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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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Classify a path on tire by using Logistic Regression and Support Vector Machine (SVM)Based on VGG-16, VGG-19, and INCEPTION V3 Modes Sukma Sufryanto; Imam Yuadi
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.
Opinion mining toward work from office policies on post-pandemic covid-19 by using supervised learning Tri Hadi Wicaksono; Imam Yuadi; Ira Puspitasari
TEKNOSAINS : Jurnal Sains, Teknologi dan Informatika Vol 11 No 1 (2024): TEKNOSAINS: Jurnal Sains, Teknologi dan Informatika
Publisher : LPPMPK- Universitas Muhammadiyah Cileungsi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37373/tekno.v11i1.525

Abstract

Post-pandemic COVID-19, many companies have re-implemented work from office policies for their employees. However, the policy has been controversial among social media activists, especially in Twitter. The sentiment arose according to them, during the pandemic COVID-19 they believe that working from home has many advantages over working in an office. The emergence of 'work from office' sentiments is an interesting target for opinion-mining research. Opinion mining or sentiment analysis is a general research area of ​​data mining that helps to explore and analyze existing views and opinions to obtain useful information. The analysis process involves the use of machine learning with several supporting algorithms. This study used four classification algorithm models of supervised learning, including naive Bayes, support vector machines, k-nearest neighbors, and random forests.  The selection of those algorithms also aims to find out which model produced a good performance for the results. The performance results of each model were evaluated by the confusion matrix and the k-nearest neighbor algorithm model with an accuracy value of 96.62% was found to give the best results and to be the most used model in the classification process. On the other hand, the algorithm model that obtains the lowest accuracy is a random forest with 72.08%.  
Integrated framework of curriculum mapping through keyword tagging using Orange: public service logic approach Fairus Faqih; Imam Yuadi
Pustaka Karya : Jurnal Ilmiah Ilmu Perpustakaan dan Informasi Vol. 14 No. 1: Juni 2026
Publisher : Program Studi Ilmu Perpustakaan dan Informasi Islam, Fakultas Tarbiyah dan Keguruan, Universitas Islam Negeri Antasari Banjarmasin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18592/pk.v14i1.19005

Abstract

Comparable to other public service domains in which service provision involves a wide array of resource-integrating actors, higher education faces the challenge of delivering meaningful educational experiences. Adopting a service-logic perspective, this study proceeds from the assumption that such experiences can be shaped through the co-production of library services aligned with course curricula. Public Service Logic (PSL) provides the conceptual foundation for this co-creative integration, while the operationalization employs keyword tagging and relevance modeling in a case study of library practice at Politeknik Perkapalan Negeri Surabaya (PPNS). The analysis uses a neural network configured with a hidden layer of 100 neurons, ReLU activation, the Adam optimizer, L2 regularization (alpha), a specified maximum number of iterations, and a fixed random state. The model achieved precision and recall of 70 percent, indicating successful projection of similarity and relevance between the book collection and course offerings. Theoretically, the study advances the service-logic narrative in higher education by demonstrating integration of library resources with curriculum design
Analisis Bibliometrik Pelayanan Publik untuk Penyandang Disabilitas Salsabiila, Marsaa; Yuadi, Imam
Jurnal Pemerintahan dan Kebijakan (JPK) Vol. 4 No. 3 (2023): August
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jpk.v4i3.18782

