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All Journal Jurnal Simetris Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) BIOEDUSAINS: Jurnal Pendidikan Biologi dan Sains INOVTEK Polbeng - Seri Informatika
Feddy Setio Pribadi
Universitas Negeri Semarang
Computer Science & IT Education Engineering

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Penggunaan Metode Random Forest, Support Vector Machine dan Artificial Neural Networks dalam Prediksi Suhu Udara di Balikpapan Erika Meinofelia; Mochamad Aryono Adhi; Achmad Fahruddin Rais; Djunaidi Djunaidi; Feddy Setio Pribadi
BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains Vol. 8 No. 6 (2025): BIOEDUSAINS:Jurnal Pendidikan Biologi dan Sains
Publisher : Institut Penelitian Matematika, Komputer, Keperawatan, Pendidikan dan Ekonomi (IPM2KPE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31539/9s0xxr05

Abstract

This study aimed to compare the performance of three algorithmic models, namely Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN), in predicting air temperature in Balikpapan. Changes in air temperature influenced by various climatic and geographical factors present a major challenge in urban planning; thus, accurate predictions are crucial to support sustainable and climate-adaptive city planning. The dataset used consists of observational data from the Balikpapan Meteorological Station, BMKG, over ten years, from January 2014 to December 2024. The models were evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R Squared (R²) metrics. The results show that the SVM method produced an MAE of 0.17, RMSE of 0.21, and R² of 0.95, providing better predictions than ANN and Random Forest. In conclusion, SVM is an effective method for air temperature prediction in Balikpapan. Keywords: Artificial Neural Networks, Random Forest, Support Vector Machine, Machine Learning, Air Temperature Prediction
The Role of Big Data Analytics and Artificial Intelligence in Business Strategy: A Systematic Review Nazli Rahmeisi; Mu'alfi Fahrul Fanani Sachroni; Yehezkiel Nesta Andyanto; Rizky Ajie Aprilianto; Feddy Setio Pribadi
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 16 No. 2 (2025): JURNAL SIMETRIS VOLUME 16 NO 2 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i2.14909

Abstract

The accelerating digital transformation across industries has intensified the need for datadriven approaches in strategic business management. This study conducts a Systematic Literature Review (SLR) to examine how Big Data Analytics (BDA) and Artificial Intelligence (AI) influence business strategy formulation, risk management, and organizational competitiveness. Guided by the PICOC framework and PRISMA 2020 protocol, 27 peer-reviewed journal articles published between 2020 and 2024 were analyzed through thematic synthesis and bibliometric visualization using VOSviewer. The results indicate that BDA and AI enhance strategic decision-making, operational efficiency, and risk mitigation through predictive insights and real-time analytics. However, their strategic integration remains limited due to socio-technical challenges such as inadequate analytical capability, weak data governance, and organizational resistance. The review highlights that the true strategic value of BDA and AI emerges when these technologies are embedded within long-term strategic planning, data governance, and sustainability frameworks, rather than treated merely as operational tools. This study contributes to strategic management literature by synthesizing cross-sectoral evidence and offering insights into how data-driven intelligence fosters long-term competitiveness and sustainable business transformation.
Modelling, Simulation, and Analysis of Sequence-Based Models for Smart Lighting Voice Command Classifiers with MFCC-Based Data Augmentation Yohanes Batara Setya; Feddy Setio Pribadi
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/r4p60871

Abstract

Voice command classification is essential for smart lighting systems in IoT environments. However, existing approaches often struggle in real-world scenarios with background noise and speaker variability due to limited and imbalanced training data. This indicates a need for models that maintain high accuracy under such conditions. To address this, the study evaluates three deep learning architectures: a Deep Neural Network (DNN), a Gated Recurrent Unit (GRU), and a bidirectional Long Short-Term Memory (LSTM) network, run on the Google Speech Commands dataset. The classification targets six voice commands (“right”, “off”, “left”, “on”, “down”, “up”) using Mel-Frequency Cepstral Coefficients (MFCCs) as features. Data augmentation techniques, including pitch shifting, time stretching, mix-up, and noise injection, are used to expand the dataset, balance class distributions, and simulate acoustic conditions such as background noise and speaker differences. Model performance is assessed through confusion matrices and receiver operating characteristic curves (ROC-AUC) across training, validation, and test sets. The bidirectional LSTM achieves the highest test accuracy (94%), followed by GRU (92%) and DNN (79%). The LSTM model also generalizes well, showing no signs of overfitting and maintaining stable performance in the presence of acoustic variation. These results suggest that combining bidirectional LSTM with MFCC-based augmentation provides a more robust approach to voice command recognition, particularly in IoT-based smart lighting contexts, where environmental variability is common.
CVI-Validated Indo-Transformer Framework for Intelligent Cooperative Supervision Syahroni Hidayat; Afriani Fajar Navissaturrisqi; Feddy Setio Pribadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7606

Abstract

Cooperative supervision reports contain complex narrative structures and overlapping administrative terminology, complicating automatic classification into governance, risk profile, financial performance, and capital adequacy. Reliable automation is particularly important for accelerating the analysis of supervisory findings while addressing limited labeled data and imbalanced categories. This study aimed to develop and externally evaluate a text-classification framework combining quantitatively validated Generative Artificial Intelligence (GenAI) labeling with conventional and Transformer-based models. Data comprised 294 preprocessed sentences collected from the Department of Cooperatives, Small and Medium Enterprises, Industry, and Trade of Semarang Regency during 2023–2025. Few-shot annotations were generated using ChatGPT, Gemini, Perplexity, and DeepSeek, and three-model combinations were evaluated using the Content Validity Index (CVI); majority voting from the best combination established ground truth. TF-IDF with Logistic Regression and Support Vector Machine served as baselines, whereas IndoBERT and IndoRoBERTa represented contextual models. Performance was assessed through stratified five-fold cross-validation and external testing on 58 unseen sentences. ChatGPT–Gemini–Perplexity achieved the highest Scale-Level CVI of 0.898. IndoBERT obtained the best cross-validated F1-score of 0.9099, exceeding IndoRoBERTa (0.8217), Logistic Regression (0.8004), and SVM (0.7863). On unseen data, IndoBERT retained an F1-score of 0.862, compared with 0.759 for IndoRoBERTa. These findings demonstrate that CVI-validated ensemble GenAI can construct consistent labels for low-resource administrative texts and that IndoBERT provides the strongest and most stable generalization for cooperative supervision classification. The framework offers a practical basis for scalable annotation and reliable automated support for evidence-based supervisory decision-making.
Co-Authors Achmad Fahruddin Rais Afriani Fajar Navissaturrisqi Djunaidi Djunaidi Erika Meinofelia Mochamad Aryono Adhi Mu'alfi Fahrul Fanani Sachroni Nazli Rahmeisi Rizky Ajie Aprilianto Syahroni Hidayat Yehezkiel Nesta Andyanto Yohanes Batara Setya