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All Journal Jurnal Buana Informatika JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Edukasi dan Penelitian Informatika (JEPIN) Annual Research Seminar CESS (Journal of Computer Engineering, System and Science) Jurnal Ilmiah KOMPUTASI Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JTT (Jurnal Teknologi Terpadu) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab IJIE (Indonesian Journal of Informatics Education) Jurnal Manajemen STIE Muhammadiyah Palopo MBR (Management and Business Review) JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jiko (Jurnal Informatika dan komputer) Digital Zone: Jurnal Teknologi Informasi dan Komunikasi The IJICS (International Journal of Informatics and Computer Science) JURIKOM (Jurnal Riset Komputer) JURTEKSI JOISIE (Journal Of Information Systems And Informatics Engineering) INFOMATEK: Jurnal Informatika, Manajemen dan Teknologi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Darma Agung Jurnal Bisnis, Manajemen, dan Ekonomi Jurnal Generic Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi Jurnal Algoritma Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Indonesian Journal Computer Science (ijcs) Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Indonesia Sosial Teknologi The Indonesian Journal of Computer Science Management Analysis Journal Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Buffer Informatika JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ken Ditha Tania
Jurusan Sistem Informasi, Universitas Sriwijaya
Humanities 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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Social Sciences Other

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Sentiment-Based Knowledge Discovery pada Aplikasi iPusnas Menggunakan Metode Machine Learning dan Deep Learning Ayuningtiyas, Pratiwi; Tania, Ken Ditha; Sari, Winda Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10258

Abstract

iPusnas is a digital library application developed by the National Library of the Republic of Indonesia since 2016, with over 1.5 million users. Despite its potential to improve literacy, the application has only received a rating of 2.0. This study conducted sentiment analysis on 7.596 reviews obatained through web scraping using the Google Play Scraper Library. The data then underwent preprocessing steps including case folding, data cleaning, tokenization, stopword removal, and stemming. Reviews were automatically labeled based on the rating score, where scores of 1-3 were categorized as negative, with 5.174 entries, and scores 4-5 as positive, with 2.422 entries. The dataset was split in an 80:20 ratio, with 80% for training, and 20% for testing. The machine learning models tested were SVM, Random Forest, CNN, LSTM, and RNN. The evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix. CNN and LSTM achieved the highest accuracy (82%), Random Forest and CNN achieved the highest precision (81%), RNN the highest recall (79%) and LSTM the highest F1-score (79%). McNemar test showed a significant difference between Random Forest and CNN, Random Forest and LSTM, and between RNN and LSTM, while CNN and LSTM, as well as CNN and RNN, showed no significant difference.
Sentiment-Based Knowledge Discovery of Wondr by BNI App Reviews Using SVM, KNN, and Naive Bayes for CRM Enhancement Tri Zafira, Zahra; Ditha Tania, Ken; Kurnia Sari, Winda
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10323

Abstract

The rapid development of digital banking services has necessitated a deeper understanding of user perceptions and satisfaction levels. This study analyzes sentiment from user reviews of the Wondr by BNI app using a Knowledge Discovery approach and machine learning methods. Three classification algorithms were compared: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes, evaluated with accuracy, precision, recall, and f1-score. The results show that SVM and Naive Bayes achieved the best performance with F1-scores of 0.88 and 0.87, while KNN lagged behind with 0.77. An ANOVA test further confirmed that the performance differences were statistically significant (p < 0.05), with SVM and Naive Bayes consistently outperforming KNN. Word Cloud analysis revealed dominant positive terms such as "easy," "fast," and "transaction," alongside negative terms like "login," "difficult," and "verification." These findings highlight user appreciation for simplicity and speed, while pointing out functional issues that require attention. This research not only enriches the literature on Indonesian-language sentiment analysis in the financial sector but also provides practical insights for Customer Relationship Management (CRM), particularly in strengthening customer retention strategies and guiding UX redesign for digital banking services.
Knowledge Discovery on E-Commerce Customer Churn Using Interpretable Machine Learning: A Comparative Study of SHAP-Based Classifiers Amanda Ardhani, Dhita; Tania, Ken Ditha
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10811

