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All Journal Elkom: Jurnal Elektronika dan Komputer Jurnal Teknik Informatika (JUTIF) J-Proteksion : Jurnal Kajian Ilmiah dan Teknologi Teknik Mesin
Nurdiniyah, Elsa Sari Hayunah
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Automotive Engineering Computer Science & IT Education Engineering Mechanical Engineering

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Predictive Modeling of Microstrip Antenna Slot Dimensions Using Random Forest Regression Yusuf, Aisya Nur Aulia; Nurdiniyah, Elsa Sari Hayunah; Amalia, Norma
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 1 (2025): Juli : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i1.2915

Abstract

This study presents a machine learning approach for predicting the dimensions of microstrip antenna slots based on antenna performance parameters such as frequency, gain, directivity, return loss (S11), radiation efficiency, and VSWR. A two-phase methodology was employed. In the first phase, ten regression algorithms were evaluated, and Random Forest was identified as the most effective model based on Mean Absolute Error (MAE) and R-squared (R²) scores. In the second phase, hyperparameter tuning was conducted using Grid Search to further improve the model’s performance. The optimized Random Forest model demonstrated consistent improvements in predictive accuracy, with R² values increasing across all output variables. These results indicate that the combination of regression-based modeling and systematic hyperparameter tuning is effective for capturing complex relationships in antenna design tasks. The proposed approach offers a promising data-driven alternative for geometric prediction in microstrip antenna development, particularly when analytical models are insufficient.
Rancang Bangun dan Evaluasi Sistem Peringatan Dini Panel Pompa Air Pamsimas Berbasis Esp32 dan Telegram Bot Prasetyo, Rahardian Luthfi; Darmawan, Isra’ Nuur; Nurdiniyah, Elsa Sari Hayunah; Sucipto
J-Proteksion: Jurnal Kajian Ilmiah dan Teknologi Teknik Mesin Vol. 10 No. 1 (2025): J-Proteksion
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/jp.v10i1.3580

Abstract

Ketersediaan air bersih di wilayah pedesaan melalui program PAMSIMAS sering terkendala oleh habisnya token listrik, kegagalan pompa, dan trip overload yang menyebabkan terganggunya pasokan air bagi komunitas. Penelitian ini bertujuan merancang dan memvalidasi sistem peringatan dini panel kontrol pompa PAMSIMAS berbasis mikrokontroler ESP32 dan Telegram Bot, dengan target lag time notifikasi di bawah 10 detik. Metode penelitian meliputi desain dan implementasi perangkat keras (sensor arus ACS712, sensor tegangan ZMPT101B, relay, UPS cadangan) serta pemrograman firmware ESP32 untuk pembacaan sensor dan pengiriman pesan melalui API Telegram. Pengujian dilakukan dalam tiga tahap: unit testing laboratorium, integrasi end-to-end dengan 12 skenario panel, dan uji lapangan selama 14 hari di lokasi Sidabowa. Hasil menunjukkan rata-rata lag time 5,01 detik (std=0,95 s), akurasi deteksi 98,0%, false positive rate 1,5%, serta stabilitas operasi dalam simulasi pemadaman listrik dan overload. Pengalaman pengguna mengindikasikan tingkat kemudahan tinggi dalam penggunaan perintah teks dan format notifikasi. Kesimpulannya, sistem ini efektif, terjangkau, dan mudah direplikasi, menawarkan solusi IoT-Chatbot untuk meningkatkan keandalan layanan air PAMSIMAS. Rekomendasi meliputi perluasan uji di berbagai jaringan seluler dan integrasi SMS fallback untuk memperkuat robustness komunikasi.
Classification of Worker Productivity and Resource Allocation Optimization with Machine Learning: Garment Industry Yusuf, A’isya Nur Aulia; Alkaf, Zakiyyan Zain; Nurdiniyah, Elsa Sari Hayunah; Wisudawati, Tri; Fawzi, Muhammad Ihsan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5263

Abstract

This study presents an integrated predictive–prescriptive framework for improving workforce management in the garment industry by combining machine learning classification with linear programming optimization. Using a publicly available dataset of 1,197 production records, productivity levels were categorized into low, medium, and high classes. Data preprocessing included handling missing values, one-hot encoding of categorical variables, and class balancing using SMOTE. Eleven classification algorithms were evaluated, with LightGBM achieving the highest performance (accuracy 78.3%, weighted F1-score 78.3%, Cohen’s Kappa 63.4%) after hyperparameter tuning via Bayesian Optimization. The optimized model’s predictions were then incorporated into a linear programming model, implemented with PuLP, to maximize the allocation of high-productivity workers across production departments under capacity constraints. The results yielded an allocation plan assigning 117 high-productivity workers, significantly enhancing potential operational efficiency. The novelty of this work lies in integrating an optimized ensemble learning model with mathematical programming for end-to-end productivity classification and resource allocation, a combination rarely explored in labor-intensive manufacturing contexts. This framework offers a scalable decision-support tool for data-driven workforce planning and could be adapted to other manufacturing domains with similar operational structures. 
Co-Authors Alkaf, Zakiyyan Zain Darmawan, Isra’ Nuur Muhammad Ihsan Fawzi Norma Amalia, Norma Prasetyo, Rahardian Luthfi Sucipto Wisudawati, Tri Yusuf, Aisya Nur Aulia Yusuf, A’isya Nur Aulia