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All Journal Jurnal Algoritma Jurnal Wawasan Ilmiah Jurnal Teknologi Informasi dan Ilmu Komputer JAIS (Journal of Applied Intelligent System) JOIN (Jurnal Online Informatika) Edu Komputika Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal ULTIMATICS Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Teknika Journal of Community Development Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal PkM MIFTEK Jurnal Algoritma Engineering Science Letter Journal of Novel Engineering Science and Technology IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi
Asri Mulyani
Institut Teknologi Garut
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Nursing Public Health Social Sciences Other

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Penerapan Algoritma K-Means Untuk Pengelompokan Faktor Perceraian Di Kabupaten Garut Mulyani, Asri; Padilah, Eva Nurul
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2198

Abstract

Perceraian dapat terjadi apabila suami atau istri sudah sama-sama merasakan adanya ketidakcocokan dalam menjalani rumah tangganya. Beberapa permasalahan penyebab perceraian yang diajukan oleh penggugat yaitu karena suami selingkuh, penghasilan suami tidak cukup, suami yang sudah lama tidak bekerja, maupun adanya ketidakcocokan dalam menjalani rumah tangga dan beberapa penyebab perceraian yang lainnya. Namun data faktor perceraian belum mendapatkan kategorisasi, oleh karena itu diperlukan adanya pengelompokan data pada periode-periode sebelumnya. Penelitian ini bertujuan untuk mengelompokkan faktor-faktor perceraian dikabupaten Garut menggunakan metode CRISP-DM dan algoritma K-Means Clustering. Data yang digunakan sebanyak 500 record kasus perceraian pada tahun 2023. Evaluasi performa dilakukan dengan menggunakan Davies-Bouldin Index (DBI) dimana nilai DBI terendah yaitu 0.668 pada K=3 menunjukan kualitas Clustering terbaik dibanding nilai DBI K=2 dan K=4-9 yang memiliki nilai DBI antara 0.774 hingga 0.807. hasil Clustering menunjukan bahwa kelompok dengan kasus terbanyak ada di Cluster 1 yaitu sebanyak 381 kasus yang sebagian besar disebabkan oleh faktor ekonomi, seperti penghasilan suami tidak cukup dan suami lama tidak bekerja. Selanjutnya hasil evaluasi Silhouette Index menghasilkan nilai terbesar berada di K3 yaitu 0.630. Penelitian ini memberikan kontribusi praktis berupa rekomendasi kebijakan berbasis hasil Clustering, yang dapat dimanfaatkan oleh Pengadilan Agama Garut dan pemerintah daerah. Rekomendasi tersebut seperti edukasi pranikah mengenai kesiapan mental dan finansial serta perluasan akses lapangan kerja untuk mengurangi beban ekonomi dalam rumah tangga.
Implementasi Virtual Tour Berbasis Web Menggunakan Kamera 360° Sebagai Media Promosi Mulyani, Asri; Erwin Gunadhi Rahayu, Raden; Qalam Ilmayasa, Muhammad
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2230

