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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 Jurnal Ekonomi Manajemen Sistem Informasi 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) Jurnal Pustaka Ilmiah 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) 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 Jurnal Ilmu Multidisplin 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
Imam Yuadi
Departemen Ilmu Informasi Dan Perpustakaan, Fakultas Ilmu Sosial Dan Ilmu Politik, Universitas Airlangga
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Optimizing Library Visitor Satisfaction Analysis with Machine Learning Nurahman, Yeni Fitria; Yuadi, Imam
TEKNOLOGI DITERAPKAN DAN JURNAL SAINS KOMPUTER Vol 8 No 1 (2025): June
Publisher : Universitas Nahdlatul Ulama Surabaya

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

Abstract

In today’s increasingly digital era, libraries continue to play a vital role as centers of information, knowledge, and culture. Despite the widespread availability of online information, libraries remain essential for providing diverse resources, services, and convenient facilities. The role of libraries has evolved to meet the needs and expectations of visitors, requiring ongoing innovation in services and amenities to ensure user satisfaction. This study aims to assess the level of visitor satisfaction at UNUSA Library regarding the services provided. The research utilized questionnaire data, initially collected from 802 respondents, of which 224 valid responses were analyzed. Furthermore, this study compares the predictive performance of three machine learning methods K-Nearest Neighbor, Decision Tree, and Support Vector Machine to determine which method achieves the highest accuracy in predicting visitor satisfaction. The analysis was conducted using the Orange Data Mining application as the prediction model. The results indicate that library visitors generally report a high level of satisfaction, with certain services rated more positively than others, and that machine learning models can effectively predict satisfaction levels based on visitor feedback.
FOSTERING DIGITAL LITERACY THROUGH GLAM COLLABORATION: THE STRATEGIC ROLE OF LIBRARIES IN EDUCATIONAL TRANSFORMATION Putra, Nawwaf Faruq Adina; Yuadi, Imam; Margono, Hendro
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.
Classify a path on tire by using Logistic Regression and Support Vector Machine (SVM)Based on VGG-16, VGG-19, and INCEPTION V3 Modes Sufryanto, Sukma; Yuadi, Imam
Eduvest - Journal of Universal Studies Vol. 5 No. 8 (2025): Eduvest - Journal of Universal Studies
Publisher : Green Publisher Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59188/eduvest.v5i8.50960

Abstract

This study focuses on the classification of tire tread patterns using machine learning and deep learning approaches, emphasizing Logistic Regression (LR) and Support Vector Machine (SVM) combined with feature extraction methods like Inception V3, VGG-16, and VGG-19. Results indicate that Inception V3 outperformed other feature extraction methods, yielding the highest classification accuracy (CA) of 93.2% when used with SVM. SVM demonstrated superior robustness and adaptability, especially in handling complex data, as evidenced by its high AUC values (up to 0.987) across multiple configurations. Logistic Regression, while slightly less robust, performed consistently well with simpler features, achieving stable metrics with VGG-16 (AUC: 0.976, CA: 90.7%). These findings highlight the importance of selecting appropriate feature extraction and classification combinations to optimize performance. The study recommends using Inception V3 with SVM for high-accuracy applications and Logistic Regression for scenarios prioritizing computational efficiency. These insights contribute to developing adaptive and efficient tire classification systems suitable for diverse road and environmental conditions.
Device-Based Majapahit Inscription Classification with Multi-Filter Enhancement Muhammad Rafi Raihan; Imam Yuadi
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1792

