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All Journal Jurnal Simetris International Journal of Advances in Artificial Intelligence and Machine Learning
Adolf Asih Suprianto
Politeknik Enjinering Indorama
Computer Science & IT

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RANCANG BANGUN PROTOTIPE GARDENING SMART SYSTEM (GSS) UNTUK PERAWATAN TANAMAN ANGGREK BERBASIS WEB Deni Kurnia; Adolf Asih Suprianto
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 7, No 1 (2016): JURNAL SIMETRIS VOLUME 7 NO 1 TAHUN 2016
Publisher : Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (630.98 KB) | DOI: 10.24176/simet.v7i1.504

Abstract

Gardening Smart System (GSS) adalah protitipe sistem perawatan tanaman yang pengontrolannya dirancang melalui web. Prototipe ini dimaksudkan untuk membantu pengontrolan sistem perawatan tanaman berjenis anggrek pada tingkat perorangan (personal user) yang kedepannya dapat dikembangkan lebih luas lagi aplikasinya. Perancangan prototipe ini meliputi aspek hardware dan software. Dari sisi hardware prototipe dirancang menggunakan soil mosturise sensor, arduino, ethernet shield dan waterpump, sedangkan dari sisi software prototipe dirancang dengan membangun user interface berbasis HTML dan CSS. Komunikasi data antara software dengan hardware dilakukan melalui IP address yang dapat diakses melalui mobile phone (HP) maupun desktop/PC. Pengujian prototipe diterapkan pada salah satu tanaman anggrek berjenis Phalaenopsis. Setelah dilakukan pengujian pada aspek hardware dan software, hasil yang diperoleh sesuai dengan desain dan spesifikasi yang telah direncanakan. Kedepan, pengembangan prototipe ini masih terbuka lebar untuk mengontrol sistem berbasis web dengan aplikasi lebih kompleks lagi. Kata kunci: aplikasi berbasis web, komunikasi data, IOT (internet of thing).
Constraint-Aware Machine Learning for Ensuring Feasible Predictions in Operational Data Science Lau Meng Cheng; Adolf Asih Suprianto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.796

Abstract

Background: Large-scale machine learning models require substantial computational resources during training, particularly in GPU-intensive and distributed computing environments. Although modern training pipelines achieve high predictive performance, they often rely on static resource allocation strategies that do not adapt to evolving learning dynamics. This leads to inefficient GPU utilization, increased training time, and unnecessary computational cost, limiting the scalability and sustainability of large-scale AI systems.Aims: This study aims to improve training efficiency by proposing a Dynamic Resource Allocation (DRA) framework that integrates real-time learning signals into computational resource management. The framework dynamically adjusts GPU allocation based on convergence behavior, enabling efficient alignment between computational demand and model training stages.Methods: The proposed framework employs a descriptive, analytical, and comparative experimental design, using secondary, confidential machine learning training logs comprising 1,500 training runs. The system integrates a training-monitoring module, a convergence-analysis mechanism, and an adaptive resource controller. Performance evaluation is conducted by comparing static allocation and dynamic allocation strategies using metrics such as training time, GPU utilization, model accuracy, and computational efficiency.Results: Experimental results demonstrate that the proposed framework significantly improves training efficiency. The dynamic allocation strategy reduces training time by approximately 30–32%, increases GPU utilization by up to 17%, and improves overall computational efficiency without degrading model accuracy. Furthermore, the convergence analysis shows that the proposed method achieves faster, more stable convergence than static allocation strategies.Conclusion: The findings confirm that integrating training-aware resource allocation into machine learning pipelines significantly enhances both efficiency and sustainability. By dynamically aligning computational resources with learning behavior, the proposed framework reduces wasteful computation while maintaining predictive performance. This approach provides a scalable solution for efficient large-scale model training in cloud and high-performance computing environments.
Learning from Heterogeneous Label Quality: Strategies for Robust Model Training Wu Shukun; Hafiz Muhammad Kurniawan; Adolf Asih Suprianto
International Journal of Advances in Artificial Intelligence and Machine Learning Vol. 3 No. 2 (2026): International Journal of Advances in Artificial Intelligence and Machine Learni
Publisher : CV Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijaaiml.v3i2.799

Abstract

Background: Training data collected from crowdsourcing platforms, automated labeling systems, and multiple annotators often contain labels with unequal levels of reliability. Conventional machine-learning methods generally treat all labels as equally trustworthy, which can reduce model performance when label errors vary across samples and annotation sources.Aims: This study proposes a quality-aware training method that explicitly models the heterogeneity of label reliability. It examines whether sample-level quality weighting can improve predictive performance and robustness under synthetic and real-world label-noise conditions.Methods: The proposed method combines annotation or neighborhood agreement, loss-based confidence, and source-level reliability to estimate a continuous quality score for each training sample. The scores are incorporated into a weighted cross-entropy objective and periodically updated during training. Experiments were conducted on CIFAR-10, CIFAR-100, CIFAR-10N, and CIFAR-100N using ResNet-18. The method was compared with cross-entropy, label smoothing, generalized cross-entropy, Co-teaching, and DivideMix.Result: Under high heterogeneous noise, the proposed method achieved accuracies of 83.21% on CIFAR-10 and 54.62% on CIFAR-100, compared with 73.48% and 45.92% for conventional cross-entropy. It also achieved the highest accuracy under the evaluated real-world noisy-label conditions. Ablation and sensitivity analyses showed that the combined quality indicators were complementary and that the method remained effective under moderate quality-estimation errors.Conclusion: Explicitly modeling label reliability improves robustness when training data contain unequal annotation quality. The proposed method provides a practical and computationally efficient strategy for learning from crowdsourced, weakly supervised, and multi-source datasets.
Co-Authors Hafiz Muhammad Kurniawan Jayawarsa, A.A. Ketut Lau Meng Cheng Wu Shukun