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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) Jurnal Manajemen STIE Muhammadiyah Palopo MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING 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 Journal of Information Systems and Informatics Zonasi: Jurnal Sistem Informasi JATI (Jurnal Mahasiswa Teknik Informatika) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Darma Agung Jurnal Bisnis, Manajemen, dan Ekonomi Jurnal Generic Jurnal Pendidikan dan Teknologi Indonesia Jurnal Algoritma Jurnal Teknologi dan Manajemen Industri Terapan Jurnal Indonesia Sosial Teknologi Electronic Journal of Education, Social Economics and Technology The Indonesian Journal of Computer Science Management Analysis Journal Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Buffer Informatika Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Ken Ditha Tania
Jurusan Sistem Informasi, Universitas Sriwijaya
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Pengembangan Dan Evaluasi Knowledge Management System Berbasis Android dengan Pendekatan Rational Unified Process (RUP) untuk Manajemen Rumah Kos Fahmi Aulia Hakim, Adzka; Alfarizi Ramadhiyansa, Muhammad; Dwiansyah, Octa; Afdhal Nadzif, Muhammad; Dzaky Agusman, Muhammad; Ditha Tania, Ken; Rifai, Ahmad
Jurnal Teknologi dan Manajemen Industri Terapan Vol. 4 No. 2 (2025): Jurnal Teknologi dan Manajemen Industri Terapan
Publisher : Yayasan Inovasi Kemajuan Intelektual

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55826/jtmit.v4i2.618

Abstract

Kemajuan teknologi informasi telah memicu digitalisasi di berbagai sektor, termasuk dalam manajemen rumah kos. Sistem manajemen kos tradisional yang masih dilakukan secara manual sering mengalami masalah dalam pencatatan data penghuni, pembayaran, dan perawatan fasilitas, yang dapat mengakibatkan kesalahan dan ketidakefisienan dalam pengelolaan. Studi ini bertujuan menciptakan sistem manajemen pengetahuan (KMS) berbasis Android untuk Arina Kos dengan menerapkan metode Rational Unified Process (RUP) untuk meningkatkan efisiensi operasional serta kualitas layanan bagi penghuni kos. Proses pengembangan perangkat lunak melibatkan tahap Inception, Elaboration, dan Construction dengan pemodelan sistem memakai Unified Modelling Language (UML). Hasil pengujian dengan metode Black Box Testing menunjukkan bahwa semua fitur sistem beroperasi dengan baik sesuai dengan spesifikasi yang telah dirancang, dengan tingkat keberhasilan uji mencapai 100%. Evaluasi dengan System Usability Scale (SUS) menghasilkan nilai 84,21, yang menunjukkan sistem sangat mudah digunakan. Penelitian ini juga mengisi celah yang belum banyak diteliti, yaitu penerapan KMS Android dengan RUP untuk manajemen rumah kos, serta kontribusi untuk peningkatan efisiensi dan komunikasi dalam pengelolaan kos.
Analisis Usability dan User Experience pada Aplikasi CISEA PT. Bukit Asam Menggunakan Metode System Usability Scale (SUS) dan User Experience Questionnaire (UEQ) Amelia, Rita; Tania, Ken Ditha
CESS (Journal of Computer Engineering, System and Science) Vol. 9 No. 1 (2024): January 2024
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/cess.v9i1.54728

Abstract

Suatu organisasi dapat mengatasi persaingan yang semakin ketat dengan menggunakan teknologi informasi. Suatu organisasi dapat menjadi lebih baik dalam melakukan segala kegiatan operasionalnya jika dapat memanfaatkan keunggulan teknologi. Aplikasi CISEA adalah aplikasi yang digunakan perusahaan sebagai media informasi dan komunikasi karyawan. Peneliti mengevaluasi pengalaman dan kemampuan pengguna menggunakan aplikasi CISEA. Analisis dilakukan menggunakan metode yaitu pada aspek pengalaman pengguna menggunakan metode User Experience Questionnaire (UEQ) dan aspek Usability menggunakan System Usability Scale (SUS). Hasil pengukuran kebergunaan (usability) menggunakan pengujian SUS yang diberikan kepada 30 responden diperoleh nilai rata-rata skor SUS mencapai 82.5 maksudnya aplikasi CISEA dalam kategori œexcellent dengan penilaian grade skor yaitu œB yang menyatakan aplikasi CISEA bisa diterima oleh pengguna. Hasil kepada 30 responden dengan enam elemen pengukuran kuesioner UEQ, yang meliputi apsek attractivesness (Daya Tarik) mendapat nilai rata-rata 1,22 memberikan nilai posistif/baik, aspek perspicuity (Kejelasan) mendapat nilai 1,26 memberikan nila positif/baik, aspek efficiency (efisiensi) mendapat nilai 1,18 memberikan nilai positif/baik, aspek dependability mendapat nilai 0,96 memberikan nilai positif/baik, aspek stimulation (stimulasi) mendapat nilai 0,82 memberikan nilai yang positif atau baik serta elemen inovatif (kebaruan) mendapatkan nilai 0,89 memberikan nilai positif/baik.
Peran Knowledge Sharing dan Motivasi Kerja Terhadap Kinerja Pegawai Dengan Kompetensi Sebagai Variabel Intervening Rahmah, Atika Nur; Tania, Ken Ditha
Jurnal Manajemen STIE Muhammadiyah Palopo Vol 9, No 2 (2023)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35906/jurman.v9i2.1842

