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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TEKNIK INFORMATIKA Teknika Jurnal Teliska Proceedings of KNASTIK Elkom: Jurnal Elektronika dan Komputer PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Prosiding SNATIF Teknika: Jurnal Sains dan Teknologi Annual Research Seminar SMATIKA Jurnal Ampere Proceeding of the Electrical Engineering Computer Science and Informatics PROtek : Jurnal Ilmiah Teknik Elektro Jurnal Informatika Upgris Tech-E International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal TIPS : Jurnal Teknologi Informasi dan Komputer Politeknik Sekayu Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal RESISTOR (Rekayasa Sistem Komputer) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Qua Teknika Jurnal Fokus Elektroda : Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Informatika dan Rekayasa Elektronik bit-Tech Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) International Journal of Advances in Data and Information Systems Jurnal Teknik Informatika (JUTIF) Fokus Elektroda: Energi Listrik, Telekomunikasi, Komputer, Elektronika dan Kendali) Advance Sustainable Science, Engineering and Technology (ASSET) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Bangsa Enrichment: Journal of Multidisciplinary Research and Development Prosiding Seminar Hasil Penelitian dan Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Sultan Indonesia Journal of Environment and Sustainability Education JEPEmas: Jurnal Pengabdian Masyarakat (Bidang Ekonomi) Jurnal Pengabdian Masyarakat Mentari Smatika Jurnal : STIKI Informatika Jurnal
Ade Silvia Handayani
Politeknik Negeri Sriwijaya
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Competency Mapping of Vocational Education Graduates of South Sumatra Province Based on Leading Industries Using Input-Output Analysis Lupikawaty, Marieska; Novianti, Leni; Handayani, Ade Silvia; Rakhman, M.Arief
Enrichment: Journal of Multidisciplinary Research and Development Vol. 3 No. 2 (2025): Enrichment: Journal of Multidisciplinary Research and Development
Publisher : International Journal Labs

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55324/enrichment.v3i2.369

Abstract

The alignment between educational and industrial needs is an important factor in driving economic growth and workforce readiness. In South Sumatra Province, vocational high schools and polytechnics play a crucial role in equipping students with the technical and practical skills necessary for employment in key sectors such as plantations, feed, oil and gas, and coal-based industries. However, there is a gap between the competencies offered by educational institutions and the demands of the growing industry, especially in emerging areas such as digital transformation and sustainability. This study aims to map the competence of vocational schools and polytechnics to leading industries in South Sumatra using Geographic Information Systems (GIS) and input-output analysis. GIS is used to analyze the spatial distribution of educational institutions and their proximity to industrial centers, while input-output analysis assesses competency flows into industry-specific needs. Data on educational programs, graduate output, and industry requirements are compiled and analyzed to identify gaps and areas for improvement. The results reveal strong alignment in traditional industries such as rubber, palm oil, and mining, while significant gaps are observed in emerging areas such as green energy and digital technologies. Urban areas such as Palembang were found to have a higher concentration of educational institutions and diverse programs, while rural areas faced limited access to specialized training. The study emphasizes the importance of modernizing curriculum, investing in infrastructure, and encouraging industry-education collaboration to address these gaps.
Sosialisasi dan Pendampingan Pengelolaan Sampah Rumah Tangga di Daerah Pinggir Sungai Kelurahan 27 Ilir dan Kelurahan Lorok Pakjo Kota Palembang Handayani, Ade Silvia; Angguna, Welan Mauli; Rizkiyanti, Shally; Husni, Nyayu Latifah; Meileni, Hetty; Rahman, M. Arief
Jurnal Pengabdian Masyarakat Bangsa Vol. 3 No. 4 (2025): Juni
Publisher : Amirul Bangun Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59837/jpmba.v3i4.2501

