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All Journal J@TI (TEKNIK INDUSTRI) Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Ilmu Perpustakaan Techno.Com: Jurnal Teknologi Informasi MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Forum Ilmu Sosial Jurnal Adabiya Edulib Lentera Pustaka Jurnal Kajian Informasi & Perpustakaan JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Jurnal Tamaddun Populis : Jurnal Sosial dan Humaniora Publication Library and Information Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Khatulistiwa Informatika HIGIENE: Jurnal Kesehatan Lingkungan JBMP (Jurnal Bisnis, Manajemen dan Perbankan) Jurnal Pilar Nusa Mandiri Jurnal Penelitian Pendidikan IPA (JPPIPA) JURNAL YAQZHAN: Analisis Filsafat, Agama dan Kemanusiaan Indonesian Journal of Artificial Intelligence and Data Mining JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Management and Economics Journal (MEC-J) Jurnal Manajemen Kesehatan Yayasan RS.Dr. Soetomo Angkasa: Jurnal Ilmiah Bidang Teknologi Martabe : Jurnal Pengabdian Kepada Masyarakat International Journal of Community Service Learning JURNAL GOVERNANSI Cakrawala: Jurnal Litbang Kebijakan Tibanndaru : Jurnal Ilmu Perpustakaan dan Informasi JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Transparansi Jurnal Ilmiah Ilmu Administrasi Jurnal Kesehatan Medical Technology and Public Health Journal Applied Technology and Computing Science Journal Journal of Information Systems and Informatics Dinasti International Journal of Education Management and Social Science Journal of Economics, Business, and Government Challenges MUKADIMAH: Jurnal Pendidikan, Sejarah, dan Ilmu-ilmu Sosial Jurnal Informasi dan Teknologi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jatilima : Jurnal Multimedia Dan Teknologi Informasi Responsive: Jurnal Pemikiran dan Penelitian Administrasi, Sosial, Humaniora dan Kebijakan Publik Bubungan Tinggi: Jurnal Pengabdian Masyarakat J-3P (Jurnal Pembangunan Pemberdayaan Pemerintahan) Info Bibliotheca: Jurnal perpustakaan dan ilmu Informasi Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K) Journal of Computer Networks, Architecture and High Performance Computing Unilib: Jurnal Perpustakaan Jurnal Teknik Informatika (JUTIF) Jurnal Pemerintahan dan Kebijakan (JPK) Dialogue: Jurnal Ilmu Administrasi Publik BIOLOVA Journal La Multiapp Journal of Technology and Informatics (JoTI) International Journal of Social Science, Educational, Economics, Agriculture Research, and Technology (IJSET) Az-Zahra: Journal of Gender and Family Studies Media Pustakawan Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Bidik : Jurnal Pengabdian kepada Masyarakat Journal of Law, Poliitic and Humanities Malcom: Indonesian Journal of Machine Learning and Computer Science Research and Development in Education (RaDEn) MIMBAR INTEGRITAS Journal of Governance and Social Policy Eduvest - Journal of Universal Studies SATIN - Sains dan Teknologi Informasi Journal of Economics and Management Scienties Riwayat: Educational Journal of History and Humanities (Journal of Environmental Sustainability Management) Indonesian Governance Journal : Kajian Politik-Pemerintahan Jurnal Wacana Kinerja: Kajian Praktis-Akademis Kinerja dan Administrasi Pelayanan Publik Al Maktabah Jurnal kajian Ilmu dan Perpustakaan Jurnal Informatika TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Other

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Comparative Analysis of CNN Architectures for Clean and Non-Clean Outfit Classification in Fashion Images Noviana Wahyu Basuki; Imam Yuadi
Journal La Multiapp Vol. 7 No. 3 (2026): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v7i3.3256