Abstract

Abstract Libraries play an important role in providing access to knowledge and information. When performing their duties, library staff play a crucial role in providing high-quality services to library users, as well as being the center and core of the library. The involvement of library staff in knowledge dissemination activities is important. It is therefore important for library staff to have the motivation, skills and performance to support the performance of their duties. This study aims to solve the problems that occur by analyzing the effect of knowledge sharing and work motivation on employee performance with competence as an intervening variable. This research uses quantitative methods. The sampling applied was non-probability using saturated sample technique, involving 26 respondents from Sriwijaya University Library employees. Data collection was done through questionnaires because the population was proportional to the number of respondents. Data analysis was conducted through descriptive analysis approach and PLS-SEM with SmartPLS analysis tool. The study results show that knowledge sharing has no significant influence on employee competence and performance. On the other hand, work motivation has a significant effect on employee competence and performance. Competence also has a significant effect on employee performance, and knowledge sharing and work motivation have no direct effect on performance through competence as an intervening variable.Keywords: Knowledge Sharing, Work Motivation, Competence, Performance.Abstrak Perpustakaan memainkan peran penting dalam menyediakan akses terhadap pengetahuan dan informasi. Saat menjalankan tugas, pegawai perpustakaan memiliki peran krusial dalam menyediakan layanan berkualitas tinggi kepada pengguna perpustakaan, serta menjadi pusat dan inti dari perpustakaan. Keterlibatan pegawai perpustakaan dalam kegiatan penyebaran pengetahuan dianggap penting. Maka penting bagi pegawai perpustakaan memiliki motivasi, keterampilan, dan kinerja yang berguna untuk mendukung pelaksanaan tugasnya. Penelitian ini bertujuan untuk menyelesaikan permasalahan yang terjadi dengan cara menganalisis pengaruh berbagi pengetahuan dan motivasi kerja terhadap kinerja pegawai dengan kompetensi sebagai variabel intervening. Penelitian ini menggunakan metode kuantitatif. Pengambilan sampel yang diterapkan ialah non-probabilitas dengan menggunakan teknik sampel jenuh, melibatkan 26 responden dari pegawai UPT Perpustakaan Universitas Sriwijaya. Pengumpulan data dilakukan melalui kuesioner karena jumlah populasi sebanding dengan jumlah responden. Analisis data dilakukan melalui pendekatan analisis deskriptif dan PLS-SEM dengan alat analisis SmartPLS. Hasil studi menunjukkan bahwa berbagi pengetahuan tidak memiliki pengaruh signifikan terhadap kompetensi dan kinerja pegawai. Di sisi lain, motivasi kerja memberikan pengaruh yang signifikan terhadap kompetensi dan kinerja pegawai. Kompetensi juga berpengaruh signifikan terhadap kinerja pegawai, serta berbagi pengetahuan dan motivasi kerja tidak berpengaruh langsung terhadap kinerja melalui kompetensi sebagai variabel intervening.Kata Kunci: Knowledge Sharing, Motivasi Kerja, Kompetensi, Kinerja.
Peran Knowledge Sharing Lingkungan Kerja dan Kompetensi Kerja Dalam Meningkatkan Kinerja Karyawan Sanjaya, Riska Amelia; Tania, Ken Ditha
Jurnal Manajemen STIE Muhammadiyah Palopo Vol 9, No 2 (2023)
Publisher : Lembaga Penerbitan dan Publikasi Ilmiah (LPPI) Universitas Muhammadiyah Palopo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35906/jurman.v9i2.1838