Abstract

Customer churn remains one of the most pressing issues in the e-commerce sector, as it directly erodes revenue and reduces customer lifetime value. This study proposes an interpretable machine learning approach designed not only to predict churn but also to uncover practical insights that can inform retention strategies. The analysis draws on a publicly available dataset containing customer behavior and transaction records. Data preparation involved handling missing values, applying label encoding, and addressing class imbalance with SMOTE. Five classification models—Logistic Regression, Random Forest, XGBoost, Support Vector Machine, and Gradient Boosting—were trained on an 80:20 stratified split, with performance assessed through accuracy, precision, recall, F1-score, and AUC. Among these, XGBoost delivered the most consistent results, achieving 96% accuracy, 95% precision, 92% recall, and a near-perfect AUC of 0.999, followed closely by Random Forest. Logistic Regression produced the lowest AUC at 0.886. To ensure transparency in decision-making, SHAP (SHapley Additive exPlanations) was applied, revealing Tenure, Complain, and CashbackAmount as the most influential predictors. Longer customer relationships were linked to reduced churn risk, while frequent complaints and higher cashback usage indicated a greater likelihood of leaving. These findings contribute knowledge by blending robust predictive performance with interpretability, enabling e-commerce businesses to design more targeted and proactive customer retention measures.
User Review Automation: Detecting Actionable Complaints on Gojek in the Play Store using the LSTM Method Ramadhani, Indira Nailah; Sari, Winda Kurnia; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5708

Abstract

This study aims to develop an automatic complaint detector for Gojek app reviews using Long Short Term Memory (LSTM). The dataset consists of 225,002 user reviews on the Google Play Store. The purpose of this study itself is to facilitate the service team in understanding the shortcomings of the application complained by users. Automatic complaint detection will facilitate the service team to take action to resolve the problems experienced by users. Therefore, the review data provided by users is properly processed using LSTM to create an effective and efficient detection system. Processing is carried out using three different data sharing ratios, namely 90:10, 80:20, and 70:30 to ensure that the system is stable and effective. The accuracy results of the three data sharing ratios reached above 90%, thus proving that the system is able to detect complaints well. A pre-built dashboard is used to visualize the results of the system built using LSTM to facilitate monitoring the classification results. This system is expected to facilitate companies in detecting all user complaints and finding solutions to improve services to provide comfort for users.
Perbandingan Kinerja LSTM, Random Forest, dan SVR Berbasis Knowledge Discovery untuk Prediksi Harga Beras Sumatera Selatan Bahri, Cheisya Andini; Tania, Ken Ditha
JURNAL RISET KOMPUTER (JURIKOM) Vol. 12 No. 5 (2025): Oktober 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v12i5.9140

Abstract

Rice is a primary staple food in Indonesia, particularly in South Sumatra Province. In February 2024, BBC News Indonesia reported that the price of premium rice surged to Rp18,000 per kilogram, marking the highest price in the country’s history. To anticipate and predict similar spikes in the future, this study applies a Knowledge Discovery approach and compares three machine learning models: LSTM, Random Forest, and SVR. The approach follows the stages of data selection, cleaning, transformation, modeling, and evaluation to uncover hidden patterns in historical data. The dataset, obtained from the official PIHPS Nasional website, consists of 1,412 daily rice price records from January 2020 to May 2025. Model performance was evaluated using MAPE, MAE, and RMSE metrics. The findings indicate that the SVR model outperformed LSTM and Random Forest, delivering the most accurate results. For the Super Quality II rice category, SVR achieved a MAPE of 0.00 percent, MAE of 40.93, and RMSE of 52.54. SVR also consistently produced the lowest prediction errors in other categories, such as Low Quality I (MAE 59.39) and Medium Quality I (MAE 38.92). This research is expected to serve as a foundation for developing machine learning–based food price monitoring systems to support more responsive policies and maintain rice price stability in the future.
PENGARUH KNOWLEDGE MANAGEMENT, TALENT MANAGEMENT, DAN STRESS KERJA TERHADAP KINERJA KARYAWAN Siade, Shalya Yunia; Ken Ditha Tania
ZONAsi: Jurnal Sistem Informasi Vol. 6 No. 1 (2024): Publikasi Artikel ZONAsi Periode Januari 2024
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v6i1.17902

