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Charis Fathul Hadi
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Prodi Teknik Elektro, Fakultas Teknik , Universitas PGRI Banyuwangi Jl.Ikan Tongkol No. 22 Banyuwangi 68416, Jawa Timur
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Journal Zetroem
Published by Universitas PGRI Banyuwangi
ISSN : 2656081X     EISSN : 2656081X     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering
jurnal zetroem yang dapat dimuat dalam jurnal ini meliputi bidang keilmuan Teknik Elektronika, Teknik Kendali, Sistem Tenaga, Telekomunikasi, Informatika, Sistem Distribusi. Makalah dapat berupa ringkasan laporan hasil penelitian atau kajian pustaka ilmiah. Makalah yang akan dimuat hendaknya memenuhi format yang telah ditentukan.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 157 Documents
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Sintesis CaTiO3 Berbasis Limbah Cangkang Telur Bebek Untuk Aplikasi Sel Surya Muhammad, Al Jalali; Wa Ode Sitti Ilmawati; Aslan Ndita
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6261

Abstract

Duck’s eggshell is one type of organic waste that has not been used optimally. Duck eggshell contain CaCO3 as a CaO source, so they are potentially processed as CaTiO3. The purpose of this study was to investigate the effect of sintering temperature on morphology, phase, functional groups, and optical properties of CaTiO3. The sintering temperatures were used in this research from 800oC, 900oC and 1000oC for 4 hours. SEM analysis show that there was the different of morphology properties to each samples. Homogeneity of CaTiO3 particles can be seen from spherical distribution of CaTiO3 particles, especially at 800oC. In this research was found orthorombic crystal structure of CaTiO3 to each sintering temperatures.According to the XRD patterns, it can be seen that there was a simillar patterns to each samples. Each peaks of CaTiO3 that formed on 2 (23,329o; 29,960o; 33,152o; 40,990o; 47,568o; and 49,211o) have the growth orientations respectively are (400), (510), (440), (444), (800), and (820). Particle size at sintering temperature of 800oC, 900oC and 1000oC respectively are 43,60438 nm, 33,43218 nm, and 29,35699 nm).
Perancangan Alat Pemantauan Konsumsi Listrik Multipoint Berbasis IOT untuk Meningkatkan Efisiensi Energi Charlie William; Hugeng; Lamto Widodo
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6287

Abstract

Konsumsi energi listrik yang tidak efisien dapat menyebabkan pemborosan daya dan peningkatan biaya operasional. Penelitian ini bertujuan untuk merancang dan mengimplementasikan alat pemantauan konsumsi listrik multipoint berbasis Internet of Things (IoT) yang mampu mengirimkan data penggunaan daya secara nirkabel dan real-time. Sistem dirancang menggunakan sensor PZEM-004T (Closed CT) sebagai pengukur daya, mikrokontroler ESP32 sebagai unit pemroses utama, modul NRF24L01 sebagai media komunikasi antar node, serta WiFi untuk konektivitas ke Google Spreadsheet sebagai basis data dan antarmuka pengguna daring. Proses penelitian meliputi tahapan perancangan perangkat keras, pemrograman sistem, integrasi modul, serta pengujian fungsionalitas pada dua skenario beban, yaitu kombinasi charger laptop dan 3D printer. Hasil pengujian menunjukkan bahwa sistem mampu memantau parameter listrik—tegangan, arus, daya aktif, energi, dan faktor daya—dengan akurasi tinggi dan latensi rendah. Pada pengujian pertama, Node 1 mencatat rata-rata 212,42 V dan 21,64 W, sedangkan Node 2 mencatat 211,81 V dan 35,59 W. Pada pengujian kedua, Node 1 menunjukkan daya rata-rata 35,55 W, sementara Node 2 sebesar 85,10 W. Sistem terbukti dapat bekerja stabil untuk pemantauan multipoint tanpa kabel tambahan, dengan kemampuan pencatatan data otomatis dan akses daring. Hasil penelitian ini menunjukkan bahwa rancangan alat ini efektif digunakan sebagai solusi monitoring energi berbasis IoT untuk mendukung efisiensi dan manajemen energi listrik di lingkungan rumah tangga maupun industri.
Optimalisasi Teknik Image Enhancement untuk Klasifikasi Varietas Apel Menggunakan SVM dan CNN Johan, Anju Alicia; Fitri, Zilvanhisna Emka; Imron, Arizal Mujibtamala Nanda; Arif, Praditya Zainal
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.5513

