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All Journal Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Jurnal Teknologi International Journal of Electronics and Communications Systems
Laili, Maria Bestarina
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Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Performance Evaluation of Electronic Control System in Series-Parallel Hybrid Vehicle: A Simulation Study Permatasari, Jelita; Santoso, Dian Budhi; Sunardi, Egi; Laili, Maria Bestarina
International Journal of Electronics and Communications Systems Vol. 5 No. 1 (2025): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v5i1.27629

Abstract

The increasing contribution of the transportation sector to global emissions has driven the development of hybrid electric vehicles (HEVs) as a practical solution to reduce environmental impact. The effectiveness of HEVs is highly dependent on electronic control systems that regulate power distribution between the internal combustion engine (ICE), electric motor, generator, and battery in real time under various operating conditions. This study aims to evaluate the performance of the electronic control system implemented using Stateflow in a simulated series-parallel hybrid electric vehicle. The research methodology involves simulating the vehicle model in MATLAB/Simulink, which integrates Stateflow to design and manage the logic and operational mode transitions. A continuous closed-loop feedback structure is used to facilitate real-time control decisions, guided by input variables such as throttle position, vehicle speed, and battery State of Charge (SoC). Various driving scenarios are simulated, including acceleration, steady cruising, deceleration, and energy recovery during braking. Simulation results show that the designed electronic control system can maintain operational stability with engine efficiency reaching 92%, battery power utilization up to 65%, and electronic transitions between modes (EV, HEV, regenerative) in less than 0.2 seconds, demonstrating a 40% improvement in response compared to conventional electronic control models. These findings confirm the potential of Stateflow-based electronic control approaches in creating more responsive and efficient hybrid vehicle propulsion systems, while supporting the development of low-emission transportation technology
Penerapan Metode QoS pada Sistem Monitoring Telur Berbasis IoT Nurzamilah, Zulia; Rahmadewi, Reni; Laili, Maria Bestarina
Jurnal Mekanova : Mekanikal, Inovasi dan Teknologi Vol 11, No 2 (2025): Oktober
Publisher : universitas teuku umar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35308/jmkn.v11i2.12169

Abstract

Kemajuan teknologi Internet of Things (IoT) telah mendorong penerapan otomatisasi dalam berbagai bidang, salah satunya adalah sistem pemantauan proses penetasan telur. Penelitian ini bertujuan untuk mengevaluasi kualitas layanan jaringan pada sistem monitoring berbasis IoT dengan menggunakan pendekatan Quality of Service (QoS) melalui perangkat lunak Wireshark. Evaluasi dilakukan dengan mengukur empat parameter utama, yaitu throughput, delay, jitter, dan packet loss. Berdasarkan hasil pengujian, nilai rata-rata throughput yang diperoleh sebesar 7,88 Kbps, dengan rentang nilai antara 2,89 Kbps hingga 21,41 Kbps. Parameter delay menunjukkan nilai rata-rata sebesar 67,71 ms, di mana nilai terendah mencapai 2,72 ms dan tertinggi 160 ms, masih dalam kategori sangat baik menurut standar TIPHON. Untuk jitter, nilai rata-rata tercatat sebesar 1,63 ms, dengan variasi dari 0,2 ms hingga 3,0 ms, yang menandakan kestabilan jaringan dalam hal waktu tunda. Adapun packet loss tercatat sebesar 0% pada seluruh pengujian, menunjukkan tidak adanya kehilangan paket data selama transmisi berlangsung. Temuan ini menunjukkan bahwa sistem monitoring IoT yang diuji memiliki kualitas jaringan yang cukup andal dan stabil, serta layak digunakan untuk pemantauan suhu dan kelembaban secara real-time dalam aplikasi inkubasi telur.
Evaluasi Kinerja YOLOv11 pada Deteksi Penyakit Tanaman Cabai: Studi Komparatif dengan YOLOv8, YOLOv5, dan SSD Permatasari, Jelita; Armin, Edmund Ucok; Sunardi, Egi; Laili, Maria Bestarina; Putri, Salsanabila Mariestiara
Jurnal Teknologi Vol 25, No 3 (2025): Desember 2025
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/teknologi.v25i3.8400

Abstract

Early and accurate detection of chili plant diseases is essential to support precision agriculture and minimize crop losses. Conventional visual inspection performed by farmers is often subjective and inconsistent, particularly under varying lighting conditions and complex field environments. Recent developments in deep learning, especially object detection models, enable the automation of disease identification with higher reliability. This study evaluates the performance of the YOLOv11 architecture for detecting three classes related to chili plant conditions—anthracnose, fruit fly, and healthy fruit—using a primary dataset of 1,062 field images collected in Karawang, Indonesia. The model was trained using a standardized configuration and compared with three widely used object detection models: YOLOv8, YOLOv5, and SSD. The training process was conducted for 100 epochs, with evaluation metrics including precision, recall, mAP50, mAP50–95, and inference time. Experimental results show that YOLOv11 achieved the highest detection performance, with an mAP50 of 86.94%, outperforming YOLOv8 by 3.8%, YOLOv5 by 6.8%, and SSD by 12.7%. The model also demonstrated the fastest inference speed at 10.9 ms, making it suitable for real-time field applications. Training analysis indicated stable convergence at the 61st epoch, supported by balanced precision (0.82391) and recall (0.77967) values as well as consistent reductions in both training and validation losses. These findings demonstrate that YOLOv11 provides more accurate and efficient detection of chili plant diseases compared with previous YOLO variants and SSD, and it offers strong potential for implementation in practical agricultural environments.
Co-Authors Armin, Edmund Ucok Nurzamilah, Zulia Permatasari, Jelita Putri, Salsanabila Mariestiara Reni Rahmadewi, Reni Santoso, Dian Budhi Sunardi, Egi