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JUTI: Jurnal Ilmiah Teknologi Informasi
Published by Institut Teknologi Sepuluh Nopember Surabaya
ISSN : 24068535     EISSN : 14126389     DOI : http://dx.doi.org/10.12962/j24068535
Core Subject : Health, Science, Education,
Computer Science & IT Dentistry Education Library & Information Science
JUTI (Jurnal Ilmiah Teknologi Informasi) is a scientific journal managed by Department of Informatics, ITS.
Arjuna Subject : -
Articles 408 Documents
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Comparative analysis of deep learning models for multi-horizon rainfall forecasting in flood-prone tropical highlands Supri Amir; Amran Rahim; Fitrah Ramadhan; Edy Saputra; Octavian
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1518

Abstract

Accurate rainfall forecasting is critically important for the effective mitigation of floods and the management of water resources in tropical highland regions that serve as principal upstream catchment areas for major reservoirs. Heavy periods of rainfall in the upper watershed can exceed a reservoir’s capacity for storage and release, thus contributing to the recurrence of flooding in downstream urban areas and their surrounding regions. This study addresses this practical model-selection problem by systematically comparing Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Fusion Transformer  (TFT), and AutoTFT with multiple daily forecasting horizons for rainfall prediction. Historical rainfall records were combined with meteorological variables to develop the forecasting models. Model performance was evaluated across short- and medium-term forecasting horizons using Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). The results indicate that AutoTFT consistently achieved the lowest MAE across all forecasting horizons, ranging from 6.75 mm (1-day) to 7.79 mm (14-day). At the 3-day and 9-day horizons, AutoTFT also produced the lowest MSE (201.36 and 215.91 mm²) and RMSE (14.19 and 14.69 mm), demonstrating superior predictive accuracy. At the 7-day and 14-day horizons, TFT slightly outperformed AutoTFT in terms of RMSE (14.66 and 14.88 mm, respectively), although AutoTFT maintained the lowest MAE. Meanwhile, LSTM achieved the lowest RMSE (12.79 mm) at the 1-day horizon, indicating competitive performance for very short-term forecasting. Overall, the transformer-based models, particularly AutoTFT and TFT, consistently outperformed the recurrent architectures at medium- and long-range forecasting horizons, highlighting their potential as reliable and generalizable approaches for rainfall forecasting in flood-prone tropical highlands and supporting more effective early warning systems and water resource management.
Bean leaf disease classification using two-phase transfer learning with MixUp and classical machine learning Syah Alam Afif Sahubawa; Kumara Ari Yuana
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1519

Abstract

Bean leaf disease classification remains challenging due to limited annotated datasets and high visual variability in lesion size, color intensity, illumination, leaf orientation, and background conditions. Although convolutional neural networks and transfer learning have shown promising results, many existing bean leaf disease classification methods still rely on single-stage fine-tuning and end-to-end softmax classification, which may reduce generalization on small agricultural datasets. Furthermore, the combined use of staged transfer learning, MixUp regularization, multi-level deep feature extraction, and classical machine learning classifiers remains insufficiently explored. This gap highlights the need for a more robust and validation-controlled framework to improve classification performance under limited-data conditions. Therefore, this study proposes a hybrid classification framework that integrates two-phase VGG19 transfer learning, validation-selected MixUp regularization, multi-level deep feature extraction, and classical machine learning classifiers. In the first phase, the pretrained VGG19 backbone is frozen to train a tasks-pecific classification head, while in the second phase, selected deeper layers are fine-tuned to adapt high-level representations to bean leaf disease patterns. Deep features extracted from multiple representation levels are classified using SVM, KNN, and LightGBM under a strict validation-based model selection protocol. Experiments on the public IBean dataset show that the proposed hybrid approach outperforms the end-to-end CNN baseline. The best configuration, combining two-phase transfer learning, MixUp regularization, and LightGBM, achieves 98.44% test accuracy on the IBean dataset. Additional validation on an independent Bean Leaf Disease Dataset achieves 98.33% test accuracy. These findings indicate that the proposed framework is effective for improving bean leaf disease classification performance.
Evaluating deterministic asynchronous disk benchmarking using the linux asynchronous I/O Rahmalia Syahputri; Rizky Juniardi
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1539