Abstract

One of the largest export commodities in Indonesia is fruit commodities, one of which is apples. Apples have many varieties that differ in shape, color and size, which can cause identification and highlighting of apples to have limitations by requiring manual inspection from experts. This manual inspection is influenced by the expert's ability and experience in assessing the texture, color pattern, smell and characteristics of apples. In addition, the large diversity of apple varieties does not guarantee the completeness and ease of access related to information and data on apple varieties. The availability of this information is very important in supporting increased fruit production and determining superior apple varieties. So, a system is made that can classify apple varieties such as ana apples, manalagi apples, fuji apples, red delicious apples and rome beauty apples automatically. The apple variety classification methods used are SVM and CNN. The accuracy result of the SVM method is 94% based on texture feature parameters. While the CNN accuracy result is 100% Using learning rate 0.001 and epoh 20.
Analisis Kinerja Multimodal Dense Neural Network untuk Deteksi Hipoksia Janin pada Dataset Tidak Seimbang Yusuf, Dianni; Subono, Subono
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6204

Abstract

This study aims to develop a Multimodal Dense Neural Network (MDNN) for detecting fetal hypoxia using an imbalanced Cardiotocography (CTG) dataset. The primary challenges in fetal hypoxia diagnosis include the imbalance between Normal, Suspect, and Hypoxia classes and the limited interpretability of conventional deep learning models. To address these issues, a robust preprocessing pipeline was designed, consisting of Physiological Clipping (50–200 bpm), Median Absolute Deviation (MAD) normalization, SMOTETomek balancing, and Gaussian noise augmentation. The MDNN architecture integrates two parallel branches: Fetal Heart Rate (FHR) signals and clinical parameters (pH, Apgar score, and base deficit), fused through a Dense Fusion Layer to generate compact multimodal representations. Experimental results demonstrate that the proposed MDNN achieved 99.7% accuracy, 99.5% F1-score, and 0.993 AUC, outperforming CNN (84.6%), ResNet18 (82.3%), and MLP (87.5%). The confusion matrix showed good generalization capability with per-class accuracies of 69% (Normal), 56% (Suspect), and 67% (Hypoxia). SHAP feature importance analysis identified FHR pattern (0.45) and pH level (0.25) as the most influential features in classification. These findings confirm that the proposed MDNN is robust, computationally efficient, and clinically interpretable, making it a promising framework for real-time fetal hypoxia diagnosis in modern clinical environments.
Analisis Efektivitas Metode Responsible, Accountable, Consulted, Informed (RACI) dalam Sistem Manajemen Process Approval subono, subono; Yusuf, Dianni
ZETROEM Vol 7 No 2 (2025): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v7i2.6450

Abstract

The approval management process plays an essential role in improving efficiency and accountability in organizational decision-making. PT Asta Berkah Autonomous, a company specializing in automation system development, faces challenges in transparency and efficiency due to manual approval procedures conducted through Google Forms and email. This study aims to design and implement a web-based approval management system integrated into the Asta Project application using the Responsible, Accountable, Consulted, Informed (RACI) method. The RACI method is applied to clearly define the roles and responsibilities of each stakeholder, ensuring a structured and transparent approval workflow. The system development process adopts the Rapid Application Development (RAD) approach, emphasizing iterative design and user involvement. System testing was conducted using Blackbox Testing and User Acceptance Testing (UAT) based on ISO 9126 quality standards. The results demonstrate that the implementation of the RACI method enhances role clarity, process efficiency, and transparency among participants. The developed system successfully reduces submission time, simplifies approval tracking, and supports faster and more accurate decision-making. This implementation significantly contributes to improving productivity and governance of the approval process within PT Asta Berkah Autonomous. System testing using Blackbox Testing and User Acceptance Testing (UAT) based on ISO 9126 quality standards. The results show that all system functions operated successfully (100% valid), with an average user satisfaction score of 84.44%, categorized as excellent. The application of the RACI method significantly improved efficiency, transparency, and accountability in the company’s approval process. Overall, the developed system contributes to digital transformation efforts and enhances corporate governance effectiveness.
Performance Evaluation of IoT Communication Protocols (LoRa, Wi-Fi and Zigbee) for Smart Environment Fahmi, Arif Fahmi; Indra Kurniawan; Junaedi Adi Prasetyo
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.6414

