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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Panrita Abdi - Jurnal Pengabdian pada Masyarakat Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JMM (Jurnal Masyarakat Mandiri) Jurnal Otomasi Kontrol dan Instrumentasi Journal of Electronics, Electromedical Engineering, and Medical Informatics Journal of Novel Engineering Science and Technology Eduvest - Journal of Universal Studies Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics
Suto Setiyadi
Universitas Telkom
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Neuroscience Social Sciences Other

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IMPLEMENTASI ALAT PEMANTAUAN AKTIVITAS DAN PELACAKAN LOKASI JATUH (E-CARE) UNTUK LANSIA DI DAERAH CIKUTRA KOTA BANDUNG Istiqomah; Hesty Susanti; Husneni Mukhtar; Willy Anugrah Cahyadi; Bambang Setia Nugroho; Suto Setiyadi; Nicola Akmal Afrinaldi; Nadia Husnul; Fikri Ardian
Panrita Abdi - Jurnal Pengabdian pada Masyarakat Vol. 7 No. 1 (2023): Jurnal Panrita Abdi - Januari 2023
Publisher : LP2M Universitas Hasanuddin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/pa.v7i1.19312

Abstract

Indonesia has a percentage of an elderly population of as much as 9.03%, so it can be stated that Indonesia is a country with an old structure. This makes Indonesia pay attention to the health, activity, and productivity of the elderly. Changes in physical condition can affect the activities of the elderly significantly and increase the risk of falling. Therefore, a monitoring tool for elderly activities and location points for elderly falls was developed called e-care for the elderly. E-care Elderly is designed to be able to monitor the position of the elderly via GPS and detect if the elderly have fallen. This tool is connected to the application installed on the smartphone. In addition, this elderly E-care is equipped with a camera that can monitor the situation around the elderly. This service will be given to 10 elderly in Cikutra Village, Cibeunying Kidul District, Bandung City. There will be several preparations, implementation, and assistance activities for E-care for the Elderly. According to a survey of respondents who took part in this community service, more than 80% felt the tool had a performance that followed the needs, and 100% thought it was appropriate and very suitable for this activity with the goals and conditions of the community and it was hoped that the continuity of this activity. The survey results illustrate that the community understands all community service activities and how to use and benefit from E-Care tools.  ---  Indonesia memiliki persentase penduduk lansia sebanyak 9,03%, sehingga dapat dinyatakan indonesia merupakan negara berstruktur tua. Hal tersebut membuat Indonesia harus memperhatikan  kesehatan, keaktifan, dan produktivitas dari lansia. Perubahan kondisi fisik dapat mempengaruhi aktivitas lansia terutama dan menambah resiko jatuh. Oleh karena itu dikembakan alat pemantau aktivitas lansia dan titik lokasi jatuh lansia yang dimamakan e-care lansia. E-care Lansia dirancang agar dapat memantau posisi lansia melalui GPS dan mendeteksi jika lansia terjatuh. Alat ini terhubung ke aplikasi yang terpasang pada telepon pintar. Selain itu, E-care lansia ini dilengkapi dengan kamera yang dapat memantau keadaan di sekitar lansia. Pada abdimas ini akan diberikan kepada 10 lansia di Kelurahan Cikutra, Kecamatan Cibeunying Kidul, Kota Bandung.  Akan ada beberapa kegiatan persiapan, pengimplementasian dan pendampingan penggunaan E-care Lansia. Menurut survei responden yang mengikuti kegiatan abdimas lebih dari 80 % merasa alat memiliki kinerja yang sesuai dengan kebutuhan dan 100 % merasa sesuai dan sangat sesuai kegiatan ini dengan tujuan dan kebutuhan masyarakat dan diharapkan keberlangsungan kegiatan ini. Dari hasil survei tersebut juga bisa menggambarkan bahwa masyarakat sasar paham tentang seluruh kegiatan abdimas dan paham cara penggunaan dan manfaat dari alat E-Care.
E-Growth Monitoring System (EGMS) sebagai Upaya Penurunan Prevalensi Stunting MUKHTAR, HUSNENI; SUSANTI, HESTY; CAHYADI, WILLY ANUGRAH; RAHMAWATI, DIEN; MUTTAQIEN, TEUKU ZULKARNAIN; SUDIYONO, OOY ARIE; SUSANTO, KUSNAHADI; SETIYADI, SUTO; GERALDI, ARIK; PUJIRAHARJO, YOGA; SARI, SHEIZI PRISTA; SARI, EKA AFRIMA; YUDIARTI, DIENA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 4: Published October 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i4.903