Abstract

Accurate storage performance characterization in virtualized environments requires benchmarking tools with minimal runtime overhead. Conventional asynchronous I/O introduces scheduling overhead and latency variability, degrading measurement stability. This study designs and evaluates Calyx, a deterministic disk benchmarking tool built on Linux io_uring that integrates submission queue polling, fixed resource registration, and an allocation-free I/O path. Evaluation on HyperV virtualized NVMe compared Calyx against fio. Under sequential reads on ext4, Calyx achieves 4,576.92 MB/s throughput with 0.18% CV and 3,979.54 μs P99 latency, versus fio's 4,560.49 MB/s, 0.73% CV, and 5,210.11 μs P99 latency. For sequential writes on XFS, Calyx maintains 3,320.09 MB/s (2.25% CV, 9,529.98 μs P99) while fio drops to 1,388.92 MB/s (49.57% CV, 39,059.46 μs P99), which is plausibly attributed to SSD cache exhaustion. CPU utilization for Calyx scales flatly from 49.5% (QD=8) to 53.1% (QD=64), whereas fio scales linearly from 35.2% to 59.8%. Calyx's deterministic pipeline reduces measurement variability without sacrificing throughput, confirming that registered-resource execution improves benchmarking reliability in virtualized systems.
Understanding linguistic patterns of depression and anxiety in Indonesian social media text using machine learning classification and topic modeling Dita Pramesti; Hilda Nuraliza
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1547

Abstract

The increasing prevalence of mental health disorders such as depression and anxiety calls for effective approaches to analyze psychological expressions in textual data. This study explores the linguistic markers of depression and anxiety in Indonesian social media text through an integrated model of machine learning classification and topic modeling. In contrast to earlier work primarily centered on classification performance, this work emphasizes interpretability through comparative machine learning analysis and LDA-based thematic analysis. Classification determines the expressed condition, LDA determines thematic structures that account for distinguishing patterns beyond accuracy metrics alone. The dataset consisted of 17,096 records collected from Facebook groups, reduced to 7,199 instances after removing neutral labels. TF-IDF-based feature extraction was applied using unigram and bigram representations with a maximum of 5,000 features. A comparative analysis was conducted using six classification algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest, Decision Tree, XGBoost, and Naïve Bayes, evaluated under three train-test split scenarios (70:30, 80:20, and 90:10). SMOTE was applied to the training data to address class imbalance. SVM achieved the best performance with an F1-score of 0.903 under an 80:20 split, followed by Naïve Bayes and XGBoost, while KNN performed lowest consistently. LDA topic modeling revealed that depression-related texts were dominated by internal emotional expression, social isolation, and suicidal ideation, whereas anxiety-related texts were characterized by sudden fear, somatic physical symptoms, and social anxiety. These findings suggest that combining classification with topic modeling offers a practical foundation for developing early detection tools and Indonesian-language NLP resources for mental health discourse analysis.
Hybrid U-Net and ConvLSTM-based spatial forecasting of marine heatwave intensity and occurrence probability Daniar Ihza Carundyatama; Yosafat Donni Haryanto; Avrionesti; Purwanti Lelly Sabrina
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1552

Abstract

Marine Heatwaves (MHW) are extreme sea surface temperature events that significantly affect marine ecosystems, fisheries, and coral reef environments, highlighting the need for accurate forecasting systems in vulnerable regions such as the Lesser Sunda Islands. This study aims to develop a hybrid deep learning-based MHW prediction system by integrating U-Net and ConvLSTM models over the regions of Bali, West Nusa Tenggara, and East Nusa Tenggara. The datasets used consist of NOAA OISST V2.1 daily sea surface temperature data for the period 1985–2024, along with Niño 3.4 and Dipole Mode Index (DMI) atmospheric indices. The U-Net model was applied to predict Sea Surface Temperature Anomaly (SSTA) intensity, while ConvLSTM was used to estimate the probability of MHW occurrence. The results demonstrate that the best forecasting accuracies of the U-Net intensity model for 1-, 3-, 5-, and 7-day lead times reached 0.8896, 0.8597, 0.8462, and 0.8674, respectively, with optimal thresholds of 0.92 °C, 0.56 °C, 0.51 °C, and 0.31 °C. The ConvLSTM probability model produced maximum FAR values of 0.9101, 0.8962, 0.8941, and 0.8854 with optimal probability thresholds of 0.25, 0.1, 0.1, and 0.1. RMSE evaluation increased gradually from 0.27 °C on day-1 to 0.94 °C on day-7 forecasts. Overall, the hybrid deep learning framework demonstrated robust and stable performance in representing both the intensity and probability of MHW events up to a seven-day forecasting horizon.
End-to-end encryption scheme for text and media exchange using AESGCM-SIV and elliptic curve diffie-hellman Akhdan Rafif Nugraha; Nur Widiyasono; Randi Rizal
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1556