Abstract

Permasalahan sampah di kawasan perkotaan masih menjadi tantangan serius, terutama pada wilayah padat penduduk seperti bantaran sungai yang memiliki risiko pencemaran tinggi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kesadaran dan mengubah perilaku warga dalam pengelolaan sampah rumah tangga melalui pendekatan edukatif dan pendampingan partisipatif. Lokasi kegiatan difokuskan di Kelurahan 27 Ilir dan Kelurahan Lorok Pakjo, Kota Palembang. Metode yang digunakan meliputi sosialisasi secara langsung dan pendampingan rumah tangga selama dua minggu. Hasil kegiatan menunjukkan peningkatan pemahaman warga terhadap pemilahan sampah serta penerapan praktik pengelolaan sampah yang lebih bertanggung jawab. Temuan ini dianalisis menggunakan Theory of Planned Behavior (Ajzen, 1991), yang menekankan pentingnya sikap, norma sosial, dan persepsi kontrol dalam membentuk niat dan perilaku. Kegiatan ini menunjukkan bahwa pendekatan edukatif dan partisipatif dapat menjadi strategi efektif dalam membangun budaya pengelolaan sampah yang berkelanjutan di tingkat komunitas.
Pendeteksi Masker dan Monitoring Suhu menggunakan Webcam dan Sensor Suhu GY-906 untuk Pencegahan Penularan Covid-19 Evelina, Evelina; Putra, Yogie Dwi; Husni, Nyayu Latifah; Handayani, Ade Silvia; Rasyad, Sabilal; Sobri, M.
Jurnal Ampere Vol. 7 No. 2 (2022): JURNAL AMPERE
Publisher : Universitas PGRI Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31851/ampere.v7i2.9514

Abstract

Covid-19 is a pandemic that occurs in various parts of the world and is a form of public concern. Coronavirus Disease (Covid-19) is a mutation of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which is highly infectious. The use of masks, keeping a distance and monitoring body temperature are ways that can be done to prevent the transmission of the Covid-19 virus. Many people have to give up their lives because of their ignorance of the dangers of this virus. Based on this, the authors try to develop a system that is able to detect and monitor automatically. That is by applying the use of mask detection and monitoring human body temperature before entering the room. The design of this detection system uses a webcam connected to a raspberry pi and a GY-906 Temperature Sensor and a HC-SR04 Proximity Sensor that is connected to an arduino nano. The way this detection system works is that the system can find out whether the user is wearing a mask or not and the user can find out his body temperature before entering the room. Besides, the output of this detection system is in the form of a display on the monitor and LCD as well as supporting outputs such as LEDs and Buzzers.ABSTRAKCovid-19 merupakan pandemi yang terjadi di berbagai belahan dunia dan merupakan bentuk kepedulian masyarakat. Coronavirus Disease (Covid-19) adalah mutasi dari Sindrom Pernafasan Akut Parah Coronavirus 2 (SARS-CoV-2) yang sangat menular. Penggunaan masker, menjaga jarak dan memantau suhu tubuh menjadi cara yang bisa dilakukan untuk mencegah penularan virus Covid-19. Banyak orang yang harus merelakan nyawanya karena ketidaktahuan mereka akan bahaya virus ini. Berdasarkan hal tersebut, penulis mencoba mengembangkan suatu sistem yang mampu mendeteksi dan memonitoring secara otomatis. Yaitu dengan menerapkan penggunaan masker pendeteksi dan pemantauan suhu tubuh manusia sebelum memasuki ruangan. Perancangan sistem pendeteksi ini menggunakan webcam yang terhubung dengan raspberry pi dan Sensor Suhu GY-906 serta Sensor Proximity HC-SR04 yang terhubung dengan arduino nano. Cara kerja sistem pendeteksi ini adalah sistem dapat mengetahui apakah pengguna memakai masker atau tidak dan pengguna dapat mengetahui suhu tubuhnya sebelum memasuki ruangan. Selain itu, keluaran dari sistem pendeteksi ini berupa tampilan pada monitor dan LCD serta keluaran pendukung seperti LED dan Buzzer.
Web-Based Monitoring System for Automatic Coffee Drying in a Smart Dryer Dome Nofriyanti, Duwi; Handayani, Ade Silvia; Suroso, Suroso; Novianti, Leni; Rakhman, M Arief; Asriyadi, Asriyadi
International Journal of Advances in Data and Information Systems Vol. 6 No. 2 (2025): August 2025 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i2.1416

Abstract

This study developed a web-based monitoring system integrated into a smart dryer dome for automatic coffee drying. The system utilized the RN-GZWS-RS485 sensor to measure critical drying parameters: temperature, humidity, and light intensity. Data acquisition relied on an ESP32 microcontroller, transmitting real-time measurements to a server using the MQTT protocol, while sensor-actuator interactions operated through the Modbus protocol. Actuator performance adhered to predefined threshold values, maintaining drying temperature within 45–50?°C and relative humidity between 20–40%. Real-time monitoring and system status visualization were implemented via a Laravel-based web interface. Experimental tests demonstrated that 71.76% of temperature readings, 64.71% of humidity readings, and 68.24% of light intensity readings consistently fell within optimal ranges. Low standard deviation values confirmed the system’s effectiveness in maintaining stable drying conditions. Additionally, the integration of solar power facilitated system deployment in remote locations without conventional electricity infrastructure. These findings highlight the system's potential to improve the reliability, accuracy, and efficiency of automatic coffee drying processes.
Real-Time Retail Shelf-Stock Detection with YOLOv7 Alquratu SeptriaPS, Annies; Silvia Handayani, Ade; Nasron, Nasron
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46448