Abstract

The Internet of Things (IoT) constitutes a core component in the development of Smart Environment systems for real-time environmental monitoring. Selecting an appropriate communication protocol remains a major challenge, particularly in semi-rural areas characterized by limited network infrastructure and heterogeneous geographical conditions. This study aims to evaluate and compare the performance of three IoT communication protocols, namely LoRa, WiFi (ESP32), and Zigbee (XBee), through real-field prototype-based testing conducted in the semi-rural region of Banyuwangi. The evaluated parameters include communication range, transmission latency, and packet delivery ratio (PDR). The experiments were performed by periodically transmitting data packets under multiple distance scenarios. The results indicate that LoRa achieves the longest communication range of approximately ±1750 meters with an average latency of 2.1 seconds, WiFi exhibits the lowest latency of about 0.2 seconds with an effective range of ±200 meters, while Zigbee demonstrates stable transmission performance with a PDR of 100% up to a distance of ±600 meters. The main contribution of this study lies in providing empirical performance data obtained from real-field experiments in a semi-rural Indonesian environment, which can serve as a reference for selecting appropriate IoT communication protocols for Smart Environment implementations.
Comparison of CNN, ResNet50, and Xception for Deepfake Image Detection Rachmat; Mohammad Zainuddin; Handini Arga Damar Rani
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7524

Abstract

This study compares the performance of three deep learning architectures—Convolutional Neural Network , ResNet50, and Xception—for frame-based deepfake image detection and identifies the most effective model in terms of accuracy, precision, recall, F1-score, and generalization. The study followed the Knowledge Discovery in Databases (KDD) framework using the Deepfake Detection Dataset (DFD Entire Original) from Kaggle, which consists of 3,432 videos, including 3,068 fake and 364 real videos. Videos were converted into frames using OpenCV, followed by face detection and cropping using MTCNN. The resulting face images were resized to 224×224 pixels, normalized, augmented, and labeled. To reduce classification bias caused by class imbalance, the training data were balanced using random undersampling, resulting in real frames and  fake frames. The dataset was then split into training, validation, and testing sets using a stratified 60:20:20 ratio. The results show that Xception achieved the best performance among the three models, with an accuracy of 95.21%, precision of 0.95, recall of 0.95, and F1-score of 0.95, followed by ResNet50 with an accuracy of 93.42% and CNN with an accuracy of 87.65%. These findings indicate that transfer learning-based architectures, particularly Xception, are more effective than conventional CNNs for deepfake image detection under a consistent experimental setting. This study is limited to a single dataset and frame-based evaluation, thus future work will explore the potential of hybrid models, such as Vision Transformer (ViT) combined with Capsule Networks , to improve detection performance and address challenges like temporal analysis and cross-dataset validation.
Digital Signal Feature Extraction for Graph-Based Host Classification in VM Placement Hidayat, Taufik; Medriavin Silalahi, Lukman; Hamid, Abdul
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7559

