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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JURNAL NASIONAL TEKNIK ELEKTRO Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar ILKOM Jurnal Ilmiah SENTIA 2017 SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Lectura : Jurnal Pendidikan Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Infotekmesin Buletin Ilmiah Sarjana Teknik Elektro International Journal of Visual and Performing Arts Generation Journal Jurnal Mnemonic Frontier Energy System and Power Engineering Masyarakat Berdaya dan Inovasi Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Science in Information Technology Letters International Journal of Engineering, Science and Information Technology International Journal of Robotics and Control Systems ALINIER: Journal of Artificial Intelligence & Applications Ilmu Komputer untuk Masyarakat SinarFe7 Jurnal Maklumatika Applied Engineering and Technology Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Jurnal Inovasi Teknologi dan Edukasi Teknik PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO Bulletin of Social Informatics Theory and Application Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal Informatika Polinema (JIP) ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Journal of Engineering and Technological Sciences Jurnal ilmiah teknologi informasi Asia
Anik Nur Handayani
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PENGEMBANGAN LEMBAR KERJA PESERTA DIDIK (LKPD) BERBASIS TEKA-TEKI SILANG PADA PEMBELAJARAN INFORMATIKA UNTUK SISWA SMP Farah Nisa’ Salsabila; Anik Nur Handayani; Baskoro Arif Widodo
Jurnal Ekonomi, Bisnis dan Pendidikan Vol. 3 No. 12 (2023)
Publisher : Universitas Negeri Malang

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Abstract

Pendidikan memegang peranan penting dalam pengembangan individu dan kemajuan suatu bangsa. Namun, tantangan dalam meningkatkan minat dan motivasi siswa di SMP, terutama dalam pembelajaran Informatika, masih menjadi perhatian. Dalam era digital yang cepat berkembang, pembelajaran memerlukan inovasi dalam penyampaian materi. Salah satu tantangan utama adalah kebosanan akibat penyampaian materi yang monoton dan kurang menarik. Dalam konteks ini, pendekatan pembelajaran yang berpusat pada peserta didik, dengan penggunaan teknologi dan metode yang inovatif, menjadi solusi efektif. Penelitian ini mengembangkan Lembar Kerja Peserta Didik (LKPD) berbasis permainan Teka Teki Silang (TTS) untuk pembelajaran informatika di SMP, dengan tujuan memfasilitasi pemahaman konsep siswa dan sebagai bahan ajar tambahan bagi guru. Model ADDIE digunakan sebagai kerangka kerja dalam pengembangan ini, yang mencakup tahapan Analisis, Desain, Pengembangan, Implementasi, dan Evaluasi. Hasil penelitian menunjukkan LKPD berbasis TTS sangat efektif meningkatkan pemahaman siswa terhadap materi informatika dan sangat layak digunakan dilihat dari segi media dan materi. LKPD ini dapat menjadi solusi untuk meningkatkan minat dan motivasi belajar, serta memfasilitasi pemahaman konsep siswa dalam pembelajaran informatika di SMP.
PEMBUATAN MEDIA AJAR INTERAKTIF SEBAGAI BENTUK INOVASI DALAM PEMBELAJARAN INFORMATIKA UNTUK SISWA SMP Reza Setyawan; Anik Nur Handayani; Baskoro Arif Widodo
Jurnal Ekonomi, Bisnis dan Pendidikan Vol. 3 No. 12 (2023)
Publisher : Universitas Negeri Malang

