IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), a two times annually provides a forum for the full range of scholarly study. IJEIS scope encompasses all aspects of Electronics, Instrumentation and Control. IJEIS is covering all aspects of Electronics and Instrumentation including Electronics and Instrumentation Engineering. The journal publishes original research papers, short communications, and review articles both written in English or Bahasa Indonesia. The paper published in this journal implies that the work described has not been, and will not be published elsewhere, except in abstract, as part of a lecture, review or academic thesis. Paper may be written in English or Indonesian, however paper in English is preferred. Accreditated "Sinta 3" by the Ministry of Research-Technology and Higher Education Republic of Indonesia: 51/E/KPT/2017 PERIOD: 2023-2027. Initials IJEIS Abbreviation Indonesian J. Electron. Instrum. Syst. DOI prefix 10.22146 by Impact 2.69 |in Sinta Citation Analysis SCOPUS | Google Scholar Please read these journal guidelines and template carefully. Authors who want to submit their manuscript to the editorial office of IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) should obey the writing guidelines. If the manuscript submitted is not appropriate with the guidelines or written in a different format, it will BE REJECTED by the editors before further reviewed. The editors will only accept the manuscripts which meet the assigned format. IJEIS is published two times annually, April and October by the Indonesian Computer, Electronics and Instrumentation Support Society (IndoCEISS) in colaboration with the Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada, Yogyakarta. Since 2016, Open Journal Systems (OJS) has been applied for all business process in IJEIS. Therefore, the authors are required to register in advance and upload the manuscript by online. The process of the manuscript could be monitored through OJS. Authors, readers, editorial board, editors, and peer review could obtain the real time status of the manuscript. Several other changes are informed in the Journal History. IJEIS (Indonesian Journal of Electronics and Instrumentation Systems) is indexed and has been listed in: Directory of Open Access Journal (DOAJ) ASEAN Citation Index (ACI) Crossref Science and Technology Index (SINTA) Google Scholar Microsoft Academic Search (MAS) Publick Knowledge Project (PKP) Index Bielefeld Academic Search Engine (BASE) WorldCat libraries Directory Of Open Access Scholarly Resources (ROAD) IJEIS (Indonesian Journal of Electronics and Instrumentation Systems), with registered number ISSN 2088-3714 (print), ISSN 2460-7681 (online). 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Implementasi Logika Fuzzy Dalam Sistem Pendingin Otomatis Kandang Ayam Broiler Closed House
Industri peternakan ayam broiler di Indonesia memiliki peran penting dalam memenuhi kebutuhan protein nasional, tetapi suhu tropis tinggi menjadi tantangan yang dapat memicu heat stress pada ayam. Kondisi ini berdampak pada penurunan produktivitas, kesejahteraan ayam, dan kerugian ekonomi. Oleh karena itu, sistem otomatisasi pengontrol suhu dan kelembaban menjadi kebutuhan penting, khususnya dalam menghadapi tantangan tersebut. Penelitian ini merancang sistem kontrol otomatis suhu dan kelembaban menggunakan logika Fuzzy Sugeno dengan mengintegrasikan sensor kelembaban dan suhu serta pompa dan blower sebagai aktuator. Hasil penelitian menunjukkan bahwa sistem ini berhasil meningkatkan stabilitas suhu kandang, dengan kategori aman berdasarkan Temperature-Humidity Index (THI) meningkat dari 27,60% menjadi 50,56%. Uji performa sistem menghasilkan nilai MAE 1,475; RMSE 3,903; dan R² Score 0,988 untuk kontrol blower, serta MAE 0,037; RMSE 0,124; dan R² Score 0,923 untuk kontrol pompa. Selain itu, sistem ini meningkatkan indeks performa ayam broiler dari 366 menjadi 406. Solusi ini terbukti efektif dalam mendukung efisiensi serta keberlanjutan industri peternakan ayam broiler.
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Integrasi Robot Lengan Beroda Holonomic dan Pengindraan Visual Menggunakan Yolov5 untuk Pemilah Sampah
The issue of waste management has become a critical topic, particularly in the sorting process, which relies on human labor and poses hygiene risks and inaccuracy. Several technologies have been explored to address this problem, including robotic arms with visual sensing, which are widely used but face challenges such as limited working areas and relatively complex installation processes. This study develops a waste-sorting robot based on a holonomic-wheeled robot integrated with visual sensing and the YOLOv5 algorithm for waste classification. The robot is equipped with a vacuum gripper for waste pickup and placement, as well as sensors for navigation and position control. Tests were conducted on four types of waste: bottles, leaves, metals, and paper. The results demonstrate a classification accuracy rate of 100%, with an average waste placement success rate of 90% for leaves and paper, and 80% for bottles, influenced by the surface characteristics of the waste and the consistency of the robot's positioning. This robotic system offers enhanced efficiency and accuracy compared to manual methods, although there remains room for improvement in the gripping mechanism and synchronization of the robot's movements. Overall, the robot system shows performance with accuracy above 80% with a wider working area than using a robot arm.
