Article Per Year (5 Year)
p-Index From 2021 - 2026
3.022
P-Index
This Author published in this journals
All Journal Jusikom : Jurnal Sistem Komputer Musirawas SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan JOURNAL OF SCIENCE AND SOCIAL RESEARCH Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Community Development Journal: Jurnal Pengabdian Masyarakat JPM: JURNAL PENGABDIAN MASYARAKAT Journal Software, Hardware and Information Technology Jurnal Pengabdian Masyarakat IPTEK Jurnal Komtekinfo Jurnal Masyarakat Madani Indonesia Electronic Journal of Education, Social Economics and Technology Innovative: Journal Of Social Science Research Jurnal Informatika Polinema (JIP) Jurnal Pengembangan Teknologi Informasi dan Komunikasi (JUPTIK) JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Feri Irawan
Teknik Informatika, Universitas Maritim Raja Ali Haji
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Veterinary Other

Published : 22 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 22 Documents
Search

 1   2   3 
PREDIKSI PENUTUPAN HARGA SAHAM HARIAN MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM BERBASIS FUZZY C-MEANS Aras Hardi Cusinia; Tekad Matulatan; Feri Irawan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.6824

Abstract

This study develops a stock price prediction model based on the Adaptive Neuro-Fuzzy Inference System integrated with Fuzzy C-Means clustering (ANFIS-FCM) for three information technology stocks listed on the Indonesia Stock Exchange: Anabatic Technologies (ATIC), Elang Mahkota Teknologi (EMTK), and Metrodata Electronics (MTDL). Daily OHLC data from 2015 to 2025 were transformed using a five-day sliding window and normalized with Min-Max scaling. Fuzzy C-Means was used to generate cluster centers for the fuzzy inference system, avoiding the rule explosion problem associated with grid partitioning on twenty input features. Hyperparameters (number of rules, epochs, and learning rate) were tuned separately for each stock using grid search based on validation RMSE. Testing results show MAPE values of 1.95% for ATIC, 3.01% for EMTK, and 1.64% for MTDL, all well below the 10% accuracy threshold commonly used in time-series forecasting. Model performance was found to be data-dependent, with the lowest error obtained for the most stable stock and the highest for the most volatile one.
PERBANDINGAN KINERJA LONG SHORT-TERM MEMORY (LSTM) DAN BIDIRECTIONAL LONG SHORT-TERM MEMORY (BiLSTM) PADA PREDIKSI HARGA PENUTUPAN SAHAM PERBANKAN INDONESIA Muhammad Septi; Tekad Matulatan; Feri Irawan
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 9 No. 4 (2026): August 2026 (1)
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i4.6898

Abstract

Pergerakan harga saham yang bersifat fluktuatif dan nonlinier menuntut penggunaan model prediksi yang mampu menangkap pola deret waktu secara efektif. Penelitian ini bertujuan membandingkan kinerja metode Long Short-Term Memory (LSTM) dan Bidirectional Long Short-Term Memory (BiLSTM) dalam memprediksi harga penutupan saham sektor perbankan Indonesia, yaitu BBCA, BBRI, dan BMRI. Data yang digunakan berupa data historis harian yang diperoleh dari Yahoo Finance periode 1 Januari 2019 hingga 30 Desember 2024 dengan jumlah 1.469 data untuk setiap emiten. Tahapan penelitian meliputi preprocessing, uji korelasi, normalisasi Min-Max, pembentukan data menggunakan sliding window, pembagian data secara kronologis dengan proporsi 70% data pelatihan, 15% data validasi, dan 15% data pengujian, serta hyperparameter tuning. Kinerja model dievaluasi menggunakan Root Mean Square Error (RMSE), Mean Absolute Error (MAE), dan Mean Absolute Percentage Error (MAPE). Hasil penelitian menunjukkan bahwa model LSTM memberikan performa terbaik pada saham BBRI dengan MAPE 1,85% dan BMRI dengan MAPE 1,57%, sedangkan model BiLSTM menghasilkan performa terbaik pada saham BBCA dengan MAPE 1,57%. Secara keseluruhan, kedua model mampu menghasilkan prediksi dengan tingkat akurasi yang tinggi (MAPE < 2%), namun model yang optimal bergantung pada karakteristik masing-masing saham.
Co-Authors Akhir Abadi Tanjung Aldo Eko Syaputra Aras Hardi Cusinia Asfahani, Asfahani Ayumi Chairunisa Basyaruddin Ismail Bendra Wardana Daniel Sinaga Deviyanti, Wulan Efranda, Nolan Euis Ammelia Fortia Magfira Fortia Magfira Hollanda Arief Kusuma Iwan Adhicandra Januardi Rosyidi Lubis, Marisha Pertiwi Marisha Pertiwi Matulatan, Tekad Muhamad Fadli Muhammad Fajar Fajri Fardilah Muhammad Septi Nikentari, Nerfita Nolan Efranda Nolan Efranda Novrizal Fattah Fahmitra Novrizal Fattah Fahmitra Perra Budiarti Rayahu Putri Purnomo, Nopi Putri, Perra Budiarti Rahayu Raden Mohamad Herdian Bhakti Rahayu, Perra Budiarti Rahmi Herawati Harahap Raja Muda Harahap Rifaldi Herikson Rifaldi Herikson Sepli Yandri Siregar, Selamat Riadi Siti Syifa Alifah SLAM, Berta Erwin Yuskana Harahap Yustida Bellini