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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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INDONESIA
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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Development of an Air Quality Classification System Using SMOTE-Based Random Forest and XAI Analysis Arip Kristiyanto; Hirawati Lubis
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.v8i1.7586

Abstract

South Tangerang City is a critical environmental issue that requires an accurate and transparent classification system. This study aims to develop an air quality classification model using a machine learning algorithm integrated with data balancing techniques and model interpretation methods. The methodology used includes pre-processing of Air Pollutant Standard Index (ISPU) data for the 2020–2022 period into three categories: Good, Moderate, and Unhealthy. The dataset used is 1096, Synthetic Minority Over-sampling Technique (SMOTE) is applied to handle class imbalance, and hyperparameter optimization is performed using GridSearchCV. The experimental results show that the Random Forest algorithm outperforms the baseline SVM and KNN models, achieving an accuracy of 0.81 and an F1-Score of 0.75 after SMOTE and tuning. Explainable AI (XAI) analysis using SHAP reveals that sulfur dioxide (SO₂) is the most dominant feature influencing model decisions, and it is spatially correlated with industrial activities and heavy transportation in the South Tangerang area. The final model was then deployed to the Hugging Face Spaces cloud platform via the Gradio interface to provide publicly accessible classification services. This study demonstrates that integrating Random Forests and SHAP produces a classification system that is not only highly performant but also scientifically transparent, supporting air pollution mitigation.
Density-Optimized Lookup Table with Piecewise Linear Interpolation for ESP32 ADC Precision Enhancement Antonius Irianto Sukowati; Linza Mawadda Rahmah
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.v8i1.7602

Abstract

The inherent non-linearity of built-in Analog-to-Digital Converters (ADCs) in low-cost microcontrollers like the ESP32 significantly impacts measurement accuracy, often exceeding 10% error in critical ranges. This research aims to enhance ESP32 ADC precision without expensive external hardware through a novel software-based correction method. The proposed approach combines a density-optimized Lookup Table (LUT) with piecewise linear interpolation. Unlike conventional uniform distribution, this technique strategically concentrates 65% of calibration points in the critical mid-voltage region (0.5–2.5 V) where non-linearity is most pronounced. Experimental validation was conducted using precise input voltages from 0 V to 3.2 V across multiple ESP32 units. Results demonstrate remarkable improvements: the average absolute error was reduced from 0.112 V (3.42% of full scale) to 0.008 V (0.24% of full scale), with Root Mean Square Error (RMSE) decreasing by over 92.5%. The method achieves a sub-1% maximum error while maintaining minimal resource consumption, requiring only 264 bytes of memory and 2.3 ms processing time per measurement. These findings confirm that high-accuracy measurements are achievable using commodity hardware, challenging the notion that precision requires expensive external ADCs. This work offers significant implications for cost-sensitive IoT, environmental monitoring, and healthcare applications requiring reliable data acquisition without increased hardware complexity.
Smart Seismic Intelligence Machine Learning for Spatial Clustering and Earthquake Magnitude Prediction in Indonesia Setya Hadi, Harry; Rauf, Rosnita; Agus Salim; Kevin Maulana Firdaus
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.v8i1.7615

Abstract

Indonesia is located within the Pacific Ring of Fire, one of the most seismically active regions in the world due to the interaction of multiple major tectonic plates. Understanding the spatial distribution of earthquakes and accurately estimating their magnitudes is essential for effective disaster risk assessment and mitigation planning. This study aims to analyze earthquake distribution patterns and develop a machine learning-based approach to predict earthquake magnitude using seismic data from the Meteorology, Climatology, and Geophysics Agency (BMKG). The study employs two machine learning methods: K-Means Clustering to identify spatial groupings of earthquake events and Random Forest Regression to predict magnitude based on spatial and temporal features. The dataset consists of 67 earthquake events recorded in February 2026, including attributes such as latitude, longitude, depth, magnitude, and occurrence time. Clustering results indicate that the optimal number of clusters is k = 4, with a Silhouette Score of 0.3444, suggesting a moderate clustering structure. This implies that spatial patterns are present, although cluster separation is not yet well-defined. The Random Forest model achieved an R² of 0.7382 on training data and 0.0975 on testing data, indicating overfitting likely due to the limited dataset size. Feature importance analysis reveals that longitude contributes the most (43.7%), followed by depth (29.6%), latitude (20.6%), and time (6.0%). These findings highlight the dominant role of spatial factors in Indonesia’s seismic activity. However, the limited dataset restricts model generalization; therefore, future studies should use larger datasets and incorporate additional geophysical parameters to improve predictive performance.
Web-Based Competency Test Information System with Automated Scoring Using the Waterfall Method Damanik, Burhanuddin; Sadarmanis Halawa
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.v8i1.7622

