Prediksi Kedatangan Turis Asing Ke Indonesia Menggunakan Backpropagation Neural Networks

In this paper, a backpropagation neural network (BPNN) method with time series data have been explored. The BPNN method to predict the foreign tourist's arrival to Indonesia datasets have been implemented. The foreign tourist's arrival datasets were taken from the center agency on statistics (BPS) Indonesia. The experimental results showed that the BPNN method with two hidden layers were able to forecast foreign tourist's arrival to Indonesia. Where, the mean square error (MSE) as forecasting accuracy has been indicated. In this study, the BPNN method is able and recommended to be alternative methods for predicting time series datasets. Also, the BPNN method showed that effective and easy to use. In other words, BPNN method is capable to producing good value of forecasting.Keywords - BPNN; foreign tourists; BPS; MSEPemanfaatan backpropagation neural network (BPNN) dengan data deret waktu telah digunakan dalam paper ini. Metode BPNN telah digunakan untuk memprediksi data kedatangan turis asing ke Indonesia, dimana data turis tersebut diambil dari badan pusat statistik Indonesia (BPS). Hasil pengujian menunjukkan bahwa metode BPNN dengan dua lapisan tersembunyi mampu memodelkan dan meramalkan data kedatangan turis asing ke Indonesia yang diindikasikan dengan nilai mean square error (MSE). Penelitian ini merekomendasikan bahwa metode BPNN mampu menjadi alternative metode dalam memprediksi data yang berjenis deret waktu karena metode BPNN efektif dan lebih mudah digunakan serta mampu menghasilkan akurasi nilai peramalan yang baik

PENGARUH MODEL PEMBELAJARAN PENDEKATAN TAKTIS TERHADAP HASIL BELAJAR FLYING SHOOT PADA PERMAINAN BOLA TANGAN : Studi Eksperimen di SMP Negeri 1 Kecamatan Sliyeg Kabupaten Indramayu

Penelitian ini meneliti pengaruh pendekatan taktis terhadap hasil belajar flying shoot dalam permainan bolatangan di SMP Negeri 1 Sliyeg Kabupaten Indramayu. Tujuan penelitian ini adalah untuk mengetahui seberapa besar pengaruh pendekatan taktis terhadap hasil belajar flying shoot. variabel bebas dalam penelitian ini adalah pendekatan taktis dan variabel terikatnya adalah hasil belajar flying shoot. metode yang digunakan adalah metode eksperimen, dengan desain penelitian pretest and posttes control group design. Populasi penelitian ini adalah siswa-siswi SMP Negeri 1 Sliyeg Kabupaten Indramayu. Sampel penelitian adalah siswa kelas 7 di SMP Negeri 1 Sliyeg, dengan jumlah 30 orang dengan teknik pengambilan sampel menggunakan random sampling. Instrumen penelitian yang digunakan adalah tes keterampilan flying shoot. tes keterampilan flying shoot ini memiliki validitas 0,927 dan reliabilitas 0,92. Berdasarkan hasil penghitungan dan pengujian signifikansi kedua kelompok antara kelompok eksperimen dan kontrol, ternyata kelompok eksperimen lebih memberikan pengaruh yang signifikan terhadap hasil belajar flying shoot dalam permainan bolatangan di SMP Negeri 1 Sliyeg Kabupaten Indramayu

Performance Measurement in ITG Based on Balanced Scorecard

The article reports on our work in conducting performance measurement for the management of Information Technology (IT) by applying the Balanced Scorecard (BSC) at Mulawarman University, Samarinda, East Kalimantan, Indonesia. The results of this study are then used to propose a hybrid framework that applies both BSC and a artificial intelligence (AI) techniques in order to measure the performance of IT governance generally. This article also examines BSC’s abilities and its flexibility compared to other methods in measuring the performance of IT governance. The proposed hybrid framework is expected to yield a management that produces a scorecard measures that are more rigorous, accurate and consistent with the objectives and organizational strategies in non-profit organizations, especially educational ones