Abstract

The preservation of cultural heritage through digitalization has become increasingly essential in modern archaeological and information technology research. This study focuses on classifying Majapahit inscription images based on the recording device using machine learning approaches enhanced by multiple image filtering techniques. A dataset comprising seven inscriptions photographed with seven different devices was used to evaluate the performance of three classification models: Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). Four preprocessing filters Grayscale, Sobel, Histogram Equalization, and Canny Edge Detection were applied to assess their effects on model accuracy. The results revealed that the SVM consistently achieved the highest accuracy and robustness, particularly under Sobel and Histogram Equalization filters, confirming its superior ability to capture discriminative texture and edge-based features. In contrast, KNN showed unstable results due to its sensitivity to noise and intensity variations, while Logistic Regression performed moderately well in linearly separable data conditions. Paired t-test analysis further validated that SVM’s performance advantage was statistically significant. These findings highlight that edge-preserving preprocessing techniques can substantially enhance the accuracy of device-based image classification and provide a computational framework that supports digital preservation efforts in cultural heritage research.
Multi-Device Image Dataset With Manual And Python-Based Augmentations For Cross-Device Robustness In Image Classification Research Erika Putri; Imam Yuadi
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 04 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i04.1887

Abstract

This study presents a multi-device image collection and a reproducible VSCode/Python pipeline for analyzing image classification and the effects of data augmentation under real hardware variation. Images were captured at the Galeri Inovasi Institut Teknologi Sepuluh Nopember (GRITS) using four devices Infinix Note 4, LG G6, Samsung S23+, and Xiaomi Pad 6s Pro with 31 images per device. We applied manual and Python-based augmentations (rotation, flips, brightness, sharpening, contrast) and organized outputs by device and augmentation type for controlled comparisons. Using stratified 80:20 splits, we evaluated Logistic Regression (LR), SVM (RBF), and KNN. Results: LR reached accuracy 0.90 (macro-F1 0.88; weighted-F1 0.90), SVM 0.89 (macro-F1 0.88; weighted-F1 0.89), and KNN 0.67 (macro-F1 0.65; weighted-F1 0.68). Augmentation enhanced robustness and cross-device generalization, though Xiaomi Pad 6s Pro remained the most challenging class, indicating a persistent device-specific shift. The dataset and scripts provide a transparent, baseline-ready testbed for research on image classification, cross-device variability, and the impact of augmentation.
Pemetaan Bibliometrik Tren Penelitian Artificial Intelligence dalam Bidang Pendidikan Tahun 2015–2025 Salsabila, Chyntia Shafa; Yuadi, Imam
Populis : Jurnal Sosial dan Humaniora Vol. 10 No. 2 (2025)
Publisher : Universitas Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47313/ppl.v10i2.4260

Abstract

Penelitian ini bertujuan untuk memetakan tren penelitian tentang Artificial Intelligence (AI) dalam bidang pendidikan pada kurun tahun 2015–2025 dengan menggunakan metode bibliometrik. Analisis dilakukan secara deskriptif berbasis data publikasi yang diperoleh dari basis data Scopus melalui distribusi publikasi per tahun, identifikasi penulis, jurnal, institusi, serta pemetaan keywoard co-occurance dan kolaborasi antar penulis dengan memanfaatkan perangkat VOSViewer. Hasil penelitian menunjukkan bahwa terdapat pertumbuhan publikasi yang signifikan dari tahun ke tahun terutama di tahun 2020 disertai lonjakan yang tinggi pada tahun 2023 – 2025 yang dipicu dengan adanya generative AI seperti ChatGPT. Analisis kata kunci mengungkapkan tiga kluster utama, yaitu pengembangan teknologi (machine learning, natural language processing, intelligent tutoring systems), isu sosial dan etika (AI ethics, student perceptions), serta aspek pedagogis yang menekankan peran guru dan pengalaman belajar. Jejaring kolaborasi memperlihatkan dominasi peneliti dari Tiongkok, Amerika Serikat, dan Eropa, dengan beberapa tokoh berperan sebagai penghubung lintas negara. Pada temuan ini juga menyoroti bahwa penelitian AI tidak hanya fokus pada aspek teknis namun juga menyoroti etika, sosial, dan pedagogis. Abstract This research aims to map the research trends on Artificial Intelligence (AI) in the field of education during the period 2015–2025 using a bibliometric method. The analysis is conducted descriptively based on publication data obtained from the Scopus database through the distribution of publications per year, identification of authors, journals, institutions, and mapping of keyword co-occurrence and collaboration between authors using the VOSViewer tool. The results show a significant growth in publications from year to year, especially in 2020, accompanied by a high spike in 2023 and 2025, triggered by the presence of generative AI such as ChatGPT. Keyword analysis revealed three main clusters: technology development (machine learning, natural language processing, intelligent tutoring systems), social and ethical issues (AI ethics, student perceptions), and pedagogical aspects that emphasize the role of teachers and the learning experience. Collaboration networks show the dominance of researchers from China, the United States, and Europe, with several figures acting as cross-border liaisons. This finding also highlights that AI research does not only focus on technical aspects but also highlights ethical, social, and pedagogical aspects.
Penerapan Rapidminer dengan Metode Decision Tree Pada Tingkat Motivasi Berkunjung Pemustaka di Perpustakaan UIN Sunan Ampel Surabaya Hary Supriyatno; Imam Yuadi
AL Maktabah Vol 10, No 1 (2025): JUNI
Publisher : Pusat Publikasi Ilmiah UIN Fatmawati Sukarno Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29300/mkt.v10i1.7086

