research advances time series forecasting by predicting electronic journal visiting patterns. The literature benefits from evaluating smoothing strategies and their effects on predicting. The study helps electronic journal practitioners evaluate visitor patterns and journal performance by boosting prediction accuracy.

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Backpropagation Neural Network with Combination of Activation Functions for Inbound Traffic Prediction

Cited by 7 publications

References 17 publications

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Bank Soundness Level Prediction: ANFIS vs Deep Learning

Penerapan Metode Neural Network untuk Prediksi Harga Bawang Putih di Kota Singkawang

Mean-Median Smoothing Backpropagation Neural Network to Forecast Unique Visitors Time Series of Electronic Journal

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