Science, Technology, and Communication Journal

Systematic literature review using deep learning in plant genomic prediction

Rizki Darmawan — Mon, 01 Jun 2026 00:00:00 +0700

Genomic prediction (GP) is a critical approach in plant breeding used to forecast agronomic traits based on genetic marker data. In recent years, machine learning and deep learning methods have been increasingly applied in genomic prediction to address the limitations of traditional methods such as genomic best linear unbiased prediction (GBLUP). This systematic literature review aims to evaluate research trends over the past decade (2015–2025) regarding the application of deep learning in genomic prediction for crops, encompassing publication trends, publication sources, dataset types, key research topics, methods/algorithms used, implementation schemes, and commonly used evaluation metrics. The study follows the PRISMA protocol, with literature searches conducted on the primary databases Scopus and SpringerLink. From thousands of identified articles, 43 studies meeting the inclusion criteria were selected. The data revealed a significant increase in publications since 2018, peaking in 2024. The majority of articles were published in open-access journals, with notable contributions in Frontiers in Plant Science, Scientific Reports, and G3. The research covers various crop types, including wheat, maize, rice, soybean, sugarcane, and others, with diverse deep learning schemes such as convolutional neural networks (CNN), Ensemble Stacking, and Multi-Task Learning, often compared to conventional methods. The synthesis indicates that deep learning frequently enhances the accuracy of genomic prediction, particularly for complex multi-environment or multi-trait data. However, in some cases, classical linear/Bayesian models remain competitive. The application of deep learning in plant genomic prediction is rapidly advancing and demonstrates significant potential for improving crop selection performance.

Security audit of a pharmacy information system using blackbox testing and CIA triad: A case study

Rahmalia Syahputri, M Rivaldi Arwin Hadi Wijaya — Wed, 03 Jun 2026 00:00:00 +0700

Pharmacy information systems are essential for managing drug inventory, sales, financial reports, and user administration, yet they are exposed to security risks like data manipulation, account misuse, and information leakage. This study integrates Blackbox Testing and the CIA Triad (Confidentiality, Integrity, Availability) to audit a pharmacy application. Testing employed 19 security scenarios, supported by tools such as SQLmap, Burp Suite, OWASP ZAP, and Apache JMeter to detect vulnerabilities without accessing source code. Results show that the system meets availability requirements and provides audit logging for user activity monitoring. However, confidentiality and integrity weaknesses were identified: input validation allowed illogical data like negative stock, potential SQL Injection existed on the login page, and password encryption was insufficient. Strengthening input sanitization, adopting strong encryption, and enhancing authentication are necessary to close security gaps and improve system reliability.

QoS aware traffic shaping for TikTok live streaming over congested LAN using MikroTik queue tree

Diah Risqiwati, Wiryawan Ananta Pratama Panigoro, Hanugra Aulia Sidharta — Thu, 04 Jun 2026 00:00:00 +0700

Real time video streaming applications such as TikTok Live are highly sensitive to network instability, especially under bandwidth contention on shared access networks. This paper evaluates a Quality of Service (QoS)-aware traffic shaping scheme for TikTok live streaming in a congested local area network using a MikroTik router with a queue tree configuration. TikTok traffic is identified and assigned minimum bandwidth guarantees and highest priority, while non streaming traffic is treated as best effort. Network performance is assessed under two scenarios: baseline (without shaping) and experiment (with shaping), sing the TIPHON standard and four QoS parameters: throughput, delay, jitter, and packet loss. The experimental results show that although the average throughput only increases slightly from 70.67 Kbps to 80 Kbps and remains in the “Poor” category, the proposed scheme significantly improves temporal QoS metrics: average delay is reduced from 88.65 ms to 45.16 ms (“Very Good”), jitter decreases from 95.61 ms to 89.80 ms (“Good”), and packet loss drops from 7.66% to 3.78% (“Good”). These findings indicate that priority-based traffic shaping using a queue tree can effectively stabilize latency and data delivery for TikTok live streaming on bandwidth-limited networks without requiring capacity upgrades.

Robust cryptocurrency price forecasting using a Bayesian-optimized CNN–LSTM hybrid model

Tue, 09 Jun 2026 00:00:00 +0700

The rapid growth of cryptocurrency has caused the price movements of digital assets such as Bitcoin (BTC) and Ethereum (ETH) to become highly volatile and difficult to predict. This study aims to develop a cryptocurrency price prediction model using a hybrid convolutional neural network–long short-term memory (CNN-LSTM) architecture optimized with Bayesian Optimization. The data used in this study consisted of daily historical data for Bitcoin and Ethereum from January 1, 2018, to December 31, 2025, obtained from Yahoo Finance. The research stages included data preprocessing, normalization using Min-Max Scaling, sequence generation using the sliding window method (window sizes of 30, 60, and 90), CNN-LSTM model development, hyperparameter optimization using Bayesian Optimization (30, 50, and 100 trials), and evaluation using regression metrics including MSE, RMSE, MAE, MAPE, and R². The results showed that the hybrid CNN-LSTM model outperformed the standalone CNN and LSTM models, with RMSE reductions of 27% – 59% for BTC and 18% – 19% for ETH. For Bitcoin data, the best model was obtained using 30 trials with a window size of 30, achieving an RMSE of $2,588.33, MAE of $2,004.23, MAPE of 1.99%, and R² of 0.9468. Meanwhile, for Ethereum data, the best model was obtained using 50 trials with a window size of 60, achieving an RMSE of $138.56, MAE of $99.75, MAPE of 3.27%, and R² of 0.9737. These results indicate that the combination of CNN-LSTM and Bayesian Optimization is effective for predicting cryptocurrency prices with non-linear and volatile characteristics.

