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All Journal Jurnal Matematika Jupiter Jurnal Matematika Sains dan Teknologi DWIJA CENDEKIA: Jurnal Riset Pedagogik Jurnal Informatika Global Diseminasi : Jurnal Pengabdian kepada Masyarakat JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Pengabdian Multidisiplin
Hartati
Universitas Terbuka
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Public Health 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.
Aplikasi GARCH dalam Mengatasi Volatilitas Pada Data Keuangan , Hartati; Imelda Saluza
Jurnal Matematika Vol 7 No 2 (2017)
Publisher : Mathematics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JMAT.2017.v07.i02.p87

Abstract

The financial market is a place or means convergence between demand and supply of a wide range of financial instruments Long-term (over one year). Activities that occur in the financial markets in the long term will form a series of data is often called a time series that contains a set of information from time to time. Practical experience shows that many time series exhibit their periods with great volatility. The greater the volatility, the greater the chance to experience a gain or loss. Important properties are often owned by the data time series in finance, especially to return data that the probability distribution of returns are fat tails (tail fat) and volatility clustering or often referred to as a case heteroskedastisitas. Not all models are able to capture the nature of heteroscedasticity, one of the models that are able to do is Generalized Autoregressive Heteroskedasticity Condition (GARCH). So the purpose of this study was to determine the GARCH model in dealing with the volatility that occurred in the financial data. The results showed that the GARCH model is best suited to see volatility in the financial data.
MEMANFAATKAN LIMBAH PLASTIK MENJADI PAVING BLOCK TEGUH; HARTATI; Steven ANTHONY; Bonita HIRZA; Yetty HASTIANA
Diseminasi: Jurnal Pengabdian kepada Masyarakat Vol. 2 No. 1 (2020)
Publisher : Pusat Pengabdian kepada Masyarakat- LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (168.001 KB) | DOI: 10.33830/diseminasiabdimas.v2i2.748

Abstract

Plastic waste becomes one of the sources of environmental pollution problems. Landfill in the final landfill in Palembang, will be a serious problem if it is not sought solution. This community service is in the form of counseling and processing training of plastic waste into paving block given to the partners of the community in the location where the garbage disposal. The provision of counseling and training is expected to be the process of utilizing science and technology in the utilization of plastic waste so it is economically beneficial for the community because it has a high selling value. The implementation phase of this activity in the form of 1) establishment of partnership by determining the community, 2) fulfillment of production equipment, 3) counseling on the innovation of entrepreneurship and marketing of products, 4) training of paving block making. The result of this activity is that partners can make paving blocks from plastic waste and have entrepreneurial innovation and product marketing
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.
Pengembangan Kewirausahaan Melalui Pelatihan Pembuatan Bunga Hias dari Sampah Plastik Kelurahan Sako Baru Hartati, Hartati; Saputra, Alpin H.; Diana, Mustika; Iisnawati, Iisnawati; Hermansyah, Hermansyah; Teguh, Teguh; Anthony, Steven
Jurnal Pengabdian Multidisiplin Vol. 4 No. 1 (2024): Jurnal Pengabdian Multidisiplin
Publisher : Kuras Institute & Scidac Plus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51214/00202404728000

Abstract

Kelurahan Sako Baru terbentuk Tanggal 18 Agustus 2007. Kelurahan Sako Baru memiliki Bank sampah yang bernama “Bank Sampah Melati”. Kelurahan Sako Baru juda terdapat salah Satu Prioritas Program PKK Sako Baru adalah Pengembangan Kehidupan Berkoperasi. Adapun Pelaksanaan Programnya adalah Pemberian keterampilan keluarga dalam upaya peningkatan dan pemberdayaan ekonomi kelurga melalui pengembangan ekonomi kreatif dan usaha mikro kecil, serta pengembangan kehidupan berkoperasi. Pelatihan Pembuatan Bunga Hias dari Sampah Plastik Bank Sampah Melati PKK Kelurahan Sako Baru dapat menghasilkan produk-produk kreativitas atau produk inovatif yang mudah dikerjakan dan dipasarkan. Metode Pengabdian yang digunakan adalah Participatory Action Research (PAR) Dari data yang kami peroleh dari Kelurahan Sako Baru termasuk warga yang tingkat pendapatannya rata-rata rendah. Selanjutnya juga berdasarkan hasil wawancara di lapangan sebagian besar wanita Kelurahan Sako Baru tidak bekerja, ataupun bekerja musiman, ibu rumah tangga sehingga sangat menunjang apabila diberikan tambahan keterampilan untuk “Pelatihan Membuat Bunga Hias dari Sampah Plastik“. Dengan demikian kegiatan PKM ini dapat mengembangkan kewirausahaan Kelurahan Sako Baru dan meningkatkan keterampilan PKK dalam membuat Bunga Plastik dari sampah kresek plastik yang siap dipasarkan. Metode pelaksanaan diawali dengan pemberian penyuluhan, Sosialisasi, pelatihan, praktik, dan penilaian bagaimana layaknya barang untuk dipasarkan. Harapan dari PKM ini adalah adanya peningkatan ekonomi dikalangan keluarga melalui pemanfaatan lingkungan sebagai sumber penghasilan masyarakat.
Membangun Literasi Masyarakat melalui Perintisan Taman Baca Masyarakat di Desa Muara Telang Marga Diana, Mustika; Hartati, Hartati; Anthony, Steven; Rahmawati, Rahmawati
Jurnal Pengabdian Multidisiplin Vol. 4 No. 2 (2024): Jurnal Pengabdian Multidisiplin
Publisher : Kuras Institute & Scidac Plus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51214/00202404996000

