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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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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.
The Influence of Knowledge Management and Digital Competence on Employee Performance: Mediating Role of Innovative Behavior Sabila, Amalia; Afrina, Mira; Tania, Ken Ditha
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.v9i6.11529

Abstract

Rapid technological changes in the era of Industry 4.0 and 5.0 have made digital knowledge and skills more important in improving the way employees perform their tasks. Earlier research has given mixed results. This shows there is still a lot to learn. Based on the KBV (Knowledge Based-View) theory, this study looks at how knowledge management and digital competence directly and indirectly affect employee performance through innovative work behavior. Data were obtained using a questionnaire that had been compiled and analyzed with Partial Least Squares-Structural Equation Modeling (PLS-SEM) method with SmartPLS 4.1.1.4. The research sample included all employees in the case study (N = 56), with census sampling method. The study found that KM had a significant impact on IWB (p < 0,05), but did not have a significant direct impact on EP (p > 0,05). DC had a significant impact on EP (p < 0,05), but did not have a significant impact on IWB (p > 0,05). IWB played an important role in improving EP and also mediated the relationship between KM and EP. Theoretically, this study adds value to both the KBV theory by explaining how KM boosts performance through indirect ways, and by showing that digital capital plays a limited role in improving performance. Practically, the findings offer actionable implications for HR practitioners in designing performance systems that reward innovative behaviour, thereby motivating employees to utilize knowledge and digital tools more creatively to enhance productivity and service quality in medium enterprises.
Performance Analysis of YOLO, Faster R-CNN, and DETR for Automated Personal Protective Equipment Detection Naufaldihanif, Rihan; Kurniawan, Dedy; Tania, Ken Ditha
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.v9i6.11593

Abstract

Automated monitoring of Personal Protective Equipment (PPE) is crucial for enhancing safety in high-risk environments like construction sites, yet selecting the optimal detection model requires careful evaluation of accuracy versus efficiency trade-offs. This study presents a comparative performance analysis across distinct object detection paradigms represented by YOLO (YOLOv8, YOLOv11n), Faster R-CNN, and DETR to benchmark their suitability for real-time PPE detection. However, this study moves beyond a simple technical benchmark by also proposing a logical process to transform raw model detections (e.g., 'person', 'hardhat') into actionable compliance verification information (e.g., 'Compliant'/'Non-Compliant'). Using a curated construction site safety dataset, models were evaluated based on standard accuracy metrics (including mAP@.5:.95) and efficiency measures (inference latency). Results indicate that DETR and YOLOv11n achieved the highest overall accuracy with an identical mAP@.5:.95 of 0.770, closely followed by YOLOv8 (0.763), while the YOLO family demonstrated significantly superior real-time efficiency (6-7 ms latency). Faster R-CNN recorded a lower mAP (0.703) and the highest latency. Conclusively, YOLOv11n offers the most compelling balance for the detection phase, and the proposed logical process provides a practical method for integrating this technical output into automated safety monitoring systems.
COMPARISON OF NAÏVE BAYES, SVM, K-NN, DECISION TREE, AND RANDOM FOREST IN SENTIMENT ANALYSIS BASED ON SEABANK APPLICATION ASPECTS Fachrozi, Muhammad Al; Tania, Ken Ditha
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v12i1.4189

Abstract

Abstract: The increasing use of digital banking applications has led to the need for a deeper understanding of user perceptions, especially through aspect-based sentiment analysis. This study aims to classify the sentiment of SeaBank app users by focusing on four main aspects: learnability, efficiency, technical issues or errors, and satisfaction. Review data totaling 1,971 comments were collected from the Google Play Store and labeled with sentiments based on the scores (ratings) given by users. The CRISP-DM approach serves as the methodological framework for this study, which includes five classification algorithms: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Random Forest. The evaluation results show that the SVM algorithm provides the best performance with the highest average value of the four aspects achieving accuracy of 93.91%, Precision of 91.16%, recall of 97.96% and F1-Measure of 94.33%. According to the research findings, the Support Vector Machine (SVM) algorithm provides the best performance when performing aspect-based sentiment analysis on text data from digital banking application reviews. The findings are expected to serve as a reference for the development of automated evaluation systems that rely on user opinions as the basis for decision making. Keywords: aspects; CRISP-DM; digital Banking; seabank; sentiment analysis Abstrak: Peningkatan pemakaian aplikasi perbankan digital mendorong perlunya pemahaman yang lebih dalam mengenai persepsi pengguna, terutama melalui analisis sentimen berbasis aspek. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi SeaBank dengan berfokus pada empat aspek utama: kemudahan dipelajari (learnability), efisiensi penggunaan (efficiency), kendala atau kesalahan teknis (error), serta tingkat kepuasan (satisfaction). Data ulasan berjumlah 1.971 komentar dikumpulkan dari Google Play Store dan diberi label sentimen berdasarkan skor (rating) yang diberikan oleh pengguna. Pendekatan CRISP-DM berfungsi sebagai kerangka metodologis untuk penelitian ini, yang mencakup lima algoritma klasifikasi: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, dan Random Forest. Hasil evaluasi menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan nilai rata-rata dari ke empat aspek tertinggi yang mencapai accuracy sebesar 93.91%, Precision sebesar 91.16%, recall sebesar 97.96% dan F1-Measure sebesar 94.33%. Menurut temuan penelitian, algoritma Support Vector Machine (SVM) memberikan kinerja terbaik saat melakukan analisis sentimen berbasis aspek pada data teks dari ulasan aplikasi Seabank. Temuan ini diharapkan dapat menjadi referensi bagi pengembangan sistem evaluasi otomatis yang mengandalkan opini pengguna sebagai dasar pengambilan keputusan. Kata kunci: Analisis Sentimen, Aspek, Bank Digital, SeaBank, CRISP-DM
Knowledge Discovery in Sharia Mobile Banking Reviews Using Aspect-Based Sentiment Analysis and Machine Learning Nashiroh Ramadhani, Muthia; Ditha Tania, Ken; Afrina, Mira
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.v10i1.11753