Abstract

Pasca pandemi Covid-19, perpustakaan memiliki tugas berat dalam upaya peningkatan kunjungan fisik pemustaka. Salah satu penyebabnya ialah adanya pergeseran budaya akses informasi dari cetak ke digital. Kondisi ini menyebabkan turunnya angka kunjungan onsite di perpustakaan, tidak terkecuali UIN Sunan Ampel Surabaya. Salah satu strategi yang dilakukan perpustakaan adalah melalui inovasi penyediaan layanan, seperti koleksi corner. Tujuan peneltian untuk mengetahui tingkat motivasi berkunjung pemustaka di Perpustakaan UIN Sunan Ampel Surabaya berdasarkan teori ERG (Existence, Relatedness, Growth) Clayton Alderfer. Instrumen yang digunakan adalah kemudahan akses wifi, koleksi yang relevan, fasilitas lengkap, tempat nyaman untuk berkegiatan, diskusi, penyelesaian tugas akademik, dan sumber inspirasi. Metode penelitian menggunakan decision tree melalui aplikasi RapiMiner untuk menentukan aturan atau rule motivasi pemustaka. Hasil analisis penelitian menggunakan Decision Tree menunjukkan tingkat akurasi pada angka 98%. Sedangkan untuk tingkat motivasi berkunjung pemustaka di Layanan Koleksi Corner Perpustakaan UIN Sunan Ampel Surabaya menciptakan tiga rule dalam tiga kategori, yakni cukup, tinggi, dan sangat tinggi. Rule pertama, jika Layanan Koleksi Corner memenuhi kebutuhan pemustaka sebagai tempat yang nyaman untuk penyelesaian tugas akademik dan memiliki fasilitas lengkap maka motivasi berkategori cukup. Kedua, jika ekspektasi kebutuhan sebagai tempat penyelesaian tugas akademik, adanya fasilitas lengkap, dan koleksi relevan dapat dipenuhi oleh layanan, maka berdampak pada motivasi tinggi. Ketiga, jika layanan koleksi corner memiliki spesifikasi sebagai tempat penyelesaian tugas akademik, adanya dukungan fasilitas lengkap, kemudahan akses wifi, dan dilengkapi koleksi relevan maka motivasi berkategori sangat tinggi.
Comparative Analysis of Foot Sole Classification Models: Evaluating Logistic Regression, SVM, and Random Forest Purba, Trie Dinda Maharani; Yuadi, Imam
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11550

