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Tri A. Sundara
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The Indonesian Journal of Computer Science
Published by STMIK Indonesia Padang
ISSN : 25497286     EISSN : 25497286     DOI : https://doi.org/10.33022
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering Engineering
The Indonesian Journal of Computer Science (IJCS) is a bimonthly peer-reviewed journal published by AI Society and STMIK Indonesia. IJCS editions will be published at the end of February, April, June, August, October and December. The scope of IJCS includes general computer science, information system, information technology, artificial intelligence, big data, industrial revolution 4.0, and general engineering. The articles will be published in English and Bahasa Indonesia.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,193 Documents
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A Hybrid CNN-Transformer Architecture with Bidirectional Cross-Attention Fusion for Efficient Flower Recognition Hersh HAMA; Shayan Jalal; Saman Omer; Mohammed Ahmed
The Indonesian Journal of Computer Science Vol. 15 No. 3 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v15i3.5181

Abstract

Automated flower recognition is crucial to the fields of agriculture and biodiversity monitoring, but deep learning models are extremely high in computing demand for resource-constrained devices. In this paper, a compact and efficient hybrid model based on EfficientNet-B4 and ViT-Small/16 is introduced. The design uses a bidirectional cross-attention fusion mechanism that uses both local edge-level features and global context to learn highly discriminative representations for fine-grained classification. The model was tested using 3,500 images from 35 species of flowers from a curated, augmented database, using 5-fold cross-validation. It outperformed the state-of-the-art architectures such as Swin Transformer and ResNet50, with 98.71% accuracy and a 98.80% F1 score with high statistical stability (95% CI:98.34%–99.09%) and with faster convergence rate. Statistically, these improvements were confirmed by pair-wise and Wilcoxon signed-rank statistical tests, which indicate the model's potential in resource-efficient real-world automated plant identification.
Design and Implementation of an Offline-Capable Drone Tracking System for Agricultural Spraying Verification Using GPS and Multi-Level Water Tank Monitoring Handri Santoso; Haryono
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Agricultural spraying drones have become an effective solution for increasing productivity and spraying uniformity in modern agriculture. However, landowners often face difficulties verifying whether spraying operations have actually been performed within designated areas. Conventional verification methods rely heavily on manual observation, which is labor-intensive, time-consuming, and prone to inaccuracies. This research presents the design and implementation of an offline-capable Drone Tracker system capable of recording drone operational activities through the integration of high-precision GPS tracking and multi-level water tank monitoring. The proposed system records location, speed, positioning quality, and tank liquid level every second, storing all information locally on an SD Card. Since agricultural areas frequently lack reliable internet connectivity, a store-and-forward synchronization mechanism is implemented, allowing data to be uploaded automatically once internet access becomes available. Experimental implementation demonstrates that the system can provide objective evidence of spraying activities while maintaining reliable operation in remote agricultural environments. The proposed solution improves transparency, accountability, and operational monitoring of agricultural drone services.
TrustPhish-AI: Safety-Gated Source-Aware Evaluation of Three-Class Phishing Detection Under Email-to-SMS Shift Akam Aziz
The Indonesian Journal of Computer Science Vol. 15 No. 4 (2026): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Source shift can make phishing detectors appear stronger while concealing unsafe class-specific errors. This study evaluated a three-class workflow (Legitimate, Suspicious, and Phishing) using source-aware governance, controlled augmentation, family-disjoint diagnostics, and pre-specified safety gates. The frozen training set contained 3,732 records from four source families. Evaluation used a locked 584-record validation set, a 587-record internal test, a 788-record SMS compound test, and a 200-record SpamAssassin family-disjoint diagnostic. Classical word/character TF-IDF logistic regression with cell-balanced augmentation achieved internal macro-F1 of 0.9633 and compound macro-F1 of 0.5461. DistilRoBERTa augmentation improved compound macro-F1 by 0.1524 versus its reference (95% CI 0.1143-0.1914), but reduced phishing recall by 0.2374 and failed the severe-error gate. A hierarchical transformer repair achieved internal macro-F1 of 0.9668 and compound macro-F1 of 0.5119; its compound gain was 0.1774 (95% CI 0.1410-0.2152), while phishing recall remained 0.2323 below the reference. Governance review included 492 dual-human consensus records, eight independently adjudicated disagreements, and protected-overlap screening. Candidate replacement sources were excluded under provenance, licensing, channel, privacy, taxonomy, or overlap criteria, so no records were added after the training freeze. The results show that source-aware augmentation can improve targeted and aggregate metrics, but aggregate gains are insufficient when phishing-recall and severe-error safety criteria fail. The study supports a governance and evaluation contribution, not global source resistance or autonomous deployment.

Page 120 of 120 | Total Record : 1193

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