Abstract

Abstrak Salah satu perusahaan BUMN pertambangan adalah PT Bukit Asam Tbk. PT Bukit Asam Tbk jelas menghadapi tantangan dalam proses bisnisnya selama pandemi COVID-19. Beberapa kegiatan yang melibatkan banyak orang harus ditangguhkan. Hal ini berdampak pada kinerja karyawan, masalah seperti ini pasti membutuhkan sumber daya manusia berkualitas tinggi. Tujuan penelitian ini menganalisis pengaruh knowledge management, stres kerja, dan talent management, terhadap kinerja karyawan karena penting bagi PTBA Tanjung Enim untuk memastikan bahwa knowledge management dan talent management berfungsi dengan baik dan karyawan tidak mengalami stress kerja. Untuk melakukan pengujian validitas model yang dikembangkan, studi ini menggunakan PLS-SEM (Modeling Equation Structural Partial Least Squares) menggunakan perangkat lunak SmartPLS sesuai dengan data yang dikumpulkan. Hasil studi menunjukkan bahwa knowledge management, talent management, dan stres kerja memberikan pengaruh terhadap kinerja karyawan. Kata kunci: Knowledge Management, Talent Management, Stress Kerja, Kinerja Karyawan, Sem-PLS Abstract In the mining industry, PT Bukit Asam Tbk is a state-owned enterprise. PT Bukit Asam Tbk is clearly facing challenges in its business processes during the COVID-19 pandemic. Several activities involving many people had to be stopped. This has an impact on employee performance, problems like this definitely require quality human resources. Because it is crucial for PTBA Tanjung Enim to guarantee that knowledge management, talent management, and work stress are not experienced by employees, The purpose of this study is to determine how these factors affect worker performance. This study tests the validity of the model created using SmartPLS software and primary data from the questionnaire applying the Partially least squares structural equation modeling, or PLS-SEM, approaches. Employee performance is recognized to be impacted by job stress, talent management, and knowledge management, according to study findings. Keywords: Knowledge Management, Talent Management, Work Stress, Employee Performance, Sem-PLS
PREDIKSI GANGGUAN PANIK MENGGUNAKAN KNOWLEDGE DISCOVERY IN DATABASE DENGAN ALGORITMA GRADIENT BOOSTING Maulizidan, Muammar Ramadhani; Hermanto, Muhammad Lucky; Ardhillah, Onky; Azra, Muhammad Azyumardi; Purba, Kevin Agustin; Zidan, Umar Rahman; Tania, Ken Ditha; Meiriza, Allsella
Jurnal Teknologi Terpadu Vol 13, No 2 (2025): JTT (Jurnal Terpadu Terpadu)
Publisher : Pusat Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32487/jtt.v13i2.2518

Abstract

In an effort to enhance the diagnosis and intervention of panic disorder, this study develops a predictive model for determining the severity level of panic disorder using the Knowledge Discovery in Databases (KDD) approach. The dataset comprises variables such as age, gender, personal and family history, current stressors, symptom severity, impact on daily life, demographics, medical history, psychiatric history, substance use, coping mechanisms, social support, and lifestyle factors. The Gradient Boosting algorithm was employed to analyze the data and uncover complex patterns among the variables. The results indicate that the proposed model is capable of classifying the severity of panic disorder with high accuracy, aligning with findings from previous studies that utilized similar approaches. Other research also supports the effectiveness of machine learning algorithms in predicting panic attacks using data from wearable devices and mobile applications. These findings are expected to contribute to the development of decision support systems in the field of mental health. 
Organizational Culture and Organizational Innovation Capability through the Mediation of Knowledge Sharing Mufidah, Luthfiah; Tania, Ken Ditha
Management Analysis Journal Vol. 13 No. 1 (2024): Management Analysis Journal
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/maj.v13i1.1491

