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Sistem Pendukung Keputusan dengan Aplikasi
Published by Ali Institute of Research and Publication
ISSN : 28292820     EISSN : 28292189     DOI : https://doi.org/10.55537/spk
Core Subject : Science,
Decision Sciences, Operations Research & Management
Artikel yang diterbitkan dalam Sistem Pendukung Keputusan dengan Aplikasi adalah relevansinya dengan masalah teoretis dan teknis dalam mendukung pengambilan keputusan yang ditingkatkan. Naskah dapat diambil dari beragam metode dan metodologi, termasuk dari teori keputusan yang didukung komputer.
Arjuna Subject : Ilmu Pengambilan Keputusan (semua kategori) - Ilmu Pengambilan Keputusan (Lain-Lain)
Articles 41 Documents
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Prediksi Gap-Up Saham Berbasis Logistic Regression Menggunakan Indikator Teknikal Anisa, Yuan; Hafiz, Muhammad; Hadijah, Hadijah; Gani, Abdul
Sistem Pendukung Keputusan dengan Aplikasi Vol 4 No 2 (2025)
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/spk.v4i2.1334

Abstract

This research aims to construct and test a predictive model for the gap-up phenomenon in stocks on the Indonesia Stock Exchange (IDX), with a case study on PT Bank Mandiri Tbk. (BMRI) stock. The research uses a quantitative approach by applying a Binary Logistic Regression model to analyze 1,213 daily historical data points from January 1, 2019, to January 1, 2024.Five technical analysis-based independent variables—vol_spike, rsi, prev_return, macd, and stochastic—were used to predict the probability of a stock gap-up occurrence.The analysis results show that the model as a whole is statistically significant (LLR p-value < 0.05), with an LLR p-value of (8.544e-11) and a Pseudo R-squared of 0.03389. Of the five variables, stochastic, macd, and prev_return were identified as significant predictors. Specifically, a high Stochastic Oscillator value has a strong positive influence on the probability of a gap-up. On the other hand, Moving Average Convergence Divergence (MACD) and Previous Return show a significant negative influence.These findings provide empirical evidence that a combination of technical indicators can be used to model and predict stock price movements at market opening. The implications of this research offer valuable insight for investors who rely on technical analysis as a basis for decision-making.

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