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All Journal JUITA : Jurnal Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Jurnal Informatika Teknologi dan Sains (Jinteks) Intechno Journal : Information Technology Journal
Kumara Ari Yuana
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering

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MODELING MATEMATIS DAN SIMULASI DROPLET UNTUK PENDINGINAN ALAT-ALAT TEKNOLOGI INFORMASI DAN KOMPUTER DENGAN METODE LATTICE-BOLTZMANN: MODELING MATEMATIS DAN SIMULASI DROPLET UNTUK PENDINGINAN ALAT-ALAT TEKNOLOGI INFORMASI DAN KOMPUTER DENGAN METODE LATTICE-BOLTZMANN Wiji Nurastuti; Kumara Ari Yuana
Jurnal Informatika Teknologi dan Sains Vol 3 No 3 (2021): EDISI 9
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (768.38 KB) | DOI: 10.51401/jinteks.v3i3.1039

Abstract

Abstrak : Kebutuhan inovasi skema pendinginan untuk pemeliharaan perangkat elektronik dengan suhu aman dibawah batas yang telah ditentukan oleh batasan material dan kendala realibilitas yang terkait pada miniaturisasi microchip yang agresif pada komponen elektronik. Pergeseran dari ketergantungan pada sistem berpendingin kipas menjadi ke skema pendinginan yang memanfaatkan pendingin cairan dielektrik menggunakan berbagai skema pendinginan fase tunggal. Perekayasa (engineer) sistem pendingin memusatkan perhatian pada skema pendinginan dua fase, untuk memanfaatkan kedua system pendingin. Sifat yang harus dimiliki perekayasa sistem pendingin ini yaitu konveksi fluida dan panas laten untuk memindahkan jumlah kalor yang jauh lebih besar dari pada skema fase tunggal, sambil mempertahankan suhu perangkat yang lebih rendah. Beberapa skema pendingin cairan dua fase telah direkomendasikan untuk menghilangkan fluks kalor tinggi dari perangkat yang digunakan diaplikasi. Momentum droplet memungkinkan cairan menembus penghalang uap yang dibuat oleh gelembung nukleasi dan secara lebih efektif mengisi kembali permukaan, keduanya sangat bermanfaat untuk pendinginan fluks tinggi. Pada model dan simulasi pengembangan droplet menggunakan metode LBM multi fase, parameter penting yang selalu didapatkan adalah arus semu maksimum (maximum spurious currents) yang menetukan stabilitas komputasi. Kata kunci : Modeling Matematis, Simulasi Droplet, Metode Latice-Boltzman Abstract: The need for innovative cooling schemes for maintaining electronic devices with safe temperatures below predetermined limits by material limitations and reliability constraints associated with aggressive microchip miniaturization of electronic components. Shifting from reliance on fan-cooled systems to cooling schemes that utilize dielectric liquid cooling using a variety of single-phase cooling schemes. The cooling system engineer focuses on two-phase cooling schemes, to take advantage of both cooling systems. Properties that these cooling system engineers must possess are fluid convection and latent heat to transfer a much greater amount of heat than a single-phase scheme, while maintaining a lower device temperature. Several two-phase liquid cooling schemes have been recommended to remove the high heat flux from the apparatus used in the application. The droplet momentum allows the liquid to penetrate the vapor barrier created by the nucleation bubbles and more effectively replenish the surface, both of which are very beneficial for high flux cooling. In droplet development models and simulations using the multi-phase LBM method, an important parameter that is always obtained is the maximum spurious currents which determine the computational stability. Keywords: Mathematical Modeling, Droplet Simulation, Latice-Boltzman Method
METODE DIGITAL IMAGE PROCESSING UNTUK MENENTUKAN DISTRIBUSI UKURAN DIAMETER GELEMBUNG UDARA PADA MICROGELEMBUNG GENERATOR Drajat Indah Mawarni; Indarto Indarto; Deendarlianto Deendarlianto; Kumara Ari Yuana
Journal of Information System Management (JOISM) Vol. 4 No. 2 (2023): Januari
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/joism.2023v4i2.977

