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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 Wikrama Parahita : Jurnal Pengabdian Masyarakat 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 Journal of Innovation Information Technology and Application (JINITA) 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 semanTIK Smatika Jurnal : STIKI Informatika Jurnal
Ade Silvia Handayani
Politeknik Negeri Sriwijaya
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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.
Implementation of Fuzzy Logic Method to Get Estimation of Fluid Depletion on Smart Infusion Permata Sari, Mira; Taqwa, Ahmad; Silvia Handayani, Ade
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.25589

Abstract

Technology plays an important role in improving healthcare, especially in the field of medical care, particularly in infusion. Infusions are essential in hospitals, requiring constant monitoring by healthcare professionals to ensure patient safety.  The system tracks the remaining infusion fluid and displays this data on the nurse's mobile device, enabling remote control of infusion levels in each patient room. The solution incorporates a load cell sensor to measure infusion weight and an optocoupler sensor to measure infusion drip speed. In addition, the solution uses a fuzzy logic control system to make decisions based on drip speed and infusion weight, estimating when the infusion will run out.Applying this automatic infusion drip monitoring device significantly improves the accuracy and reliability of infusion management, leading to substantial improvements in patient care and safety.In this test, the results can be seen that there is a difference between the weight weighed manually and the weight on the device. with the largest weight difference of 2.49%.
Perancangan Sistem Monitoring Infus Menggunakan Mikrokontroler Arduino Uno Secara Real - Time Handayani, Ade Silvia; Mardiani, Mega; Taqwa, Ahmad
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Terapi intravena adalah prosedur medis yang menggunakan jarum untuk menggantikan cairan atau menyuntikkan obat ke dalam pembuluh darah. Namun kenyataan di lapangan, umumnya pemantauan infus dilakukan secara manual, yang seringkali menimbulkan masalah seperti infus habis tanpa sepengetahuan perawat. Dalam penelitian ini, dengan memanfaatkan teknologi Internet of Things (IoT) untuk pemantauan infus dari jarak jauh dan sejumlah komponen, termasuk sensor load cell, Optocoupler, Webcam, dan Arduino Uno sebagai pengendali utama, serta modul NodeMCU ESP32 yang digunakan untuk menghubungkan sistem ke internet, sehingga data pemantauan dapat diakses melalui server dengan komunikasi serial dan protokol MQTT secara real-time. Hasil penelitian menunjukkan bahwa sensor berkinerja baik, dengan tingkat akurasi sekitar 80% dalam pengujian. Data pemantauan infus dan kontrol pasien dapat diakses melalui aplikasi Android, memungkinkan pemantauan yang lebih efisien. Dengan teknologi ini, perawat dapat memantau infus dari jarak jauh, sehingga dapat meningkatkan kualitas perawatan dan keselamatan pasien secara keseluruhan.
Perbandingan Polaritas VV dan VH dalam Penerapan Algoritma NDFI pada Pemetaan Banjir Kota Palembang Az-zahra, Maudhy; Handayani, Ade Silvia; Lindawati, Lindawati
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.36209

Abstract

Frequent floods in Indonesia, particularly in Palembang, South Sumatra, pose significant economic and social challenges due to land-use alterations, river overflow, and intense precipitation. The region's geographical, geological, and demographic features, compounded by global climate change, worsen the situation. Flood mapping and monitoring, utilizing satellites like Sentinel-1 with Synthetic Aperture Radar (SAR), are pivotal for mitigating these disasters. Sentinel-1's SAR technology aids wetland monitoring, acting as a natural water absorber and minimizing flood risks. Data from Sentinel-1, especially in VV and VH polarizations, offer profound insights into hydrological systems influencing floods. SAR efficiently comprehends Earth's environment, facilitating high-precision and rapid flood mapping using the NDFI method in Palembang. This study compares VV and VH polarizations in the NDFI algorithm to identify the most suitable polarization for accurate flood mapping. Results show VV data achieves 97.8% mapping accuracy compared to VH's 50%. The high accuracy of VV data signifies superior flood area detection. Moreover, VV's sigma0 value (backscatter) at -1.47 dB exceeds VH's approximately -20.47 dB, indicating stronger signal intensity. Hence, VV-polarized data, considering its performance, proves more effective for flood mapping.   Keywords: NDFI; Flood; Polarization; Sentinel
Peningkatan Keterampilan Kreatif Dan Berpikir Kritis Melalui Pendidikan Vokasi Untuk Masa Depan Berkelanjutan Pada Kegiatan Expo First 2024 M Arief Rahman; Handayani, Ade Silvia
Sehati Abdimas Vol 7 No 1 (2024): Prosiding Sehati Abdimas 2024
Publisher : PPPM POLTESA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47767/sehati_abdimas.v7i1.928

