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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi JURNAL ELEKTRO Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat Jurnal Indonesia Sosial Teknologi eProceedings of Engineering LOGIC: Jurnal Penelitian Informatika
Deni Saepudin
Universitas Telkom Bandung, Indonesia
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Journal : Building of Informatics, Technology and Science

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Stock Industry Sector Prediction Based on Financial Reports Using Random Forest Zhafran, Kamil Elian; Saepudin, Deni
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5743

Abstract

This study aims to predict the stock industry sector on the Indonesia Stock Exchange (IDX) based on financial reports using the Random Forest method. Implementing this machine learning approach is crucial due to the complexity of financial data, which demands robust and adaptive methods for accurate predictions. The dataset comprises financial data from companies across 10 industrial sectors on the IDX, spanning 2010-2022, and includes 17 features from each financial report. Notably, there is an imbalance in the number of companies per sector, with sector B representing 14.76% and sector G only 1.98%. This imbalance introduces bias in data analysis, thus necessitating the application of the SMOTE oversampling method to address it. The research process involves data cleaning, splitting the data into 80% training and 20% testing sets, applying the SMOTE oversampling technique, and comparing predictions from imbalanced and balanced datasets. The Random Forest method is chosen for its capability to handle complex datasets for industrial sector classification. Evaluation results indicate that without oversampling, the model achieves an accuracy of 73.57%, precision of 74.29%, recall of 73.57%, and an F1-score of 73.51%. With oversampling, these metrics improve to an accuracy of 80.21%, precision of 81.34%, recall of 80.21%, and an F1-score of 80.45%.
Clustering-Based Stock Return Prediction using K-Medoids and Long Short-Term Memory (LSTM) Sofyan, Denny; Saepudin, Deni
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.5744

Abstract

This research focuses on predicting stock returns using the K-Medoids clustering method and the Long Short-Term Memory (LSTM) model. The primary challenge lies in forecasting stock prices, which are then converted into return predictions. Clustering is performed to group stocks with similar price movements, facilitating the preparation of data for training the LSTM model within each cluster. This issue is crucial for aiding investors in making more informed investment decisions by leveraging predictions within specific stock clusters. Through clustering with K-Medoids, based on average returns and return standard deviation, the LSTM model is trained to predict daily returns for each stock within different clusters using the average stock price in each cluster. The data is divided into training (2013-2019) and testing (2020-2022) datasets, with model evaluation conducted using Root Mean Square Error (RMSE). The implementation results indicate prediction performance measured by RMSE for each cluster, with Cluster 3 showing the best performance with a testing RMSE of 0.0300, while Cluster 4 exhibited the worst performance with an RMSE of 0.3995. In the formation of an equal weight portfolio, tested from May 2020 to January 2023, the portfolio value grew from 1 to 2.50, with an average return of 0.0014 and a return standard deviation of 0.0158, indicating potential gains with lower risk compared to the LQ45 index.
Co-Authors Abdurrahman Muttaqiin Achmad Fadholy Achmad Rizal Aditya Firman Ihsan Adiwijaya Aisyah Aisyah Alberila Fraida Loceseima Putri Almaya Sofariah Andhika Rama Putra Anggia Parsaoran Exaudi Aniq Antiqi Rohmawati Aniq Atiqi Rohmawati Aniq Rohmawati Anjar Pratiwi Annas Wahyu Ramadhan Annisa Aditsania Annisa Resnianty Anton Sri Haryanto Arfananda, Muhammad Ghifari Arifin Dwi Kandar Saputro Ayunda Firsty Trisnowati Azizah , Nakhwa Benedikto Krisnandy Wijaya Caramoy, Senza Danar Satrio Aji Dara Ayu Lestari Defy Ayu Dewa Made Rai Widyadarma Diah Fitri Wulandari Diani Sarah Kamilial Diani Sarah Kamilial Didit Adytia Dimas Rizqi Guintana Dini Apriliani Lestari Dio Navialdy Egi Shidqi Rabbani Elvina Oktavia Erlina Febriani Esther Laura Christy Fadhlika Hadi Fahmi Muhamad Fauzi Farah Diba Faturachman Nugraha Sasmita Fazlur Rahman Amri Febry Triyadi Fhira Nhita Fikri Nur Hadiansyah Fitriaini Amalia Freyssenita Kanditami P Furqon Hidayat Gharyni Nurkhair Mulyono Ghufron, Sayid Giali Ghazali Gilang Rachman Perdana Gilang Rachman Perdana Hadyatma Dahna Marta Hario Adi Ghufron Herlansyah, Ridhwan Rifky Himatul Zulfa Husain Athfal Hidayat Ihsan Hasanudin Irfan Fauzan Prasetyo Irma Palupi Isman Kurniawan Izzata Izzata Jondri Jondri Kaisa Sekaring Pertiwi Kautsar Abdillah Kemas Muslim Lhaksmana Khoirunnisa Ulayya Kuntjoro Adji Sidarto Lani Rohaeni Laode Muhammad Ali Al-Qomar Lesmana, Rangga Made Larita Ditakristy Mailia Putri Utamil Maulid Fathurachman, Rizaldi Mayriskha Isna Indriyani Mega Silvia Desvi Muhamad Aziz, Reihan Muhammad Fadhil Maulana Muhammad Iqbal Cholil Muhammad Rifqi Arrahim Natadikarta Muhammad Taufiq Raihan Nanda Putri Mintari Narestha Adi Pratama, Putu Agus Naufal Abdurrahman Burhani Nisrina Nur Faizah Novelya Nababan Novi Syafira, Muthia Nur Roza Fitriyana Putri Nuvaisiyah Putu Harry Gunawan Rahmi Putri Amalia Raisa Betha Meiliza Ratih Puspita Furi Rauf, Khalifatur Razaq, Kukuh Sanddi Reima Agustina Kusumawardani Reiza Krisnaviardi Resi Annisa Nur Reza Pratama Rian Febrian Umbara Ridhwan Rifky Herlansyah Rizaldi Maulid Fathurachman Rizq Athariq, Muhammad Sabilla Fitriyantini Saputra, Muhammad Ridho Semeidi Husrin Shabrina Nanggala Sheila Nur Fadhila Sofyan, Denny Sri Rezeki Hardiyanti Susy Sundari Syaifrijal Zirkon Radion Tasya Salsabila Tifani Intan Solihati Triandini Nurislamiaty Triyana Kadarisman Uggi Stivani Savitri Vina Putri Damartya Widyasari, Felicia Dina Yanuar Ishaq Zhafran, I Kamil Elian Zhafran, Kamil Elian