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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Rekayasa Mesin Sinergi Jurnal Teknik Mesin International Journal of Advanced Technology in Mechanical, Mechatronics and Material (IJATEC) JTTM: Jurnal Terapan Teknik Mesin International Journal of Community Service Akuntansi dan Humaniora: Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Bhinneka International Journal of Innovation in Mechanical Engineering and Advanced Materials Journal of Engineering and Technological Sciences
Wiwit Suprihatiningsih
Universitas Mercu Buana
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Public Health Social Sciences Other

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Electroencephalogram-Based Multi-Class Driver Fatigue Detection using Power Spectral Density and Lightweight Convolutional Neural Networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Feleke, Aberham Genetu
Journal of Engineering and Technological Sciences Vol. 57 No. 4 (2025): Vol. 57 No. 4 (2025): August
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.4.2

Abstract

Driver fatigue is the primary factor contributing to traffic accidents globally. To address this challenge, the electroencephalogram (EEG) has been proven reliable for assessing sleepiness, fatigue, and performance levels. Although alertness monitoring through EEG analysis has shown progress, its use is affected by complicated methods of collecting data and labelling more than two classes. Based on previous research, the original form of EEG signals or power spectral density (PSD) has been extensively applied to detect driver fatigue. This method needs a large, deep neural network to produce valuable features, requiring significant computational training resources. More observations regarding feature extraction and classification models are needed to reduce computational cost and optimize accuracy values. Therefore, this research aimed to propose a PSD-based feature optimization on a lightweight convolutional neural network (CNN) model. Five types of statistical functions and four types of signal power ratios were applied, and the best features were selected based on ranking algorithms. The results showed that feature optimization using the Relief Feature (ReliefF) algorithm had the highest accuracy. The proposed lightweight CNN model obtained an average intra-subject accuracy of 71.01%, while the cross-subject accuracy was 69.07%.
Pelatihan Pengoperasian Mesin Pengurai Sabut Kelapa Di RPTRA Menara Kelurahaan Kembangan Selatan Jakarta Barat Subekti, Subekti; Indah, Nur; Pratiwi, Swandya Eka; Wahyudi, Haris; Anggara, Fajar; Sudarma, Andi Firdaus; Carles, Henry; Sari, Andarany Kartka; Suprihatiningsih, Wiwit
Jurnal Pengabdian Masyarakat Bhinneka Vol. 3 No. 4 (2025): Bulan Juli
Publisher : Bhinneka Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58266/jpmb.v3i4.250

Abstract

\ Proses pengolahan limbah sabut kelapa dengan menggunakan mesin pengurai sabut kelapa dapat memudahkan proses produksi. Hasil produksi dari penguraian sabut menghasilkan cocofiber dan cocofeat yang dapat di manfaatkan dalam berbagai macam produk seperti jok mobil, matras, keset, kerajinan tangan, papan serat, serta produk ramah lingkugan lainnya. Hal tersebut menunjukkan bahwa penggunaan produk-produk rumahan tidak hanya berasal dari bahan baku sintetis. Pemanfaatan sabut kelapa dengan sumber bahan baku yang relatif mudah didapatkan dapat membantu perindustrian ekonomi kecil dan menengah, dengan demikian pengabdian masyarakat melalui merancang mesin pengurai sabut kelapa menggunkan motor bakar bensin sebagai penggerak mesin untuk mempermudah proses penguraian sabut kelapa sebelum diproduksi. Sehingga tujuan pengabdian masyarakat dalam menanfaatkan limbah serabut kelapa menjadi barang dapat bernilai ekonomis sehingga dapat meningkatkan taraf hidup masyarakat sekitar. Kegiatan ini dilakukan dengan ceramah dan demo mesin pengurai sabut kelapa. Masyarakat yang akan terlibat dari kegiatan ini sekitar 40 orang yang diatur oleh RT/RW setempat, Kegiatan ini sangat diapresiasi oleh para peserta dimana hampir 88 % peserta memahami isi materi dan praktek Mesin Serabut Kelapa. Sedangkan untuk nilai terendah sekitar 72 % menyatakan bahwa kegiatan ini sangat diperlukan oleh para peserta dan berlangsung sukses dengan banyaknya pertanyaan dan saran agar kegiatan ini dilanjutkan.
Designing an intelligent system for vibration diagnosis of centrifugal water-cooling pumps using Bayesian networks Suprihatiningsih, Wiwit; Romahadi, Dedik; Genetu Feleke, Aberham
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4390-4402

Abstract

Implementing monitoring methods is a viable method to reduce substantial damage to cooling water centrifugal pumps. Engaging in manual vibration analysis requires considerable time and a requisite level of competence. Small datasets pose challenges when applying classification systems that utilize linear classification models and deep learning. Given these issues, our proposal entails developing a system capable of autonomously, precisely, and accurately diagnosing vibrations using a limited dataset. The system is anticipated to possess the capability to detect multiple categories of mechanical defects, such as static imbalance, dynamic imbalance, misalignment, cavitation, looseness, and bearing corrosion. The Bayesian network (BN) structure was constructed using the MATLAB software. The input data parameters comprise vibration signals measured in the frequency domain and values representing phase differences. The constructed intelligent system was subsequently assessed using a dataset including 120 samples. The smart system can rapidly anticipate and precisely identify every form of harm with exceptional accuracy and sensitivity, relying on test outcomes. The test data analysis reveals that the intelligent system attained an average accuracy of 94.74%, precision of 95.32%, sensitivity (recall) of 93.67%, and F-score of 94.36%. 
Co-Authors Abdul Hamid Adji Nur Fadilah Anggara, Fajar Arief Priyanto Arief Suardi Nur Chairat ARIYANTO , Satrio Muhammad Ayu Arwati, I G. Carles, Henry Dedik Romahadi Dedik Romahadi Denny Prumanto Erna Setiany Erna Sofriana Imaningsih Feleke, Aberham Genetu G A Arwati, I G. Ayu Arwati, I Genetu Feleke, Aberham Gian Villany Golwa Hadi Pranoto Hari Setiyawati Hui Xiong Mahesh Kumar Muhammad Fikri Muhammad Fitri, Muhammad Muhammad Idil Fadil Nur Indah, Nur Nurato Nurato Pratiwi, Riri Puji Rahayu PUTRA, Rangga Aji Romahadi, Dedik Safira, Safira Sagir Alva SALMAN, Kina Bahru Sari, Andarany Kartka Sawarni Hasibuan Setiawan, Arip Siti Choiriah Sri Purwaningsih Subekti Subekti Sudarma, Andi Firdaus Suharmadi Suharmadi Supaat Zakaria Swandya Eka Pratiwi, Swandya Eka Villany Golwa, Gian Wahyudi, Haris Winnugroho Wiratman, Manfaluthy Hakim, Tiara Aninditha, Aru W. Sudoyo, Joedo Prihartono Yudha Aji Pramono Yudhi Herliansyah