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All Journal Jurnal Matematika Sains dan Teknologi DWIJA CENDEKIA: Jurnal Riset Pedagogik Jurnal Informatika Global JUSTIN (Jurnal Sistem dan Teknologi Informasi)
Hartati Hartati
Universitas Terbuka
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Education Mathematics Social Sciences Other

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PENGGUNAAN METODE ARIMA DALAM MERAMAL PERGERAKAN INFLASI Hartati Hartati
Jurnal Matematika Sains dan Teknologi Vol. 18 No. 1 (2017)
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (818.822 KB) | DOI: 10.33830/jmst.v18i1.163.2017

Abstract

Inflation is a problem which haunts the economy of each country. Its development is which continually increasing make a drag on economic growth to a better direction. Inflation tends to occur in developing countries like Indonesia which is an agricultural country. To overcome the instability of inflation, one way to do is to predict the time series data. Methods Autoregressive Integrated Moving Average (ARIMA) has the ability to capture the necessary information about the wood as well as able to cope with the instability of inflation of inflation. This is because ARIMA is a method of forecasting time series are suited to predict the number of variables in a fast, simple, inexpensive, accurate, and only requires the data variables to be predicted. Inflasi merupakan suatu masalah yang menghantui perekonomian setiap negara. Perkembangannya yang terus-menerus mengalami peningkatan menjadi hambatan pada pertumbuhan ekonomi ke arah yang lebih baik. Perubahan laju inflasi cenderung terjadi pada negara-negara berkembang seperti halnya Indonesia yang merupakan negara agraris. Untuk menanggulangi terjadinya ketidakstabilan laju inflasi, salah satu cara yang dapat dilakukan adalah dengan meramalkan data time series. Metode Autoregressive Integrated Moving Average (ARIMA) memiliki kemampuan untuk menangkap informasi-informasi yang diperlukan mengenai laju inflasi serta mampu menanggulangi ketidakstabilan dari laju inflasi. Hal ini dikarenakan ARIMA merupakan suatu metode peramalan time series yang cocok digunakan untuk meramal sejumlah variabel secara cepat, sederhana, murah, dan akurat serta hanya membutuhkan data variabel yang akan diramal.
Optimisasi Backpropagation Neural Network dalam Memprediksi IHSG Hartati Hartati; Alpin Herman Saputra; Imelda Saluza
Jurnal Informatika Global Vol 13, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v13i1.2066

Abstract

Covid-19 has become a global epidemic and has spread to many countries in the world, including Indonesia. The COVID-19 pandemic is one source of uncertainty that causes financial data to fluctuate and cause data to be volatile. This outbreak had an impact on financial data, not only on the Rupiah exchange rate but also on the Jakarta Composite Index (JCI). The uncertainty of the JCI makes it difficult for investors, data managers, and business people to predict data for the future. JCI is one indicator of the capital market (stock exchange). The uncertainty of the JCI data causes the need for predictions, so that investors, data managers, and business people can make the right decisions so that they can reduce risk and optimize profits when investing. One of the factors causing the JCI's decline was sentiment caused by investor panic over the rapid spread of COVID-19 in various cities in Indonesia. This research uses Backpropagation Neural Network (BPNN) in making predictions and continues with optimization of BPNN using ensemble techniques. Historical data from the JCI used were obtained from yahoo.finance. The ensemble technique used consists of two approaches, namely combining different architectures and initial weights with the same data and combining different architectures and weights. The results of network performance using ensemble technique optimization show good performance and can outperform the individual network performance of BPNN. Keywords: prediction, JCI, Optimization, BPNN, volatile
Pembelajaran Orang Dewasa: Tutorial Webinar (Tuweb) melalui Microsoft Teams Mahasiswa PGSD Universitas Terbuka di Era Pandemi Alpin Herman Saputra; Hartati Hartati; Steven Anthony
DWIJA CENDEKIA: Jurnal Riset Pedagogik Vol 5, No 1 (2021): DWIJA CENDEKIA: Jurnal Riset Pedagogik
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (472.564 KB) | DOI: 10.20961/jdc.v5i1.45915

Abstract

Primary Education of Universitas Terbuka students are students of Elementary School Teacher Education (PGSD) and Early Childhood Education (PGPAUD) study programs who have been teachers in schools for at least 1 (one) year of teaching. PGSD students in normal circumstances carry out direct learning through Face-to-Face Tutorials (TTM) but in the pandemic era, they adapt to the Webinar Tutorial (Tuweb) using the Microsoft team. Tuweb is synchronous learning. Adult learning styles (andragogy) in the aspect of self-concept (emotionally stable students, they are adults whose age, cognitive, and development are mature), the concept of experience (the requirement to become a student in the teaching field of at least one year of teaching, is proven with a Decree (SK) from the relevant agency, the concept of learning readiness, time perspective or learning orientation. The average score of the andragogy ability of the students is 84.8 or in the high category
NEURAL NETWORK OPTIMIZATION USING ENSEMBLE METHOD IN FORECASTING FINANCIAL DATA Imelda Saluza; Hartati Hartati
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 10, No 4 (2022)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v10i4.50771

Abstract

Forecasting is a time series data analysis technique for predicting future data by obtaining patterns of change in past data. Exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Box-Jenkins are common forecasting algorithms for linear time series data. Meanwhile, models such as Artificial Neural Networks (ANN), Fuzzy, and others are frequently utilized for nonlinear time series data. One of the most generally used model selection procedures is to evaluate each model that has been trained in time series data learning and then used to predict the model's performance, and then allow the forecaster determine if the model is acceptable or choose the best model from a list of candidates. Forecasts created with the best model, on the other hand, rarely produce generalized outcomes for the full data set. As a result, it's crucial to put the results of the learning training to the test. The ensemble method is employed instead of learning from a large number of models. The objective of this research is to apply ANN and the Ensemble Approach to optimize a forecasting model. When forecasting with a neural network, the ensemble approach is used to limit the occurrence of over fitting so that the resulting model can beat individual NN models and be consistent in lowering mistakes.
Co-Authors Alpin Herman Saputra Imelda Saluza, Imelda Steven ANTHONY