Abstract

ABSTRAKPemerintah Indonesia melalui Stranas Percepatan Pencegahan Anak Kerdil (stunting) 2018-2024 memprioritaskan pencegahan dan penurunan prevalensi stunting dengan menyasar berbagai penyebab langsung dan tidak langsung yang memerlukan kerjasama dan koordinasi lintas sektor di seluruh tingkatan pemerintah, swasta, dunia usaha dan masyarakat, terutama di Posyandu. EGrowth Monitoring System (EGMS) yang dirancang ini berfungsi untuk mengukur tumbuh kembang bayi dan balita, terutama mendeteksi terjadinya stunting dan gizi buruk sejak dini agar dapat dilakukan upaya perbaikan secara tepat. Implementasi produk inovasi ini menggunakan sensor ultrasonik dan load cell untuk mengukur tinggi badan dan berat badan bayi dan balita, di mana rentang error pembacaan sensor-sensor tersebut secara berturut-turut adalah 0,01 – 4,36% dan 0,00 – 1,43%.Kata kunci: stunting, e-growth monitoring system, berat badan, tinggi badan, posyandu ABSTRACTIndonesian government, through National Strategy for Preventing Child Stunting 2018-2024, prioritize both prevention and reduction of stunting prevalence by targeting the causes. It requires inter-sector coordinations between government, private companies, businesses, and citizens, especially in local clinics. E-Growth Monitoring System (EGMS) is proposed to measure the growth of infant and toddler, specifically to detect a possible stunting and bad nutrition as early as possible in order to devise an improvement effectively. The implementation of this innovation employs the use of both ultrasonic sensor and load cell to measure the body height and weight of infant and toddler. Its expected margin of error for the sensors are 0.01 – 4.36% and 0.00 – 1.43% for ultrasonic sensor and load cell, respectively.Keywords: stunting, e-growth monitoring system, weight, height, posyandu
Pengukuran Somatotype dan Center of Pressure (CoP) dengan Force Platform untuk Mengetahui Pengaruh Morfologi Tubuh terhadap Keseimbangan Postur Berdiri Susanti, Hesty; Mukhtar, Husneni; Rahmawati, Dien; Arik Geraldy Fauzi, Muhammad; Setiyadi, Suto
Jurnal Otomasi Kontrol dan Instrumentasi Vol 14 No 2 (2022): Jurnal Otomasi Kontrol dan Instrumentasi
Publisher : Pusat Teknologi Instrumentasi dan Otomasi (PTIO) - Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/joki.2022.14.2.3