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Abstract

Pesatnya perkembangan ilmu pengetahuan dan teknologi mendorong perlunya pengembangan wawasan dan kemampuan dalam bidang pendidikan. Media pembelajaran, terutama yang digital, memiliki peran penting dalam memfasilitasi proses pembelajaran dengan menawarkan konten interaktif dan aksesibilitas yang tinggi. Namun, sebagian besar pendekatan pengajaran masih mengandalkan metode ceramah dan buku paket, mengakibatkan minat belajar siswa menurun dan kurangnya pemahaman konsep. Penelitian ini bertujuan untuk mengembangkan media ajar interaktif sebagai inovasi dalam pembelajaran informatika untuk siswa SMP, dengan fokus pada materi kolaborasi dalam masyarakat digital. Melalui pengembangan media ajar ini, diharapkan dapat meningkatkan pemahaman siswa terhadap materi pelajaran dan mendukung proses pembelajaran di dalam kelas. Media ajar interaktif ini juga diharapkan dapat memfasilitasi siswa untuk belajar secara aktif dan praktis, meningkatkan minat belajar serta mengatasi keterbatasan fasilitas yang tersedia. Penelitian ini diharapkan dapat memberikan kontribusi positif bagi pendidikan dengan memanfaatkan teknologi untuk meningkatkan efektivitas pembelajaran.
Pemodelan Sistem Deteksi Kadar Unsur Hara Tanah Berdasarkan Nilai NPK Menggunakan Metode Fuzzy Mamdani Dityo Kreshna Argeshwara; Zulkham Umar Rosyidin; Aji Prasetya Wibawa; Anik Nur Handayani; Mokh. Sholihul Hadi
Jurnal Sains dan Informatika Vol. 9 No. 1 (2023): Jurnal Sains dan Informatika
Publisher : Teknik Informatika, Politeknik Negeri Tanah Laut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34128/jsi.v9i1.523

Abstract

Profil kesuburan tanah merupakan hal yang penting dalam pertanian karena merupakan media utama dalam bercocok tanam. Penggunaan pupuk kimia dan pestisida secara terus menerus dan berlebihan akan dapat menimbulkan perubahan sifat fisika dan kimia tanah yang pada akhirnya akan dapat menyebabkan tanah menjadi kritis. Hal ini akan berpengaruh pada produktivitas hasil panen para petani. Salah satu upaya untuk mengetahui tingkat kesuburan tanah adalah melalui diagnosa unsur hara dalam tanah. Tujuan penelitian ini adalah untuk membuat pemodelan sistem optimalisasi deteksi kadar unsur hara dalam tanah menggunakan fuzzy. Melalui simulasi ini akan didapatkan data kadar unsur hara tanah dengan parameter unsur hara N (Nitrogen), P (Fosfor) , K (Kalium) menggunakan labview. Berdasarkan parameter tersebut kemudian dihasilkan berapa nilai kadar unsur hara NPK dalam tanah apakah rendah, sedang atau tinggi.
Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems Hayati, Lilis Nur; Handayani, Anik Nur; Gunawan Irianto, Wahyu Sakti; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.948

Abstract

This research explores the optimization of YOLO-based computer vision algorithms for real-time recognition of Indonesian Sign Language (BISINDO) letters under diverse environmental conditions. Motivated by the communication barriers faced by the deaf and hearing communities due to limited sign language literacy, the study aims to enhance inclusivity through advanced visual detection technologies. By implementing the YOLOv5s model, the system is trained to detect and classify correct and incorrect BISINDO hand signs across 52 classes (26 correct and 26 incorrect letters), utilizing a dataset of 3,900 images augmented to 10,920 samples. Performance evaluation employs k-fold cross-validation (k=10) and confusion matrix analysis across varied lighting and background scenarios, both indoor and outdoor. The model achieves a high average precision of 0.9901 and recall of 0.9999, with robust results in indoor settings and slight degradation observed under certain outdoor conditions. These findings demonstrate the potential of YOLOv5 in facilitating real-time, accurate sign language recognition, contributing toward more accessible human-computer interaction systems for the deaf community.
Optimized image-based grouping of e-commerce products using deep hierarchical clustering Pranoto, Yuliana Melita; Handayani, Anik Nur; Herwanto, Heru Wahyu; Kristian, Yosi
International Journal of Advances in Intelligent Informatics Vol 11, No 3 (2025): August 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v11i3.1979

Abstract

Managing large and constantly evolving product catalogs is a significant challenge for e-commerce platforms, especially when visually similar products cannot be reliably distinguished using text-based methods. This study proposes a product grouping method that combines a fine-tuned EfficientNetV2M model with an adaptive Agglomerative Clustering strategy. Unlike conventional CNN-based approaches, which have limited scalability and a fixed number of clusters, the proposed method dynamically adjusts similarity thresholds and automatically forms clusters for unseen product variations. By linking deep visual feature extraction with adaptive clustering, the method enhances flexibility in handling product diversity. Experiments on the Shopee product image dataset show that it achieves a high Normalized Mutual Information (NMI) score of 0.924, outperforming standard baselines. These results demonstrate the method’s effectiveness in automating catalog organization and offer a scalable solution for inventory management and personalized recommendations in e-commerce platforms.
Comparative Analysis Using Xception and MobileNetV2 Deep Learning Models for Brain Tumor Detection in MRI Images Mumtaazah, Muhammad Athar; Anik Nur Handayani
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15332