Abstract

User reviews provide important insights into the quality of digital banking applications; however, their large volume makes manual analysis inefficient. This study applies Aspect-Based Sentiment Analysis (ABSA) to examine user perceptions of the BYOND by BSI application based on three aspects: interface, features and performance, and services. Three classification algorithms were compared: Naïve Bayes, Support Vector Machine (SVM), and Random Forest, evaluated with accuracy, precision, recall, F1-score, and ROC-AUC. The results indicate that SVM and Naïve Bayes achieved the best performance, with an accuracy of 0.95 and an F1-score of 0.92, whereas Random Forest exhibited slightly lower performance with an F1-score of 0.89. Furthermore, sentiment analysis reveals the features and performance aspect exhibits the highest proportion of negative sentiment (39.6%), primarily associated with system reliability issues, login problems, transaction failures, and application instability. These findings demonstrate that ABSA can serve as an effective knowledge discovery approach for identifying critical functional issues and supporting data-driven prioritization in improving digital banking services, particularly within the context of sharia banking applications.
Implementation Of Naïve Bayes Algorithm In Predicting Alumni Waiting Time To Secure Employment (Case Study: Universitas Sriwijaya) Shelly Putri; Ken Ditha Tania
Jurnal Indonesia Sosial Teknologi Vol. 6 No. 2 (2025): Jurnal Indonesia Sosial Teknologi
Publisher : Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59141/jist.v6i2.8929

Abstract

In education, alums' success in getting a job after graduation is a significant benchmark for educational institutions in assessing the quality of education they provide. This study aims to estimate the waiting period category of alums based on the ability of alums to graduate when they are related to the waiting period category and design software that can predict the waiting period category of alums by classification method. The method applied is CRISP-DM. The data used is tracer study data in 2021 with 4,734 records. With a significant level of 5% (0.05), it was found that the waiting period category had a positive and detrimental relationship with the variables of GPA, Waiting Period, First Work Province, First Income, Ethics, Expertise, and English language ability. In this study, 10-fold cross-validation was applied, which resulted in the accuracy of the decision tree algorithm of 84.33%, the K-NN algorithm of 75.45%, the Naive Bayes Classifier algorithm of 85.21%, and the Random Forest algorithm of 84.04%. Furthermore, a different test (T-Test) was carried out, which showed that the Naive Bayes Classifier algorithm was the most dominant algorithm among the other three algorithms so that it could classify and predict the waiting period category well. This study concludes that applying the Naïve Bayes algorithm can effectively predict the waiting period for alums to get a job. The implication of this study is the development of web-based software that educational institutions can use to analyze the waiting period of alumni, provide recommendations for educational policies, and assist students in planning better career strategies.
Pengaruh Knowledge Sharing Factor Terhadap Keberlanjutan Penggunaan E-Learning Pasca Covid-19: The Influence of Knowledge Sharing Factors on the Continuity of Using E-Learning Post-Covid-19 Ariyanti, Putri; Ditha Tania, Ken; Wedhasmara, Ari; Meiriza, Allsela
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3382

Abstract

E-learning includes learning methods that use information technology and can be accessed via the internet, making it possible to learn remotely without face-to-face meetings. E-learning functions to implement knowledge management practices, especially in sharing knowledge. Studies on various knowledge-sharing factors influencing the adoption of e-learning in the post-pandemic context are still limited in the existing literature. Therefore, this study has the objective of developing an Expectation Confirmation Model by taking into account the factors of knowledge sharing (communication openness, personal trust, sharing motivation, use of technology, and perceptions of ease of use of technology) to test the viability of using e-learning, especially at Srivijaya University. This study uses the Partial Least Squares Structural Equation Modeling (PLS-SEM) method to test the validity of the developed model. Study data was collected from active students at Sriwijaya University who used or are currently using e-learning in lectures. The results of this study show that knowledge sharing factors, including openness of communication, personal trust, motivation to share, usefulness of technology, and perceived ease of use of technology, are important factors in determining the continued use of e-learning services at Sriwijaya University.
Sentiment Analysis Performance Value Optimization Using Hyperparamater Tunning With Grid Search On Shopee App Reviews Muhammad Luthfi Al-Ghifari; Ken Ditha Tania
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3384