Abstract

Perintisan pendirian Taman Baca Masyarakat di desa muara telang marga kecamatan marga telang kabupaten banyuasin adalah program Pengabdian Kepada Masyarakat (PKM) UPBJJ-UT Palembang dengan menggunakan anggaran 2022. Pendirian Taman Baca Masyarakat ini bertujuan untuk meningkatkan minat minat baca masyarakat dan menjadi tempat belajar para pelajar dan anak-anak putus sekolah yang ada di desa muara marga telang khususnya dan di kecamatan muara telang pada umumnya. TBM merupakan sarana untuk pembelajaran dan pendidikan masyarakat secara nonformal. TBM diarahkan untuk memberikan pelayanan kepada warga masyarakat yang belum sekolah, buta aksara, putus sekolah, dan warga masyarakat yang kebutuhan pendidikannya tidak dapat terpenuhi melalui pendidikan formal. Perintisan TBM ini bertujuan meningkatkan literasai masyarakat sehingga dapat menciptakan masyarakat yang berdaya secara pengetahuan, ekonomi dan sosial.desa marga telang muara merupakan bagian daerah perarian yang memiliki keterbatasan akses perpustakaan secara fisik dan juga keterbatasan akses informasi digital dikarekan daerah ini masih belum tersedia jaringan internet yang memadai. Membangun literasi masyarakat pedesaan menjadi tanggung jawab perguruan tinggi sebagai salahsatu dari Tri Dharma Perguruan Tinggi. Metode yang dilakukan dalam pengabdian kepada masyarakat ini adalah Participatory Action Research (PAR), PAR merupakan penelitian yang melibatkan secara aktif semua pihak-pihak yang relevan (stakeholder) dalam mengkaji tindakan yang sedang berlangsung (dimana pengalaman mereka sendiri sebagai persoalan) dalam rangka melakukan perubahan dan perbaikan kearah yang lebih baik. Kegiatan ini akan dilakukan dengan tiga tahap yaitu persiapan, pelaksanaan dan monitoring. Pada persiapan tim abdimas akan membeli sarana dan prasarana yang dibutuhkan, tahap pelaksanaan tim akan menyerahkan baha-bahan dan akan memberikan materi terkait perpustakaan. Pada kegiatan monitoring tim akan mengevaluasi apakan TBM dimanfaatkan secara efektif dan efisien oleh masyarakat. Kegiatan perintisan pendirian taman baca masyarakat ini memberikan hasil berupa terwujudnya sebua taman baca masyarakat di desa muara telang marga sebagai upaya peningkatan literasi masyarakat dalam berbagai sector. Berdasarkan hasil evaluasi, dapat disimpulkan bahwa kegiatan pengabdian ini berhasil dalam mencapai tujuan yaitu meningkatnya minat baca masyarakat yang dapat dari penggunaan koleksi yang ada pada TBM baik membaca do tempat maupun dibawa pulang kerumah, hal ini dapat meningkatkan literasi masyarakat muara telang marga. Manfaat yang bisa diperoleh melalui kegiatan ini yaitu terjadi peningkatan lietasi masyarakat desa Muara Telang Marga.
Prediksi Harga Saham Menggunakan Empirical Mode Decomposition dan Feed Forward Neural Networks Saluza, Imelda; Mohammad Taufikurrahman; Lastri Widya Astuti; Hartati; Dhamayanti; Evi Yulianti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 2 (2023): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10068884

Abstract

Stocks are a very market and shares are a characteristic of a company and their movements are influenced by the market. So if a company experiences problems, the company's shares may experience a spike. As happened with the Bank Syariah Indonesia (BSI) company which experienced service problems on the 8th to 20th. May 11, 2023, which caused a sharp decline in the company's shares. Volatile spikes can cause a risk of loss for investors and business people in the company. So both need to estimate their portfolio. Therefore, it is necessary to predict the share price, the closing price of BSI shares. This research uses time series data from the closing price of BSI shares, which is followed by decomposition using Empirical Model Decomposition (EMD) to break down the original data into several signals which then select these signals using Correlation Based Feature Selection (CFS) for feature selection and ends with make predictions using the Feed Forward Neural Networks (FFNN) algorithm. Based on the proposed model, the Mean Square Error (MSE) (training: 3.84E-02, testing: 1.73E-05) and Mean Absolute Error (MAE) (training: 1.48E-01, testing: 3.40E-03) values ​​are low for both training and testing data compared to without perform EMD and CFS from original data.
Co-Authors Alpin Herman Saputra Bonita Hirza Dhamayanti Diana, Mustika EVI YULIANTI Hermansyah Hermansyah Iisnawati, Iisnawati Imelda Saluza, Imelda Lastri Widya Astuti Mohammad Taufikurrahman Rahmawati, Rahmawati Saputra, Alpin H. Steven ANTHONY Teguh Teguh Teguh Teguh