Abstract

The purpose of this study is to create and contrast image classifiers which are able to identify clean clothing from dirty using machine learning and deep learning techniques. Our utilized dataset contains 200 images of an outfit which are obtained from different official fashion brand sites and well-known e-commerce platforms in Indonesia. The information is used from secondary data which are digital images of the two style categories. Image preprocessing (resizes, normalizations and data augmentations), feature extraction from VGG-16, VGG-19 and inception v3 is done. The extracted features are then fed into the classifiers namely Logistic Regression, Neural Network and Support Vector Machine (SVM). The evaluation of the model is performed by different metrics (e.g., AUC, accuracy, F1-score, precision, recall and MCC) and visual examination using MDS plot and Silhouette Plot. The results demonstrate that the integrated model involving VGG-16 and Logistic Regression performs best obtaining highest AUC when compared with other model combinations. The MDS and Silhouette Plot visualizations also supported that VGG-16 has the most superior feature separation between clean outfits and non-clean outfits. In a word, our study unveils that fashion style recognition accuracy can be improved significantly through CNN-based feature extraction and traditional classification model. We hope that our work will encourage the comparison of CNN feature extraction and classification algorithms, and also can lay the foundation for further research in image-based outfit guidance systems serving a range of fashion industry and service sectors where professional appearance is a criterion.
ANALISIS BIBLIOMETRIK TENTANG NETWORK GOVERNANCE PADA PELAYANAN PUBLIK Martina Fitria Wulandari; Imam Yuadi
INDONESIAN GOVERNANCE JOURNAL : KAJIAN POLITIK-PEMERINTAHAN Vol 6 No 2
Publisher : Universitas Pancasakti Tegal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24905/igj.v6i2.97

Abstract

Antusiasme terhadap teori Network Governance semakin meningkat dalam beberapa tahun terakhir. Lalu, bagaimana perkembangan implementasi teori network governance pada pelayanan publik? Penelitian ini bertujuan untuk memberikan pemahaman terkait teori network governance pada pelayanan publik melalui analisis bibliometrik perkembangan penelitian-penelitianl terkait selama 10 tahun terakhir. Analisis bibliometrik pada penelitian ini memanfaatkan databes jurnal Web of Science yang divisualisasikan menggunakan aplikasi VosViewer dan R Biblioshiny guna menganalisis 332 jurnal publikasi mengenai network governance dan pelayanan publik. Berdasarkan analisis yang telah dilakukan, salah satu penelitian dengan sitasi terbanyak menemukan bahwa teori ini relevan dalam menjawab kebutuhan publik. Penelitian terkait network governance pada pelayanan publik paling banyak diproduksi oleh Amerika Serkat. Berdasarkan analisis biblioshiny, beberapa sumber jurnal yang paling relevan terhadap topik penelitian ini dalam area penelitian Public Administration antara lain Public Management Review, American Review of Public Administration, International Journal of Public Sector Management, International Review of Administrative Science, dan Journal of Public Administration Research and Theory.
Advancing Winged Animal Classification Through Image Analysis Prasetya Triputra Nugraha; Imam Yuadi
Jurnal Penelitian Pendidikan, Psikologi Dan Kesehatan (J-P3K) Vol 7, No 2 (2026): J-P3K
Publisher : Yayasan Mata Pena Madani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51849/j-p3k.v7i2.994

Abstract

This study is to assess how well two classification algorithms, Support Vector Machine (SVM) and Logistic Regression, work with deep learning-based feature extraction techniques, including Inception V3, VGG-16, and VGG-19. The methodology comprised preprocessing a collection of photos of flying animals, using the three convolutional neural network (CNN) designs to extract features, and applying the two algorithms to do classification. AUC, Classification Accuracy (CA), F1 Score, Precision, Recall, and MCC were among the important metrics used to assess the models. According to the findings, Inception V3 performed better than VGG-16 and VGG-19 on every parameter, with Logistic Regression obtaining nearly flawless scores (AUC = 1.000, CA = 0.987, F1 = 0.987). Although it was marginally less effective than Logistic Regression (AUC = 0.998, CA = 0.943, F1 = 0.946), SVM also did well with Inception V3. The feature extraction techniques that performed the worst were VGG-16 and SVM in particular (CA = 0.890, F1 = 0.891). These results highlight the effectiveness of Logistic Regression for classification in this setting and the improved multi-scale feature extraction capabilities of Inception V3. This study demonstrates how effective classifiers and cutting-edge CNN architectures, such as Inception V3, may be combined to automatically classify winged animals.
Color Psychology on Book Covers: An Analysis of Visual Preferences Across Adult and Children’s Books for Enhanced Audience Targeting at Sampoerna Academy Grand Pakuwon Surabaya Arum Karisma Nadya Lashita; Imam Yuadi
Jurnal Ilmu Perpustakaan Vol 15, No 1 (2026): April 2026
Publisher : Library and Information Science Study Program, Faculty of Humanities, Univ. Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jip.v15i1.77-100

