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An Intelligent System for Light and Air Conditioner Control Using YOLOv8 Ikharochman Tri Utomo; Muhammad Nauval Firdaus; Sisdarmanto Adinandra; Suatmi Murnani
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2446

High energy consumption in classrooms is a significant concern, often resulting from inefficient lighting and air conditioning systems. Specifically, the problem lies in the lack of automated control mechanisms that adjust energy use based on real-time occupancy data. This study aims to develop and evaluate a system that employs a camera integrated with the YOLOv8 algorithm to detect human presence and optimize energy usage by controlling lights and air conditioning. The system's performance was assessed in three different classroom environments: two large and one small. The system's accuracy for occupancy detection varied from 13.64% to 100%, depending on lighting conditions and room size. Light control accuracy was highest in the classrooms with consistent lighting, reaching 99.77%. Air conditioning control achieved perfect accuracy of 100% in the classroom with a SHARP brand AC, with a maximum remote-control range of 7 meters. These findings indicate that the system's performance is influenced by lighting conditions and room size, with smaller rooms showing better results. The system demonstrates promising potential for reducing energy consumption in classroom settings, thereby contributing to more sustainable energy practices.

Penerapan IoT dalam Sistem Monitoring Kesehatan: Inovasi dan Implementasi Ramadhan, Irfan Wahyu; Firdaus, Firdaus; Adinandra, Sisdarmanto
Techno.Com Vol. 23 No. 4 (2024): November 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i4.11482

Penelitian ini mengkaji penerapan Internet of Things (IoT) dalam sistem monitoring kesehatan melalui tinjauan literatur yang mendalam. Inovasi dalam perangkat wearable, platform cloud, dan algoritma analitik memungkinkan pengumpulan data fisiologis secara real-time, pemantauan jarak jauh, dan deteksi dini anomali kesehatan. Implementasi teknologi ini telah menunjukkan hasil yang menjanjikan, seperti akurasi tinggi dalam pengukuran parameter kesehatan, efektivitas dalam mendeteksi kondisi kritis, dan peningkatan akses terhadap perawatan kesehatan untuk populasi yang kurang terlayani. Namun, adopsi sistem monitoring kesehatan berbasis IoT menghadapi beberapa tantangan, termasuk keamanan data, interoperabilitas, dan validasi klinis. Untuk memaksimalkan potensi teknologi ini, penelitian masa depan perlu difokuskan pada penguatan keamanan dan privasi data pasien. Dengan memanfaatkan kemajuan teknologi seperti Artificial Intelligence (AI), Machine Learning (ML), dan integrasi Electronic Health Records (EHR), sistem monitoring ke6sehatan berbasis IoT dapat mencapai potensi penuh mereka dalam mentransformasi perawatan kesehatan dan memberdayakan individu untuk mengambil peran proaktif dalam mengelola kesehatan. Kata kunci: Catatan Kesehatan Elektronik, Internet of Things, Kecerdasan Buatan, Pembelajaran Mesin, Sistem Pemantauan Kesehatan.

Single Channel Electrogastrogram Frequency Domain Analysis and Correspondence to Brain Activity in a Resting State Condition Sahroni, Alvin; Miladiyah, Isnatin; Adinandra, Sisdarmanto; Sofyan, Pramudya Rakhmadyansyah; Anora, Levina; Hanafi, Mhd.
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.590

An electrogastrogram (EGG) is a well-known method to record gastric myoelectrical activity. However, some researchers believe that EGG measures the gastric slow wave and can be used as a surrogate for gastric motility, whereas others claim that EGG is flawed. Our proposed study broadens the scope of EGG research, particularly by offering the opportunity to observe gut-brain signaling pathways, which can enhance our understanding of brain properties and behavior in response to psychological changes. This study focuses on how to confirm single-channel EGG's setup with public datasets and previous studies and how to observe the relationship of gut-brain axis pathways. We gathered four subjects utilizing a 250 Hz bioamp to monitor brain wave activity on the head and scalp including gastric activity, and used Zenodo's EGG dataset for the confirmation phase. We placed single-channel electrodes around the stomach to investigate gastric myoelectrical activity and extracted the EGG's power spectrum using a specific band-pass filter (0.03 - 0.07 Hz). We extracted the EGG's power spectrum and dominant frequency as our main features. Regarding brain electricity activities, we applied the FIR filter to obtain each brain wave's properties. We found that each subject had different responses during pre- and postprandial, both from primary and secondary resources. We found that the increase in EGG activity caused a change in EEG properties, particularly in the alpha band (8-12 Hz). Additionally, the EEG P3 site in the parietal lobe followed the power change rates of the EGG between 0 to 0.015 of relative power. We conclude that P3 and slow-wave gastric movement from EGG correspond to each other and reflect gut-brain axis pathways. However, future studies with larger samples must strengthen our findings according to the gut-brain axis pathways in the P3 site and EGG

Early Detection of Diabetes Mellitus in Women via Machine Learning Arrayyan, Ahmad Zaki; Adinandra, Sisdarmanto
Journal of Electrical Technology UMY Vol. 8 No. 2 (2024): December
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jet.v8i2.24287

Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes. The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the best-performing model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0.74), resulting in an F1-score of 0.82.