Abstract

This study presents a comparative analysis of two deep learning models, Xception and MobileNetV2, for brain tumor detection using MRI images. The selection of these models is based on their respective advantages. Xception is known for its ability to handle large and complex datasets due to its deep architecture and the use of depthwise separable convolutions. It also features a deep structure capable of extracting complex features from high-resolution images, making it well-suited for detailed image recognition tasks. In contrast, MobileNetV2 is designed to be lighter and more computationally efficient, making it ideal for deployment on mobile devices or in resource-constrained environments without significantly compromising performance. These characteristics make both models highly relevant for medical image analysis, particularly in brain tumor detection, which demands both accuracy and efficiency.This study uses a public dataset that has been preprocessed through augmentation and normalization. Both models were trained and evaluated using accuracy, loss, and confusion matrix metrics. The results show that MobileNetV2 achieved higher accuracy (97.8%) compared to Xception (94.9%) with a lower error rate. For precision, recall, and F1-score metrics, the results were identical up to four decimal places, further supporting that MobileNetV2 is more suitable for brain tumor detection in resource-limited settings. Based on the findings, MobileNetV2 demonstrates superior performance compared to Xception, making it the favorable choice.
Determining Quality of Service (QoS) of End-User Internet Networks with Data Sniffing and Classification Algorithms Rosyidin, Zulkham Umar; Muladi, Muladi; Handayani, Anik Nur
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1444

Abstract

The development of telecommunications technology in this world has changed very rapidly. Changes are made to access technology using the transmission media, which uses fiber optic technology, which has the advantage of being free from interference, large and fast data delivery capacity. An Internet Service Provider (ISP) is a provider of construction services and management of network infrastructure that always meets customer needs. Customer satisfaction with the services provided by ISP is also important in the era of increasingly tight market competition. Quality of Service (QoS) testing in internet networks needs to be done so that customers get optimal service. This study analyzes the quality of internet networks with fiber optic media on the end user side with the data sniffing method using Wireshark software that records video data traffic on the YouTube platform. The results of the data recording are processed using the QoS method with Throughput, Packet Loss, Delay, and Jitter parameters. The QoS assessment index is divided into Excellent, Good, Fair, and Poor classes according to the TIPHON standard. Data from these parameters is classified using the Naive Bayes, KNN, and Decision Tree methods. The results of applying the algorithms show the highest Accuracy value in the Decision Tree algorithm of 97%, while the highest Precision and Recall are in the KNN algorithm with values of 94% and 85%.
Yoga Posture Recognition and Classification Using YOLOv5 Maqbullah, Afwatul; Handayani, Anik Nur; Kurniawan, Wendy Cahya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.228

Abstract

Yoga, a centuries-old health practice from India, has gained global recognition for its benefits to physical, mental, and emotional well-being. However, incorrect execution of yoga poses can lead to injuries or diminished results. This research develops an automated system for recognizing and classifying yoga postures using YOLOv5, a state-of-the-art deep learning algorithm. YOLOv5, part of the YOLO (You Only Look Once) series, is designed for real-time object detection and offers enhanced performance through features like anchor-free detection and adaptive training strategies. The study collects a dataset of 1,000 images across 20 yoga pose categories, followed by manual annotation and training using transfer learning. Validation results show strong performance, achieving an accuracy of 90% with precision and recall scores of 0.942 and 0.941, respectively, and mAP@50 and mAP@50-95 values of 0.976 and 0.866. Despite challenges with certain poses showing lower accuracy due to variations in posture and dataset limitations, the model demonstrates robustness in detecting and classifying yoga postures effectively. This system has potential applications in artificial intelligence-driven yoga education, enabling practitioners to train independently with real-time feedback
Integration of Yolov8 And Instance Segmentation in The Chinese Sign Language (CSL) Recognition System Wijaya, Mikel Ega; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.247

