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Researcher · Educator · Software developer

Arif Mu’amar Wahid

PhD Student, Kanazawa University, Japan Lecturer, Universitas Amikom Purwokerto, Indonesia

I study plant bioelectric signals, biological time-series representation learning, reliable machine-learning evaluation, and edge/IoT sensing. My broader work spans data mining, educational analytics, and applied intelligent systems. I also develop web applications and software tools for practical workflows.

MEXT Scholarship recipient · 2nd place, Indoneris Data Mining Competition (2024)

Google Scholar ↗ ORCID ↗ GitHub ↗

Selected publications

Full publication list →
  1. 2026

    A Systematic Review of Plant Bioelectrical Potential Acquisition Systems for Edge Sensing

    Arif Mu'amar Wahid, Hidetaka Nambo

    2026 International Conference on Current Research in Artificial Intelligence and Data Science (ICCRAIDS)

    DOI ↗
  2. 2025

    Transformer-Based Models for Enhanced Plant Bioelectric Signal Classification

    Arif Mu'amar Wahid, Hidetaka Nambo

    2025 9th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

    DOI ↗
  3. 2025

    Deep Learning for Malaria Classification Leveraging GAN-Augmented with Transfer Learning

    Chyntia Raras Ajeng Widiawati, Arif Mu'amar Wahid, Hikmalul A'la Syahrizaldy, Karina Puspita Prameswari

    2025 1st International Conference on Artificial Intelligence Technology (ICoAIT)

    DOI ↗

Research interests

Research overview →

My work focuses on data-driven analysis of biological and other complex signals, with particular attention to how measurements and models can be validated.

Plant bioelectric signals

Plant electrophysiology, potential variations, and responses to environmental stimuli.

Machine learning for time series

Classification, sequence modeling, and reproducible evaluation of biological signal data.

Applied intelligent systems

Multisensor measurement, data mining, and useful computational tools.

Selected research projects

  • GAN-Augmented Deep Learning for Malaria Classification Applied computer-vision research combining GAN-based data augmentation and transfer learning for malaria classification from microscopy images.
  • Transformer-Based Models for Plant Bioelectric Signal Classification Research on machine-learning approaches for classifying plant bioelectric signals, including Transformer-based models for noisy biological time-series data.
  • Plant Bioelectrical Potential Acquisition for Edge Sensing Research on plant bioelectrical-potential acquisition, signal analysis, and sensing architectures for environmental and edge-sensing applications.
Explore all research projects →

Software projects

Software portfolio →

Alongside academic research, I build practical web applications. Case studies distinguish implemented source code from features that have not yet been independently verified.

Recent writing

All articles →

  • Small Changes

    Unexpected positive impacts of integrating daily walking into a busy student routine.

  • When AI Learns to See Malaria

    An accessible explanation of our research on malaria classification using GAN-augmented deep learning.

  • Home for the Holidays

    Reflections on the Eid holiday homecoming (mudik) and rural life.