Abstract

This study developed a real-time shelf stock monitoring system for retail environments, leveraging the You Only Look Once version 7 (YOLOv7) deep learning-based object detection framework. The system effectively addresses the inefficiencies, delays, and errors inherent in manual stock auditing processes. The underlying model was trained on a comprehensive dataset comprising 15,397 annotated object labels across fifteen distinct retail product categories. The fully trained model was then integrated into a web-based platform designed to capture real-time shelf images via a webcam. These captured images undergo automated processing for product detection and counting. The detection results are dynamically displayed on an interactive dashboard and securely stored in a backend database. The system also incorporates voice alerts, which are triggered automatically when stock levels fall below predefined thresholds, thereby facilitating immediate restocking. Experimental validation indicates high performance, with both precision and recall exceeding 96%, and an average processing latency of less than one second per frame. The model achieved an mAP@0.5 of 0.996 and an mAP@0.5:0.95 of 0.86. These findings underscore the system's effectiveness in providing a rapid, accurate, and efficient monitoring solution specifically tailored for small to medium-sized retail businesses. The primary contribution of this research lies in its comprehensive, end-to-end system integration, combining robust YOLOv7-based object detection with real-time web visualization and automated voice alerts, successfully addressing existing gaps in prior implementations.
Food Image Classification and Recipe Recommendation for South Sumatran Cuisine Using EfficientNetB1 Salsabillah, Farhah; Silvia Handayani, Ade; Anugraha, Nurhajar
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.46449

Abstract

Visual-based food classification and recipe recommendation systems remain underexplored in the context of local culinary traditions. To address this gap, a system was developed using the EfficientNetB1 architecture of Convolutional Neural Networks (CNN), integrated with a Large Language Model (LLM) to generate South Sumatran recipes from food images, adapting suggestions to classification results. The model was trained using transfer learning on eight food ingredient classes selected for their prevalence in local cuisine. It achieved a validation accuracy of 98.2% and a test accuracy of 98%, with average precision, recall, and F1-score all exceeding 98%, indicating consistent and reliable performance. The system was deployed as a web-based application, DapoerKito, allowing users to upload food images, receive classification results, and obtain generated recipe suggestions. LLM-generated recipes are produced instantly, matched to ingredients, and shown in a clear format. These findings demonstrate the value of integrating computer vision and language generation in an AI-based platform that supports usability and cultural relevance. In addition to its technical capabilities, the system contributes to the digital preservation of regional culinary heritage through interactive AI. This CNN–LLM integration offers a novel approach for advancing food AI with diverse ingredients, personalized nutrition, and multilingual support.
Comparative Analysis of LSTM and GRU for River Water Level Prediction Faris, Fakhri Al; Taqwa, Ahmad; Handayani, Ade Silvia; Husni, Nyayu Latifah; Caesarendra, Wahyu; Asriyadi, Asriyadi; Novianti, Leni; Rahman, M. Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5054

Abstract

Accurate river water level prediction is essential for flood management, especially in tropical areas like Palembang. This study systematically analyzes the performance of two deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for real-time water level forecasting using hourly rainfall and water level data collected from automatic sensors. A series of experiments were conducted by varying window sizes (10, 20, 30) and the number of layers (1, 2, 3) for both models, with model performance assessed using RMSE, MAE, MAPE, and NSE. The results demonstrate that both window size and network depth significantly influence prediction accuracy and computational efficiency. The LSTM model achieved its highest accuracy with a window size of 30 and a single layer, while the GRU model performed best with a window size of 20 and two layers. This work contributes by systematically analyzing hyperparameter configurations of LSTM and GRU models on hourly rainfall and water level time series for flood-prone regions, offering empirical insight into parameter tuning in recurrent neural architectures for hydrological forecasting. These findings highlight the importance of careful parameter selection in developing reliable early warning systems for flood risk management.
Pengembangan Website Berbasis Machine Lerning untuk Klasifikasi Kesehatan Pasien Diabetes Safitri, Rahmi Dian; Handayani, Ade Silvia; Sopian Soim
Tech-E Vol. 8 No. 1 (2024): TECH-E (Technology Electronic)
Publisher : Fakultas Sains dan Teknologi-Universitas Buddhi Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31253/te.v8i1.3184