Abstract

Book covers play a crucial role in capturing readers’ attention and shaping initial perceptions, making color an essential element in influencing emotional engagement and guiding audience targeting across children’s and adult literature. Color functions as a visual communication tool that reflects the tone, theme, and intended readership, while also aligning with psychological and developmental preferences. The objective is to identify how color attributes such as brightness, saturation, and dominant hues differentiate book categories and support effective visual classification. A quantitative content analysis approach is applied using 6,093 book cover images collected from the Sampoerna Academy Library. Data extraction is conducted using Python libraries, including OpenCV and NumPy, to measure RGB (Red, Green, Blue), HLS (Hue, Lightness, Saturation), and colorfulness attributes. The analysis is supported by visualization techniques such as Scatter Plot and Radial Visualization (RadViz) through Orange software to explore relationships between color variables and book categories. The results reveal clear visual distinctions between the two categories. Children’s book covers predominantly appear in higher brightness and color intensity ranges, with strong associations to warm and vibrant hues such as red, yellow, and orange. In contrast, adult book covers are more widely distributed across lower brightness levels and are associated with cooler and more subdued tones such as blue, green, and purple. Some overlap is identified, indicating that certain colors can function across categories depending on context. The findings confirm that color attributes serve as reliable indicators for audience targeting and visual classification. Future research is recommended to integrate additional design elements, including typography and layout, to provide a more comprehensive understanding of book cover design.
ANALYSIS OF BOOK BORROWING PATTERNS IN STUDENTS OF THE FACULTY OF ECONOMICS AND BUSINESS, NATIONAL UNIVERSITY USING APRIORI ALGORITHM Chyntia Shafa; Imam Yuadi
JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Vol 11, No 1 (2026)
Publisher : Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jipi.v11i1.28679

Abstract

This study aims to analyze book borrowing patterns at the Library of the Faculty of Economics and Business, National University using the Apriori algorithm. The analysis was conducted on borrowing transaction data to identify relationships among book categories that are frequently borrowed together. The dataset consisted of 68 borrowing transactions covering 11 book categories. Although the number of transactions was relatively limited, the data were selected based on the availability of complete borrowing records during the observation period and were considered sufficient to identify initial borrowing patterns. The results reveal several significant patterns. The “Management” category obtained the highest support value of 40%, indicating that it was the most frequently borrowed category, while the rule “Management → General Management” achieved a confidence value of 70%, showing a strong tendency for both categories to be borrowed together. These findings demonstrate that the Apriori algorithm can effectively identify user borrowing preferences from circulation data. This study contributes to the development of data mining applications in library science, particularly in the use of association analysis to support evidence-based library management. The findings may assist librarians in optimizing collection arrangement, developing recommendation systems, and improving collection development strategies. Furthermore, this study highlights the potential of transaction data analysis as a practical approach for understanding user information needs in academic libraries.
ENHANCING JOB FIT PREDICTION IN CORPORATIONS – A COMPARATIVE MACHINE LEARNING STUDY UTILIZING GRADIENT BOOSTING Bondan Ari Wijaya; Imam Yuadi
International Journal of Social Science, Educational, Economics, Agriculture Research and Technology (IJSET) Vol. 5 No. 4 (2026): MARCH
Publisher : RADJA PUBLIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.20035466

Abstract

The test results demonstrated how well the Gradient Boosting model could predict outcomes, with the model achieving the best performance metrics, such as an overall accuracy of 98% with 10-fold cross-validation. using group learning techniques to evaluate job fit. This remarkable performance was attained despite the organizational dataset's inherent class imbalance. Crucially, the model showed constant effectiveness in every aspect of job fit. The majority class, Perfect Match (98.8%), is divided into groups based on the difference between PeG and PoG. The minor groups, Overqualified (96.2%) and Underqualified (96.5%), are also divided into groups with strong accuracy and memory. "Jenjang - Main Grp "Text" and "PeG" are the two most important things that can tell you work fit," according to the feature importance analysis. These data give us a solid, objective basis for future talent management and placement decisions by clearly demonstrating that there are distinct, data-driven patterns in placing people in jobs at a company. Machine Learning, Job Fit, Human Resources, Gradient Boosting and Personnel Analytics.
FOSTERING DIGITAL LITERACY THROUGH GLAM COLLABORATION: THE STRATEGIC ROLE OF LIBRARIES IN EDUCATIONAL TRANSFORMATION Nawwaf Faruq Adina Putra; Imam Yuadi; Hendro Margono
JIPI (Jurnal Ilmu Perpustakaan dan Informasi) Vol 10, No 2 (2025)
Publisher : Progam Studi Ilmu Perpustakaan UIN Sumatera Utara Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/jipi.v10i2.27880