Abstract

AbstractPT Konverta Mitra Abadi Lampung is a manufacturing company that produces carton boxes and packing. The company strives to be a good company with knowledgeable and natural human resources. A successful company is one that is able to increase its capacity through innovation and new ideas and improve the performance of its employees to achieve company goals. The purpose of this research is to study how knowledge sharing, work environment, and work competence affect worker performance. This study involved 60 respondents who were permanent employees of PT Konverta Mitra Abadi Lampung. The data for this study were collected through the questionnaire distribution method, and then analyzed using the PLS-SEM (Partial Least Squares-Structural Equation Modeling) method used with SmartPLS. Businesses can use the findings of this study to improve employee performance so that they can achieve their goals. The results showed that knowledge sharing did not have a significant impact on the performance of employees of PT Konverta Mitra Abadi Lampung. This is because the company pays less attention to knowledge sharing activities between employees. On the other hand, a good and comfortable work environment and high competence greatly affect employee performance.Keywords: Knowledge Sharing; Work Environment; Work Competency; Employee Performance.Abstrak PT. Konverta Mitra Abadi Lampung adalah perusahaan manufaktur yang memproduksi carton box and packing. Perusahaan tersebut berusaha menjadi perusahaan yang baik dengan sumber daya manusia yang berpengetahuan luas dan alami. Perusahaan yang berhasil adalah perusahaan yang mampu meningkatkan kapasitasnya melalui inovasi dan ide-ide baru serta meningkatkan kinerja karyawannya untuk mencapai tujuan perusahaan. Tujuan penelitian ini adalah untuk mempelajari bagaimana knowledge sharing, lingkungan kerja, dan kompetensi kerja mempengaruhi kinerja pekerja. Penelitian ini melibatkan 60 responden yang merupakan karyawan tetap PT. Konverta Mitra Abadi Lampung. Data untuk penelitian ini dikumpulkan melalui metode penyebaran kuesioner, dan kemudian dianalisis menggunakan metode PLS-SEM (Partial Least Squares-Structural Equation Modeling) yang digunakan dengan SmartPLS. Bisnis dapat menggunakan temuan penelitian ini untuk meningkatkan kinerja karyawan agar mereka dapat mencapai tujuan mereka. Hasil penelitian menunjukkan bahwa knowledge sharing tidak berdampak signifikan pada kinerja karyawan PT. Konverta Mitra Abadi Lampung. Ini karena perusahaan kurang memperhatikan kegiatan berbagi pengetahuan antar karyawan. Lain halnya, lingkungan kerja yang baik dan nyaman serta kompetensi yang tinggi sangat memengaruhi kinerja karyawan.Kata Kunci: Knowledge Sharing; Lingkungan Kerja; Kompetensi Kerja; Kinerja Karyawan.
Sentiment Analysis on Google Play Store Reviews to Measure User Perception of the Gojek Application Using CNN Anissa, Cahya Rahmi; Tania, Ken Ditha; Sari, Winda Kurnia
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11084

Abstract

This study was conducted to analyze sentiment towards user reviews from the Google Play Store regarding the Gojek application. The analysis aims to measure user perceptions using a Convolutional Neural Network (CNN). This study aims to understand user views on the Gojek application. By understanding user perceptions, the information obtained can be utilized by the company's service team to improve the quality of the application for users. User perceptions are grouped into three labels: positive, neutral, and negative. To produce an effective model, this study uses three data sharing ratios simultaneously with the same parameters: 90:10, 80:20, and 70:30. Due to the large amount of data, random sampling is needed to balance the data and thus increase accuracy in the data processing process. Model evaluation was carried out using a confusion matrix, precision, recall, and F1-Score. The results obtained with the highest accuracy of 84.29%. This study successfully demonstrates that CNN is able to process user review data well.
Deteksi Komentar dan Analisis Sentimen Promosi Judi Online pada Youtube Menggunakan IndoBERT dan XGBoost Putri, Naila Raihana; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8421