Abstract

Pelayanan publik untuk penyandang disabilitas saat ini masih menjadi topik pembahasan yang perlu banyak dikaji. Masih banyak pelayanan publik yang tidak ramah akses untuk para penyandang disabilitas.  Pelayanan publik yang tidak merata untuk para penyandang disabilitas yang dimana jumlah penyandang disabilitas tergolong cukup banyak menimbulkan adanya rasa iri atas ketimpangan yang diberikan oleh pemerintah kepada masyarakat. Tujuan penelitian ini adalah mengetahui perkembangan riset dalam melihat perkembangan pelayanan publik untuk penyandang disabilitas.  Metode penelitian menggunakan analisis bibliometrik dalam memberikan gambaran terkait sebaran publikasi studi pada bidang pelayanan publik terkhususnya pelayanan publik untuk penyandang disabilitas. Analisis bibliometrik ini menerapkan limit dalam penentuan data yang akan digunakan dalam penelitian dengan melihat pertumbuhan penelitian terkait pelayanan publik untuk penyandang disabilitas. Hasil dari penerapan limit kemudian di export dalam format RIS dan BibTex. Format RIS digunakan pada visualisasi menggunakan bantuan aplikasi VOSviewer. Sedangkan format BibTex digunakan pada visualisasi menggunakan  bantuan aplikasi Biblioshiny. Hasil akhir analisis bibliometrik ini adalah memvisualisasi jurnal dan penulis yang relevan dengan topik bahasan dan analyze result yang menghasilkan visualisasi dari bidang kategori yang relevan, jenis dokumen, afiliasi yang turut berkontribusi, persebaran wilayah, dan tingkat publikasi setiap tahun dari data 145 data dokumen. Visualisasi yang dihasilkan dari analisis bibliometrik ini selain dapat mempermudah memperoleh data dan memahami aspek-aspek pendukung dalam pembahasan pelayanan publik untuk penyandang disabilitas. Dapat juga memahami ada beberapa aspek yang kurang diteliti dalam adanya pelayanan publik untuk disabilitas.
Implementation of Machine Learning to Predict The Timeliness of Graduation of Employees on Study Assignment at Company X Parenda Rizkya Permata; Imam Yuadi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2474

Abstract

The energy transition requires workers in the energy sector who have relevant skills that can be applied in the future. Company X implements a study assignment program to improve its employees' skills, but delays in completing their studies hinder their readiness to enter the workforce. Identifying the factors that influence graduation timeliness can improve the program's effectiveness. This study aims to develop a predictive model to determine whether employees in Company X's work-study program will graduate on time. The main purpose of this model is to provide early warnings about employees at risk of delays, enabling more targeted interventions to improve human resource management. We applied the CRISP-DM framework and used Machine Learning to analyze data from 317 employees who participated in the study program. Four machine learning algorithms were tested, namely Gradient Boosting, Decision Tree, Random Forest, and Naive Bayes. 17 factors were trained to cover academic, demographic, and administrative aspects to predict timely graduation. Among the algorithms tested, Gradient Boosting showed the best performance with an AUC of 0.956 and an accuracy of 0.909. These results were supported by high ROC and confusion matrix values, indicating the model's excellent predictive ability. 
Unsupervised Text Mining of Employee Feedback for Identifying Organizational Strengths and Improvement Areas Febri Ari Wicaksono; Imam Yuadi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 2 (2026): MALCOM April 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i2.2482

Abstract

Employee feedback provides rich signals about organizational performance, yet its free-text format makes systematic analysis at scale difficult. This study proposes an unsupervised text mining workflow in Orange Data Mining to extract actionable themes from continuous employee comments by separating two semantic polarities: strength feedback (“What went well?”) and improvement feedback (“What could be improved?”). After cleaning and Indonesian-language preprocessing (Sastrawi stemming, custom stopwords), 3,406 strength and 3,172 improvement entries were represented using TF–IDF. Improvement feedback was clustered using K-Means and assessed with silhouette-based validation, while both feedback types were explored using LDA topic modeling supported by topic coherence checks for interpretability. The results reveal recurring organizational themes related to goal execution and performance, supervision, communication/coordination, and motivation, with notable vocabulary overlap between strengths and areas for improvement. Scientifically, this work demonstrates how polarity-aware unsupervised analytics improves interpretability compared to treating feedback as a single corpus, and practically, it provides a scalable way for managers to transform unstructured feedback into structured insights for targeted improvement initiatives.
Klasifikasi Kondisi Isolator Menggunakan Logistic Regression dan Support Vector Machine dengan Analisis Citra Berbasis VGG-16, VGG-19, dan Inception V3: Classification of Insulator Conditions Using Logistic Regression and Support Vector Machine with Image Analysis Based on VGG-16, VGG-19, and Inception V3 Purwaningtyas, Aris; Yuadi, Imam
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2652