Abstract

The implementation of competency tests in vocational high schools (SMK) is still largely conducted manually, leading to several issues such as delays in score processing, data recording errors, and inefficiencies in managing examination records. This study aims to design and develop a web-based competency test information system using the Waterfall method to improve efficiency, accuracy, and transparency in the examination process. The system was developed using PHP, MySQL, HTML, CSS, and JavaScript, incorporating six main features: user login, participant registration, question management, online examination, automatic scoring, and result reporting. System evaluation was conducted using Black Box Testing to assess functional performance and the System Usability Scale (SUS) to measure user satisfaction. The results show that all system features operated successfully with a functional success rate of 100% across all test scenarios, indicating high system reliability. In addition, usability evaluation involving 30 respondents, consisting of 20 students and 10 teachers, resulted in an average SUS score of 92, which falls into the “excellent” category. The system also significantly improves efficiency by reducing result processing time from 2–3 days to real-time processing, while minimizing data entry errors. Therefore, the developed system not only improves the efficiency and accuracy of competency test implementation but also provides a reliable and user-friendly solution, contributing to the development of educational information systems based on user experience.
Pollutant Monitoring System for Mushroom Factory Liquid Waste using Arduino with 4 Sensor Integration Sigitta H., Rito Cipta; Mubarok, Rizky; Rakhman, Arif; Prastyo, Firman Ardy; Prastyono, Rizki Noor; Arsiandro, Fadly Haris
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.v8i1.7623

Abstract

Mushroom production requires a specially designed medium for mushroom growth. Factory-scale composting methods utilize several media, which involve water to maintain moisture and chemical/organic fertilizers. The water used for mushroom cultivation contains hazardous substances, which, if dissolved and flowing into sewers, water sources, or community irrigation, can cause several problems, resulting in liquid pollutants. The purpose and main contribution of this research is to develop and implement pollutant monitoring system in mushroom factory liquid waste based on Arduino with the integration of four sensor parameters. The 4D method used includes the stages of define, design, develop, and disseminate. The goal is to develop pollutant monitoring system using four sensors. The feasibility analysis results have a percentage above 80% in the good category and are suitable for use. Accuracy analysis results are >80% in the good category. Temperature and pH also tend to be stable without significant changes. The pH value tends to be stable in the neutral range of 7.2–7.3, which is generally still suitable for supporting the survival of aquatic organisms. The dissolved oxygen (DO) levels in water are generally low, in the range of 2.46 to 3.01 mg/L, which is already below the standard for good water quality (generally >5 mg/L). Meanwhile, the turbidity parameter indicates pollution, marked by very high turbidity reaching 300 NTU. Then it shows a drastic decrease from very high conditions at the beginning of 300 NTU to approaching zero after further distance, indicating an effective natural sedimentation process in improving water clarity.
Application of K-Means and Decision Tree for Disease Prediction Using Data Mining Approach Riah Ukur Ginting; Fernando H Sinaga; Rianto Sitanggang; Ivan Elisabeth Purba; Aprima A Matondang
ZETROEM Vol 8 No 1 (2026): ZETROEM
Publisher : Prodi Teknik Elektro Universitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

This study aims to analyze the distribution patterns of patient diseases using a data mining approach at UPTD Puskesmas Pakkat. The dataset consists of secondary data from 4,633 patients collected between January 2022 and December 2023, obtained from digital medical records, with variables including age, gender, and 22 disease diagnosis categories. The K-Means Clustering method was employed to identify disease grouping patterns based on patient characteristics. The optimal number of clusters was determined using the Silhouette Score, with the best value of 0.5556 at K=6. Cluster quality was further evaluated using the Davies-Bouldin Index (DBI) with a value of 0.6722, indicating good cluster separation. To support the classification process, the Decision Tree algorithm was applied to predict cluster membership for new patient data. Model evaluation was conducted using a train-test split scheme and k-fold cross-validation to enhance reliability and minimize the risk of overfitting. The results indicate distinct disease patterns across age groups, where infectious diseases such as acute respiratory infections (ARI) and diarrhea dominate in children, while non-communicable diseases such as hypertension and diabetes are more prevalent among adults and the elderly. This study contributes by integrating clustering and classification methods and provides data-driven epidemiological insights that can support decision-making in primary healthcare services.
Analysis of the Effect of Temperature Variations on HC-SR04 Ultrasonic Sensor Distance Measurement Accuracy Based on Tinkercad Simulation Arunglabi, Rismawaty; Allu, Nicolaus; Patandianan, Hendrick Jackson Andreas
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.v8i1.7637

Abstract

Ultrasonic sensors are used to measure distance based on the reflection of sound waves. However, their accuracy is influenced by temperature changes because temperature determines the speed of sound propagation in air. This study analyzes the effect of temperature variations on the performance of ultrasonic sensors through theoretical analysis and Tinkercad simulation within a range of 0°C to 50°C. The method includes calculating the speed of sound as a function of temperature, analyzing the wave travel time, and simulating distance measurements in a virtual environment. The error value is obtained by comparing the sensor readings with the actual distance. The results show that higher temperatures increase the speed of sound, causing the measured distance to appear shorter, while lower temperatures result in longer readings. The smallest error occurs at 19°C–20°C, when the speed of sound approaches 343 m/s. At extreme temperatures, the measurement error can exceed 5%. These findings confirm that temperature is a key factor affecting ultrasonic sensor accuracy and can serve as a reference for the development of automation and industrial monitoring systems

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