Abstract

This research aims to develop an advanced recognition system for Chinese Sign Language (CSL) by integrating YOLOv8 and instance segmentation techniques. Communication through sign language is essential for the deaf community, and although CSL has been standardized in China, recognizing complex hand movements remains a significant challenge. YOLOv8 is employed for real-time object detection, while instance segmentation is used to provide more detailed analysis of hand gestures. This integration seeks to improve hand gesture recognition under varying lighting and background conditions, which is crucial for more effective communication between the deaf community and the wider society. The study evaluates the system’s performance using common metrics such as Mean Average Precision (mAP), precision, recall, and F1-score. The findings indicate that the non-segmentation model performs better than the segmentation model in terms of precision, recall, and mAP, especially when trained with a larger dataset ratio. The non-segmentation model provides faster and more accurate detection, while the segmentation model, despite using the same amount of data, shows potential for more detailed recognition of gestures. Although the segmentation model shows improvements in the F1-score with more detailed accuracy, the non-segmentation model remains superior in overall detection speed and accuracy. This research highlights the importance of integrating YOLOv8 and instance segmentation for improving CSL recognition, with better results on the non-segmentation model for more effective communication for the deaf
Optimization of Nglegena Javanese Script Recognition With Machine Learning Based on Zoning And Normalization of Feature Extraction Graciello, Manuel Tanbica; Handayani, Anik Nur; Wibawa, Aji Prasetya
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.256