Abstract

Dalam era digital saat ini, kemajuan teknologi telah mengubah cara wisatawan ketika akan mencari informasi terkait dengan pemilihan akomodasi saat akan merencanakan kegiatan berlibur. Villa Andhita, sebagai salah satu destinasi penginapan di Kabupaten Garut, memiliki potensi besar untuk menarik wisatawan, namun promosi yang dilakukan masih terbatas pada media sosial dengan konten berupa foto, Video dan teks, yang kurang interaktif dan tidak memberikan gambaran menyeluruh mengenai fasilitas serta lingkungan villa. Akibatnya, calon wisatawan sering kali kesulitan mendapatkan informasi yang jelas dan harus datang langsung ke lokasi untuk memastikan kesesuaian dengan kebutuhan mereka. Untuk mengatasi kendala tersebut, penelitian ini bertujuan untuk merancang dan mengimplementasikan Virtual Tour berbasis web menggunakan kamera 360° sebagai media promosi yang lebih inovatif dan imersif. Metode pengembangan yang digunakan dalam penelitian ini adalah Multimedia Development Life Cycle (MDLC). Hasil penelitian menunjukkan bahwa Virtual Tour yang dikembangkan mampu memberikan pengalaman eksplorasi yang lebih interaktif dibandingkan media promosi konvensional. Berdasarkan pengujian beta, sistem yang dibuat memperoleh rata-rata skor 4,43 dalam skala Likert, yang menunjukkan bahwa Virtual Tour ini sangat layak digunakan sebagai media promosi. Dengan adanya inovasi ini, Villa Andhita dapat meningkatkan daya tariknya di kalangan wisatawan serta memberikan kemudahan bagi calon pengunjung dalam mendapatkan informasi yang lebih lengkap sebelum melakukan reservasi.
Klasifikasi Keaslian Uang Kertas Menggunakan Algoritma K-Nearest Neighbor dan Metode Gabor Filter Mulyani, Asri; Nurazizah, Neng Putri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2375

Abstract

Counterfeiting is currently on the rise in Indonesia. Based on Law No. 7 of 2011 concerning counterfeit rupiah currency, counterfeit currency is defined as any object whose material, size, color, image, and/or design resembles the rupiah that is made, formed, printed, duplicated, circulated, or used as a means of payment illegally. The objectives of this study are: To obtain a paper money classification model by applying the KNN and Gabor Filter algorithms. To improve the evaluation results of the paper money classification model by applying the KNN and Gabor Filter algorithms with Confusion Matrix and ROC-AUC Curve. The results of testing the banknote classification model show the effectiveness of the KNN algorithm model and the Gabor Filter method, as well as the assistance of PCA, producing the best performance with an accuracy value of 97.14%, precision of 95.72%, recall of 95.77%, f1-score of 95.74%, and specificity of 99.62%. The AUC value obtained on the ROC-AUC curve based on the test results produced a banknote classification model with an average AUC performance for all classes of 97.35%, which is classified as excellent in classifying banknotes, so that the model can be implemented into the system.
Rancang Bangun Sistem Pengelolaan Inventaris Barang Dengan Verifikasi Pengguna Berbasis Face Recognition Sutedi, Ade; Indrakusumah, Muhamamad Rafi; Deddy Supriatna, Asep; Mulyani, Asri
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2672

Abstract

Inventaris barang di lingkungan sekolah membutuhkan sistem yang mampu mencatat dan mengelola data secara terstruktur serta aman. Penelitian ini bertujuan merancang dan mengembangkan sistem informasi inventaris barang berbasis web dengan penambahan fitur keamanan autentikasi pengguna menggunakan teknologi face recognition. Sistem ini ditujukan untuk mendukung kinerja bagian sarana dan prasarana dalam melakukan pencatatan inventaris secara lebih terintegrasi dan terlindungi dari akses tidak sah. Metode pengembangan menggunakan pendekatan Rational Unified Process (RUP) dengan empat tahapan utama: Inception, Elaboration, Construction, dan Transition. Teknologi yang digunakan meliputi bahasa pemrograman PHP dengan framework Laravel, MySQL sebagai basis data, dan JavaScript untuk penerapan face recognition. Hasil pengujian menunjukkan bahwa sistem berhasil dibangun dengan fitur utama berupa input data inventaris, pencarian, pembuatan laporan, serta autentikasi wajah dengan tingkat akurasi tinggi. Dengan demikian, sistem ini menjadi solusi digital yang tidak hanya menyediakan pengelolaan inventaris, tetapi juga menghadirkan peningkatan aspek keamanan melalui integrasi teknologi face recognition.
PENDAMPINGAN MASYARAKAT DALAM PENGGUNAAN APLIKASI KAWAL DESA DI DESA CISURUPAN Mulyani, Asri; Nugraha, Aldi; Jamaludin; Khaifa, Raisha Sarah; Maulud, Restu Bagja; Oktavian, Muhamad Ar Rasyid Rizki; Sugriantha, Irham; Ismaya, Karina; Salsabila, Kailla; Setiawan, Ahmad Dandi; Homsatin, Asyifa Azsma; Muhamad, Zaki; Rizki, Riyandi Muhamad; Janatunnisa, Raisya Agni; Ramdani, Dikri; Pasundan, Gia Aghista; Abdurrohman, Muhammad Akmal; Saputri, Amellia; Akbar, Fazri Haikal; Oktavian, Zordan; Rangga, Wisnu
Jurnal PkM MIFTEK Vol 6 No 2 (2025): Jurnal PkM Miftek
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/miftek/v.6-2.2624