Abstract

This study proposes a host classification methodology based on signal analysis and graph representation as a pre-placement stage for virtual machines (VM) in a cloud computing environment. The Bitbrains dataset is utilized as a source of time-series data representing CPU, memory, disk, and network utilization. Each parameter is modeled as a discrete signal and analyzed in both time and frequency domains. The analysis is conducted using a fixed observation window and frequency-domain transformation to capture workload characteristics across multiple resources. The Fourier transform results indicate the dominance of low-frequency components, suggesting gradual workload variations. Spectral energy is calculated and normalized to identify quantitative differences between host conditions. The results show that the overloaded class contributes 98.9% of the total spectral energy, while the underloaded and balanced classes contribute only 0.7% and 0.4%, respectively. The extracted features are then integrated using a four-node graph model that connects all resource dimensions into a single structure. The aggregated graph score is employed for dynamic percentile-based classification. From a total of 1,239 analyzed hosts, the proposed method classifies 421 hosts as overloaded, 409 as underloaded, and 409 as balanced. These findings demonstrate that spectral characteristics combined with graph integration provide a quantitatively structured and adaptive host segmentation mechanism, where the resulting classification can support VM placement decisions by identifying underloaded, balanced, and overloaded host conditions.
Smart Attendance System: AI Technology for Digital Attendance Using Computer Vision Technology Natsir, Fauzan; Redo Abeputra Sihombing; Triana Dewi Salma; Millati Izzatillah; Ega Shela Marsiani; Farhan Maulana Arramsy; Anuj Kumar
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7569

Abstract

Employee attendance is a crucial aspect of human resource management, particularly in maintaining discipline and ensuring the operational effectiveness of a company. PT KAMM currently uses a fingerprint-based attendance system which, although effective, often encounters issues such as sensor sensitivity to finger conditions, potential device damage caused by continuous physical contact, and employee inconvenience. This research aims to develop a face recognition-based attendance system as a more efficient and hygienic alternative. The dataset comprises 1,400 facial images from 20 PT KAMM employees (20 classes), split into 80% training, 10% validation, and 10% testing data. The method applied combines the Haar Cascade algorithm for face detection and a Convolutional Neural Network (CNN) for face recognition. The CNN architecture consists of four convolutional layers with 32 to 256 filters, ReLU activation, max pooling, flatten, a 512-neuron fully connected layer, dropout of 0.5, and softmax classification. The model was trained for 50 epochs using the Adam optimizer with a learning rate of 0.001 and batch size of 32. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics. Results show the system achieved an accuracy of 95.71%, precision of 95.80%, recall of 95.60%, and an F1-score of 95.70%, with an average inference time of 0.12 seconds/frame in real-time. However, the system has limitations: accuracy drops by up to 12% under extreme lighting conditions and when employees wear masks. This study is expected to serve as a reference for other companies seeking to adopt similar face recognition technology for contactless attendance management systems.
Design and Evaluation of a Virtual Tour–Based Application for the Placement Criteria of the Instrument Landing System (ILS) Rusman; Moch Rifai; Prayitno, Hadi; Artadarma, Nyoman
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/ztr.v8i1.7573

Abstract

The Instrument Landing System (ILS) is a critical component of aviation navigation that ensures aircraft landing accuracy and safety. However, public understanding of restricted ILS areas and equipment placement criteria remains limited, as existing informational media—such as regulatory documents or slide presentations—lack interactivity and spatial visualization. This study aims to design and evaluate a Virtual Tour-based ILS Application as an interactive educational medium to visualize and explain the placement criteria of ILS equipment in a three-dimensional (3D) environment. The research used the ADDIE (Analysis, Design, Development, Implementation, Evaluation) instructional design model to systematically develop the application. The content and functional design were derived from the Indonesian Civil Aviation Authority’s SKEP/113/VI/2002 and ICAO Annex 10 Volume I standards. Data collection methods included observation, expert validation, and user testing. The application was evaluated using three quality dimensions of the ISO 25010 software standard: functional suitability, compatibility, and usability. Participants included media experts, subject matter experts, and 20 community members residing near Budiarto Airport, Indonesia. As a result, the Virtual ILS application achieved high evaluation scores across all dimensions—functional suitability (97.5%), compatibility (100%), and usability (90.16%)—indicating excellent performance, ease of use, and educational value. The interactive 3D visualization effectively improved users’ comprehension of ILS restricted zones and equipment placement requirements, compared to conventional informational media.

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