The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence Dzulfikar, Helmy; Adinandra, Sisdarmanto; Ramadhani, Erika
JOIN (Jurnal Online Informatika) Vol 6 No 2 (2021)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v6i2.702

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.

An Intelligent System for Light and Air Conditioner Control Using YOLOv8 Ikharochman Tri Utomo; Muhammad Nauval Firdaus; Sisdarmanto Adinandra; Suatmi Murnani
Journal of Innovation Information Technology and Application (JINITA) Vol 6 No 2 (2024): JINITA, December 2024
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v6i2.2446

High energy consumption in classrooms is a significant concern, often resulting from inefficient lighting and air conditioning systems. Specifically, the problem lies in the lack of automated control mechanisms that adjust energy use based on real-time occupancy data. This study aims to develop and evaluate a system that employs a camera integrated with the YOLOv8 algorithm to detect human presence and optimize energy usage by controlling lights and air conditioning. The system's performance was assessed in three different classroom environments: two large and one small. The system's accuracy for occupancy detection varied from 13.64% to 100%, depending on lighting conditions and room size. Light control accuracy was highest in the classrooms with consistent lighting, reaching 99.77%. Air conditioning control achieved perfect accuracy of 100% in the classroom with a SHARP brand AC, with a maximum remote-control range of 7 meters. These findings indicate that the system's performance is influenced by lighting conditions and room size, with smaller rooms showing better results. The system demonstrates promising potential for reducing energy consumption in classroom settings, thereby contributing to more sustainable energy practices.

LITERATURE REVIEW: PERAN SISTEM SMART HEALTH SEBAGAI INOVASI DIGITAL DALAM UPAYA PENCEGAHAN STUNTING Malini, Regina Septient; Firdaus; Adinandra, Sisdarmanto
Jurnal Elektro Kontrol (ELKON) Vol. 5 No. 2 (2025): Jurnal ELKON
Publisher : Teknik Elektro Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/elkon.v5i2.15706

Stunting merupakan permasalahan kesehatan masyarakat yang berdampak jangka panjang terhadappertumbuhan fisik dan perkembangan kognitif anak. Seiring dengan perkembangan teknologi digital,pendekatan smart health berbasis Internet of Things (IoT), Artificial Intelligence (AI), dan Machine Learning(ML) mulai diterapkan sebagai solusi inovatif dalam upaya deteksi dan pencegahan stunting. Penelitian inimenggunakan metode systematic literature review terhadap 20 jurnal ilmiah untuk mengidentifikasipenerapan teknologi smart health dalam tiga kategori utama: deteksi, pemantauan, dan pencegahan stunting.Hasil studi menunjukkan bahwa teknologi ini mampu meningkatkan efektivitas deteksi dini, efisiensipemantauan secara real-time, serta memperluas cakupan edukasi gizi kepada masyarakat melalui mediadigital yang interaktif. Namun demikian, implementasinya masih menghadapi sejumlah tantangan, sepertiketerbatasan infrastruktur, rendahnya literasi digital, serta kurangnya integrasi dengan sistem informasikesehatan nasional. Oleh karena itu, keberhasilan penerapan smart health membutuhkan dukungan kebijakan,infrastruktur yang memadai, serta evaluasi berkelanjutan agar dapat diimplementasikan secara optimal danberkelanjutan di berbagai wilayah, khususnya di daerah dengan sumber daya terbatas.

Optimization of Electromyography (EMG) Signal Parameters for Assistive Device Control Using a Convolutional Neural Network (CNN) Ramadhan, Irfan Wahyu; Adinandra, Sisdarmanto
Communications in Science and Technology Vol 10 No 2 (2025)
Publisher : Komunitas Ilmuwan dan Profesional Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21924/cst.10.2.2025.1758

Facial Electromyography (EMG) signals offer a promising modality for intuitive human-machine interfaces (HMIs). The development of robust control systems, however, remains challenging in view of the inherent complexity, noise susceptibility, and significant inter-subject variability of EMG signals in the facial region. This study addresses these technical challenges by developing and validating an optimized Deep Learning framework for facial gesture recognition. The primary objective of this study is to create a reliable classification model for five essential facial movements: 'Rest', 'Smile', 'Eyebrow Raise', 'Right Lip Movement', and 'Left Lip Movement'. The model will serve as precise control inputs for assistive devices. The proposed methodology employs a systematic workflow comprising signal preprocessing (filtering, normalization, and segmentation) followed by the automated hyperparameter optimization of a one-dimensional (1D) Convolutional Neural Network (CNN). The experimental results demonstrate that the optimized model achieved a classification accuracy of 90% on internal test data, with the learning rate identified as the most critical hyperparameter influencing performance. Furthermore, validation of the model on entirely new participants yielded an accuracy of 71%. While this result underscores the persistent challenge of generalizing across different users, it establishes a reliable baseline. Ultimately, this work provides a validated, optimization-based framework that utilizes low-cost instrumentation, thereby offering a substantial pathway towards more accessible and personalized hands-free assistive technologies to restore autonomy for individuals with severe motor impairments.

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