Abstract

This research aims to explore the enhancement of the organizational innovation capability (OIC) in the South Sumatra Balitbangda through the role of organizational culture (OC), with the help of knowledge sharing (KS) as an intervening variable to help South Sumatra Balitbangda, which is a supporting element of the government responsible for government innovation in the South Sumatra region, maximize their competence in innovation capability. This research uses a quantitative approach with census sampling method which covers all of Balitbangda’s active employees. The examination of the collected data uses the PLS-SEM method with a total of 53 valid questionnaire responses from Balitbangda’s employees. The results indicate that OC and KS positively and significantly influence enhancing OIC. It has also proved that KS effectively mediates the relationship between OC and OIC. This study suggests a bigger sample size and scope, and the exploration of other potential variables in enhancing innovation capabilities for future research.
Implementation of Business Intelligence for Data Visualization at PT PP London Sumatra Indonesia Lubis, Muhammad Ali; Tania, Ken Ditha
Sistemasi: Jurnal Sistem Informasi Vol 13, No 6 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i6.4663

Abstract

This research project examines the deployment of Business Intelligence (BI) utilising Tableau to enhance data visualisation at PT PP London Sumatra Indonesia Tbk, with a particular focus on the palm oil processing and sales sector. Palm oil is a significant commodity in the Indonesian economy. This study highlights the significance of accessing accurate data in order to inform business decisions. The implementation of Business Intelligence (BI) facilitates the processing of data from disparate sources into informative visualisations, thereby enabling the identification of operational trends and patterns. The methodology comprised the collection of data on a daily basis, the cleansing of said data using ETL processes, and the presentation of the data in a visual format through Tableau. The results demonstrated an improvement in operational efficiency and a reduction in the time taken to make decisions, while also providing insights that could inform strategic decisions made by management. It is anticipated that this implementation will assist the company in meeting market challenges and improving product quality and business processes.
Application of RESTful Web Service in the Development of Letter Management Information System of Inspectorate Fikri, M Fauzan; Tania, Ken Ditha
Journal of Information System Research (JOSH) Vol 5 No 2 (2024): Januari 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i2.4791