Abstract

The human ability to carry out daily activities is influenced by the balance of body posture in various positions, especially in a standing position. One of the factors of body balance is a biomechanical factor which is influenced by the morphology of the human body. Somatotype is a method of classifying the morphology of the human body, while the balance of human body posture can be measured based on the center of pressure (COP), which is the center of pressure generated when humans stand up. In this study, a system was designed to classify somatotypes using the Heath-Carter formula and assessment of body posture balance. Somatotype grouping is done with inputs in the form of body height measured with an ultrasonic sensor, body mass measured using a set of load cells to determine body mass index (BMI), and 8 anthropometric parameters. While the measurement of body balance is carried out by measuring the COP values using 8 load cell sensors. Eight anthropometric parameters were used, namely skin folds in the triceps, subscapular, supraspinal, and medial calf areas, the width of the femur and humerus bones, and the triceps and calf circumference. Statistical parameter values ​​in the form of mean, range, and approximate entropy (ApEn) are then calculated from the obtained COP values, i.e., for the medial-lateral (ML) and anterior-posterior (AP) directions, so that the center point of the COP, the displacement distance of each axis (ML and AP directions), and their stability can be determined. The measurement results are grouped and combined to display the type of somatotype associated with the COP value to obtain a relationship between the type of somatotype and a person's standing posture. Measurements were carried out on 14 male subjects with an age range of 15-23 years. The measurement results show an average error range of 0.37-2.58% for ultrasonic sensors, 0.64-4.67% for load cells, 0.1-0.6% for ApEn values ​​at COP in the medial-direction direction (ML COP), and 0% for ApEn in COP in the anterior-posterior direction (AP COP). The average difference range for ML COP is 0.03-0.84 cm, while the average for AP COP is 0.14-0.58 cm. These differences indicate a shift in the center of pressure on the subject's body when measurements are taken compared to the original state. The ectomorphic somatotype group had the greatest instability compared to 2 other somatotype groups, namely endomorphic and mesomorphic. In each somatotype group, the value of the AP COP range which is greater than in the ML COP indicates a greater tendency of body sway in the anterior-posterior direction. Keywords: somatotype; balance posture; center of pressure; body morphology
PENERAPAN SISTEM PENYIRAMAN OTOMATIS PADA PROSES PENYEMAIAN BIBIT PADI KERING BERBASIS PANEL SURYA DI DESA BODEH Wahmisari Priharti; Diyah Widiyasari; Suto Setiyadi
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 4 (2025): Agustus
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i4.32962

Abstract

Abstrak: Sistem penyemaian bibit padi kering merupakan metode alternatif yang diprakarsai oleh kelompok tani Ponco Tani di Desa Bodeh. Meskipun metode ini terbukti efektif untuk mempercepat proses penanaman dan memperbanyak hasil tanaman padi, terdapat kelemahan yaitu kebutuhan akan sistem penyiraman yang efisien dan terjadwal. Hal ini sulit dipenuhi menggunakan sumber listrik konvensional karena seringnya terjadi pemadaman yang menghambat proses penyiraman bibit.Untuk mengatasi permasalahan tersebut, kegiatan pengabdian masyarakat ini dilaksanakan untuk mengimplementasikan suatu sistem penyiraman otomatis berbasis panel surya. Pada kegiatan ini, 11 petani anggota Ponco Tani ikut dilibatkan sebagai pengguna dan diberikan sosialisasi serta pelatihan terkait cara penggunaan serta pemeliharaan sistem. Umpan balik yang diperoleh dari sosialisasi menunjukkan tingkat pemahaman sebesar 97% yang mengindikasikan bahwa sistem yang diberikan dapat dipahami serta dijaga secara mandiri oleh petani. Harapannya, sistem ini mampu menggantikan ketergantungan pada sumber listrik konvensional dan dapat terus digunakan selama 15-20 tahun ke depan untuk meningkatkan produktivitas petani bibit padi kering di Desa Bodeh.Abstract: The dry rice seedling system is an alternative method initiated by the Ponco Tani farmer group in Bodeh Village. Although this method has proven effective in accelerating the planting process and increasing rice crop yields, it has a drawback the need for an efficient and scheduled irrigation system. This need is difficult to meet using conventional electricity sources due to frequent power outages, which hinder the seedling watering process. To address this issue, this community service activity was carried out to implement an automatic irrigation system powered by solar panels. In this program, 11 farmers who are members of Ponco Tani were involved as users and received socialization and training regarding the usage and maintenance of the system. Feedback obtained from the sessions indicated a 97% level of understanding, showing that the system can be understood and maintained independently by the farmers. It is hoped that this system will replace the dependence on conventional electricity sources and continue to be used for the next 15–20 years to improve the productivity of dry rice seedling farmers in Bodeh Village.
Multimodal Gait Analysis Using IMU and EMG Sensors with HMM Classification to Differentiate Obese and Normal Body Types Setiyadi, Suto; Muhammad Ridho Rosa; Nigel Bryan Tang; Muhammad Sabiq Al Muttaqin; Muhammad Rafi Haykal Gumelar
Journal of Novel Engineering Science and Technology Vol. 4 No. 03 (2025): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v4i03.1272