An IndoBERT-based framework for emotion classification in Indonesian song lyrics

Wed, 10 Jun 2026 00:00:00 +0700

Emotion classification in song lyrics represented a significant research area within natural language processing, yet studies targeting Indonesian-language lyrics remained scarce due to the limited availability of labeled datasets and the absence of domain-specific models. This study developed and evaluated an emotion classification model for Indonesian song lyrics using fine-tuned IndoBERT-base-p2, a transformer-based language model pre-trained on a large Indonesian corpus. A dataset of 1,025 labeled lyric entries was compiled from Kaggle, Genius, and KapanLagi, covering four emotion categories: joy, sadness, fear, and anger. Preprocessing encompassed duplicate removal, case folding, structural marker removal, and non-alphabetic character cleaning. Nine fine-tuning experiments were conducted by systematically varying learning rate and dropout rate, with early stopping applied based on validation loss. The optimal configuration employed a learning rate of 3 × 10^-5 and a dropout rate of 0.1, achieving 75.73% accuracy and 75.85% macro-averaged F1-score on the held-out test set. Joy and anger were classified most reliably, attaining F1-scores of 82.76% and 76.47% respectively, while sadness presented the greatest challenge, exhibiting the lowest precision of 64.10% alongside a recall of 80.65%, indicating a systematic tendency of the model to over-predict this class. These findings demonstrated that IndoBERT-base-p2, when fine-tuned with appropriate hyperparameter configuration, served as an effective approach for domain-specific emotion classification in Indonesian song lyrics.

Interpretative comparative analysis of LSTM and random forest for multi-label classification of English Qur’an translation

Nur Delifah, Nazruddin Safaat Harahap, Okfalisa Okfalisa, Elvia Budianita — Thu, 11 Jun 2026 00:00:00 +0700

The rapid growth of digital Qur'anic resources has created a need for automated systems capable of accurately categorizing verses by thematic content. The thematic complexity of Qur'anic text, in which a single verse may simultaneously convey multiple moral, spiritual, and social messages, presents a significant challenge for automated classification systems. This study conducts a comparative and explainable evaluation of long short-term memory (LSTM) and random forest (RF) for multi-label classification of English Qur'an translations across six thematic categories: arkanul Islam, iman, amal, human and community relations, akhlak, and history and story. To address severe class imbalance, synthetic minority over-sampling technique (SMOTE) was applied per label, expanding the training set from 4,489 to 19,658 samples. LSTM captured sequential contextual relationships through integer token embeddings, while RF relied on TF-IDF vector representations. Evaluated on 1,248 unseen test verses, RF achieved a higher macro F1-score (0.2748) compared to LSTM (0.2432), while LSTM retained marginally higher accuracy (79.61% vs. 79.55%). Per-label analysis revealed that both models performed best on lexically explicit labels such as arkanul Islam and iman, but consistently failed on abstract categories such as akhlak, where LSTM recorded near-zero recall of 0.61% and RF only 6.10%. This study contributes empirical evidence that TF-IDF-based SMOTE interpolation is more effective for minority-class augmentation than token-sequence interpolation, and demonstrates that macro F1-score is a more appropriate evaluation metric than accuracy for imbalanced multi-label religious text classification.

Workflow reproducibility and observability layer for Tomofast-x gravity inversion experiments

I Wayan Pio Pratama — Fri, 12 Jun 2026 00:00:00 +0700

Tomofast-x is an open-source parallel platform for gravity and magnetic inversion; however, reproducible execution, runtime observability, and workflow traceability are commonly managed outside the inversion software itself. This study presented a provenance-aware and telemetry-aware experimentation environment for reproducible Tomofast-x workflows and evaluated it using four publicly available reference scenarios archived on Zenodo. Each scenario was reproduced three times, resulting in 12 platform-managed runs executed through isolated workspaces with structured provenance and telemetry collection. The reproduced solutions showed close agreement with the reference outputs, with mean absolute root mean square error differences ranging from approximately 2.47 × 10^-11 to 4.72 × 10^-9. Runtime telemetry revealed substantial operational differences between scenarios. The uncompressed baseline required approximately 10.9 GB peak memory, whereas compressed scenarios required approximately 202 – 247 MB. Runtime decreased from approximately 436 s in the baseline case to approximately 300 – 340 s in compressed executions. Telemetry was successfully collected for all runs, including processor utilization, observed process-level RAM, runtime progression, and message passing interface (MPI) worker activity. Workflow robustness was further evaluated using injected failure cases involving corrupted parameter files, missing data grids, invalid execution paths, and simulated MPI failures. The results demonstrated that the proposed platform provided reproducible, provenance-aware, and telemetry-aware experimentation support for workstation-scale Tomofast-x workflows.