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All Journal Bulletin of Computer Science Research Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi
Iskandar, Muhammad Irsyad
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Other

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Prediksi Pola Pergerakan Saham Adro.Jk Melalui Model LSTM Berbasis Data Historis Iskandar, Muhammad Irsyad; Mudzakir, Tohirin Al; Cahyana, Yana; Pratama, Adi Rizky
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.554

Abstract

The fluctuating nature of stock price movements presents a significant challenge in investment decision-making. To address this issue, a predictive model capable of capturing historical patterns and accurately forecasting stock prices is required. This study aims to develop a stock price prediction model for PT Alamtri Resources Indonesia Tbk (ADRO.JK) using the Long Short-Term Memory (LSTM) algorithm. The dataset comprises daily closing prices from January 1, 2020, to December 30, 2024, obtained from Yahoo Finance. The data was processed in a time series format using a sliding window approach, employing 30 historical data points to predict the next price point. The model was constructed using two LSTM layers, one Dense layer, and techniques such as Dropout and EarlyStopping to prevent overfitting.The training and testing results indicate that the model performs exceptionally well, achieving a Mean Absolute Percentage Error (MAPE) of 0.0341 or 3.41%, corresponding to a prediction accuracy of 96.59%. In a short-term prediction scenario over seven days, the model achieved an accuracy of 99.07% (MAPE = 0.0093), while in a medium-term scenario up to May 19, 2025, it achieved an accuracy of 98.76% (MAPE = 0.0124). The predicted stock price on May 19, 2025, is estimated at IDR 1,913.76. With its high accuracy and low error rate, the LSTM model has proven to be a reliable tool for forecasting stock prices based on historical data.
Konten Reels Instagram @half.spacemedia terhadap Tingkat Keterlibatan Digital Audiens Iskandar, Muhammad Irsyad; Santoso, Hudi; Dharmawan, Leonard
Edutik : Jurnal Pendidikan Teknologi Informasi dan Komunikasi Vol. 6 No. 1 (2026): EduTIK : Februari 2026
Publisher : Jurusan PTIK Universitas Negeri Manado

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Abstract

Penelitian ini bertujuan untuk menganalisis pengaruh konten Reels Instagram pada akun @half.spacemedia terhadap keterlibatan digital audiens. Pendekatan penelitian yang digunakan adalah kuantitatif dengan metode survei terhadap 83 responden yang merupakan pengikut aktif akun tersebut. Instrumen penelitian diuji melalui uji validitas dan reliabilitas, dan seluruh item pernyataan dinyatakan valid serta reliabel. Hasil uji linearitas menunjukkan adanya hubungan linear antara variabel konten Reels dan keterlibatan audiens dengan nilai signifikansi 0.000 (<0.05). Analisis regresi linear sederhana menghasilkan nilai koefisien determinasi (R²) sebesar 0.539, yang menunjukkan bahwa sebesar 53.9% variasi keterlibatan digital audiens dapat dijelaskan oleh konten Reels Instagram, sedangkan sisanya dipengaruhi oleh faktor lain di luar penelitian. Persamaan regresi Ŷ = 5.288 + 0.672X mengindikasikan adanya pengaruh positif dan signifikan antara konten Reels dan keterlibatan audiens. Dengan demikian, konten Reels Instagram terbukti memiliki peran yang kuat dalam meningkatkan keterlibatan digital audiens.
Co-Authors Leonard Dharmawan Mudzakir, Tohirin Al Pratama, Adi Rizky Santoso, Hudi Yana Cahyana