Abstract

YouTube, as a highly interactive platform, has become a medium for online gambling promotions, raising legal issues under the Electronic Information and Transactions (ITE) Law and social risks, particularly for adolescents. This study aims to analyse public responses to gambling-related comments and to develop an automatic detection system using Natural Language Processing (NLP). The research follows the Knowledge Discovery in Databases (KDD) stages, including web scraping, preprocessing, text transformation, model training, and evaluation. Sentiment analysis was performed on 999 comments labelled positive, negative, and neutral. Detection of promotional content was tested using IndoBERT and TF-IDF-based XGBoost, with 587 training samples and 885 external testing samples at an 80:20 ratio. The results show that the majority of comments (52.65%) are positive with a fairly high average confidence score (0.914), indicating public support for the eradication of online gambling. Meanwhile, negative comments (24.72%) with a confidence score of 0.888 generally contained criticism of the rampant practice of gambling promotion or YouTube's weak moderation system. For automatic detection, IndoBERT achieved superior performance with 0.94 accuracy and F1-score and only 10 misclassifications, significantly outperforming XGBoost, which reached 0.73 accuracy with 47 errors. This study highlights the effectiveness of transformer-based models in detecting gambling promotions while also indicating strong public support for eradication efforts. These findings provide an empirical foundation for advancing research on adaptive automated moderation systems capable of identifying concealed patterns of illicit content in digital platforms, particularly in the detection of online gambling promotional comments within the YouTube ecosystem.
Komparasi Klasterisasi Data Historis Gempa Bumi Menggunakan DBSCAN, K-Means, dan Agglomerative Clustering Lakeisyah, Eka Therina; Tania, Ken Ditha; Afrina, Mira
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8426

Abstract

Earthquakes are one of the natural disasters that are prone to occur on the island of Sumatera and pose a serious challenge because they can have a devastating impact on human life, such as loss of life, material losses, and environmental damage. Therefore, earthquake hazard zone mapping is needed to provide information about the potential and history of disasters and is an important tool for disaster mitigation efforts. This study aims to map earthquake vulnerability in Sumatra by comparing three clustering algorithms, namely DBSCAN, K-Means, and Agglomerative Clustering, based on earthquake data in Sumatra from 1973 to 2023. This is to find the best algorithm so that it can provide recommendations for appropriate earthquake risk mitigation strategies. The results show that the K-Means algorithm is the best because it obtained the highest Silhouette Coefficient value, namely 0.3948 with a total of 3 clusters. It is hoped that this research can improve understanding of earthquake hazard zones on the island of Sumatra and provide practical contributions in the form of mitigation strategy recommendations tailored to the characteristics of each cluster to support the application of this research for the government and local communities.
Comparison of XGBoost and LSTM in Knowledge Discovery for GrokAI Mobile Application Sentiment Analysis Risyahputri, Aliyananda; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8651

Abstract

Generative AI has provided real benefits in key sectors of the public sector. However, the rapid expansion of AI assistant services also raises concerns about whether newly released products can consistently meet user expectations, especially as negative experiences are increasingly expressed through public reviews. Its positive impacts encourage competitive rivalry among AI assistant product developers, including xAI, which also participates by formulating the Grok AI application. As a relatively new product with over 50 million downloads, GrokAI needs to perform an evaluation to maintain its competitiveness. This condition leads to the research goal of analyzing user sentiment toward GrokAI application through reviews on Google Play Store and comparing the performance of Machine Learning and Deep Learning classification models within the framework of Knowledge Discovery in Databases (KDD). This study uses 11,108 review data classified using the VADER Lexicon method, resulting in 7,633 positive reviews and 3,475 negative reviews. The data is then tested on XGBoost (Extreme Gradient Boosting) and LSTM (Long-Short Term Memory) models. The results show that the XGBoost model performs slightly better with an accuracy of 87.22%, compared to LSTM, which reaches 86.58%. However, both models exhibit significant performance disparities in classifying negative classes due to the extreme difference in data quantity. The knowledge discovery process reveals that the majority of positive sentiment appreciates the free access and general functions of the application. Meanwhile, negative sentiment focuses on complaints related to response time, output quality, and specific features such as image and voice. The main recommendation is to maintain the advantage of free access also improve features and processing logic to sustain loyalty and service quality. Future research is suggested to test models with more balanced data and optimize dataset cleaning to improve accuracy in minority classes.
Review: A Hybrid Approach of Aspect-Based Sentiment Analysis and Knowledge Extraction for Evaluating Security Perceptions in Digital Payment Applications Fatihaturrahmah, Aisyah; Tania, Ken Ditha
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.31557