Abstract

The rapid development of technology today has provided convenience for us in today's civilization. One of these developments is the invention of the internet due to high internet penetration and rapid growth in mobile usage, online shopping has increased tremendously. This online shopping is now often referred to as e-commerce. E-commerce is one of the trade models that has been widened under the effect of extensive use of technology. Specifically, e-commerce refers to the usage of the Internet or other networks. Shopee is one of the popular marketplaces in Indonesia that has the highest number of visitors of 129 million per month and can be downloaded on the Google Play Store. Play Store itself has several features such as Reviews that can allow users to give opinions. All complaints and opinions from shopee users can be channeled into this feature. With this a research aims to optimize the performance value of sentiment analysis with the Term Frequency-Inverse Document Frequency (TF-IDF) method and Hyperparameter Tuning with Gridsearch for the Shopee application on the Google Play Store. Based on research the reviews resulting in 3000 data where 2015 user data is positive and 985 data is negative. Testing data was split by a ratio of 90:10 for 300 data test in each classification model to find the accuracy score. With hyperparameter tuning using gridsearch we can see the result of each accuracy score of KNN, DCT, RF, and LR is increasing from 0.73 to 0.77, 0.823 to 0.826, 0.856 to 0.87, and 0.856 to 0.866. This indicated that among the machine learning model that had been tuning using gridsearch, KNN is the one that highly increased.
Pengaruh Knowledge Management Factor Terhadap Keberlanjutan Penggunaan E-learning Elna Sari, Cici; Tania, Ken Ditha; Wedhasmara, Ari; Apriansyah Putra
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3406

Abstract

E-learning adalah pemanfaatan teknologi informasi sistem pembelajaran. Fungsi e-learning menerapkan praktik manajemen pengetahuan (KM). Beberapa penelitian telah menyelidiki faktor-faktor tertentu yang mempengaruhi penerimaan pembelajaran melalui pembelajaran daring. Namun, kajian tentang faktor manajemen pengetahuan yang mempengaruhi keberlangsungan adopsi e-learning pada masa transisi pascapandemi relatif baru dan belum dilaporkan dalam literatur yang ada. Oleh karena itu, tujuan utama dari penelitian ini adalah mengembangkan Expectation Confirmation Model (ECM) dengan faktor KM (acquisition, sharing, implement and protection) untuk menguji keberlanjutan penggunaan e-learning khususnya di Universitas Sriwijaya. . Penelitian ini menggunakan Partial Least Squares Structural Equation Modeling (PLS-SEM) untuk memvalidasi model yang dikembangkan. Data dikumpulkan dari 267 mahasiswa aktif Universitas Sriwijaya yang menggunakan atau sedang menggunakan e-learning dalam perkuliahan. Hasil penelitian ini menunjukkan bahwa faktor manajemen pengetahuan, dalam hal ini knowledge acquisition (KA), knowledge sharing (KS), knowledge application (KAP), dan knowledge protection (KP), merupakan faktor penting dalam menentukan keberlangsungan penggunaan layanan. e-learning di Universitas Sriwijaya.
ANALYSIS OF DEMOGRAPHIC AND SOCIOECONOMIC FACTORS ON THE INCIDENCE OF DIABETES MELLITUS IN DKI JAKARTA USING LOGISTIC REGRESSION M. Ilham Fahlevi; Jackson Imanuel Manurung; Mohd Rizky Putra Pratama; M Naufal Hisyam; Allsela Meiriza; Ken Ditha Tania; Zaqqi Yamani
SOSIOEDUKASI Vol 15 No 1 (2026): SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL
Publisher : Fakultas Keguruan Dan Ilmu Pendidikan Universaitas PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36526/sosioedukasi.v15i1.7722

Abstract

Diabetes mellitus (DM) is a non-communicable disease with a significant global impact and an increasing incidence rate. Indonesia records one of the highest diabetes rates, particularly in the province of DKI Jakarta, which shows the highest national prevalence. This observational study with a cross-sectional design aims to evaluate the factors influencing the onset of DM in the Jakarta area using data from the 2023 Indonesia National Health Survey (SKI). This research involves participants over the age of 15. Analysis was conducted using univariate, bivariate (chi-square test), and multivariate methods with the Logistic Regression method, while considering the complexity of the research design. Research findings indicate that age, education level, and comorbidities are factors that significantly influence the incidence of DM. Those below the productive age group are at a higher risk of experiencing DM (OR = 2.268). Secondary education lowers the risk compared to higher education (OR = 0.611). Comorbidity is the main risk factor, increasing the probability of DM incidence by 6.229 times. These findings emphasize the importance of managing comorbidities and implementing appropriate preventive measures for at-risk individuals in efforts to manage diabetes in major cities.
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