Abstract

Penelitian ini dilakukan untuk mengetahui distribusi ukuran diameter gelembung yang dihasilkan oleh alat pembangkit gelembung udara mikro (microgelembung generator/MBG). Gelembung yang dihasilkan direkam dengan menggunakan kamera berkecepatan tinggi (high speed camera). Metode yang digunakan untuk mengetahui ukuran gelembung yang dihasilkan adalah dengan menggunakan metode digital image processing (MDIP). MDIP ini memiliki keunggulan yaitu dapat mengukur dimensi distribusi gelembung pada aliran yang tidak teratur secara akurat serta pada operasinya tidak mempengaruhi aliran yang terbentuk. Hasil akhir dari ekstraksi data image processing ini adalah distribusi diameter microgelembung yang dinyatakan dalam Probability Distribution Function (PDF) dan digunakan untuk mempresentasikan perbandingan disribusi dimensi gelembung udara yang dihasilkan oleh MBG Kata Kunci: Gelembung Udara Mikro, Digital Image Processing, Highspeed Camera, Probability Distribution Function
FOREST FIRE LOCATION AND TIME RECOGNITION IN SOCIAL MEDIA TEXT USING XLM-ROBERTA Hafidz Sanjaya; Kusrini Kusrini; Kumara Ari Yuana; Arief Setyanto; I Made Artha Agastya; Simone Martin Marotta; José Ramón Martínez Salio
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 4 (2025): JITK Issue May 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i4.6194

Abstract

Forest fires have become a serious global threat, significantly impacting ecosystems, communities, and economies. Although remote sensing technology shows potential, limitations such as time delays, limited sensor coverage, and low resolution reduce its effectiveness for real-time forest fire detection. Additionally, social media can serve as a multimodal sensor, presenting multilingual text data with rapid and global coverage. However, it may encounter challenges in obtaining location and time information on forest fires due to limitations in datasets and model generalization. This study aims to develop a multilingual named entity recognition (NER) model to identify location and time entities of forest fires in social media texts such as tweets. Utilizing a transfer learning approach with the XLM-RoBERTa architecture, fine-tuning was performed using the general-purpose Nergrit corpus dataset containing 19 entities, which were relabeled into 3 main entities to detect location, date, and time entities from tweets. This approach significantly improves the model's ability to generalize to disaster domains across multiple languages and noisy social media texts. With a fine-tuning accuracy of 98.58% and a maximum validation accuracy of 96.50%, the model offers a novel capability for disaster management agencies to detect forest fires in a scalable, globally inclusive manner, enhancing disaster response and mitigation efforts.
A Memory-Efficient and Gradient-Stable Lightweight ANFIS for Real-Time Humidity Prediction in Precision Agriculture Eddy Nurraharjo; Ema Utami; Kusrini; Kumara Ari Yuana
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 3, August 2026 (Article in Progress)
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i3.2700

Abstract

Precision agriculture demands artificial intelligence solutions that are both accurate and deployable on resource-constrained hardware, yet conventional machine learning models require excessive memory while traditional ANFIS architectures suffer from training instability. This study developed a memory-efficient and gradient-stable lightweight Adaptive Neuro-Fuzzy Inference System (ANFIS) for real-time humidity prediction on microcontroller-class devices. The proposed architecture strategically reduced the rule base from 27 to only 4 interpretable fuzzy rules and limited membership functions to two per input, achieving an 85.2% reduction in learnable parameters. A gradient-stable training mechanism was introduced, combining physics-informed parameter initialization with adaptive gradient clipping to prevent gradient explosion. The model was trained and validated using 31,474 real-world greenhouse samples collected over 218 days, with 80% allocated for training and 20% for temporal testing. Experimental results demonstrated that the gradient-stable architecture successfully converged from a catastrophic R² of -64.08 to 0.9148, with a root mean square error of 1.32% and mean absolute error of 1.05%. The model required only 0.211 KB of memory, representing a 99.9% reduction compared to baseline Random Forest models, while achieving inference time of 8.2 milliseconds on Arduino UNO. The system was successfully deployed on three independent hardware modules, maintaining consistent performance with average RMSE of 1.99% over 168 hours of continuous operation. This study concludes that strategic simplification and stability-aware training enable interpretable neuro-fuzzy systems to operate effectively on ultra-low-resource devices, bridging the gap between predictive accuracy and hardware feasibility in embedded agricultural IoT applications.
Robust Deep Learning Approach for Automatic Age and Gender Recognition Based on Voice M. Nasyid Yunitian Rizal; Theopilus Bayu Sasongko; Arifiyanto Hadinegoro; Kumara Ari Yuana; Wahid Miftahul Ashari
Intechno Journal : Information Technology Journal Vol. 8 No. 1 (2026): July
Publisher : Universitas AMIKOM Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2026v8i1.2827