Abstract

Purpose: The rapid expansion of digital wallets in Indonesia has heightened concerns regarding user security and trust. This study evaluates user sentiment toward the security features of the DANA digital payment application using Aspect Sentiment Classification (ASC), a subtask of Aspect-Based Sentiment Analysis (ABSA). It aims to compare multiple classification models and generate structured, machine-readable sentiment outputs to support knowledge extraction and system integration. Methods: A total of 4,846 security-related reviews were collected from the Google Play Store using keyword-based filtering, supplemented by 3,000 unfiltered reviews for robustness evaluation. Sentiment labeling was performed using a hybrid rule-based and manual annotation approach. From 300 proportionally sampled reviews (150 positive and 150 negative), the validation achieved 0.8504 accuracy and a Cohen’s κ of 0.951, indicating near-perfect agreement. Five models Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and IndoBERT were evaluated using 5-fold stratified cross-validation with random oversampling to address class imbalance. Result: IndoBERT achieved the highest performance with 98% accuracy, an F1-score of 0.974, and an AUC-ROC of 0.996, followed by CNN and BiLSTM. Robustness testing across temporal (DANA June–October) and cross-domain (GoPay) datasets confirmed IndoBERT’s strong generalization with minimal F1-score variation. Novelty: Unlike previous ABSA studies that addressed multiple aspects, this research focuses exclusively on the security aspect, providing fine-grained insights into user trust. The integration of XML-based structured output enhances interpretability and interoperability in digital financial sentiment analysis, contributing to the development of more secure and transparent fintech ecosystems.
Penerapan Metode Machine Learning Dan Teknik SMOTE untuk Prediksi Diabetes Sembiring Depari, Alrayssa Davinka; Tania, Ken Ditha; Sevtiyuni, Putri Eka
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9032