Abstract

Digital transformation has fundamentally changed the ways information is accessed, evaluated, and utilized, making digital literacy an essential competence in contemporary education. Within this context, Galleries, Libraries, Archives, and Museums (GLAM) hold significant potential as providers of cultural and knowledge resources that support learning in the digital era. This article examines the strategic role of libraries in fostering digital literacy through GLAM collaboration as part of educational transformation. The study employs a Systematic Literature Review (SLR) method by analyzing 17 peer-reviewed articles published between 2013 and 2024, retrieved from Google Scholar, Scopus, ScienceDirect, DOAJ, and JSTOR. The findings indicate that libraries are well positioned to act as central coordinators of GLAM collaboration due to their established digital infrastructure, expertise in information organization, and metadata management capabilities. Five key themes emerge from the analysis: the urgency of GLAM integration in digital education, libraries as strategic connectors among GLAM institutions, the contribution of GLAM to digital literacy development, collaborative learning models supported by GLAM, and socio-technical challenges in implementation. Overall, GLAM collaboration led by libraries enhances critical thinking, information evaluation, and contextual understanding through access to authentic and multimodal resources. This study highlights the transformative leadership role of libraries within the GLAM ecosystem in higher education.
Pikachu Image Classification Using Neural Network and Support Vector Machine with Painters, VGG-16, and Inception-V3 Feature Representations Muthia Andriana Putri; Imam Yuadi
Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Vol 10 No 4 (2026): OCTOBER 2026
Publisher : Lembaga Komunitas Informasi Teknologi Aceh (KITA), Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jtik.v10i4.6905

Abstract

This study aims to evaluate the effectiveness of multiple feature representations in classifying Pikachu images into three distinct visual categories: anime, action figures, and hand-drawn illustrations. The primary challenge lies in limited data availability and the high visual variability across styles, resulting in significant inter-class similarity and intra-class diversity. To address this issue, the study employs a transfer learning approach utilizing pre-trained Convolutional Neural Networks (CNNs), namely VGG-16 and Inception-V3, alongside painterly feature descriptors. The dataset comprises 351 images collected from open-access sources with balanced class distribution. Extracted features are subsequently classified using Support Vector Machines (SVM) and shallow Neural Networks. The findings demonstrate that integrating deep semantic features with artistic representations significantly improves classification accuracy compared to single-feature approaches. These results highlight the critical role of hybrid feature engineering and classifier selection in achieving robust image classification performance under data-constrained conditions.
ANALISIS BIBLIOMETRIK: KEBIJAKAN KONSUMSI ALKOHOL Dea Roseliana Putri; Imam Yuadi
Dialogue : Jurnal Ilmu Administrasi Publik Vol 8, No 1 (2026)
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/dialogue.v8i1.18617

Abstract

Sejak satu dekade terakhir, publikasi tentang kebijakan publik terkait dengan konsumsi alkohol terus mengalami peningkatan dengan fokus yang terbatas pada terbatas .analisis hubungan antara kebijakan publik terhadap tingkat konsumsi alkohol. Studi ini bertujuan untuk menyajikan analisis tren literatur akademik tentang kebijakan publik terkait konsumsi alkohol selama satu dekade terakhir (2014-2023), termasuk perkembangan publikasi tentang kebijakan publik terkait konsumsi alkohol, dokumen dan penulis yang paling banyak dikutip, institusi dan negara tempat mereka berafiliasi, serta jaringan kata kunci dan penulis dari publikasi yang diterbitkan selama 2014-2023. Metode analisis bibliometrik digunakan untuk mengetahui tren tersebut, dengan memanfaatkan data bibliografi yang diambil dari database Scopus. Sebanyak 349 publikasi yang telah melalui tahap penyaringan dianalisis dengan bantuan perangkat lunak VOSviewer dan Biblioshiny. Hasil analisis menunjukkan perkembangan yang signifikan dalam hal publikasi dan tema yang dicakup selama tahun 2014-2023. USA menempati peringkat tertinggi di antara negara-negara dengan produksi terbanyak, dan University of Toronto merupakan institusi yang paling produktif. Dokumen teratas yang paling banyak dikutip fokus pada keterkaitan antara faktor risiko dengan disability-adjusted life years (DALY), termasuk di dalamnya adalah penggunaan alkohol.
Lung X-ray Image Classification for Distinguishing Tuberculosis and Pneumonia Using Pretrained CNN Feature Extractors and Supervised Classifiers Ardian Mohib; Imam Yuadi; Ira Puspitasari; Yusi Dyah Patriani
Journal of Information System and Informatics Vol 8 No 3 (2026): June
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i3.1595

