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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Media Statistika Sosiohumaniora Jurnal Ilmu Dasar Statistika Proceedings of Annual International Conference Syiah Kuala University - Life Sciences & Engineering Chapter CAUCHY: Jurnal Matematika Murni dan Aplikasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics JUTI: Jurnal Ilmiah Teknologi Informasi Journal of the Indonesian Mathematical Society Jurnal Matematika & Sains Jurnal Optimasi Sistem Industri SEMIRATA 2015 International Journal of Advances in Intelligent Informatics Indonesian Journal of Science and Technology Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal TAM (Technology Acceptance Model) BAREKENG: Jurnal Ilmu Matematika dan Terapan International Journal of Supply Chain Management Eksakta : Berkala Ilmiah Bidang MIPA Jurnal Aplikasi Statistika & Komputasi Statistik Journal of Islamic Monetary Economics and Finance Indonesian Journal of Electrical Engineering and Computer Science JINAV: Journal of Information and Visualization Journal of Applied Data Sciences Prosiding Seminar Nasional Official Statistics Jurnal Matematika Integratif Literatur : Jurnal Bahasa dan Sastra Jurnal TAM (Technology Acceptance Model)
Dedi Rosadi
Department Of Mathematics, Universitas Gadjah Mada
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Journal : International Journal of Advances in Intelligent Informatics

 1 
Portfolio optimization based on self-organizing maps clustering and genetics algorithm Fajri Farid; Dedi Rosadi
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.587

Abstract

In this modern era, gaining additional income is necessary to fulfill daily needs since inflation is unavoidable. Investing in stocks can give passive income to help people deal with the increasing prices of necessities. However, selecting stocks and constructing a portfolio is the major problem in investing. This research will illustrate the stock selection method and the optimization method for optimizing the portfolio. Stock selection is carried out by clustering using Self-organizing Maps (SOM). Clustering will show the best stocks formed for a portfolio to be optimized. The best stocks that have the best performance are selected from each cluster for the portfolio. The best performance of the stock can be determined using the Sharpe Ratio. Optimization will be carried out using a Genetic Algorithm. The optimization is carried out using software R i386 3.6.1. The optimization results are then compared to the Markowitz Theory to show which method is better. The expected return on the portfolio generated using Genetic Algorithm and Markowitz Theory are 3.348458 and 3.347559975, respectively. While, the value of the Sharpe Ratio is 0.1393076 and 0.13929785, respectively. Based on the results, the best performance of the portfolio is the portfolio produced using Genetic Algorithm with the greater value of the Sharpe Ratio. Furthermore, the Genetics Algorithm optimization is more optimal than the Markowitz Theory.
Optimization hybrid weighted switching filtering (OHWSF) using SVD and SVD++ for addressing data sparsity Muhammad, Malim; Gunardi, Gunardi; Danardono, Danardono; Rosadi, Dedi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1796

Abstract

Recommender systems are crucial for filtering vast amounts of digital content and providing personalized recommendations; however, their effectiveness is often hindered by data sparsity, where limited user-item interactions lead to reduced prediction accuracy. This study introduces a novel hybrid model, Optimization Hybrid Weighted Switching Filtering (OHWSF), to overcome this challenge by integrating two complementary strategies: Hybrid Weighted Filtering (HWF), which linearly combines predictions from SVD and SVD++ using a weighting parameter (α), and Hybrid Switching Filtering (HSF), which dynamically selects predictions based on a threshold rating (θ). The OHWSF framework introduces a tunable optimization mechanism governed by the parameter σ₁ to adaptively balance weighting and switching decisions based on actual rating deviations. Unlike existing static or manually tuned hybrid methods, the proposed model combines dynamic switching with weight optimization to minimize prediction error effectively. Extensive experiments on four benchmark datasets (ML-100K, ML-1M, Amazon Cell Phones Reviews, and GoodBooks-10K) demonstrate that OHWSF consistently outperforms traditional collaborative filtering (UBCF, IBCF), matrix factorization techniques (SVD, SVD++), and standalone hybrid models across all evaluation metrics (MAE, MSE, RMSE). The model achieves optimal performance within the range of α = 0.6–0.9 and θ = 1.0–1.5, demonstrating robustness across varying sparsity levels. Notably, OHWSF achieves up to 742.16% MAE improvement over the UBCF model, with significantly reduced training time compared to SVD++. These findings confirm that OHWSF significantly improves prediction accuracy, scalability, and adaptability in sparse data environments. This research contributes a flexible, interpretable, and efficient hybrid recommendation framework suitable for real-world applications.
Co-Authors Abdul Hoyyi Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Abdurakhman Adhitya Ronnie Effendie, Adhitya Ronnie Adi Marhadi Asrirawan Asuri, Afriza Budi Nurani Ruchjana Danardono Danardono Danardono, Danardono Dara Puspita Anggraeni Deasy Arisanty Dede Maryana Devni Prima Sari Di Asih I Maruddani Dina Agustina, Dina Edi Faizal Epha Diana Supandi Ezra Putranda Setiawan Ezra Putranda Setiawan Faihatuz Zuhairoh Fajri Farid Firmansyah, Hilmi Gubu, La Gumgum Darmawan Gumgum Darmawan Gunardi Gunardi Hendra Perdana Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah Hermansah, Hermansah Herni Utami Herni Utami Iman Sanjaya Iskandar, Edi Izza Dinikal Arsy Jana, Padrul Khabib Mustofa La Gubu Malim Muhammad Media Rosha Muhammad Muhajir, Muhammad Pepi Novianti Resa Septiani Pontoh Retno Subekti Rizal, Ahmad Ashril Ruchjana, Budi N Rupilu, Rina Apriany Helen Wite Setialaksana, Wirawan - Sifriyani, Sifriyani Solly Aryza Sri Redjeki Sri Redjeki Ulfasari Rafflesia