Abstract

Gait analysis is essential for diagnosing movement disorders and monitoring rehabilitation progress; conventional methods are often costly and complex. This study aims to differentiate gait characteristics between individuals with obesity and those with normal body composition using a multimodal approach that integrates Inertial Measurement Unit (IMU) and electromyography (EMG) sensors. Data were collected from ten male participants (five classified as obese and five with normal body composition). IMU sensors were used to measure acceleration, angular velocity, and step count, while EMG sensors recorded muscle activity from the tibialis anterior and gastrocnemius muscles. We developed a real-time acquisition using ESP32 microcontrollers and Bluetooth Low Energy (BLE), and gait phase classification was performed using the Hidden Markov Model (HMM). Using heel-mounted sensors, the average step detection error ranged from 2.5% to 3.6%. IMU signals from obese participants indicated a shift in dominant gait phase from Initial Contact during slow walking to Loading Response during fast walking, with relative errors up to 27%. In contrast, participants with normal body composition exhibited more diverse and accurate phase distributions. EMG-based analysis provided more precise segmentation (with error rates as low as 0.47%). It revealed distinct muscle activation patterns: gastrocnemius activity was dominant during the Midswing or Midstance phases, while tibialis anterior activity peaked during Initial Contact, Initial Swing, or Loading Response. These findings suggest body composition significantly affects gait stability, phase transitions, and muscle activation patterns. Future work should explore advanced machine learning algorithms such as Long Short-Term Memory (LSTM) or Convolutional Neural Networks (CNN), integrate pressure sensors, and validate the system in real-world environments to enhance accuracy and reliability.
Non-Contact Heart Rate Detection Using FMCW Radar Based on 1-D Convolutional Neural Networks Diyah Widiyasari; Istiqomah Istiqomah; Fiky Yosef Suratman; Suto Setiyadi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 2 (2026): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i2.1547

Abstract

Non-contact heart rate (HR) estimation using frequency-modulated continuous-wave (FMCW) radar has emerged as a promising solution for unobtrusive, continuous vital-sign monitoring. However, accurately extracting HR from radar signals remains challenging because of low-amplitude cardiac-induced chest vibrations, environmental clutter, motion artifacts, and system noise. Traditional signal processing techniques, such as bandpass filtering combined with fast Fourier transform (FFT) analysis, are commonly employed to estimate HR in the frequency domain. Nevertheless, these approaches are highly sensitive to noise and often struggle to robustly capture weak cardiac components, leading to unstable or inaccurate estimates. To address these limitations, this study proposes a non-contact HR estimation framework based on FMCW radar combined with a one-dimensional convolutional neural network (1D-CNN). A systematic radar signal preprocessing pipeline is developed, including range-bin selection, phase extraction, noise suppression, filtering, and structured data labeling, to construct learning-ready input features. The 1D-CNN model is designed to automatically learn discriminative temporal patterns associated with cardiac activity directly from preprocessed radar signals. The proposed method is evaluated using two datasets: a publicly available dataset and an independently acquired dataset collected under controlled conditions. Performance is benchmarked against conventional bandpass filtering- and FFT-based HR estimation methods. The experimental results demonstrate that the proposed 1D-CNN framework achieves more accurate and stable HR predictions. On the public dataset, MAE decreases from 17.96 to 6.09 BPM, RMSE from 21.28 to 7.34 BPM, and MedAE from 17.66 to 5.43 BPM. The independent dataset yields consistent gains, with MAE decreases from 14.05 to 5.45 BPM, RMSE from 18.05 to 6.84 BPM, and MedAE from 10.74 to 4.57 BPM. These results indicate that the proposed 1D-CNN framework can effectively estimate HR from radar signals and demonstrate its capability to operate across datasets acquired with different radar frequencies
Development of a Responsive Airbag Hardware Mechanism for Fall Mitigation and Injury Reduction in the Elderly Sigit Indriyanto; Willy Anugrah Cahyadi; Husneni Mukhtar; Muhammad Fasha Aqillah; Suto Setiyadi
Eduvest - Journal of Universal Studies Vol. 6 No. 6 (2026): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v6i6.52958