Sistem Manajemen Energi Hibrida pada Sumber Energi Baterai-Superkapasitor Berbasis Automatic Switching
This research develops an automatic switching-based hybrid energy management system that integrates batteries and supercapacitors to improve power distribution efficiency in electric vehicles. The system is controlled by a microcontroller that monitors the current in real-time and activates the supercapacitor line when the current exceeds 10 A. The switching circuit uses IR2110 and IRFP4568 MOSFETs. Tests were conducted in three scenarios: battery only, hybrid without load, and hybrid with load. In the no-load condition, the supercapacitor produced a peak current of 18.23 A and an average of 5.73 A, while the battery recorded an average current of 8.52 A. In the loaded condition, the supercapacitor peak current reached 20.87 A, with an average of 10.82 A, while the battery was 11.95 A. The total energy increased from 55022.25 J (battery only) to 92000.76 J (no-load) and 147019.09 J (with load). The efficiency also increased from 1.0% to 2.4% in the hybrid configuration. The system showed a stable energy conversion efficiency of 95% under both hybrid conditions. These results prove that automatic integration of supercapacitors can improve system efficiency and performance without the complexity of control algorithms.
Pengembangan Prototipe Sistem Peringatan Dini Tabrakan Belakang Pada Truk Berbasis Arduino
Rear-end collisions with trucks often occur due to the lack of driver awareness in maintaining a safe following distance, as well as the presence of blind spots around the truck, especially at the rear. These blind spots make it difficult for drivers of vehicles too close to the truck to be detected, thus increasing the risk of collision, especially during sudden braking. To address this issue, this study develops a prototype of a rear-end collision early warning system based on Arduino, using TF02-Pro LiDAR and MK421137 speed sensors. This system is designed to detect the distance and speed of vehicles behind in real-time and provide an alarm warning if the distance and speed conditions reach dangerous thresholds. Testing was carried out using simulations with variations in the speed of the following vehicle at 15 km/h, 20 km/h, and 30 km/h. The results showed that the system successfully detected the speed of the following vehicle with an accuracy of 97.75% and an average error of 2.25%. Furthermore, the alarm status was successfully activated based on the predefined distance and speed thresholds. This prototype is expected to enhance road safety by providing effective early warnings to drivers behind trucks.
Sistem Klasifikasi Sampah Otomatis Berbasis Deteksi Objek Real-Time Pada Single Board Computer Dengan Algoritma YOLO
The development of an automatic waste classification system based on real-time object detection using the YOLO (You Only Look Once) algorithm on a Raspberry Pi 5 Single Board Computer (SBC) is the main focus of this final project. The main issue addressed is the increasing accumulation of waste, particularly in Indonesia, which requires an effective solution for automatic waste sorting. The system is designed to detect and sort plastic and metal waste in real-time using deep learning and computer vision technologies. This research employs the YOLO11n model, trained on a dataset of plastic and metal waste. The training process involves data augmentation techniques such as rotation and grayscale to enhance dataset variability. The training results show a mean Average Precision (mAP) of 98.44% on testing data. The system is implemented on a Raspberry Pi 5, with the model converted to NCNN format to improve inference speed. Testing results indicate that the system can achieve a speed of 8.90 FPS with a latency of 110 ms, meeting the criteria for a real-time system.
Deteksi Partial Discharge dengan Metode CNN VGG16
Partial discharge adalah peristiwa loncatan listrik pada bahan isolasi listrik yang menimbulkan kerusakan pada peralatan listrik. Untuk itu diperlukan suatu metode untuk mendeteksi peristiwa partial discharge. Salah satu metode yang dapat digunakan untuk deteksi partial discharge adalah metode CNN VGG16. CNN akan melakukan pemodelan dari analisa dataset gambar partial discharge VSB lalu menggunakannya untuk mengklasifikasikan data baru sebagai partial discharge atau tidak. Pada penelitian ini akan dianalisa bagaimana pengaruh parameter pemodelan dan pembagian dataset terhadap peforma. Penyesuaian parameter dilakukan dengan memvariasikan nilai learning rate, steps per epoch, dan validation steps untuk melihat nilai terbaik sehingga nantinya nilai terbaik yang akan digunakan. Pembagian dataset dilakukan dengan tiga variasi yaitu pembagian train, validasi, dan test pada dataset pertama dibagi rata, yang kedua didominankan ke train, dan yang ketiga jumlah data noPD terlebih dahulu dikurangi agar seimbang dengan PD kemudian data didominankan juga pada train. Berdasarkan penelitian, terbukti bahwa variasi dataset ketiga yang memiliki peforma terbaik dan menunjukkan bahwa CNN arsitektur VGG16 terbukti mampu untuk mengenali pola dari data sinyal partial discharge dan membuat model yang mampu mengklasifikasi data partial discharge atau tidak dengan akurasi train 95,70%, akurasi validasi 93,12% dan akurasi prediksi data test 92,50% juga dengan nilai MCC sebesar 0,75
Sistem Pakan Ikan Otomatis Berdasarkan Kualitas Air Menggunakan Metode Fuzzy Mamdani
In addressing the global food scarcity crisis associated with global warming, the development of the fisheries sector becomes crucial, particularly in Indonesia. This research discusses the innovation of an automatic fish feeding system based on water quality detection using fuzzy logic control. The system integrates sensors for temperature, pH, and TDS to measure the conditions of fish pond water. Fuzzy logic control is employed to regulate the intensity of fish feeding based on environmental conditions. The system's performance is tested by comparing the fuzzy results from Matlab simulation with the implementation on Arduino. Despite differences, the results show a high level of consistency between the two systems. However, field trials reveal result variations, emphasizing the challenges of dealing with unpredictably changing environmental conditions. The conclusion highlights the disparities between simulation and field implementation, underscoring the importance of adjustments and calibration in practical applications.