Abstract

Peningkatan keterampilan kreatif dan berpikir kritis merupakan kebutuhan penting dalam menghadapi tantangan masa depan yang berkelanjutan. Pendidikan vokasi memiliki peran strategis dalam membekali peserta didik dengan kemampuan yang relevan untuk menjawab kebutuhan dunia kerja sekaligus mendukung pembangunan berkelanjutan. Kegiatan ini bertujuan untuk mengeksplorasi efektivitas kegiatan Expo FIRST 2024 sebagai sarana pengembangan keterampilan kreatif dan berpikir kritis dalam konteks pendidikan vokasi. Expo FIRST 2024 dirancang sebagai platform kolaboratif yang mempertemukan siswa maupun mahasiswa, pendidik dosen dan guru, dan pelaku industri untuk berbagi inovasi, memperluas wawasan, dan membangun jejaring. Metode kegiatan ini menggunakan pendekatan deskriptif dengan observasi, wawancara, dan survei sebagai instrumen utama. Data dikumpulkan dari peserta expo, termasuk siswa maupun mahasiswa dari berbagai program vokasi, pendidik baik guru maupun dosen, serta perwakilan industri. Hasil pengabdian menunjukkan bahwa kegiatan ini meningkatkan kemampuan kreatif dan berpikir kritis siswa maupun mahasiswa melalui inovasi yang dipamerkan. Temuan juga mengungkapkan adanya peningkatan minat siswa, mahasiswa, dosen, dan praktisi terhadap isu-isu keberlanjutan, yang mencerminkan dampak positif dari expo dalam membangun kesadaran lingkungan. Kesimpulan dari kegiatan Expo FIRST 2024 berkontribusi dalam mendukung pendidikan vokasi sebagai instrumen pengembangan keterampilan yang relevan dengan masa depan berkelanjutan. Rekomendasi mencakup pengintegrasian kegiatan serupa secara rutin dalam kurikulum pendidikan vokasi untuk memperkuat dampak jangka panjang.
Analysis of DVB-T2 TV Broadcast Receiver with Comparison of Signal Reception Quality Dzikrillah, Muhammad; Handayani, Ade Silvia; Rakhman, Abdul
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 1 (2024): PROtek : Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i1.6429

Abstract

Television is one of the most widely used media for receiving sound and image transmissions worldwide, particularly in Indonesia. Government regulations require all television broadcasters in Indonesia to cease analog broadcasts and transition to digital broadcasts. Consequently, a device known as the Set Top Box (STB), equipped with a low noise amplifier (LNA), converts DVB-T2 digital signals into images and audio suitable for analog televisions. Tests were conducted to evaluate its signal reception capabilities. These tests took place at four Palembang city locations, utilizing indoor and outdoor antennas. The results revealed that the device's signal strength ranged from -48.7 dBm, reaching 100% signal quality, to the weakest signal at -97 dBm. Moreover, an average difference of 6.9 dB was observed between indoor and outdoor testing for each frequency. Furthermore, analyzing the average signal strength based on distance showed that the highest strength of -56.2 dBm occurred at a distance of 1.5 km from the transmitter during outdoor testing. In comparison, the weakest strength of -91.6 dBm occurred at a distance of 1.8 km during indoor testing. Additionally, a signal strength comparison between test locations indicated that the most significant difference was between the Kamboja and Plaju locations. The LNA device achieved its highest gain value of 15.2 dB. Various factors, including antenna direction, obstructions such as buildings, antenna height, signal stability, and the distance between the transmitter and receiver, influence the signal quality.
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 Ambar Sehatiningsih Amperawan Amperawan Andry Meylani Angguna, Welan Mauli Anisah Fadhilah Aryanti 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 Cantika Tri Inayah Carlos R Sitompul Carlos RS Ciksadan Ciksadan Ciksadan Ciksadan Ciksadan, Ciksadan Cinda Anugrah Citra Clara Silvia Rotua Aritonang Dely Andini Destra Andika Destra Andika Pratama Devi Indah Pujiana Devi Wahyuni Dewi Ekha Harlasyanti Dewi, Tresna Dody Novriansyah DWI RAMADHANI Dzikrillah, Muhammad Ekawati Prihatini Ekawati Prihatini Elisa Islami Putri Ella Rosita 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 Hertani Indah Lestari Hetty Meileni Hj. Lindawati Husni, Nyayu Latifah Ibnu Ziad, Ibnu Ihsan Mustaqiim 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 Latifah Husni Nyayu Leni Novianti Linda Wati Lindawati Lindawati M Arief Rahman M Arief Rahman M Lutfi Kurniawan M. A. Racka Eratama M. Ardiansyah M. Ardiansyah M. Ilham Akbar M. Sobri M.Arief Rakhman 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 Muhammad Ardiansyah Muhammad Hanif Fatin Muhammad Rafly Wijaya Muslim Muslim Nabiel Arinaullah Nabila, Puspita Aliya Nasron Nasron 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 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 Rusman Ariyanto Rusman Ariyanto Sabilal Rasyad Sabilal Rasyad Safitri, Rahmi Dian Salsabillah, Farhah Sanyyah Plowerita Sarjana Sarjana Sarjana Sarjana, Sarjana 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 Umul Salamah Wahyu Caesarendra Wahyu Caesarendra Widya, Afni Rara Wildan Putra Pratama Wirayudha, Ikhwan Adhi Yani, Iryadi Yeni Irdayanti Yudi Wijanarko