Comparing of ARIMA and RBFNN for short-term forecasting

Based on a combination of an autoregressive integrated moving average (ARIMA) and a radial basis function neural network (RBFNN), a time-series forecasting model is proposed. The proposed model has examined using simulated time series data of tourist arrival to Indonesia recently published by BPS Indonesia. The results demonstrate that the proposed RBFNN is more competent in modelling and forecasting time series than an ARIMA model which is indicated by mean square error (MSE) values. Based on the results obtained, RBFNN model is recommended as an alternative to existing method because it has a simple structure and can produce reasonable forecasts

Crude Palm Oil Prediction Based on Backpropagation Neural Network Approach

Crude palm oil (CPO) production at PT. Perkebunan Nusantara (PTPN) XIII from January 2015 to January 2018 have been treated. This paper aims to predict CPO production using intelligent algorithms called Backpropagation Neural Network (BPNN). The accuracy of prediction algorithms have been measured by mean square error (MSE). The experiment showed that the best hidden layer architecture (HLA) is 5-10-11-12-13-1 with learning function (LF) of trainlm, activation function (AF) of logsig and purelin, and learning rate (LR) of 0.5. This architecture has a good accuracy with MSE of 0.0643. The results showed that this model can predict CPO production in 2019

An audio encryption using transposition method

Encryption is a technique to secure sounds data from attackers. In this study, transposition technique that corresponds to a WAV file extension is used. The performance of the transposition technique is measured using the mean square error (MSE). In the test, the value of MSE of the original and encrypted audio files were compared; the original and decrypted audio files used the correct password is ‘SEMBILAN’ and the incorrect password is ‘DELAPAN’. The experimental results showed that the original and encrypted audio files, and the original and decrypted audio files used the correct password that has a value of MSE = 0, and with the incorrect one with a value of MSE 0.00000428 or ≠ 0. In other words, the transposition technique is able to ensure the security of audio data files

Prediction of Daily Network Traffic based on Radial Basis Function Neural Network

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21 – 24 June 2013 (192 samples series data) in ICT Unit Universitas Mulawarman, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of percentage error (MPE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic

Time Series Prediction Using Radial Basis Function Neural Network

This paper presents an approach for predicting daily network traffic using artificial neural networks (ANN), namely radial basis function neural network (RBFNN) method. The data is gained from 21-24 June 2013 (192 samples series data) in ICT Unit of Mulawarman University, East Kalimantan, Indonesia. The results of measurement are using statistical analysis, e.g. sum of square error (SSE), mean of square error (MSE), mean of absolute percentage error (MAPE), and mean of absolute deviation (MAD). The results show that values are the same, with different goals that have been set are 0.001, 0.002, and 0.003, and spread 200. The smallest MSE value indicates a good method for accuracy. Therefore, the RBFNN model illustrates the proposed best model to predict daily network traffic

Shared Voices of Indonesian Teacher-Educators from Virtual Research-Workshop-Series: Reflections on Covid-19 Pandemic Driven Professional Development

This study aims to explore the perceptions and experiences of Teacher-Educators (TEs) who participated in virtual research-workshop-series as professional development programs. Six TEs, three from natural science and three from social science, participated in a nine-month virtual research workshop series organized by the faculty. In the frame of a case study, the data were gathered from in-depth interviews and a set of questions. The findings revealed that TEs had sufficient research knowledge as they were able to identify good quality of research, read relevant reading research, and signified the importance of research as part of their professional identity. Completion of other tasks, lack of research motivation and collegiality, shortage of research skills and competencies including how to read academic articles due to vocabulary and sentence construction hindered them from conducting research. The workshop has facilitated the TEs autonomy, research skills and competencies, research collaboration, and goal-orientation. The PD program strengthened their research motivation and engagement that scaffold positive insights into their self-research awareness. Moreover, all TEs were able to complete their papers and submit them to reputable journals