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Bahtiar Imran
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bahtiarimranlombok@gmail.com
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+6285337626083
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bahtiarimranlombok@gmail.com
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Perumahan Green Asia Blok I2-04, Kecamatan Labuapi, Kabupaten Lombok Barat Nusa Tenggara Barat, Indonesia
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Kab. lombok barat,
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INDONESIA
Jurnal Computer and Technology
Published by CV. Ninety Media Publisher
ISSN : -     EISSN : 30481880     DOI : https://doi.org/10.69916/comtechno
Core Subject : Science,
Computer Science & IT
Jurnal Computer and Technology or abbreviated Comtechno is a national journal published by the Ninety Media Publisher since 2023 with E-ISSN : 3048-1880. Comtechno focuses on various issues spanning: Internet of Things (IoT), electronics engineering, software engineering, mobile technology and applications, robotics, database system, information engineering, artificial intelligence, interactive multimedia, computer networking, information system audit, accounting information system, information technology investment, information system development methodology, strategic information system (business intelligence, decision support system, executive information system, enterprise system, knowledge management), e-learning, and e-business (e-health, e-commerce, e-supply chain management, e-customer relationship management, e-marketing, and e-government). All submissions are blind and reviewed by peer reviewers. All papers can be submitted in BAHASA INDONESIA or ENGLISH.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 42 Documents
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A Dual-Pipeline Imbalance-Robust Framework for SMS Spam Detection: Achieving Flawless Precision via SMOTE-Augmented Ensembles with Rigorous Statistical Validation Zulpan Hadi; Selamet Riadi; Supardianto; Aulia Riswanti Naya; Liana Trihardianingsih
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.510

Abstract

The rapid proliferation of digital communication has exponentially increased the volume of Short Message Service (SMS) spam, exposing mobile users to systemic convenience disruptions, productivity drops, and severe financial losses through sophisticated fraudulent schemes. To construct a highly dependable filtering mechanism, this study presents a rigorous dual-pipeline machine learning framework that systematically addresses the challenges of class imbalance in statistical text mining. Utilizing a verified dataset of 5,572 Indonesian-context short messages, the raw textual corpus is subjected to uniform case normalization, structural URL extraction, and character filtering before feature projection via Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To overcome the inherent accuracy paradox of skewed class distributions, the experimental design evaluates a baseline pipeline (imbalanced data) against a synthetic data augmentation pipeline leveraging the Synthetic Minority Oversampling Technique (SMOTE) across four distinct classifiers: Logistic Regression, Naive Bayes, Linear Support Vector Machine (Linear SVM), and Random Forest. Empirical results demonstrate that while the baseline Linear SVM serves as the optimal standalone model for overall balance, achieving a peak accuracy of 98.11% and a dominant F1-Score of 92.83%, the SMOTE-augmented Random Forest configuration yields an exceptional high-security alternative by securing a flawless 100.00% precision envelope alongside an 83.89% recall rate. Advanced post-hoc evaluations including McNemar's statistical significance tests (,  for Random Forest), qualitative error analyses of semantic edge cases, and runtime profiling confirm that the developed architecture establishes a highly scalable, mathematically verified, and low-latency solution suitable for integration into real-time telecom filtering gateways.
A Leakage-Aware Ensemble Framework for Imbalanced Tabular Data: Mitigating SMOTE Contamination in Hotel Cancellation Prediction Gibran Maulana Syamroni; Bahtiar Imran; Surni Erniwati; Zaeniah; Wenti Ayu Wahyuni
Journal Computer and Technology Vol. 4 No. 1 (2026): July 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/comtechno.v4i1.514

Abstract

Predictive modeling on large-scale, imbalanced tabular data is frequently compromised by target leakage and improper resampling, leading to inflated performance metrics. While tree-based ensemble methods like Random Forest (RF) and XGBoost are widely deployed, their architectural divergence in handling complex behavioral anomalies under strict class imbalance remains underexplored. This study proposes a leakage-aware ensemble framework to mitigate SMOTE contamination and target leakage in hotel cancellation prediction. Using a rigorous CRISP-DM pipeline on 119,390 records, we applied SMOTE exclusively to the training set and engineered six behavioral features to capture non-linear contradictions, such as the counter-intuitive 99.36% cancellation rate in non-refund deposits. We systematically benchmarked RF (bagging) against XGBoost (boosting) using stratified 5-fold cross-validation, hyperparameter optimization, and loss curve monitoring. Results demonstrate that XGBoost structurally outperforms RF in minority-class detection, achieving superior Recall (0.6659), F1-Score (0.6607), and AUC-ROC (0.8657), with significantly lower variance (0.0035). Conversely, RF exhibited higher Precision (0.6588) and better cross-validation stability during hyperparameter search. Crucially, feature importance analysis revealed a structural divergence: RF prioritized temporal variables (lead_time), while XGBoost emphasized behavioral commitment signals (parking, special requests). These findings confirm that gradient boosting’s sequential residual-correction mechanism is inherently more robust than variance-reduction bagging for imbalanced tabular data containing complex, non-linear anomalies. The proposed leakage-free framework not only resolves methodological flaws in prior studies but also provides a reliable, proactive risk-scoring foundation for integrating real-time decision support systems in production environments.

Page 5 of 5 | Total Record : 42

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