Abstract

Machine learning offers promising solutions for the recognition of handwritten Javanese Nglegena script, which is crucial for preserving Indonesia's cultural heritage. This study explores the application of several supervised learning algorithms-K-Nearest Neighbors (KNN), Naïve Bayes, Decision Tree, and Random Forest-for classifying handwritten images of Nglegena Javanese script. Feature extraction is performed using a zoning technique, where each character image is divided into multiple zones (16, 25, 36, and 64) to capture local details. The extracted features are further processed using normalization methods, including Min-Max, Z-Score, and Binary normalization, to ensure uniform data distribution. The dataset, consisting of 600 images representing Javanese Nglegena characters, is split into training and testing sets using various ratios. Experimental results show that the combination of Naïve Bayes classification, 36-zone feature extraction, and Min-Max or Z-Score normalization achieves the highest accuracy of 65%. These findings demonstrate that optimizing zoning and normalization can significantly enhance the accuracy of machine learning models for Javanese script recognition. The research contributes to developing Optical Character Recognition (OCR) technology for Javanese script, supporting the digital preservation of Indonesia's historical and cultural heritage.
Co-Authors A.N. Afandi Abdul Rachman Manga' Abdullah Iskandar Syah Achmad Hamdan Achmad Safi’i Achmad Safi’i Adi Izhar Bin Che Ani Adi Prastowo, Nur Kodrad Adib Nur Sasongko Adim Firmansah Afandi, Farrel Candra Winata Agusta Rakhmat Taufani Ahmad Dardiri Ahmad Munjin Nasih Ahmad Nurdiansyah, Ahmad Ahmad Sahru Romadhon Aji Prasetya Wibawa Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro Aripriharta - Ariyanta, Nadindra Dwi Asfani, Khoirudin Atmaja, Muhammad Bayu Setya Wahyu Ayu Puspita Azhryl Assagaf Aziz, Faiz Syaikhoni Azizah, Desi Fatkhi Bagaskoro, Muhammad Cahyo Baihaqi, Dimas Imam Baihaqi, Dimas Imam Baskoro Arif Widodo Bayu Prasetyo Bayu Prasetyo, Bayu Bin Che Ani, Adi Izhar Burhanuddin, Mohd Aboobaider Chalista Yulia Hazizah Chuttur, Mohammad Yasser Damanhuri, Nor Salwa Damayanti, Farradila Ayu Damayanti, Masyita Danang Arengga Danang Arengga Wibowo Dedes, Khen Devita Maulina Putri, Devita Maulina Dewi Aprilia Lintang Didik Dwi Prasetya Difa Hananta Firdaus Am Dika Fikri L Dimas Wahyu Wibowo Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Dolly Indra Dwi Prihanto Dyah Lestari Dyah Rosita Anggraeni Edinar Valiant Hawali Edwin Meinardi Trianto Eka Rahayu Setyaningsih Erwina Nurul Azizah Eva, Nur Evania Yafie F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Fakhruddin, Dhiyaurrahman Faqih, Kamil Faradhila Saffa Dhamira Farah Nisa’ Salsabila Fauzi, Juwita Annisa Fauzi, Rochmad Felix Andika Dwiyanto Ferina Ayu Pusparani Gianika Roman Sosa Graciello, Manuel Tanbica Gunawan Budi P Guyub Raharjo Gwo-Jiun Horng Haffas Zikri Ariyandi Hakkun Elmunsyah Halimahtus Mukminna, Halimahtus Handoko, Wahyu Tri Harits Ar Rasyid Harits Ar Rosyid Hariyono Hariyono Hartarto Junaedi Hary Suswanto Heru Herwanto Heru Wahyu Herwanto Hirashima, Tsukasa Hitipeuw, Emanuel Hosen, Moh I Made Wirawan Ida Ayu Putu Sri Widnyani Ihsan Al-Fikri Imanuel Hitipeuw Ira Kumalasari Irfan Ramadhani Irham Fadlika Jehad A. H. Hammad Jehad A.H. Hammad Jevri Tri Ardiansah Jevri Tri Ardiansah Joumil Aidil Saifuddin Kamil Faqih Kartika Candra Kirana Kartika Kirana Kasmira Kasmira Katya Lindi Chandrika Khurin Nabila Kinasih, Agnes Nola Sekar Kirom, M Kohei Arai Kohei Arai Kohei Arai Kohei Arai Korba, Petr Kurniawan, Wendy Cahya Kusumawardana, Arya Laili, Mery Nur Laily, F.ti Ayyu Sayyidul Laistulloh, Dika Fikri Lalu Ganda Rady Putra Langlang Gumilar Larasati, Jade Rosida Leonel Hernandez, Leonel Lestari , Widya Liang, Yeoh Wen Liang, Yoeh Wen lilis nurhayati M. Adib Nursasongko M. Nuzuluddin M. Rodhi Faiz M. Rodhi Faiz Machumu, Paul Igunda Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahamad, Abd Kadir Manga, Abdul Rachman Maqbullah, Afwatul Ming Foey Teng, Ming Foey Moh Zainul Falah Moh. Zainul Falah Mohammad Agung Rizki Mohammad Rizky Kurniawan Mohammad Yussril Asri Mohsen Samadi Mokh Sholihul Hadi Much. Arafat Al Mubarok Muchamad Wahyu Prasetyo Muhamad Arifin Muhamad Arifin, Muhamad Muhammad Alfan Muhammad Arifin Muhammad Hafiizh Muhammad Holqi Rizki Azhari Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Ridwan Muhammad Ulinnuha Musthofa Muhammad Younas Darvish Muhammad Zaki Wiryawan Muhammad Zaky Rahmatsyah Muladi Mulya, Marga Asta Jaya Mumtaazah, Muhammad Athar Mutiara, Titi Nadindra Dwi Ariyanta Nafalski, Andrew Nandang Mufti Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Naufal Rizaldi Gunawan Nisa, Khoirotun Nizaar, Roub Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Halim Nur Rahma, Andika Bagus Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda Praja, Rafli Indar Prasetya Widiharso Prasetya Widiharso Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Pratama, Diaz Octa Priharta, Ari Primadi, Wahyu Purnomo, Purnomo Putra Utama, Agung Bella Putri Galuh Ningtiaz Qomaria, Ulfa Rahman, Nukleon Jefri Nur Rahmat Samudra Anugrah, Muhammad Raja, Roesman Ridwan Ramadhani, Lolita Resty Wulanningrum Reza Setyawan Rini Nur Hasanah Rismayanti, Nurul Rochmawati, Rochmawati Romadlon, Muhammad Rizqi Rosa Andrie Asmara Rosa Andrie Asmara Rosyidin, Zulkham Umar Rusdha Aulia Salah Abdullah Khalil Abdulrahman Salsabila, Reni Fatrisna Saodah Omar Selly Handik Pratiwi Seno Isbiyantoro Setyaningsih, Eka Rahayu Sevilla, Felix Rafael Segundo Siti Sendari Slamet Wahyudi Slamet Wibawanto Soraya Norma Mustika Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Suti Mega Nur Azizah Suziyani Mohamed Syaad Patmantara Syaad Patmanthara Syaichul Fitrian Akbar Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tsukasa Hirashima Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Utomo, Imam Tree Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Primadi Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wibawa, Aji Presetya Wibowo, Kusmayanto Hadi Wicaksana, Ardi Anugerah Widiharso, Prasetya Wijaya, Mikel Ega Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Yosi Kristian Yu, Tony Yudha Islami Sulistya Yuliana Melita Pranoto Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zufida Kharirotul Umma Zulkham Umar Rosyidin Zulkham Umar Rosyidin Zulkifli, Shamsul Aizam