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All Journal Jurnal Ilmu dan Teknologi Kelautan Tropis Lontar Komputer: Jurnal Ilmiah Teknologi Informasi ILMU KELAUTAN: Indonesian Journal of Marine Sciences Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Sisfokom (Sistem Informasi dan Komputer) Jiko (Jurnal Informatika dan komputer) JURNAL SUMBER DAYA AIR Charity : Jurnal Pengabdian Masyarakat Indonesian Community Journal eProceedings of Engineering LOGIC: Jurnal Penelitian Informatika
Didit Adytia
Telkom University
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Prediksi Return Saham Berdasarkan Data Histori dan Data Fundamental Menggunakan LSTM Naufal Abdurrahman Burhani; Saepudin, Deni; Didit Adytia
LOGIC: Jurnal Penelitian Informatika Vol. 3 No. 1 (2025): September 2025
Publisher : Universitas Telkom

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/logic.v3i1.9236

Abstract

Prediksi harga saham merupakan tantangan signifikan dalam dunia keuangan karena sifat pasar yang dinamis dan kompleks. Penelitian ini bertujuan untuk memprediksi harga saham dengan menggunakan model Long Short-Term Memory (LSTM), yang efektif dalam menangkap pola sekuensial pada data historis dan fundamental. LSTM dipilih karena kemampuannya untuk memodelkan ketergantungan jangka panjang, yang penting dalam prediksi harga saham. Evaluasi dilakukan dengan membandingkan kinerja model LSTM pada data historis dan kombinasi data historis dengan data fundamental. Hasil penelitian menunjukkan bahwa meskipun model LSTM berhasil menangkap pola pada data training, model tersebut mengalami overfitting yang signifikan pada data testing. Penambahan data fundamental tidak secara signifikan meningkatkan akurasi prediksi pada fase testing. Kesimpulannya, meskipun LSTM membantu dalam menangkap pola kompleks dari data historis dan fundamental, penelitian ini menekankan pentingnya eksplorasi lebih lanjut terhadap metode lain untuk mengatasi overfitting dan meningkatkan kinerja prediksi saham pada data testing.
Implementation of CRNN Method for Lung Cancer Detection based on Microarray Data Khoirunnisa, Azka; Adiwijaya, -; Adytia, Didit
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1339

Abstract

Lung Cancer is one of the cancer types with the most significant mortality rate, mainly because of the disease's slow detection. Therefore, the early identification of this disease is crucial. However, the primary issue of microarray is the curse of dimensionality. This problem is related to the characteristic of microarray data, which has a small sample size yet many attributes. Moreover, this problem could lower the accuracy of cancer detection systems. Various machines and deep learning techniques have been researched to solve this problem. This paper implemented a deep learning method named Convolutional Recurrent Neural Network (CRNN) to build the Lung Cancer detection system. Convolutional neural networks (CNN) are used to extract features, and recurrent neural networks (RNN) are used to summarize the derived features. CNN and RNN methods are combined in CRNN to derive the advantages of each of the methods. Several previous research uses CRNN to build a Lung Cancer detection system using medical image biomarkers (MRI or CT scan). Thus, the researchers concluded that CRNN achieved higher accuracy than CNN and RNN independently. Moreover, CRNN was implemented in this research by using a microarray-based Lung Cancer dataset. Furthermore, different drop-out values are compared to determine the best drop-out value for the system. Thus, the result shows that CRNN gave a higher accuracy than CNN and RNN. The CRNN method achieved the highest accuracy of 91%, while the CNN and RNN methods achieved 83% and 71% accuracy, respectively.
Co-Authors Aditya Firman Ihsan Adiwijaya Adiwijaya, - Alif Rizal Yonanta Andonowati Andonowati Aniq Atiqi Rohmawati Annas Wahyu Ramadhan Arionmaro Asi Simaremare Arkan Priya Anggana Hadna Arnida Lailatul Latifah Azka Khoirunnisa Cornelius Stephanus Alfredo Dede Tarwidi Deni Saepudin Derry Destian Dio Navialdy E. van Groesen Egi Shidqi Rabbani Faishal Rasyid Fakhrurroja, Hanif Fhira Nhita Four Saputra BM Hadi, Risyad Faisal Huda Bachtiar I Putu Samskerta Indira Citra Widya Indra A Aditya Isman Kurniawan Kemas Muslim Lhaksmana Leo Sembiring Lukman Hardian Mahmud Dwi Sulistiyo Mohamed, Raihani Muhammad Adzhar Amrullah Muhammad Rafiuddin Naufal Abdurrahman Burhani Navialdy, Dio Nopendri Nopendri Novalianda Jeriano Nugrahinggil Subasita Putu Harry Gunawan R. Fajrika hadnis Putra Radika Rafif Gibran Rasyadan Farouq Riam Badriana sa'adah, siti Semeidi Husrin Semeidi Husrin Seno Adi Putra Setyorini Setyorini Sharef, Nurfadhlina Mohd Tatang Mulyana Tien Fabrianti Kusumasari Widianto Soekarnen Yuninda, Alifa Puspa Zamani, Nabila Wardah ZK Abdurahman Baizal