Abstract

Diabetes merupakan salah satu penyakit tidak menular yang prevalensinya terus meningkat secara global maupun nasional. Kondisi ini menimbulkan risiko komplikasi serius seperti penyakit jantung, stroke, hingga gagal ginjal apabila tidak terdeteksi sejak dini. Oleh karena itu, dibutuhkan metode prediksi berbasis data yang mampu membantu proses deteksi awal secara cepat, akurat, dan efisien. Penelitian ini bertujuan membandingkan kinerja empat algoritma pembelajaran mesin, yaitu Random Forest, XGBoost, Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) dalam memprediksi penyakit diabetes menggunakan dataset publik dari Kaggle. Penelitian dilakukan dengan mengacu pada kerangka Knowledge Discovery in Databases (KDD) yang terdiri dari tahapan seleksi data, pra-pemrosesan (data cleaning, transformasi, dan normalisasi), penyeimbangan kelas menggunakan Synthetic Minority Over-sampling Technique (SMOTE), pembagian data latih dan data uji dengan rasio 80:20, implementasi algoritma, serta evaluasi performa model. Evaluasi dilakukan menggunakan metrik Accuracy, Precision, Recall, dan F1-Score untuk memastikan kualitas prediksi secara menyeluruh. Hasil penelitian menunjukkan bahwa Random Forest dan XGBoost memberikan performa terbaik dengan nilai Accuracy, Precision, Recall, dan F1-Score sebesar 0,97. Model KNN menunjukkan performa cukup baik dengan skor 0,94, sementara SVM memperoleh nilai terendah sebesar 0,89. Temuan ini menegaskan bahwa penerapan kerangka KDD dengan teknik SMOTE mampu menghasilkan model prediksi yang optimal. Random Forest dan XGBoost direkomendasikan sebagai algoritma unggulan pada penelitian serupa, terutama pada dataset dengan karakteristik kelas yang tidak seimbang.
Co-Authors Abdillah Putra, Muhafsyah Adeliani, Adeliani Adriansyah, Rizki Afdhal Nadzif, Muhammad Ahmad Rifai Ahmad Rifai Akbar Adiprama, Faris Akbar Kurniawan, Iqbal Akbar, Rifko Akhda, M. Dandi Al Fachrozi, Muhammad Al-Farisy, M Hadi Albani, Muhammad Syarief Albukhori, M Rafli Alfarizi Ramadhiyansa, Muhammad Alfarizi, M. Ali Ibrahim Ali Ibrahim (SCOPUS ID: 57203129436) Allsela Meiriza, Allsela Alvines, Mahendi Alzena Aisha Shakira Amanda Ardhani, Dhita 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 Aqil Zidane, Muhammad Aqilah Syahputra, M Fathan Archi Daffa Danendra, Muhammad Ardhillah, Onky Ari Wedhasmara Ariyani, Ishlah Putri Ariyanti, Putri Arvhi Randita Setia Ary Pratama, Muhammad Mayda Athallah Ubaid, Deni Attika Putri, Shopi Audia Faradhisa Ansori Aulia, Cantika Ayuningtiyas, Pratiwi Azmi Zaky, Muhammad Azra, Muhammad Azyumardi Bahri, Cheisya Andini Baidhawi, Alif Bimmo Fathin Tammam Cahya Aulia, Syifa Cahya Rahmi Anissa Cici Elna Sari Citra, Belia Clark Peter Wijaya, Adley Constancio, Elven Dedy Kurniawan Dian Febriansyah Dwiansyah, Octa Dzaky Agusman, Muhammad Eka Saputra Eka Sevtiyuni, Putri Elna Sari, Cici Endang Lestari Ruskan Epriyanti, Nadia Fachrozi, Muhammad Al Fahmi Aulia Hakim, Adzka Fajaria, Mutiara Fathoni - Fatihaturrahmah, Aisyah Fatimah, Aisyah Fauzan, Muhammad Fairuz Fikri, M Fauzan 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 Jodi Pratama, Muhammad Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Khoiriyah Harahap, Dayana Kurnia Sari, Winda Lailatur Rahmi Lakeisyah, Eka Therina Lifiano Jamot Munthe, Gabriel Lubis, Muhammad Ali M Ihsan Jambak M Luthfi Khailani, Kgs Mahdiyah Afifah Sari Mahdiyah Afifah Sari Maretta, Aulia Pinkan Mariska, Inneke Via Marshella, Siti Hariza Mas Ud, Khalid Al Maulana, Rahmat Maulizidan, Muammar Ramadhani Meiriza, Allsella Miftahul Falah Mira Afrina Miranda, Fatreisya Ayu Mufidah, Luthfiah Muhammad Adisatya Dwipansy Muhammad Dzaky Alifayoezra Muhammad Idris Muhammad Luthfi Al-Ghifari Muhammad Luthfi Al-Ghifari Munaspin, Zahra Diva Putri Nabilatulrahmah, Raihana Nachwa, Syakillah Nadrota Acta, Muhammad Fakhri Najibah Putri, Aulia Najwa Widasari, Yesya Naretha Kawadha Pasemah Gumay Nashiroh Ramadhani, Muthia Naufaldihanif, Rihan Novrizal Eka Saputra Nugraha, Allan Nuraini Kusuma, Aisha Onkky Alexander Pacu Putra Prasetia, Dika Pratiwi, Metti Detricia Purba, Kevin Agustin Putri Ariyanti Putri Casanova, Musdalifa Putri Mutiara Arinie Putri Silpiara Putri, Amelia Rizki Putri, Aulia Najibah Putri, Naila Raihana Putri, Salsa Anindya Raditya Dafa Rizki Rafika Octaria Ningsih Rafli Maulana, Muhammad Rahmah, Atika Nur Rahman, M. Fadhil Rahmat Izwan Heroza Ramadhan Putra Pratama, Muhammad Ramadhani, Indira Nailah Rangga Aderiyana, Fakih Ravi Wijayanto, Muhammad Riansyah, M. Bintang Naufal Riansyah, Muhammad Bintang Naufal Risyahputri, Aliyananda Rizka Dhini Kurnia Rizka Mumtaz, Fadia Rizki Ade Ningsih Rizky Herdiansyah, Muhammad Rizkyllah, Anabel Fiorenza Robani, M Tsabita Rositiani, Ely Sabar Manahan, Nico Sabila, Amalia Sahira, Mutia Salsabila, Adella Salsabila, Shofi Sanjaya, Riska Amelia Saputra, Marco Sasmita, Ruth Mei Sembiring Depari, Alrayssa Davinka Septhia Charenda Putri Sevtiyuni, Putri Eka Shelly Putri Siade, Shalya Yunia Siregar, Richi Nauli Juniarto Suci Amalia Suci Fitriani, Suci Sukamto, Ika Sumiyarsi Syarief Albani, Muhammad Theresia Pardede, Eva Theressa Hasioani Sianturi, Claudia Tika Octri Dieni Titiana, Nuke Merisca Tri Zafira, Zahra Triana, Ayu Triputra, Muhamad Meiko Tsabitah, Laila Wahyuni Cahnia Sari Wilantara, M Pandu Winda Kurnia Sari Wirnanti, Rintan Wulan Dari, Atikah Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Zahran Afif, Muhammad Zidan, Umar Rahman