Abstract

The rapid development of technology can increase efficiency and effectiveness in all fields by optimizing the use of technology. The Inspectorate of South Sumatra Province is a government agency located in the capital city of South Sumatra, Palembang. The problem found in the Inspectorate of South Sumatra Province is the disposition of letters that have not been digitized so that the disposition of letters cannot be monitored and the range of letters is lost. It takes more time and storage in searching for letters. With a system that is still manual, the inspectorate of the province of South Sumatra requires more budget for paper media and a lack of efficiency and effectiveness in the manual system. So a digitized letter management system is needed that can be accessed by mobile devices or PCs. With the Web Service, it is believed that it can support data integration between mobile and PC without third party applications. The application of RESTful API can increase security in user access rights. The results of this study are in the form of applications with the Rapid Application Development method adopting the Unified Modeling Language system design with Blackbox testing, it is known that the application runs well according to user needs, indicating that the application can solve problems in letter management that occur in the inspectorate of the province of South Sumatra.
Co-Authors A. Salwa Aurelya Putri Abdillah Putra, Muhafsyah Adeliani, Adeliani Adella Salsabila Adriansyah, Rizki Afdhal Nadzif, Muhammad Ahmad Fadhil Rizqi Ahmad Rifai Ahmad Rifai Ahmad Rifai Aisyah Fatimah Akbar Adiprama, Faris Akbar Kurniawan, Iqbal Akbar, Rifko Akhda, M. Dandi Akmar, Nur Salwa Fadia Al Fachrozi, Muhammad Al-Farisy, M Hadi Albukhori, M Rafli Alfarizi Ramadhiyansa, Muhammad Alfarizi, M. Ali Bardadi Ali Ibrahim Ali Ibrahim (SCOPUS ID: 57203129436) Alifa Putri Shahabiyah Aliya Faiza Aliyananda Risyahputri Allsela Meiriza, Allsela Allsella Meiriza Alsella Meiriza Alsella Meiriza Alvines, Mahendi Alzena Aisha Shakira Amanda Ardhani, Dhita Amanda, Khansa Putri Amelia Amelia Amelia Putri, Shinta Amelia, Rita Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Anggun Ramadina Anindya Putri, Salsa Anisa Basulina, Nur Anissa, Cahya Rahmi Apriansyah Putra Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi, Apriyadi Aqil Zidane, Muhammad Aqilah Syahputra, M Fathan Archi Daffa Danendra, Muhammad Ardhillah, Onky Ardina Ariani Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arvhi Randita Setia Athallah Ubaid, Deni Athiyyah Nuha Rotifa Attika Putri, Shopi Audia Faradhisa Ansori Aulia Najibah Putri Aulia Pinkasari Ayuningtiyas, Pratiwi Az-Zahra, Nanda Salsabila Azera Pramesty Azmi Zaky, Muhammad Azra, Muhammad Azyumardi Badia Inaya Sazrade Bagus Prihantoro Bahri, Cheisya Andini Baidhawi, Alif Bimmo Fathin Tammam Cahya Aulia, Syifa Cahya Rahmi Anissa Cahyo Adi Nugraha Cantika Aulia Cici Elna Sari Cindy Dinata Citra, Belia Clark Peter Wijaya, Adley Constancio, Elven Dedy Kurniawan Dian Febriansyah Dila Naila Fahria Dwiansyah, Octa Dzaky Agusman, Muhammad Edo Wicaksono Eka Saputra Eka Therina Lakeisyah Elna Sari, Cici Endang Lestari Ruskan Epriyanti, Nadia Fachrozi, Muhammad Al Fahmi Aulia Hakim, Adzka faizah, haniyah Fajaria, Mutiara Fakhri Sepriansyah Fakhri Sepriansyah Farhan Daffazka Fathoni - Fatihaturrahmah, Aisyah Fauzan, Muhammad Fairuz Ferdiansyah Fidela Tertia Alfino Fikri, M Fauzan Firmansyah, Zikri Frans Nicko Apriansyah Gabriel Sebastian Santoso Gibral Abdurahman Gustiani, Sindy Haidar Afif Mufid, Muhammad Hanggara, Bryan Hendrawan, Deni Agus Hermanto, Muhammad Lucky Hikmahwarani, Fellycia Ichsan