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Jurnal Elementer (Elektro dan Mesin Terapan)
Published by Politeknik Caltex Riau
ISSN : 24434167     EISSN : 24605263     DOI : https://doi.org/10.35143/elementer
Core Subject : Engineering,
Electrical & Electronics Engineering Engineering Mechanical Engineering
Jurnal ELEMENTER is a National journal providing authoritative sources of scientific information for researchers and engineers in academia, research institutions, government agencies, and industries. We publish original research papers, review articles, and case studies focused on Electrical Engineering, Machine Engineering, and Mechatronics Engineering fields. All papers are peer-reviewed by at least two referees. Jurnal ELEMENTER is published and imprinted by Politeknik Caltex Riau and managed to be issued twice in every volume a year ( May and November ).
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 165 Documents
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Rancang Bangun Penerjemah BISINDO Real-time Berbasis Kamera dan Deep Learning dengan Kendali Suara ESP32 WiFi I Gusti Agung Made Yoga Mahaputra; Putri Alit Widyastuti Santiary; I Ketut Swardika
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6578

Abstract

Indonesian Sign Language (BISINDO) serves as the primary means of communication for the deaf community. However, limited public understanding and the lack of practical real-time translation technology remain significant barriers to effective two-way communication. Most prior research has focused on foreign sign languages or relied on sensor-based gloves, which are less flexible for everyday use. This study proposes a real-time BISINDO translation system that converts hand gestures into speech using a camera and an ESP32 microcontroller. The system employs a CNN-LSTM deep learning model implemented in Python to classify gestures representing letters A to J, then wirelessly transmits the classification results to the ESP32, which triggers the corresponding audio output. A custom gesture dataset was collected and enhanced through preprocessing and data augmentation to support model training. Evaluation results demonstrate a classification accuracy of 91.4%, with a precision of 89.7%, recall of 90.5%, and F1-score of 89.9%. The average communication latency was recorded at 3.1 seconds, and the speech output success rate reached 86.7%. The system has proven reliable for real-time automatic gesture-to-speech translation and holds potential for further development as an inclusive communication aid for individuals with hearing impairments in Indonesia. This study serves as an initial foundation for future advancements in assistive communication technologies.
Analisis Frekuensi Domain Pada Sinyal EEG Terhadap Stimulus Gambar Kemasan Produk: Studi Neuromarketing dalam Kemasan Produk Suhendro, Jauzaa Maylia; Ni Wayan Nanik Suaryani Taro Putri; Endria, Felysia; Renanda Putra Prasetya, Gede
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6590

Abstract

Product packaging not only serves as protection but also functions as a strategic visual element that shapes consumer perception at the point of sale. With the advancement of neuroscience-based marketing, neuromarketing has emerged as a promising approach to investigate brain responses to visual stimuli. This study aims to analyze alpha wave activity in response to product packaging images categorized as attractive and unattractive. A total of 25 participants were involved, with visual stimuli selected based on a prior survey of 60 respondents. Brain activity was recorded using EEG from channels O1 and O2 and analyzed using the Power Spectral Density (PSD) method. The results indicate that attractive packaging elicited higher alpha wave activity compared to unattractive packaging and baseline, suggesting increased visual attention. Furthermore, PSD features were classified using three machine learning algorithms: Random Forest, Support Vector Machine, and K-Nearest Neighbors. Among them, Random Forest achieved the highest test accuracy at 78%. This study contributes to the objective evaluation of product packaging design through brain signal analysis and supports the development of data-driven marketing strategies.
Analysis of the Effectiveness of Fluidized Bed Dryer in Black Tea Processing Using the OEE Approach Prayitno, Hadi; Firmando, Moses; Panuju, Achmad Yahya Teguh; Lestari, Rahayu; Rachmawati, Tety
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6624

Abstract

This study evaluated the operational effectiveness of the Fluidized Bed Dryer (FBD) machine in the traditional black tea drying process using the Overall Equipment Effectiveness (OEE) framework, which includes availability, performance efficiency, and quality rate, as defined in ISO 22400-2:2014. Data collected from January to June 2023 indicated an average OEE of 83.66%. Although availability was consistently high (94–100%) and product quality maintained at 100% throughout the month, performance efficiency varied considerably, dropping to 69% in January and 66% in April, primarily due to operational delays and unstable machine utilization. These results indicate that performance efficiency was the main factor affecting OEE and total productivity. It recommends implementing condition-based maintenance procedures and real-time process monitoring systems to minimize unexpected downtime and enhance operational stability. Better regulation of input load, airflow, and drying temperature was crucial to ensure consistent performance and support product standardization in accordance with SNI 2891:2016, ultimately enhancing the competitiveness of the black tea processing industry.
Deteksi Penyakit Katarak pada Citra Mata Manusia Menggunakan Metode ResNet-50 Nugraha, Rifki Fajar; Rahmadewi, Reni
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6632

Abstract

Cataract is a leading cause of blindness that requires quick and accurate diagnosis to prevent further deterioration in vision quality. However, conventional examination methods often require a long time and specialized expertise, making them difficult to access widely. Along with technological developments, digital image processing offers a solution to detect cataracts more efficiently. This research aims to develop an image processing-based cataract identification system using a deep learning approach through the ResNet-50 architecture for pattern recognition in eye images. The research process includes image matrix transformation and file compression to improve data processing efficiency. Eye image datasets are used as training and testing data in the classification process using first-order parameters and 100 epochs. Test results showed the system was able to identify cataracts with an accuracy of 95.7% and the best computation time of 1.888 seconds, using 400 training data and 381 validation data. The resulting software simulation can be a digital image-based cataract early diagnosis tool, which is expected to support medical personnel in providing faster treatment and expanding access to eye health services.
Experimental Study of Performance and Emissions of a 4-Stroke Engine with a Capacity of 110cc Muhammad, Rouf; Pramanda, Wibi; Widyantoro, Arif; Joko Sumbogo, Yuliarto; Jiwo Satrio, Hutomo; Safi'i, Muhamad; Ramadhan Nur, Hamid; Ibrahim Soumi, Andi
Jurnal ELEMENTER (Elektro dan Mesin Terapan) Vol 11 No 1 (2025): Jurnal Elektro dan Mesin Terapan (ELEMENTER)
Publisher : Politeknik Caltex Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35143/elementer.v11i1.6643

Abstract

In addition to adding ferrite beads to the ignition coil, this study attempts to examine the effects of different spark plugs and ignition coils. Ferrite beads are employed to mitigate electrical noise and enhance electrical efficiency. This research facilitates the examination of engine performance and exhaust emissions, thereby offering insight into the combustion process within the combustion chamber. This study used a 4-stroke Otto engine with a displacement of 110 cc, powered by RON 92 gasoline. We utilize three types of spark plugs: Denso U22FS-U, NGK 14 mm, and Iridium IU27. Two varieties of ignition coils are employed: PCX 150 and COL9001, supplemented by ferrite beads. Dynotes are utilized to assess engine performance, while gas analyzers are employed to evaluate exhaust pollutants. The criteria examined for engine performance include torque and power, while exhaust emission parameters consist of CO, CO2, O2, and HC. The findings indicate that the NGK 14-mm spark plug, when paired with a COL9001 ignition coil and supplemented with a ferrite bead, achieves a peak torque of 7.67 Nm at 6514.42 rpm and a maximum power output of 5.73 kW at 72813.93 rpm. This setup yields minimal emissions and favorable fuel consumption when it is implemented. This study's findings offer significant insights and references for tactics including spark plugs, ignition coils, and ferrite beads.

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