Abstract

Falls pose a significant risk to elderly individuals and often result in serious injuries due to the lack of immediate protective mechanisms. Therefore, a real-time protective system is needed to reduce the impact of falls. The research aims to develop a wearable airbag vest system that integrates real-time fall detection with a servo-based mechanical triggering mechanism for rapid airbag deployment. The system utilized an IMU sensor to monitor body motion and orientation, a microcontroller to process detection logic, and a servo motor to activate a CO₂ inflator through a spherical buckle mechanism. Performance evaluation was conducted to assess IMU accuracy and airbag inflation response. The IMU demonstrated an average orientation error below 2% for yaw, pitch, and roll compared to an inclinometer reference. Inflation tests showed a servo current peak of 1200–2000 mA, with the airbag reaching peak pressure within 1.2–2.5 seconds after triggering, achieving pressure levels between 85 and 120 psi. The proposed servo-based triggering mechanism provides sufficient responsiveness and reliability for wearable fall protection applications.
Design of a Real-Time User Feedback for Mitigating Spurious SpO₂ Readings in Pulse Oximetry for Outpatient Monitoring Husneni Mukhtar; Dien Rahmawati; Suto Setiyadi; Istiqomah; Reza Ahmad Madani
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 1, February 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i1.2371

Abstract

Spurious SpO₂ readings—arising from motion artifacts, environmental interference, or device variability—remain a major limitation in wearable pulse oximetry, potentially triggering false alarms or missing hypoxemia during outpatient monitoring. Conventional devices often lack real-time mechanisms to detect and mitigate such errors, with previous reports indicating measurement biases of 11.2 - 24.5% across different models, underscoring the need for improved accuracy and user guidance. To address this gap, we present the design of an IoT-enabled wearable pulse oximeter with real-time user feedback, delivered through a mobile application. The system integrates a pulse oximetry and heart rate sensor (MAX30100) with a carbon monoxide gas sensor (MQ-7) and provides targeted notifications to guide corrective actions such as repositioning the probe, removing nail polish, or moving to fresh air. Validation involved controlled scenario testing (undetected SpO₂, CO >40 ppm, nail polish, and loose contact) and user trials with 15 healthy volunteers from varied academic backgrounds. The prototype demonstrated high accuracy, with low relative errors—0.92% (HR), 0.93% (SpO₂), and 0.015% (CO)—and strong usability, achieving 93.3% compliance with corrective prompts, an average response time of 4.0±0.7 seconds, and a satisfaction score of 4.3/5. Compared with commercial oximeters, the proposed system improved reliability by reducing measurement errors by at least 87% through real-time corrective feedback. Future work will focus on energy-efficient power management and large-scale community-based trials to further validate performance across diverse patient populations.
Gait Variability and Phase Segmentation in Obese and Normal Individuals Using Multi-Location IMUs and Hidden Markov Models Supervised Marginal Suto Setiyadi; Husneni Muktar; Willy Anugrah Cahyadi; Diyah Widiyasari; Mohamad Ramadhani; Nigel Bryan Tang
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 4 (2025): November
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v7i4.269