Abstract

Village fund management requires transparency and community participation. One effort to support this is the implementation of the Kawal Desa application as a digital solution. This community service activity aims to enhance the digital literacy of village officials and residents, as well as to strengthen oversight of village fund usage. Methods employed include workshops, distribution of materials, and application usage demonstrations. The results indicate an improved understanding among village officials and residents in operating the application, leading to more transparent and efficient village services. This activity contributes to fostering collaboration between village authorities and the community toward more informative and integrated village governance.
Melatih Cara Berfikir Komputasi Pada Siswa Sekolah Dasar dan Menengah di Kabupaten Garut Dewi Tresnawati; Detila Rostilawati; Ayu Latifah; Eri Satria; Asri Mulyani; Sri Rahayu; Leni Fitriani; Rinda Cahyana; Shopi Nurhidayanti; Cha Cha Nisya Asyah
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.1373

Abstract

Technological advances in the 21st century demand Computational Thinking skills that are increasingly important in the digital era. However, the application of Computational Thinking (CT) at the primary and secondary education levels in Garut Regency is still limited. This study aims to improve the understanding of CT among students in elementary schools, junior high schools, and vocational high schools through a training program that includes pre-test, interactive training, and post-test. The training method was designed according to the educational level of the participants, using Bebras questions to train CT skills. The pre-test results showed an average initial score of 47.19, while the post-test results increased by an average of 25.75 points to 72.94. The findings show that the training successfully improved participants' understanding of CT through a structured approach, including introduction of concepts through sample Bebras problems, practice problems, as well as a comprehensive question and answer session.
Natural Disaster Classification Using MobileNet with Transfer Learning Asri Mulyani; Dede Kurniadi; Gina Suciyana
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001682

Abstract

Natural disasters are events caused by natural phenomena that cause massive damage and pose a threat to human safety. Based on EM-DAT data (2000–2025), there have been more than 10,000 global disasters, resulting in millions of casualties and trillions of US dollars in losses. Notably, 2024 saw US$320 billion in losses due to extreme weather. This condition emphasizes the importance of an accurate disaster classification system for mitigation and rapid response. This study aims to develop a natural disaster image classification model using the Convolutional Neural Network (CNN) method with a Transfer Learning approach using the MobileNetV2 architecture, which is known to be efficient and lightweight. This study employs the SEMMA (Sample, Explore, Modify, Model, Assess) methodology, beginning with sampling, which involves collecting image data from various open sources, such as Kaggle and previous literature. The data is then processed through the selection, cleaning, and normalization stages. Data exploration is conducted to understand class distribution and detect data imbalance. To overcome this problem, the Synthetic Minority Over-sampling Technique (SMOTE) and image data augmentation (such as rotation, flipping, and contrast adjustment) were used to enrich the training data variation. The pre-trained MobileNetV2 model was then retrained using the modified data, with adjustments to hyperparameters to achieve optimal performance. The evaluation was conducted using various metrics, namely accuracy, precision, recall, F1-score, confusion matrix, and AUC-ROC curve. The results demonstrate that the combination of Transfer Learning, data augmentation, and SMOTE can enhance model performance, achieving an accuracy of up to 99% and an AUC-ROC above 0.99 on public test data. Additionally, testing on 26 private test images yielded an accuracy of 92.31%, with 24 of the 26 images classified correctly. These findings confirm that the combination of MobileNetV2, augmentation, and SMOTE effectively improves multi-class classification performance on natural disaster images. Furthermore, the use of a relatively lightweight model makes this system more efficient to implement on devices with limited resources, thereby supporting disaster mitigation and rapid response efforts.
Performance-Efficiency Tradeoff Analysis of YOLOv8 Variants for Real-Time Multiclass Vehicle Detection in High-Density Traffic Dede Kurniadi; Asri Mulyani; Nuraisah Nuraisah
Engineering Science Letter Vol. 5 No. 01 (2026): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001702