Farel Rachmad, Muhammad Ikhwan Najatafani, Bintang Inayah, Anna Fadilla Indira Nailah Ramadhani Ispahan, Tarisha Izzan Fieldi, Muhammad Jackson Imanuel Manurung Jeremiah Alwin Siahaan Jodi Pratama, Muhammad Jonathan Pakpahan Juliyanti, Tamara Junia Kurniati Juseia Wulandari Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Khairunnisa’ Almaududy Khalid Al Mas Ud Khoiriyah Harahap, Dayana Kurnia Sari, Winda Kurniasari, R. Nyi Pipih Lailla Syal Syabilla Lakeisyah, Eka Therina Lifiano Jamot Munthe, Gabriel Lubis, Muhammad Ali M Ihsan Jambak M Luthfi Khailani, Kgs M Naufal Hisyam M Tsabita Robani M. Fadhil Rahman M. Ilham Fahlevi M. Thoriqul Fadli Mahdiyah Afifah Sari Mahdiyah Afifah Sari Marco Saputra Maretta, Aulia Pinkan Mariska, Inneke Via Marpaung, Xenia Clarissa Valencia Maulizidan, Muammar Ramadhani Mei Intan Natasyah Meiriza, Allsella Meiriza, Alsella Meiyin Monica Amilia Putri Merizka Azzahra Miftahul Falah Mira Afrina Mohd Rizky Putra Pratama Muammar Ramadhani Maulizidan Mufidah, Luthfiah Muhammad Adisatya Dwipansy Muhammad Bayu Samudra Muhammad Dzaky Alifayoezra Muhammad Dzaky Hasyim Muhammad Fakhri Nadrota Acta Muhammad Hafiz Al Zaky Muhammad Idris Muhammad Ihsan Dirgantara Muhammad Iqbal Disriansyah Muhammad Luthfi Al-Ghifari Muhammad Luthfi Al-Ghifari Muhammad Mayda Ary Pratama Muhammad Qurhanul Rizqie Muhammad Wahyu Hikmalsyah Muhammad Yusuf Munaspin, Zahra Diva Putri Mutia Fadhila Putri Mutia Sahira Nabilaputri, Silvia Nabilatulrahmah, Raihana Nachwa, Syakillah Naila Raihana Putri Najwa Widasari, Yesya Naretha Kawadha Pasemah Gumay Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Naufaldihanif, Rihan Novrizal Eka Saputra Nugraha, Allan Nulry Izzatul Maula Nuraini Kusuma, Aisha Nurly Izzatul Maula Onkky Alexander Pacu Putra Prasetia, Dika Pratama Putra, Daffa Pratiwi, Metti Detricia Purba, Kevin Agustin Puti Chalisa Wardhana Putri Ariyanti Putri Casanova, Musdalifa Putri Mutiara Arinie Putri Rahel Alifia Putri Salsabilah Putri Silpiara Putri, Amelia Rizki Putri, Aulia Najibah Putri, Naila Raihana Putri, Salsa Anindya RA Aliffyaa Ramadhani Rabbani, Muhammad Randy Raditya Dafa Rizki Rafi Herdian Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmah, Atika Nur Rahmat Izwan Heroza Rahmat Maulana Ramadhan Putra Pratama, Muhammad Ramadhani, Indira Nailah Ramadhani, Muthia Rangga Aderiyana, Fakih Ravi Wijayanto, Muhammad Riansyah, Muhammad Bintang Naufal Risyahputri, Aliyananda Rizka Dhini Kurnia Rizka Mumtaz, Fadia Rizki Ade Ningsih Rizki Kurniati Rizky Herdiansyah, Muhammad Rizkyllah, Anabel Fiorenza Rositiani, Ely Rusdi Effendi Sabar Manahan, Nico Sabila, Amalia Sahira, Mutia Salsabila, Adella Salsabila, Shofi Sanjaya, Riska Amelia Saputra, Gerri Asa Sasmita, Ruth Mei Satria, Eka Bayu Sembiring Depari, Alrayssa Davinka Septhia Charenda Putri Sevtiyuni, Putri Eka Shafa Aurelliza Arian Shelly Putri Shofi Salsabila Siade, Shalya Yunia Siregar, Richi Nauli Juniarto Siti Hariza Marshella Suandi, M. Suci Amalia Suci Fitriani, Suci Sukamto, Ika Sumiyarsi Sukatin, Sukatin Surya, Leiden Fauzi Yoka Syakillah Nachwa Syarief Albani, Muhammad Talitha Zafirah Theonady, Oktavio Theresia Pardede, Eva Theressa Hasioani Sianturi, Claudia Tika Octri Dieni Titiana, Nuke Merisca Tri Mutiara Illahi Tri Zafira, Zahra Triana, Ayu Triputra, Muhamad Meiko Tsabitah, Laila Ummu Farida Muthmainnah Violin Juneyla Nandita Wahyuni Cahnia Sari Wilantara, M Pandu Winda Kurnia Sari Winda Kurnia Sari Wirnanti, Rintan Wulan Dari, Atikah Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Yesinta Florensia Yoga Fradana Zahran Afif, Muhammad Zaqqi Yamani Zaqqi Yamani Zaqqi Yamani A Zaskia Aulia Wulandari Zidan, Umar Rahman