Abstract

Accurate sole classification and types can aid applications in healthcare, sports, and biometrics such as diagnosis of high arch or flat foot disease, as well as in improved design of custom orthotics and enhanced gait analysis to improve sports performance. When applied to large-scale datasets, traditional methods for foot sole classification are inefficient as they are often manual, time-consuming and prone to human error. Machine learning has the ability to significantly improve accuracy and efficiency in automating this process. The proposed method uses Logistic Regression model compared to Support Vector Machines (SVM), and Random Forest using Orange Data Mining. The performance of these algorithms changes depending on the complexity of the data and model parameters. There are three types of feet that will be processed in this image analytics namely normal arch, flat foot and high arch. The pre-trained models used are Inception V3, VGG-19 and SqueezeNet. Logistic Regression model showed the best overall performance with superior parameter values such as AUC of 0.973, Classification Accuracy (CA) of 0.933, and MCC of 0.902, and demonstrated reliability and balance between precision and recall.
STUDI KOMPARATIF MODEL MACHINE LEARNING DALAM MEMPREDIKSI KETERLAMBATAN PEGAWAI: LOGISTIC REGRESSION, SVM, DAN RANDOM FOREST Palupi, Inggrid Nindia Aprila; Mardianto, M Fariz Fadillah; Yuadi, Imam; Mariyadi, Budiyan
J@ti Undip: Jurnal Teknik Industri Vol 21, No 1 (2026): Januari 2026
Publisher : Departemen Teknik Industri, Fakultas Teknik, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jati.21.1.76-87

Abstract

Keterlambatan karyawan adalah salah satu jenis pelanggaran terhadap disiplin kerja yang dapat berdampak pada produktivitas dan efektivitas organisasi. Penelitian ini bertujuan untuk mengembangkan serta membandingkan performa dari tiga algoritma machine learning Regresi Logistik, SVM, dan Random Forest dalam memprediksi keterlambatan pegawai dengan menggunakan data keterlambatan dan karakteristik individu. Dataset yang digunakan terdiri dari 1902 data, yang dibagi 80% data training dan 20% data testing dengan enam variabel, mencakup usia, lama bekerja, status pernikahan, jarak tempat tinggal ke kantor, jenis kendaraan yang digunakan, dan gaya hidup. Hasil analisis menunjukkan bahwa Random Forest memberikan kinerja prediktif yang paling baik dalam mengenali pegawai yang memiliki potensi untuk terlambat, dengan nilai akurasi tertinggi sebesar 0.82, presisi sebesar 0.93, recall sebesar 0.84, dan F1-score sebesar 0.88. Model ini terbukti dapat menunjukkan kemampuan klasifikasi yang andal dan seimbang. Analisis feature importance mengidentifikasi usia dan masa kerja sebagai faktor paling berpengaruh terhadap prediksi keterlambatan. Temuan ini tidak hanya memberikan wawasan baru dalam pengelolaan kedisiplinan pegawai, tetapi juga membuka peluang implementasi sistem peringatan dini yang dapat diintegrasikan ke dalam sistem kehadiran digital organisasi. Penelitian ini merekomendasikan perluasan variabel untuk studi lanjutan dan pemanfaatan hasil model sebagai dasar penyusunan kebijakan SDM yang lebih adaptif dan berbasis data. Abstract[Comparative Study of Machine Learning Models in Predicting Employee Delay: Logistic Regression, SVM, and Random Forest] Employee tardiness is one type of violation of work discipline that can impact organizational productivity and effectiveness. This study aims to develop and compare the performance of three machine learning algorithms Logistic Regression, SVM, and Random Forest in predicting employee tardiness using tardiness data and individual characteristics. The dataset used consists of 1902 data, which is divided into 80% training data and 20% with six variables, including age, length of service, last education level, marital status, distance from residence to office, type of vehicle used, and lifestyle. The results of the analysis show that Random Forest provides the best predictive performance in identifying employees who have the potential to be late, with the highest accuracy value of 0.82, precision of 0.93, recall of 0.84, and F1-score of 0.88. This model is proven to be able to demonstrate reliable and balanced classification capabilities. Feature importance analysis identifies age and length of service as the most influential factors in predicting tardiness. These findings not only provide new insights into employee discipline management but also open up opportunities for the implementation of an early warning system that can be integrated into the organization's digital attendance system. This study recommends expanding the variables for further studies and utilizing the model results as a basis for formulating more adaptive and data-based HR policies.Keywords: sustainability industry; developing strategy; MCDM
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ Rinarwastu, Fadilia; Yuadi, Imam
Jurnal Pilar Nusa Mandiri Vol. 21 No. 2 (2025): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Pe
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v21i2.6641