Abstract

The growing number of vehicles in Indonesia increases the need for an efficient and reliable traffic monitoring system. In Garut Regency, traffic monitoring is still carried out manually without the support of artificial intelligence, thus limiting the effectiveness of real-time traffic analysis. This study develops and evaluates a CCTV image-based vehicle classification model using YOLOv8 with a focus on application in real-world traffic conditions. The development process follows the Machine Learning Life Cycle (MLLC) stages, including data acquisition, preprocessing, training, and model evaluation. The dataset comprises 1,200 CCTV traffic images from 10 locations in Garut Regency, supplemented by 7,426 additional images from the Roboflow platform to enhance the diversity of viewpoints and visual conditions. To address class imbalance, an undersampling technique is applied so that each vehicle category, motorcycle, car, truck, bus, and public transportation, has a balanced number of instances. Three YOLOv8 variants, namely Nano, Small, and Medium, are trained and evaluated using two testing schemes: a 70:20:10 data split and a 5-fold cross-validation method. Performance evaluation was conducted using the mean Average Precision (mAP), precision, recall, and inference speed metrics. The experimental results show that YOLOv8m with the 5-Fold Cross Validation scheme produces the best performance with mAP@50 of 0.947, precision of 0.932, and recall of 0.883, while YOLOv8n excels in terms of inference speed with an average of ±8.77 ms/frame. These findings suggest that the selection of YOLOv8 variants should consider the balance between accuracy and computational efficiency and confirm the potential of YOLOv8 as an initial component of an automated CCTV-based traffic monitoring system in real-world environments with limited resources.
IoT-Based Real-Time River Monitoring and Early Flood Warning Using ESP32 and HC-SR04 Asri Mulyani; Dede Kurniadi; Rizki Esa Saputra
Journal of Novel Engineering Science and Technology Vol. 5 No. 01 (2026): 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.v5i01.1252

Abstract

Floods remain one of the most frequent and destructive natural disasters in Indonesia, primarily caused by rising river water levels. This study presents a prototype of an IoT-based river water level monitoring system using an HC-SR04 ultrasonic sensor, an ESP32 microcontroller, and the Blynk platform for real-time monitoring and alerting. The system classifies water levels into three categories: safe, alert, and danger, triggering local alarms (buzzers) and smartphone notifications accordingly. Evaluation results show a high sensor accuracy of up to 99.7%, with a notification delay averaging 1.2 seconds and a buzzer response time of less than 0.5 seconds. The system has been tested through black box testing and field simulations and has shown reliable performance for early flood warnings. These findings demonstrate the system’s potential in enhancing flood preparedness, particularly in vulnerable communities. Future enhancements include deploying waterproof sensors and integrating additional alert platforms such as SMS or sirens.
Optimization of Malaria Cell Image Classification Using Pretrained Resnet50 Architecture with Data Augmentation and Fine-Tuning Asri Mulyani; Dede Kurniadi; Agil Rahmat
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001244