Abstract

This research aims to develop a website utilizing the Support Vector Machine (SVM) algorithm for diabetes detection. The primary objective is to assist medical personnel in diagnosing diabetes efficiently by collecting and analyzing patient data to provide accurate health classifications. The SVM algorithm was chosen due to its high accuracy in managing complex and multidimensional medical data, making it ideal for diabetes detection. The website integrates SVM to process patient information and deliver precise predictions about their health status. By enhancing the diabetes diagnosis process, the system supports healthcare providers in making informed decisions and encourages patients to maintain regular check-ups. Additionally, the website features notifications for follow-up examinations, ensuring timely medical interventions and improving patient care and diabetes management. Its user-friendly interface allows medical staff to input and retrieve patient information with ease. This integration of advanced algorithms and intuitive design creates a valuable tool for both medical professionals and patients. By streamlining data collection and analysis, the website contributes to more accurate and timely diagnoses, fostering better health outcomes. This research highlights the potential of combining machine learning with healthcare to develop innovative solutions for chronic disease management, emphasizing the importance of regular monitoring and early detection in preventative healthcare.
Personalized Product Recommendations Using Restricted Boltzmann Machines To Overcome Cold-Start Challenges On A Niche Coffee E-Commerce Platform Hesti, Emilia; Handayani, Ade Silvia; Suzanzefi, Suzanzefi; Agung, Muhammad Zakuan; Rosita, Ella; Asriyadi, Asriyadi; Kaila, Afifah Syifah; Afifah, Luthfia; Ardiansyah, M.
International Journal of Artificial Intelligence Research Vol 9, No 1.1 (2025)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1.1551

Abstract

This paper examines the use of a Restricted Boltzmann Machine (RBM) to provide personalized product recommendations on a niche coffee e-commerce platform facing cold-start conditions. We train RBM variants on a binary transaction matrix derived from 100 simulated user transactions and evaluate four hidden-unit configurations (3, 5, 10, 15) using 5-fold cross-validation. Models were trained with Contrastive Divergence (CD-1) and assessed primarily by Mean Squared Error (MSE) for reconstruction fidelity, complemented by ranking metrics (Precision@3, NDCG@3). The 10-hidden-unit configuration achieved the best balance of reconstruction and ranking performance, with an average test MSE ? 0.0454, outperforming popular-item (MSE: 0.0802) and random (MSE: 0.0760) baselines. While the RBM demonstrates strong capability in modeling latent user preferences under sparse data, ranking metrics expose limitations when predicting exact top-N items in extremely sparse cases. The study highlights practical implications for early-stage niche marketplaces and suggests integrating content signals or hybridization to further improve top-N recommendation quality.
Multisensor monitoring system for detecting changes in weather conditions and air quality in agricultural environments Ramadhani, Dwi; Taqwa, Ahmad; Handayani, Ade Silvia; Caesarendra, Wahyu; Husni, Nyayu Latifah; Sitompul, Carlos R
Journal of Environment and Sustainability Education Vol. 3 No. 2 (2025)
Publisher : Education and Development Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62672/joease.v3i2.103

