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All Journal Dinamik Jurnal Teknologi Informasi dan Ilmu Komputer JOIN (Jurnal Online Informatika) JMM (Jurnal Masyarakat Mandiri) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Seminar Nasional Teknologi Informasi Komunikasi dan Administrasi [SEMINASTIKA] International Journal of Advances in Data and Information Systems Madaniya Bulletin of Information Technology (BIT) Jurnal Ilmiah SIGMA: Informatics Engineering Journal of UPB Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Pelita Pengabdian Jurnal Ilmu Komputer dan Informatika TECHNOMEDIA : Informatics and Computer Science
Antika Zahrotul Kamalia
Universitas Pelita Bangsa
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences Other

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Pemodelan Prediksi Harga Saham Emas ANTAM Menggunakan Gated Recurrent Unit dan Regresi Linear Berganda pada Time Series Kamalia, Antika Zahrotul; Wahyu Tri Utami; Arif Susilo
Tekompedia : Jurnal Ilmiah Ilmu Komputer Vol 3 No 1 (2026): Januari
Publisher : CV Nature Creative Innovation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58641/technomedia.v3i1.185

Abstract

This study investigates the prediction of PT Aneka Tambang Tbk (ANTM) stock prices using time-series data by comparing two approaches: the Gated Recurrent Unit (GRU) model and multiple linear regression. The dataset consists of daily historical ANTM data collected from Yahoo Finance spanning 2014–2024, which was preprocessed (including cleaning/normalization) and split chronologically into training (70%) and testing (30%) sets to preserve realistic forecasting conditions. Model performance was assessed using R-squared (R²), Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), supported by visual comparisons between actual and predicted values. The results indicate that the GRU model achieves superior predictive performance and better captures the dynamic and non-linear behavior of stock price movements compared to multiple linear regression. These findings suggest that GRU is more suitable for ANTM stock price forecasting in a time-series setting, while multiple linear regression remains useful as a simple and interpretable baseline model.
Hybrid Relevance and Sentiment Classification of Indonesian Gold Tweets Using Machine Learning for Market Risk Signal Extraction Kamalia, Antika Zahrotul; Indra, Indra; Wibowo, Arief; Riwurohi, Jan Everhard; Hassan, Shiza
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1517

Abstract

This study proposes a hybrid relevance–sentiment classification framework to analyze public opinion on physical Antam gold from Indonesian Twitter data and to support exploratory market-risk signal extraction. Tweets were collected during February–November 2025, after preprocessing and text-normalized deduplication, 1,271 unique tweets were retained. The approach combines weak supervision (rule-/lexicon-based silver labels) with TF-IDF-based machine learning in two stages: (1) relevance classification to separate tweets genuinely discussing physical Antam gold from non-relevant contexts (e.g., ANTM stock/capital-market discussions), and (2) two-class sentiment classification (positive vs negative) applied to relevance-filtered tweets. Random Forest achieved the strongest relevance performance (Accuracy = 0.984; macro-F1 = 0.943; 5-fold CV macro-F1 = 0.928 ± 0.033). For sentiment classification, performance was moderate and close across models; the most stable model under cross-validation (Logistic Regression/Naive Bayes) was used for downstream aggregation. Sentiment outputs were aggregated into a monthly sentiment index for descriptive comparison with gold prices; the observed association was weak, indicating that the index is better interpreted as a risk-perception proxy rather than a direct price predictor.
Klasifikasi Kondisi Pasar Harga Emas ANTAM Indonesia Menggunakan Algoritma Decision Tree Antika Zahrotul Kamalia; Choiriyatun Nisa Latansa; Zaenur Rozikin
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.800

Abstract

This study aims to classify Indonesian ANTAM gold market states using a Decision Tree model built on daily price data from 2010–2024. Market conditions are categorized into three classes: bullish, bearish, and sideways, based on forward returns with an adaptive quantile-based thresholding scheme. The feature set comprises multi-horizon rolling volatility indicators (e.g., std_5, std_10, std_20) and momentum measures (e.g., mom_5, mom_10, mom_20). A time-based split is applied, allocating 80% of observations for training and 20% for testing. Evaluation on the test set yields an accuracy of 0.337 with a macro-F1 of approximately 0.34, indicating limited predictive performance in a three-class setting. Interpretability analysis reveals that std_20 is the most influential feature, followed by std_10 and mom_5, while one-day returns contribute marginally. These findings suggest that aggregated volatility and momentum patterns are more informative than single-day fluctuations for market regime mapping. Overall, the Decision Tree is best positioned as an interpretable baseline for transparent market-state analysis, providing a foundation for future work involving richer features and more robust models.
Co-Authors Adi Rusdi Widya Agus Suwarno Agus Suwarno, Agus Ahmad Amrullahalhafizh Ahmad Turmudi Zy Amali Amali Amali, Amali Ananto Tri Sasongko Andi Al Zaroni Andriani Arief Wibowo Arif Susilo Aswan Supriyadi Sunge Buana, Aliman Fijar Choiriyatun Nisa Latansa Dedi Afandi Edy Widodo Elvan Aan Pradana Hassan, Shiza Hemdani Rahendra Herlianto Ikhsan Romli Imawan, Alifia Putri Indra Indra Ismamudi, Ismamudi Khalis Sofi Mahesa Rezky Ramadhan Miftah Wangsadanureja Nawangsih, Ismasari Noval Adi Iryanto Noval Adi Iryanto Nurhadi Surojudin Nursinta Afrianti Permana, Agung Nabawi Prameswari R, Shanti Prasetyo, Dwi Indra Qoriah Ar Raudhatul Hasanah Rahendra Herlianto, Hemdani Rangga Purnama Riady, Sasmitoh Rahmad Rijal Akmaludin Rijal Akmaludin Rozikin, Zaenur Ruwirohi, Jan Everhard Septianto, Riski Setiyadi Wicaksono Subarkah, Asuy Suderajat, Agung Sulaeman, Asep Arwan Wahyu Tri Utami Wiyarno Wiyarno Wiyarno, Wiyarno Yefta Ondi Spalanzani Simatupang Zaenur Rozikin Zulfa, Hikmatul