Komparasi Performa Model 3D CNN dalam Klasifikasi Demensia Alzheimer pada MRI Otak
Penyakit Alzheimer adalah jenis demensia akibat kerusakan pada neuron otak yang memengaruhi memori, bahasa, dan berpikir. Diagnosis manual sering rentan terhadap subjektivitas dan memakan waktu, sehingga diperlukan model otomatis seperti 3D CNN untuk klasifikasi tingkat keparahan Alzheimer. Namun, kompleksitas arsitektur 3D CNN menyebabkan waktu komputasi yang tinggi. Penelitian ini membandingkan tiga arsitektur model 3D CNN yaitu 3D ResNet, 3D ResNeXt + Bi-LSTM, dan 3D CNN + CLSTM untuk menentukan model yang optimal. Dataset yang digunakan diperoleh dari database ADNI. Performa model dievaluasi dengan menggunakan confusion matrix, akurasi, presisi, recall, F1-score dan waktu komputasi. Hasil penelitian menunjukkan bahwa 3D ResNet memiliki akurasi pelatihan tertinggi mencapai 99,54% dan waktu komputasi pelatihan sebesar 57,61 detik/epoch. Model 3D ResNeXt + Bi-LSTM mencapai akurasi pengujian sebesar 99,33% dan waktu inferensi tercepat yaitu 0,0182 detik/sampel, namun waktu komputasi pelatihan terlama yaitu 117,68 detik/epoch. Sementara itu, 3D CNN + CLSTM mencapai akurasi uji sempurna 100% tetapi memiliki waktu inferensi terlama yaitu 0,0268 detik/sampel. Penelitian ini menunjukkan bahwa arsitektur yang sederhana tetap dapat memberikan performa optimal dengan waktu komputasi yang lebih efisien dibandingkan model yang lebih kompleks.
Pengembangan Kemampuan Model Autonomous Car Terhadap Aspek Keselamatan Berkendara Saat Kondisi Ekstrem Menggunakan Carla Simulator
The advancement of automation technology, particularly in autonomous vehicles, has rapidly progressed with the integration of machine learning. However, these systems still face challenges in environments with dense traffic and dynamic conditions, making safety a primary concern. Traffic accident data indicate that the implementation of autonomous vehicles remains far from optimal, especially under extreme conditions such as severe weather and unpredictable traffic congestion. This study aims to develop an autonomous vehicle system model that can operate not only under normal conditions but also adapt to extreme situations. The model is developed using the CARLA Simulator, which enables testing in various realistic environmental scenarios. Simulations involving severe weather and high traffic density are conducted to evaluate the model’s resilience and responsiveness across different scenarios. The results show that the developed model enhances driving safety under extreme conditions with high effectiveness in obstacle avoidance and dynamic decision-making. Thus, this approach is expected to contribute to the development of more adaptive and safer autonomous vehicles for real-world applications
Rancang Bangun Sistem Pengukuran Kematangan Buah Apel Manalagi Menggunakan Sensor Ultrasonik Piezoelectric
This study aims to design and test a non-destructive ripeness measurement system for Manalagi apples (Malus Sylvestris) using a piezoelectric-based ultrasonic sensor. Manalagi apples have green skin that does not show any significant changes when ripe, so the visual method is less effective in assessing their ripeness. The designed system utilizes ultrasonic waves to measure the attenuation of waves as they propagate through the fruit. The attenuation results are then correlated with the dissolved sugar content (measured in °Brix) using a refractometer as a comparison. This method offers a faster, more efficient, and less destructive alternative to conventional methods that require fruit extraction. The system prototype consists of a microcontroller, signal amplifier circuit, piezoelectric transducer, and digital oscilloscope to display the waves. The results of the wave measurement test showed that the higher the sugar content in the fruit, the greater the attenuation value produced. Regression analysis of the data obtained provides an equation of y = –40.28 – 0.486x with a coefficient of determination (R²) of 0.6299. This finding provides an important contribution to the development of non-invasive methods for fruit quality analysis, as well as being the basis for further research aimed at improving the accuracy of fruit characteristic predictions through a multivariable approach