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Banana Peel-Derived Activated Carbon/MnO₂ Composites for Supercapacitor Electrode Applications
Herwanti, Katarina
; Saptono Nugrohadi
; Muhammad Taufiq Anwar
; Stanislaus Christo Petra Nugraha
; Dail Umamil Asri
; Noening Andrijati
Banana peel waste is an abundant agricultural by-product with high carbon content but remains largely underutilized. This study presents a preliminary investigation into the conversion of banana peel waste into activated carbon and its integration with manganese dioxide (MnO₂) to produce biomass-derived composite electrodes for supercapacitor applications. Activated carbon was prepared by carbonization followed by chemical activation using 30 wt% H₃PO₄, while MnO₂–carbon composites were synthesi...
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Data-Driven Machine Learning Models for Steam Turbine Efficiency Prediction
Increased electricity consumption in Indonesia requires coal-fired power plants to operate at high efficiency. This study proposes a data-driven approach to predict the efficiency of coal-fired steam turbines by utilizing 844 historical operational data points from the XYZ coal-fired power plant. Input variables include main steam pressure and temperature, main steam flow, final feedwater temperature, and condenser vacuum pressure. Four machine learning algorithms, namely Random Forest, Extra Tr...
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Prototype and Validation of an IoT-Based Voltage and Temperature Monitoring System for Remote ISP Point-of-Presence (POP) Infrastructure
Monitoring electricity and temperature is crucial for maintaining PoP server performance. This research developed an Internet of Things (IoT)-based tool using a NodeMCU ESP32 to directly monitor these two factors. The ZMPT101B sensor measures electrical voltage, while the DHT22 sensor monitors the temperature of the room and server rack. Information obtained from the sensor is sent in real time via a Telegram Bot to the administrator. This tool is particularly useful for PoP located remotely, as...
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Loading-Dependent Physicochemical Characteristics of LiMn2O4 Composites with Plasma-Modified and Ammonia-Functionalized Rice Husk Carbon
Harianingsih, Harianingsih
; Deni Fajar Fitriyana
; Januar Parlaungan Siregar
; Agung Budiwirawan
; Ari Dwi Nur Indriawan
; Suryo Wiroyudho Wibowo
; Rizky Ilham Fadzillah
; Nabila Khoirunisa
This study investigates LiMn2O4 composites incorporated with nitrogen-functionalized rice husk-derived carbon as a sustainable secondary phase for cathode material development. Rice husk carbon was prepared through carbonization, acid-assisted activation, plasma treatment, and ammonia functionalization, then mechanically blended with LiMn2O4 at 2, 3, and 4 wt.% to obtain LMO-NC2, LMO-NC3, and LMO-NC4, respectively. FTIR analysis showed absorption bands at approximately 3390, 1625, 1400, 1008, 83...
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Dynamic Chaotic–Adversarial Framework for High-Capacity and Imperceptible Image Steganography
Sari, Wellia Shinta
; Sari, Christy Atika
; Setiyani, Safira Hasna
; Triyono, Agus
; Ali, Rabei Raad
The rapid growth of digital communication has intensified concerns regarding data confidentiality as sensitive information transmitted through multimedia images is increasingly vulnerable to interception and unauthorized analysis. Conventional image steganography methods often struggle to simultaneously achieve high embedding capacity, strong imperceptibility, and resistance to modern steganalysis. To address this challenge, this study proposes a steganographic framework that integrates dynamic...
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Lightweight Dual-Layer Chaotic Image Encryption Using Arnold Cat Map and Henon Zigzag Diffusion
Umam, Chaerul
; Abdussalam, Abdussalam
; Nursetyo, Arif
; Sugiarto, Bambang
; Islam, Husain Md Mehedul
Digital image transmission over open networks raises significant security concerns due to the high correlation and predictable statistical properties of image data. Existing chaotic encryption schemes based on Arnold Cat Map (ACM) and Henon mapping often suffer from high computational cost, parameter sensitivity, or reliance on complex multi-stage designs. To address these limitations, this study proposes a lightweight dual-layer chaotic image encryption framework that integrates ACM-based pixel...
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Microwave-Assisted Self-Healing of AC-WC Modified with Iron Powder: Mechanical Performance and Healing Rate
Amalia Firdaus Mawardi
; Machsus Machsus
; Dadang Supriyatno
; Achmad Faiz Hadi Prajitno
; Muhammad Fikri Nadhif
; Hazen Masrafat
Conventional asphalt mixtures have limited microwave absorption, reducing the effectiveness of microwave-assisted self-healing. This study evaluates the effect of iron powder (0–10% by mass of fine aggregate) as additional fine aggregate in AC-WC mixtures on mechanical performance and microwave-activated healing behavior. Cylindrical specimens (63 mm × 100 mm; three per mixture) were tested using Marshall Stability and Indirect Tensile Strength (ITS) at 25 °C. Healing efficiency was determined b...
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Evaluating Disruption Risk to Foster Resilience in Humanitarian Supply Chain: An Integrated SV-PSI and EDAS Model
Sutrisno, Agung
; Spreafico, Christian
; Putro, Muhammad Dwisnanto
; Yusupa, Ade
; Tjolleng, Amir
; Lokaputra, Kenji
The evaluation of the risks in humanitarian supply chain operations is crucial to prevent losses due to disasters. However, many quantitative studies in the literature are aimed at the profit-oriented supply chain, while those in the field are based on the subjective preferences of decision-makers, making risk prioritization less accurate. To fill this gap, this study presents a theoretically improved approach for this purpose by integrating statistical variance, preference selection index (PSI)...
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LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping
Pramunendar, Ricardus Anggi
; Alomoush, Ashraf
; Prabowo, Dwi Puji
; Megantara, Rama Aria
; Alzami, Farrikh
; Winarsih, Nurul Anisa Sri
; Pergiwati, Dewi
; Shidik, Guruh Fajar
Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism t...
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A Sustainable Computational Framework for Breast Cancer Screening: Optimizing High-Dimensional Feature Spaces via MVP-PCA for Resource-Constrained Environments
The rapid integration of Electronic Health Records (EHR) demands the efficient processing of high-resolution medical images. However, deep learning architectures applied to mammography classification often produce massive, high-dimensional feature spaces susceptible to the curse of dimensionality and anatomical noise. Furthermore, conventional dimensionality reduction approaches tend to cause over-reduction, which destroys crucial microcalcification textures. To address these challenges, this st...
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