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All Journal Information System Diploma Program - Informatics Management JAKSTIK Journal Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Petir ComTech: Computer, Mathematics and Engineering Applications IJIES (International Journal of Innovation in Enterprise System) Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmu Teknik dan Komputer EKOMABIS: Jurnal Ekonomi Manajemen Bisnis Jurnal Ilmu Komputer dan Informatika Journal of Social Research Jurnal Computer and Technology Prosiding SeNTIK STI&K
Ire Puspa Wardhani
STMIK Jakarta STI&K
Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences

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Journal : Knowledge Engineering and Data Science

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Recurrent Session Approach to Generative Association Rule based Recommendation Armanda, Tubagus Arief; Wardhani, Ire Puspa; Akhriza, Tubagus M.; Admira, Tubagus M. Adrie
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p199-214

Abstract

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations.  The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot.  In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability.
Stacked LSTM-GRU Long-Term Forecasting Model for Indonesian Islamic Banks Sujatna, Yayat; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Yuningsih, Nia; Arif, Dody; Handayani, Sri Setya; Kardian, Aqwam Rosadi; Wardhani, Ire Puspa; Rere, L.M Rasdi
Knowledge Engineering and Data Science Vol 6, No 2 (2023)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v6i22023p215-250

Abstract

The development of the Islamic banking industry in Indonesia has become a significant concern in recent years, with rapid growth in the number of banks operating based on Sharia principles. To face emerging challenges and opportunities, a deep understanding of the long-term financial behavior of Islamic banks is becoming increasingly important. This study aims to predict the share price of PT Bank Syariah Indonesia Tbk, over 28 days using the LSTM-GRU stack. The observation stage includes importing the dataset, data separation, model variations, the training process, output, and evaluation. Observations were conducted using 10 model variations from 4 stacks of LSTM and GRU. Each model performs the training process in four epochs (200, 500, 750, and 1000). The results of observations in this study show that long-term predictions (28 days ahead) using four stacks of LSTM-GRU and daily training accumulation techniques produce better accuracy than the general method (using multiple outputs). From the observations we have made for predictions for the next 28 days, the model with the LGLG stack arrangement (LSTM-GRU-LSTM-GRU) produces the best accuracy at epoch 750 with an MSE LSTM-GRU 63.43762863. This study will undoubtedly continue in order to achieve even better precision, either by utilizing a new design or by further improving the technology we are now employing.
Co-Authors Abdul Hakim Aditya Pranata Admira, Tubagus M. Adrie Agung Slamet Riyad Agung Slamet Riyadi Agung Slamet Riyadi Ahmad Bahrudin Akhriza, Tubagus M. Akhwan Khairul Alam Alby Maulana Sidik Aldi Marwoto Aldy Wirawan Andi Herawati Andi Maulana Ibrohim Andi Perdana Anggar Prasetyo Anggar Prasetyo Annisa Indrayanti Annisa Mutia Putri Annissa Mutia Putri Aqwam Rosadi Kardian Arif Mughni Arif, Dody Armanda, Tubagus Arief Astary, Mahda Yulia Basri, Lody Saladin Bolivia Dwi Agustiani Cherry Mariz Wibowo Cherry Mariz Wibowo Damayanti, Arika Dapit Dapit Defrizal Defrizal Defrizal Defrizal Devi Devi Devita Rizky Nur Septiani Devita Rizky Nur Septiani Dewi Nur Cahyani Diah Fitaloka Dila Andriyani Dwi Cahyono, Yohanes Dyta Nigtyas Dyta Nigtyas Ega Rudy Graha Ega Rudy Graha Eko Tri Asmoro Elang M Sony Ariestono Fencing Prasetyo Ferri Yusra Ferri Yusra Fipit Aprilyanthi Firhan Okvalino Firhan Okvalino Fitriyanto Rizky Anjasmoro H. Soetirto Sadikin, Drs., MA. H. Soetirto Sadikin, Drs., MA. Hadiutama, Aryo Putra Handayani, Sri Setya hendajani, fivtatianti HENRI SAPUTRA Herlina Herlina Herman Herman Idham Adriansyah Indah Permatasari Cahyaningtyas Indrayanti, Annisa Irfan Irfan Caniago Juwita Juwita Kardian, Aqwam Rosadi Karno, Adhitio Satyo Bayangkari Kellek Kurniawan Kenya Puspita Lindri Lussiana Lussiana Lussiana Lussiana Lussiana, ETP Maria Sri Wulandari Maria Ulfah Miftachul Fuad MM SKom Fivtatianti Hendajani Mohamad Afhdal Jauhari Mohamad Chotibul Umam Mohamad Chotibul Umam Mohamad Saefudin Mohammad Afdhal Jauhari Muhammad Badruzaman Muhammad Ihsan Muhammad Riefdan Muhammad Riefdan Munich Heindari Ekasari Munich Heindari Ekasari Nenny Anggraini Noor Cholis Nur Ali Akbar Nurkholifah Nurkholifah Nurritu Praworo Pramaishella, Deva Putri Prihandoko . Pujiono Pujiono Purwo Hadi Santoso Rere, L.M Rasdi Rijayanih Rijayanih Risandi Aldino Rita Riyanti Pipit Riyadi, Agung Slamet Rojoko Rojoko Roviqoh, Vella Sabrina, Wafa Salim, Sofyan Nur Sarifuddin Madenda Shandy Juniantoro Siti Wirdayatih Soegijanto Soegijanto Sunny Arief Sudiro Supeni Siskawati Supeni Siskawati Suryanta, Asep Susi Widayati Tondi Febriyana Wahyu Hidayat Widi Hastomo Wisnu Sutrisno Wiwiek Pujiningsih Wulan Kurniawati Yayat Sujatna, Yayat Yokiansyah Sasbriantoyo Yudi Irawan Chandra Yuningsih, Nia Yusuf Yusuf