Abstract

Malaria remains a significant health concern, particularly in tropical regions such as Indonesia, where timely and accurate diagnosis is crucial for reducing transmission and mortality. Conventional diagnosis through microscopic examination is labor-intensive, time-consuming, and highly dependent on expert availability. This study proposes an automated malaria cell image classification model using a deep learning approach based on the pretrained ResNet50 architecture. The research framework adopts the SEMMA (Sample, Explore, Modify, Model, Assess) methodology to structure the development workflow. A total of 27,558 labeled blood cell images comprising two balanced classes, Parasitized and Uninfected, were used for training and evaluation. Two model configurations were tested: a baseline model without data augmentation or fine-tuning, and an optimized model that integrates both. Augmentation techniques such as rotation, flipping, shearing, zoom, and brightness adjustment were applied to increase data diversity, while fine-tuning involved unfreezing the last 20 layers of ResNet50 to adapt pretrained features to the malaria domain. Performance was evaluated using accuracy, precision, recall, F1-score, loss, and AUC-ROC. The optimized model achieved 97.63% accuracy, 0.996 AUC-ROC, and 0.2472 loss, outperforming the baseline accuracy of 92.84%. An ablation study analyzed the individual contributions of augmentation and fine-tuning, showing that both techniques play complementary roles, with fine-tuning having the greater impact. A McNemar test confirmed that the improvements were statistically significant (p < 0.05). These findings demonstrate that the optimized ResNet50 model is effective for malaria detection and holds promise for integration into real-time diagnostic systems in resource-constrained environments.
Co-Authors A. Abdul Latif AA Sudharmawan, AA Abania, Nia Abdurrohman, Muhammad Akmal Ade Sutedi Ade Sutedi, Ade Aditya Permana Aditya Permana Agil Rahmat Agung Gumelar Agus Hermawan Ahmad Badar Muttaqin, Dadan Ahmad Budiman Ahzam, Faiq Muhammad Ai Karlina Ainun, Taanafa Nurul Akbar, Fazri Haikal Alamsyah, Fathi Ridwan Aldiansah, Aldiansah Alfiansyah, Dandan Alhakim, Much Kahfi Amrulloh, Muhammad Fawaz Andarista, Hilda Dian Anggi Wandani Annisa Atmanati Ansori, Hasbi Hamdan Al Ardimansyah, Dendi Arifin, Pipin Zaenal Asep Deddy Supriatna Asri Indah Pertiwi Astri Yuliastri Aulia, Husni Aulia, Wafa Gaida Ayu Latifah Balilo Jr, Benedicto B. Banowati, Rika Burdani, Aditya Mauludin Burhanuddin, Ridwan Cha Cha Nisya Asyah Choerunisa, Alma Deddy Supriatna, Asep Dede Kurniadi Deni Heryanto Detila Rostilawati Dewi Tresnawati Dhea Arynie Noor Annisa Diar Nur Rizky Diazki, Moch. Haiqal Dini Destiani Siti Fatimah Dudy Mohammad Arifin Ependi, Nasep Eri Satria Eri Satria Erick Fernando B311087192 Erwin Gunadhi Erwin Gunadhi Rahayu, Raden Evita Prananda Dewi Fadiel Muhammad Fadillah, Hadi Bagus Fahmi Ilham Ardiansyah Fahru Nisa Aulia Faisal Nurur Ramadhan Faisal, Ridwan