Abstract

The exchange of text messages and media files in modern digital communication demands an end-to-end encryption (E2EE) scheme that simultaneously guarantees confidentiality,  integrity, authenticity, forward secrecy, and resilience to nonce mismanagement. The widely deployed AES-GCM cipher is known to collapse catastrophically under nonce reuse, enabling plaintext recovery via ciphertext XOR and universal authentication tag forgery, while prior studies have not integrated AES-GCM-SIV, ECDH Curve25519, and  HKDF-SHA256 into a unified scheme covering both text and media with comprehensive quantitative measurement. This study aims to design, implement, and evaluate an E2EE scheme based on an ECDH Curve25519, HKDF-SHA256, and AES-256-GCM-SIV pipeline across security and performance dimensions. A quantitative comparative experimental approach was employed using a Rust-based simulation program with 30 iterations per algorithm–data combination across twelve test categories (text of 100–2,000 bytes; image, audio, and video files up to 33.69 MB), accompanied by five security scenarios under the Dolev–Yao threat model (eavesdropping, tampering, replay attack, nonce reuse, and Man-in-the-Middle) and an ablation study of each cryptographic component. The results demonstrate that the proposed scheme satisfies every claimed security property within the defined threat model and tested scenarios, whereas the AES-GCM baseline fails the nonce-reuse scenario. On small data, AES-GCM-SIV incurs a 33.7%–51.6% encryption-time overhead; yet on media files it outperforms the baseline with 11.3%–16.9% higher throughput (peaking at 14.8% on large video), while memory consumption and size overhead remain identical. The ablation study confirms that each component delivers an irreplaceable security contribution. This research contributes empirical evidence that the combination of ECDH Curve25519, HKDF-SHA256, and AES-256-GCM-SIV is a viable cryptographic pipeline for E2EE simulation schemes resilient to implementation errors.
Density-aware reliability association for training-free FDTA enhancement in multi-object tracking Ignatius Aris Wibowo; Hilmil Pradana; Ahmad Saikhu
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1594

Abstract

Multi-object tracking (MOT) in crowded scenes is difficult because objects frequently occlude each other and share similar appearances, leading to identity confusion. End to-end trackers based on the From Detection to Association (FDTA) architecture reduce inter-object embedding errors but remain susceptible to ID Switches (IDSW) when ID confidence falls briefly during crowd traversal. This paper proposes a training-free density-aware association module that operates on top of a frozen FDTA model at inference time. Identity recovery activates only when both local density and frame-level detection count exceed fixed thresholds, preventing false merges in sparse regions. The method also includes a geometry-based offline tracklet linker that reconnects fragmented trajectories using bounding box constraints alone, with no learned components. On the DanceTrack validation set, the approach reduces IDSW by 13% and raises Higher Order Tracking Accuracy (HOTA) from 64.36 to 65.39, Association Accuracy (AssA) to 57.15, and ID F1 Score (IDF1) to 71.47. On the official test server, the method scores 72.01 HOTA and 78.21 IDF1
CO emission prediction in rotary dryers: a temporal fusion transformer approach Aulya Sri Utami Ilham; Anny Yuniarti
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 2, July 2026
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i2.a1599

Abstract

Carbon monoxide (CO) emission control in rotary dryer systems is critical for preventing explosive incidents within the Electrostatic Precipitator (ESP), particularly when emission levels approach the threshold of 10,000 ppm. Conventional reactive control systems relying solely on automatic shutdown upon threshold exceedance are insufficient for proactive hazard mitigation. This study proposes the application of the Temporal Fusion Transformer (TFT) for multi-horizon prediction of CO gas emissions in a nickel drying rotary dryer system, representing the first such application in this industrial context. The dataset comprised 691,182 entries of operational parameters and laboratory results from a nickel smelter, preprocessed using the Savitzky-Golay filter to reduce signal noise while preserving critical data features. The TFT model was evaluated against five benchmark models: Generalized Additive Model (GAM), Neural Network Regression (NNR), Bagged Regression Tree (BRT), Linear Support Vector Machine (LSVM), and Long Short-Term Memory (LSTM), using RMSE, MAPE, and R-squared as the evaluation metrics. The TFT achieved superior performance with an RMSE of 2.42, MAPE of 1.58%, and R-squared of 0.999, substantially outperforming all competing models. Beyond its predictive accuracy, the TFT variable selection network provides interpretable insights into the operational parameters that most strongly influence CO emission levels, enabling data-driven decision making for process operators. These results demonstrate that TFT effectively transforms emission control from a reactive to a proactive, prediction-based paradigm, thereby minimizing production disruptions while enhancing process safety.

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