Abstract

Keandalan sistem ketenagalistrikan sangat bergantung pada kondisi isolator sebagai komponen yang mencegah kebocoran arus listrik, karena kerusakannya dapat memicu gangguan hingga pemadaman berskala luas. Meskipun inspeksi menggunakan Unmanned Aerial Vehicle (UAV) semakin banyak diterapkan, analisis visual masih dilakukan secara manual sehingga cenderung subjektif, tidak konsisten, dan memerlukan waktu yang lama. Penelitian ini mengembangkan sistem klasifikasi kondisi isolator berbasis citra menggunakan platform Orange Data Mining. Model pre-trained Deep Convolutional Neural Network (DCNN), yaitu VGG-16, VGG-19, dan Inception V3, dimanfaatkan sebagai feature extractor, sedangkan Logistic Regression dan Support Vector Machine (SVM) digunakan sebagai algoritma klasifikasi untuk membedakan isolator normal dan terkontaminasi. Dataset terdiri atas 168 citra isolator keramik dan kaca yang diperoleh melalui inspeksi lapangan menggunakan drone. Evaluasi dilakukan menggunakan 5-fold stratified cross-validation untuk menjaga distribusi kelas pada setiap fold. Hasil menunjukkan bahwa kombinasi VGG-19 dan Logistic Regression memberikan performa terbaik dengan akurasi 96,4%, AUC 0,986, dan Matthews Correlation Coefficient (MCC) 0,929, serta menurunkan false positive menjadi 3,6%. Temuan ini menunjukkan efektivitas pendekatan hybrid DCNN–machine learning untuk deteksi dini kondisi isolator secara otomatis.
Mapping the Evolution of Quiet Quitting Research: A Five-Year Bibliometric and Topic Modeling Analysis Dwiky Rahardian; Imam Yuadi
MEC-J (Management and Economics Journal) Vol 10, No 1 (2026)
Publisher : Faculty of Economics, State Islamic University of Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mec-j.v10i1.32318

Abstract

Quiet quitting has emerged as a significant phenomenon in modern workplace dynamics, reflecting employee disengagement and dissatisfaction with organizational structures. This study provides a comprehensive bibliometric analysis of quiet quitting research over the past five years, utilizing data from the Scopus database and Orange Data Mining for analysis. The findings reveal key themes such as employee engagement, organizational culture, burnout, leadership, and workplace dynamics. The surge in publications related to remote and hybrid work during the period of the pandemic reflects a paradigm shift in academic literature towards the normalization of such work practices. Identifies five key thematic clusters, finding that Quiet Quitting and Organizational Structures and Employee Engagement and Workplace Analysis to be key themes. The insights underscore the need for a multidimensional approach, with implications for how organizations can foster more engaged workplaces by emphasizing supportive policies, kind and engaged leadership, and fairness in task allocation to mitigate the risk of quiet quitting. This study contributes to the literature through a new examination of research patterns to a qualitative research topic that utilized empirical methods drawing on a data-driven investigation highlighting pathways for which both researchers/academics and practitioners might consider exploring going forward.
SENTIMENT ANALYSIS ON TRAINING IMPLEMENTATION’S FEEDBACK IN PT XYZ Fadilia Rinarwastu; Imam Yuadi
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.
Enhancing the Accuracy of Competency Portfolio Assesments using Machine Learning: a Comparative Analysis of Predictive Models Aditya Cahya Saputra; Imam Yuadi
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

This study elaborates the application of various machine learning (ML) models to measure competency portfolio assessments for job grade conversion needed of employees. The purpose is choose the best ML models to enhance the accuracy, scalability, and fairness. Logistic regression and support vector machines is two traditional methods were evaluated together with random forest and gradient boosting as ensemble models and neural network as deep learning models. This study taken data of 117 employees invited to join on the competency portfolio assessment event on November 2024, all models were measured through cross-validation on parameters such as accuracy, precision and recall by Orange Data Mining. The best performance model in this study is Random Forest, achieving the highest score on Precision and Recall parameters. While Neural Networks demonstrated potential performance that almost has the same result with logistic regression. Based on this research, Random Forest can be prioritized and implemented to help the company to enhance the accuracy of competency portfolio results that needed to develop employees career, eligible competencies, and help decision making of job grade conversion assessment. Keywords: Comparative Analysis, Competency Portfolio Assessment, Machine Learning
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