Abstract

The increasing impact of climate change and the need for precision agriculture demand reliable environmental monitoring solutions.This study aims to develop a real-time, multisensor-based environmental monitoring system that displays data via an I2C LCD and a user-friendly web interface. The system utilizes an ESP32 microcontroller connected to a range of sensors, including the DHT22 (for temperature and humidity), MQ-7 and MQ-135 (for CO and COâ‚‚), LDR (for light intensity), a rain sensor, and an anemometer (for wind speed). Testing was conducted over eight hours under various environmental conditions, both indoors and outdoors. Validation was performed by comparing the sensor readings with those from standard measuring instruments. The results showed that the DHT22 sensor had a low error rate of 0.62% for temperature and 0.38% for humidity. Other sensors demonstrated low standard deviation values, indicating stable and consistent measurements. The system also exhibited responsive and accurate performance in detecting changes in environmental parameters. Therefore, this system is effective as an environmental monitoring tool for agricultural applications and can support early decision-making based on environmental condition changes.
Co-Authors A. Rahman AA Sudharmawan, AA Aan Sugiyanto Abdul Rakhman Abdurahman Abdurrahman Abu Hasan Aditya, M Rizky Vira Afifah, Luthfia Afiifa Aaliyah Maharani Agung, Muhammad Zakuan Ahmad Satrio Perdana Ahmad Taqwa Ahmad Taqwa Al Fatur Sayid Al-Kausar, Jefri Albertia Youlanda Alfarizal, Niksen Ali Nurdin Ali Nurdin Ali Nurdin Alquratu SeptriaPS, Annies Amperawan Amperawan Andry Meylani Angguna, Welan Mauli Anisah Fadhilah Aryanti Aryanti Asriyadi Asriyadi Asriyadi Asriyadi Aswarisman, Novie Rahmadani Auditra Faza Amira Az-zahra, Maudhy Banu Putri Pratiwi Br Ginting, Nurul Devani Btari Puspa Yahya C. Ciksadan Carlos R Sitompul Carlos RS Ciksadan Ciksadan Ciksadan Ciksadan Ciksadan, Ciksadan Clara Silvia Rotua Aritonang Destra Andika Destra Andika Pratama Devi Indah Pujiana Devi Wahyuni Dewi, Tresna Dody Novriansyah DWI RAMADHANI Dzikrillah, Muhammad Ekawati Prihatini Ekawati Prihatini Elisa Islami Putri Ella Rosita Emilia Hesti Endri, Jon Endri, Jon Enri, Jon Evelina Evelina Evelina Evelina Evelina, Evelina Faisal Damsi Farid Jatri Abiyyu Faris, Fakhri Al Farozi, Ahmad Felia, Okta Felisia Talitha Aprilia Firdaus Firdaus Ghina Maysya Ayu Hani Marta Putri Harlasyanti, Dewi Ekha Hertani Indah Lestari Hetty Meileni Hj. Lindawati Husni, Nyayu Latifah Ibnu Ziad, Ibnu Ihsan Mustaqiim Inayah, Cantika Tri Irawan Hadi Irawan Hadi Irdayanti, Yeni Irma Salamah Irsyadi Yani Iryadi Yani Iryadi Yani, Iryadi Iskandar Lutfi Jon Endri Kaila, Afifah Syifah Kinasih, Ayu Antika Sekar Leni Novianti Linda Wati Lindawati Lindawati M Arief Rahman M Arief Rahman M Lutfi Kurniawan M. Ardiansyah M. Ilham Akbar M. Sobri Maharani, Ullya Dwi Mardiani, Mega Marieska Lupikawaty Martinus Mujur Rose Masayu Anisah Medina Nadila Prima Putri Mega Hasanul Huda Meranda, Arganda Meutia Deli Rachmawati Mieska Despitasari Moh. Heri Kurniawan Mohammad Fadhli Msy Aulia Hasanah Muhamad Rizki Harahap Nabiel Arinaullah Nabila, Puspita Aliya Nasron Nasron Nasron Nasron Nofriyanti, Duwi Novriansyah, Dody Nur Agustini Nur Hopipah Nurhajar Anugraha Nyanyu Latifah Husni Nyayu Latifah Nyayu Latifah Husni Nyayu Latifah Husni Nyayu Latifah Husni Nyayu Latifah Husni Nyayu Latifah Husni, Nyayu Latifah Oktariani, Clara Permata Sari, Mira Permatasari, Rosmalinda Plowerita, Sanyyah Pratama, Destra Andika Prihatini, Ekawati Putra, Muhammad Rizki Ganda Putra, Yogie Dwi Putri, Amanda Kanaya Rahman, M Arief Rahman, M. Arief Rakhman, M Arief Rakhman, M.Arief Rasyad, Sabilal Riska Handayani Riswal Hanafi Siregar Rivaldo Arviando Rizkiyanti, Shally Rizky Vira Robi Robi Rosita, Ella Rossi Passarella Rumiasih Rumiasih Rumiasih Rumiasih Rumiasih Rumiasih Sabilal Rasyad Sabilal Rasyad Safitri, Rahmi Dian Salsabillah, Farhah Sanyyah Plowerita Sarjana Sarjana Sarjana, Sarjana Sehatiningsih, Ambar Selamat Muslimin Sinaga, Putri Sitangsu Sitangsu Siti Chodijah Siti Nurmaini Sitompul, Carlos R Sobri, M. Sopian Soim Sopian Soim Sopian Soim, Sopian Sri Chodidjah Sugiyanto, Aan Suroso Suroso Suroso Suroso Suroso Suroso suzan zefi Syauqiyah, Khansa Ghazalah Taqwa, Ing Ahmad Tarmidi Tarmidi Theresia Enim Agusdi Tresna Dewi Tresna Dewi Ulandari, Monica Wahyu Caesarendra Wahyu Caesarendra Widya, Afni Rara Wildan Putra Pratama Wirayudha, Ikhwan Adhi Yani, Iryadi Yeni Irdayanti Yudi Wijanarko