Abstract

Customer satisfaction is an important aspect in building a company's image, both for employees and external parties. In order to improve employee satisfaction and performance, training that organized by the company needs to receive feedback so that the training organizers can continue to provide the best service to employees who participate in the training. The large volume of feedback that must be processed in text form, leads to prolonged identification of comments and the omission of certain training programs from further analysis. This study applies text mining using sentiment analysis and Word Cloud visualization to evaluate the effectiveness of training methods and identify areas for improvement based on employee feedback on training programs at PT XYZ. The amount of data used after preprocessing was  48,910 open feedback responses from 4,314 training sessions consisting of three forms: classroom training, digital learning, and hybrid learning. The evaluation for clustering used the K-Means method, which turned out to use two optimal clusters based on the silhouette. Overall satisfaction with the training was determined through key points such as stable internet connection, overlapping of training schedule, and poor learning environment. Issues frequently that identified in the Word Cloud analysis revealed keywords describing positive and negative aspects of the situation that are requiring further improvement. This identification is useful for developing recommendations to enhance the implementation of the training and participants' experience. Further research may also involve advanced sentiment analysis and more accurate classification methods.
Co-Authors AA Sudharmawan, AA Achmad Djunawan Albigaeri, Syahruly Nizar Alifka Cellina Velby Alyusi, Shiefti Dyah Anastasya, Diva Berta Andini, Aulia Rizqi Anggraini, Pramudya Galuh Suci 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 Denaldy Oktavian Noor Rizki Dewanty, Alifia Kaltsum Dwisusilo, Aditya Endang Gunarti Enny Mar’atus Sholihah Erika Putri Fadilia Rinarwastu, Fadilia Febriano, Rizki Dwi Ferdiansah, Gilang Fitri Mutia, Fitri Gunarti, Endang Halim, Yunus Abdul Handari Niken Anggraini Hapsari, Ratih Addina Hardevianty, Melissa Yunda Hary Supriyatno Hasna, Dhia Alifia Izdihar Hendrawati, Lucy Dyah Hendro Margono Inggrid Nindia Aprila Palupi 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 M Kafi Maulana M. Fariz Fadillah Mardianto Mahardika, Synthia Amelia Putri Mariyadi, Budiyan Marsaa Salsabiila Martina Fitria Wulandari Maulidah, Nofiyah Mayasari, Sentri Indah Melati Purba Bestari, Melati Purba Mochammad Edris Effendi Muhammad Rafi Raihan Nabilla Salsabil Damayanti Zahraa Nainunis, Mas Akhmad Nazikhah, Nisak Ummi Niken Ayu Pratiwi, Bertha Novia, Asradiani Noviana Wahyu Basuki Nur Muhammad, Rizqi Nurahman, Yeni Fitria Nurul Firdausy Palupi, Inggrid Nindia Aprila Pradhana, Andrea Thrisiawan Prasetya Triputra Nugraha Prasetyo Yuwinanto, Helmy Prasyesti Kurniasari, Meinia Prayitna, Thomas Wigung Aji Purba, Trie Dinda Maharani Putra, Dwi Permana Putra, Nawwaf Faruq Adina Putri Kinanti, Novrianti Putri, Muthia Andriana Putri, Selviana Azzira Ragil Tri Atmi, Ragil Tri Rahardian, Dwiky Rahmadani, Sinta Raihanzaki, Raka Gading Ratih Addina Hapsari Rosiana, Lidya Rosyani, Widha Sabayu, Brian Sabrina Hartianingrum, Hikmah Sabrina Nur Amalia Safina Innaf Mia Ardelia Salsabila, Chyntia Shafa Saputra, Aditya Cahya Sari, Tri Kartika Setiadi, Yusuf Sherly Deasy Anjuwita Gultom Sheva Alana Brilianty Sinta Rahmadani Siswahyudianto Soesantari, Tri Sonia Tikamidia Sufryanto, Sukma Sugihartati, Rahma Suhada, Hofur Taufik Roni Sahroni 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 Yuwinanto, Helmy Prasetyo