Nur Fathon, Ahmad Fatimah, Raden Dini Destiani Siti Faturrohman, Nadhif Fauzan, Muhammad Farhan Fauzi, Rizky Ahmad Fauziyah, Asyifa Febrianti, Tiara Fikri Fahru Roji Firdaus, Raden Syaban Firmansyah, Marshal Fitri Nuraeni FITRIANI, PIPIT Gina Muhtari Gina Suciyana Gugum Gumilar Setia Permana Gustiawan, Restu Fajar Halim, Muhammad Aufa Fauza Haolilah, Siti Hilmi Aulawi Homsatin, Asyifa Azsma Iis Oktaviani Ikbal Lukmanul Hakim Inda Muliana Indra Trisna Raharja Indrakusumah, Muhamamad Rafi Indri Tri Julianto Irfanov, Muhammad Irpan Ahmad Fauzi Ismaya, Karina Jajang Romansyah JAMALUDIN Jamiludin, Irfan Janatunnisa, Raisya Agni Juliansyah, Fauzan Romi Kamal, Chaerul Syah Al Karlina, Ai Khaifa, Raisha Sarah Kharisma Wiati Gusti Khoiriyyah, Fakhrun Mahda Kurniawan, Ihsan Hafiiz Latif, A. Abdul Latifah, Ayu Leni Fitriani Leni Fitriani, Leni Lufti Lukmanurkarim Lukmanurkarim, Lufti M Rafiq Syahputa M. Mesa Fauzi Mahendra Akbar Musadad Maulud, Restu Bagja Meta Regita Mubarok, Ilham Muhamad, Zaki Muhammad Abdul Yusup Hanifah Muhammad Rikza Nashrulloh Muharam, Muhamad Riyan Muhtari, Gina Muliana, Inda Mustaatinah, Tutin Nasrullah, Muhammad Rikza Nita Novianti Firmansyah Nugraha, Aldi Nugraha, Praja Salya Nugroho, Salmanudin Nuraisah Nuraisah Nuraisah, Tintin Nurazizah, Neng Putri Nurhaliza, Nabila Putri Nurmahmudi, Raihan Nurpajar, Dini Siti Nurrifan Syabandhi Nursaidah, Syifa Nursofiana, Muhammad Fauzan Nursyaban, Dzikri Nurul Fauziah Nurusyam, Moh Algifari Oktavian, Gilang Anhari Oktavian, Muhamad Ar Rasyid Rizki Oktavian, Zordan Oktaviani, Iis Padilah, Eva Nurul Pasundan, Gia Aghista Prayoga, Hardi Putri, Elsinta Ismawati Putri, Mita Hidayani Qalam Ilmayasa, Muhammad Raharja, Indra Trisna Rahayu, Maulida Fasha Rahayu, Yari Ardiansyah Rahmawan, Muhammad Kahfi Rais, Azfa Muhammad Ramdan, Galih Muhammad Ramdani Setiawan Ramdani, Dikri Ramdani, Idham Ramdhani, Nabila Aprilia Rangga, Wisnu Ranti Rahayu Pujianti Renaldy Alamsyah Rengganis, Nadia Fauziah Revi Rexi Muhamad Fadilah Ridwan Setiawan Ridwan Setiawan Rifky Muhammad Shidiq Rima Ardianti Rinda Cahyana Rinda Cahyana Riyad Sabilul Muminin Rizal Mulyana Rizki Esa Saputra Rizki, Riyandi Muhamad Rizky Helmi Romadan, Mochamad Rizki Rosita Wulandari Ruli Ahmad Rusmana Ruspa, Rena Saadah, Roro Saepul Rochman Sahdan Hadianto Salsabila, Kailla Sambas, Keisha Aulia Saparudin, Hopid Saputri, Amellia Sarah Khoerunisa Saripudin Saripudin Setiawan, Ahmad Dandi Shopi Nurhidayanti Sidik, Muhammad Luthfi Farid Sinta Nurfatonah Siti Rima Fauziyah Slamet, Bagus Solahudin, Muhammad Husni Sopandi, Pendi Sopian, Alpi Sri Intan Multajam Sri Rahayu SRI RAHAYU Sri Rahayu Sugriantha, Irham Sukirno Sukirno Sukmawan, Tegar Sulaeman, Gilman Fajar Suwandy, Mochamad Riefky Rafliana Syabandhi, Nurrifan Tania Agusviani Wahidah Taupik Hidayat, Taupik Wahdaniah, Hamidah Nur Yana Nuryana Yoga Handoko Agustin Yosep Septiana Yuliastri, Astri Yundari, Yundari Yuni Yuliani Yusuf, Nadhif Murtadho Zaelani, Jaka Muhammad Zahra, Lubna Nur Zulkarnaen, Ade Iskandar