Abstract

Voice-based age and gender recognition plays an important role in biometric authentication, personalized human–computer interaction, and digital forensic applications. However, existing single deep learning architectures often struggle to simultaneously capture local acoustic patterns, temporal dependencies, and long-range contextual information from speech signals. This study proposes a hybrid CNN–BiLSTM–Transformer framework to improve the accuracy and robustness of multi-class age and gender classification. The proposed approach employs Mel-spectrogram representations generated from the Mozilla Common Voice dataset, followed by audio standardization and feature extraction. A Convolutional Neural Network (CNN) enhanced with Squeeze-and-Excitation blocks extracts discriminative spectral features, a Bidirectional Long Short-Term Memory (Bi-LSTM) network models bidirectional temporal dependencies, and a Transformer Encoder captures global contextual relationships through a multi-head self-attention mechanism. The model was evaluated on 40,392 speech samples across 12 age–gender categories. Experimental results achieved an overall classification accuracy of 91%, outperforming standalone CNN and Bi-LSTM models, which obtained accuracies of 84% and 74%, respectively. In addition, the proposed model demonstrated balanced performance with macro-average precision, recall, and F1-scores of 0.91, 0.92, and 0.91. The novelty of this research lies in the integration of complementary spatial, temporal, and global attention mechanisms within a unified architecture for large-scale multi-class voice-based demographic classification, providing an effective and scalable solution for intelligent biometric and speech analysis systems.
ANFIS Kuantisasi Ringan yang Terkoordinasikan di Tepi untuk Prediksi Iklim Mikro Adaptif dalam Sistem IoT yang Dibatasi Energi Eddy Nurraharjo; Ema Utami; Kusrini Kusrini; Kumara Ari Yuana
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.30160

Abstract

This study addresses the challenge of deploying collaborative adaptive intelligence on energy-constrained IoT edge nodes, a critical requirement for real-time microclimate prediction in smart agriculture. We propose a novel Edge-Coordinated Lightweight Quantized Adaptive Neuro-Fuzzy Inference System (LQ-ANFIS). The framework combines a quantized fuzzy neural model for local inference on 8-bit microcontrollers (WeMOS D1 Mini) with a lightweight MQTT-based coordination mechanism. This mechanism enables distributed nodes to achieve synchronized adaptation by periodically exchanging only scalar parameters, namely bias and learning rate, through a broker (Raspberry Pi), thereby eliminating the need for cloud infrastructure or heavy model transfers. During a seven-day experimental deployment involving three nodes, each collecting more than 10,000 temperature and humidity samples, the system demonstrated robust prediction accuracy (RMSE ≈ 1.89%, MAE ≈ 1.13%) and high energy efficiency (average power < 100 mW per node). Compared with a conventional uncoordinated ANFIS baseline, the proposed method achieved a 21% improvement in prediction accuracy and a 39% reduction in energy consumption. The results confirmed rapid inter-node bias convergence (σ < 0.05) and low coordination latency (< 50 ms), validating its real-time adaptability at the edge. These findings support scalable, cooperative intelligence for resource-constrained agricultural IoT deployments worldwide.
Co-Authors Arief Setyanto Arifiyanto Hadinegoro, Arifiyanto Ashari, Wahid Miftahul Deendarlianto Drajat Indah Mawarni Eddy Nurraharjo Eddy Nurraharjo Ema Utami Hafidz Sanjaya, Hafidz I Made Artha Agastya Indarto Indarto José Ramón Martínez Salio Kusrini M. Nasyid Yunitian Rizal Sasongko, Theopilus Bayu Simone Martin Marotta Wiji Nurastuti