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Khamdan Annas Fakhryza
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khamdanannasfakhryza@gmail.com
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+628985566531
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editor@pulsejeib.edu.eu.org
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Jl. Kartini No. 228, Kel. Ungaran, Kec. Ungaran Barat, Semarang 50511, Central Java, Indonesia
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Kab. semarang,
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INDONESIA
PULSE — Journal of Energy, Informatics & Biomedicine (JEIB)
Published by PT Khamdan Karya Cipta
ISSN : 31235549     EISSN : 31235549     DOI : -
Core Subject :
Secara substansi JEIB menyatakan fokus pada: * energy systems; * informatics; * data science; * artificial intelligence; * biomedical engineering; * original research; * review articles; * applied studies; * practical, interdisciplinary, reproducible contributions.
Arjuna Subject : -
Articles 12 Documents
 1   2 
Peningkatan Literasi Kesehatan Masyarakat melalui Edukasi Pemanfaatan Kecerdasan Buatan untuk Pengenalan Citra MRI Alzheimer khamdan Annas Fakhryza; Efa Yumna Purwono
PULSE — Journal of Energy, Informatics & Biomedicine Vol 2 No 2 (2026): July 2026 Issue
Publisher : Khamdan Karya Cipta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/pulse-jeib.v2.i2.a11

Abstract

Penyakit Alzheimer dan perkembangan kecerdasan buatan (AI) pada pencitraan medis memerlukan pemahaman yang baik agar teknologi dapat dimanfaatkan secara tepat dan bertanggung jawab. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi kesehatan dan literasi AI melalui edukasi pemanfaatan AI untuk pengenalan citra magnetic resonance imaging (MRI) Alzheimer. Kegiatan dilaksanakan di RSUD dr. Soehadi Prijonegoro Sragen pada 5–26 Agustus 2026 melalui edukasi, demonstrasi, diskusi, dan evaluasi pemahaman peserta. Materi meliputi Alzheimer, fungsi MRI, prinsip convolutional neural network (CNN), klasifikasi citra MRI, serta interpretasi explainable artificial intelligence (XAI) menggunakan Grad-CAM. Bahan demonstrasi menggunakan 23.900 citra MRI dalam empat kategori dengan distribusi kelas yang tidak seimbang. Model CNN menghasilkan akurasi pengujian 80,83%, tetapi menunjukkan perbedaan performa antar kelas, dengan recall Moderate Demented sebesar 0,06 dan Non-Demented sebesar 0,91. Kondisi tersebut digunakan sebagai contoh untuk menjelaskan bahwa akurasi keseluruhan belum cukup dalam memahami kinerja AI. Grad-CAM digunakan sebagai media visual untuk memperkenalkan interpretabilitas model, dengan penegasan bahwa hasil AI merupakan informasi pendukung dan bukan diagnosis klinis. Kegiatan ini diharapkan memperkuat pemahaman peserta mengenai manfaat, keterbatasan, dan penggunaan AI secara kritis serta bertanggung jawab dalam bidang kesehatan.
Interpretable Machine Learning on a Small, High-Quality Experimental Dataset Reveals Structure–Property Trade-Offs in Carbon Anodes for Sodium-Ion Batteries khamdan Annas Fakhryza; Yuli Siyamto
PULSE — Journal of Energy, Informatics & Biomedicine Vol 2 No 2 (2026): July 2026 Issue
Publisher : Khamdan Karya Cipta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/pulse-jeib.v2.i2.a15

Abstract

Carbon-based anodes are promising candidates for sodium-ion batteries due to their structural tunability and cost-effective precursors; however, their electrochemical performance is governed by complex and often non-linear interactions among pore structure, defect chemistry, and structural ordering. In this work, a series of carbon materials derived from pitch, phenolic resin, and biomass precursors were synthesized under controlled carbonization and activation conditions to generate a diverse microstructural space. Comprehensive physicochemical characterization including specific surface area (SSA), pore size distribution, defect degree (ID/IG), nitrogen content, interlayer spacing (d₀₀₂), and tap density was performed to establish correlations with electrochemical behavior. Electrochemical evaluation in sodium half-cells reveals a clear trade-off between reversible capacity, rate capability, and initial Coulombic efficiency (ICE). Materials with higher SSA exhibit enhanced capacity and rate performance due to improved electrolyte accessibility and ion transport, but at the expense of reduced ICE, associated with increased surface reactivity and solid–electrolyte interphase formation. Conversely, denser carbons with lower SSA and higher tap density show improved ICE and cycling stability but limited capacity. To elucidate these structure–property relationships, interpretable machine learning was employed as a complementary analytical framework. The analysis identifies SSA as the dominant descriptor, followed by ICE and interlayer spacing (d₀₀₂), revealing that electrochemical performance is governed by a non-linear interplay between surface-driven storage mechanisms and structural stability. Partial dependence analysis further demonstrates a saturation behavior of capacity at high SSA, while Pareto analysis confirms a fundamentally constrained trade-off between capacity and efficiency. These findings provide experimentally grounded and quantitatively supported design insights, highlighting that optimal performance arises not from maximizing a single parameter, but from balancing surface development, electrochemical reactivity, and structural compactness within a constrained design space.

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