Abstract

Tuberculosis (TB) and pneumonia (PNA) are infectious lung diseases with overlapping chest X-ray (CXR) manifestations, making automated differential classification clinically important and methodologically challenging. This study proposes a supervised CXR classification workflow to distinguish TB from PNA using pretrained convolutional neural network (CNN) feature extractors and supervised classifiers. A publicly available de-identified dataset comprising 390 TB and 390 PNA images was used. Images were screened to exclude duplicates, corrupted files, non-CXR images, unclear labels, and identifiable cases. Preprocessing included format standardization, resizing according to CNN input requirements, and normalization. To reduce augmentation-based leakage risk, no heavy pre-validation augmentation was applied. Image embeddings were extracted using VGG-16, Inception V3, and VGG-19, then classified using Logistic Regression, Support Vector Machine, and Neural Network models. Performance was evaluated using stratified 5-fold cross-validation with AUC, accuracy, F1-score, precision, recall, MCC, and confusion matrix analysis. The Inception V3–Logistic Regression combination achieved the best performance, with AUC of 0.999, accuracy of 0.992, F1-score of 0.992, and MCC of 0.985.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Aditya Cahya Saputra Albigaeri, Syahruly Nizar Alifka Cellina Velby Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci Ardian Mohib Artha Rachma Widiastuti Arum Karisma Nadya Lashita Azmi, Muhammad Izharul Baihaqie, Owen Berliani, Kezia Putri Bondan Ari Wijaya Cahyani, Retno Tri Christia, Tifani Dewi Chyntia Shafa Condro Rahino Mustikaning Pawestri Dama Putri, Kania Dea Roseliana Putri Dewanty, Alifia Kaltsum Dwiky Rahardian Endang Gunarti Enny Mar’atus Sholihah Erika Putri Erika Putri Fadilia Rinarwastu Fadilia Rinarwastu, Fadilia Fairus Faqih Febri Ari Wicaksono Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Gilang Ferdiansah Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hary Supriyatno Hasna, Dhia Alifia Izdihar Hendro Margono Ira Puspitasari Ira Puspitasari Ira Puspitasari Irvan Zidny Ismi Choirunnisa Prihatini Kartika Sari, Della Kezia Rahmawati Santosa Koko Srimulyo Lathifah, Lathifah Lestari, Santi Dwi Desy Lifindra, Stevanie Aurelia Lucy Dyah Hendrawati M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Marsaa Salsabiila Martina Fitria Wulandari Maulidah, Nofiyah Mayasari, Sentri Indah Melati Purba Bestari, Melati Purba Mochammad Edris Effendi Muhammad Rafi Raihan Muhammad Rafi Raihan Muthia Andriana Putri Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nawwaf Faruq Adina Putra Niken Ayu Pratiwi, Bertha Nisak Ummi Nazikhah Noor Rizki, Denaldy Oktavian Novia, Asradiani Noviana Wahyu Basuki Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Parenda Rizkya Permata Pradhana, Andrea Thrisiawan Prasetya Triputra Nugraha Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Purba, Trie Dinda Maharani Purwaningtyas, Aris Putra, Dwi Permana Putri Kinanti, Novrianti Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahmadani, Sinta Raihanzaki, Raka Gading Ratih Addina Hapsari Rosiana, Lidya Rosyani, Widha Sabayu, Brian Sabrina Hartianingrum, Hikmah Sabrina Nur Amalia Safina Innaf Mia Ardelia Salsabiila, Marsaa Salsabila, Chyntia Shafa Sari, Tri Kartika Setiadi, Yusuf Sherly Deasy Anjuwita Gultom Sheva Alana Brilianty Shiefti Dyah Alyusi Sinta Rahmadani Siswahyudianto Soesantari, Tri Sonia Tikamidia Sugihartati, Rahma Suhada, Hofur Sukma Sufryanto Tikamidia, Sonia Toetik Koesbardiati Tri Hadi Wicaksono Triandari, Ayu Ullin Nihaya Unas, Frisca Maria Vilosa, Bias Vivia Adriyanti, Elvetta Wardani, Hesti Ari Wettebossy, Anita Elizabeth Wildan Habibi Yuniawan Heru Santoso Yusi Dyah Patriani Yuwinanto, Helmy Prasetyo