Abstract

Obesity is known to disrupt motor control and biomechanics; however, detailed gait alterations in individuals with obesity remain underexplored, particularly in dynamic and real-world walking conditions. This study aims to quantitatively characterize gait differences between individuals with obesity and those of normal weight by analyzing postural and temporal gait parameters. The investigation focuses on pitch, roll, and cadence dynamics using body-worn inertial sensors, with phase transition modeling via Hidden Markov Models. This work proposes a novel framework that integrates multi-location Inertial Measurement Unit (IMU) sensors and a Hidden Markov Model–Supervised Marginal (HMM-SM) approach to detect and classify gait phases with high accuracy, offering practical value for clinical gait assessment and personalized rehabilitation. IMU sensors were placed on the waist, thigh, calf, and heel to record gait data from participants in both obese and normal-weight groups. Gait segmentation and phase modeling were conducted using 4-, 5-, and 8-state HMMs. Quantitative analysis revealed significantly greater postural variability in the obese group during slow walking, with standard deviations in roll and pitch reaching 20.68° and 9.23°, respectively—much higher than the normal-weight group (0.60° and 0.26°). Hidden state transitions from 5-state pitch HMMs showed a very strong effect size for the obese group (Cramér’s V = 0.72) compared to a moderate effect for the normal-weight group (V = 0.33). Similar patterns were observed for roll and cadence. In terms of segmentation accuracy, the 4- and 5-state HMMs outperformed the 8-state model, achieving accuracy levels above 99%, while the 8-state model reached only ~93%. The findings demonstrate that obesity significantly alters gait dynamics, particularly in postural stability and gait phase transitions. The proposed IMU-based HMM-SM framework effectively captures these changes, offering a reliable tool for gait analysis in clinical and biomechanical applications.
Design and Evaluation of a Pre-Impact Fall Detection System with Wearable Airbag Vest for Elderly Injury Mitigation Willy Anugrah Cahyadi; Husneni Mukhtar; Suto Setiyadi; Dita Puspitasari; Nigel Bryan Tang; Mohamad Ramdhani
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 8 No. 3 (2026): August
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/ijeeemi.v8i3.344

Abstract

The increase in the elderly population in Indonesia raises concerns about fall accidents. Many mitigation strategies are available to reduce fall accidents among the elderly. Two main strategies have been widely explored, i.e., fall detection and impact reduction. Despite its disadvantages, fall detection devices remain useful and important for future research. In addition, it is also urgent to address the impact reduction method by providing cushioning to minimize the physical impact on the subject of the fall. This study proposes a wearable safety vest equipped with a pre-impact fall-detection system and an automatically inflating airbag to protect the wearer during a fall. The main contributions of this study are the design and implementation of a lightweight wearable airbag vest for elderly protection, the development of a machine-learning-based pre-impact fall detection, and a quantitative evaluation of impact reduction using accelerometer-based measurements. The proposed safety vest with airbag has been trained, validated, and tested, with the subject achieving fall detection accuracy beyond 90% and the airbag deployment delay of less than 1 second after detection. The experiment results also show that the use of the airbag could reduce the impact shock down to 40-50% based on the accelerometer measurements.
Co-Authors Arik Geraldy Fauzi, Muhammad Bambang Setia Nugroho Dien Rahmawati Diena Yudiarti Dita Puspitasari Diyah Widiyasari Eka Afrima Sari Fikri Ardian Fiky Yosef Suratman GERALDI, ARIK Hesty Susanti Husneni Mukhtar Husneni Muktar Istiqomah Kusnahadi Susanto Mohamad Ramadhani Mohamad Ramdhani Muhammad Fasha Aqillah Muhammad Rafi Haykal Gumelar Muhammad Ridho Rosa Muhammad Sabiq Al Muttaqin Nadia Husnul Nicola Akmal Afrinaldi Nigel Bryan Tang Nigel Bryan Tang Nigel Bryan Tang Reza Ahmad Madani Sheizi Prista Sari Sigit Indriyanto SUDIYONO, OOY ARIE Teuku Zulkarnain Muttaqien Wahmisari Priharti Willy Anugrah Cahyadi Yoga Pujiraharjo