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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) IJIE (Indonesian Journal of Informatics Education) Jurnal Manajemen STIE Muhammadiyah Palopo MBR (Management and Business Review) JOURNAL OF APPLIED INFORMATICS AND COMPUTING METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi 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 Indonesian Journal Computer Science (ijcs) 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 Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics 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 Health Professions 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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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.
Komparasi Model Ensemble dan Algoritma Machine Learning Untuk Memprediksi Penyakit Jantung Albani, Muhammad Syarief; Kurniawan, Dedy; Tania, Ken Ditha
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8346

Abstract

This study compared the performance of nine machine learning algorithms in predicting heart disease using a dataset dating back to 1988 and consisting of four databases: Cleveland, Hungary, Switzerland, and Long Beach totaling 1025 data. The dataset used includes medical features that reflect physiological states, clinical examination results, and cardiovascular risk factors, namely age, gender, type of chest pain, resting blood pressure, serum cholesterol levels, fasting blood sugar levels, resting electrocardiography results, maximum heart rate, chest pain during physical activity, ST segment depression, ST segment slope, number of major blood vessels visible by fluoroscopy, and thalassemia status. The stages of this study include data cleaning, data transformation, and evaluation carried out using the data splitting method for training and testing as well as K-fold cross-validation with metrics of accuracy, precision, recall, F1 score, and AUC-ROC. The algorithms used in this study are Decision Tree, Random Forest, Support Vector Machine, MLP Classifier, Bagging Classifier, Gradient Boosting, CatBoost, XGBoost, and LightGBM with ensemble-based models, such as CatBoost, Random Forest, XGBoost, and LightGBM, showing consistent performance on various evaluation metrics when compared to non-ensemble models. Among all models tested, CatBoost showed the best performance, with an accuracy reaching 98%, an F1-Score of 0.980, and a Recall of 0.9875 then followed by other ensemble algorithms such as Random Forest, XGBoost and LightGBM. The results of this study indicate that ensemble models are proven to be more effective in predicting heart disease. This study aims to present an in-depth comparative study of the performance of ensemble algorithms and modern machine learning in predicting heart disease, as well as enriching the literature related to the application of Knowledge Discovery in the health sector and providing a basis for selecting more reliable prediction algorithms to support clinical decision making and the development of machine learning-based heart disease diagnosis support systems.
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.
Analisis Segmentasi Pelanggan Menggunakan RFM dan K-Means Clustering sebagai Dasar Penyusunan Aturan Pendukung Keputusan Andini, Meisya Dwi; Catra, Rafa Nadira; Homausyah, Weli Ratri; Aurelia, Haaniyah; Meiriza, Allsela; Tania, Ken Ditha; Yamani, Zaqqi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9511

Abstract

One of the important methods in supporting data-driven Customer Relationship Management (CRM) initiatives is customer segmentation. However, in practice, segmentation results are often limited to descriptive analysis and are not further utilized in decision-support processes. This study aims to utilize customer segmentation results based on the Recency, Frequency, Monetary (RFM) approach and the K-Means algorithm as a basis for developing decision-support recommendations. The research stages include data preprocessing, RFM value calculation, normalization using the Min-Max Scaling method, and determining the optimal number of clusters using the Elbow Method and Silhouette Score. The evaluation results indicate that the optimal number of clusters is four, with a Silhouette Score of 0.61, which reflects a moderately good level of cluster separation. The segmentation results classify customers into four categories: High Value/VIP Customers, Loyal Customers, Potential Customers, and Low Value/Dormant Customers, each exhibiting distinct transactional behavior characteristics. These characteristics are then interpreted into decision rules using IF–THEN logic; for example, customers with low Recency, high Frequency, and high Monetary values are recommended strategies such as loyalty rewards and upselling. The findings suggest that customer segmentation can be extended beyond descriptive analysis and utilized as a practical basis for marketing decision-making, although the approach remains relatively simple and heuristic-based. The contribution of this study is to integrate RFM-KMeans segmentation results with IF–THEN decision rules to generate more applicable marketing strategy recommendations in supporting data-driven decision making.
IMPLEMENTASI SISTEM BUSINESS INTELLIGENCE UNTUK MENDUKUNG PENINGKATAN PENGAMBILAN KEPUTUSAN MANAJERIAL KESEHATAN Putri, Mutia Fadhila; Tania, Ken Ditha; Bardadi, Ali
Djtechno: Jurnal Teknologi Informasi Vol 7, No 1 (2026): April
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v7i1.8535

Abstract

Pengelolaan data kesehatan masyarakat pada instansi pemerintah daerah umumnya masih bergantung pada mekanisme pelaporan manual yang terfragmentasi, sehingga menghambat efektivitas pengambilan keputusan berbasis bukti. Penelitian ini bertujuan merancang dan mengimplementasikan sistem Business Intelligence (BI) pada Dinas Kesehatan Kota Palembang guna meningkatkan integrasi data, kualitas analisis informasi, dan ketepatan pengambilan keputusan. Penelitian mengadopsi pendekatan Business Intelligence Roadmap yang terdiri dari fase analisis bisnis, perencanaan, desain, dan konstruksi. Data laporan oprasional dari 39 Pusat Kesehatan Masyarakat (PUSKESMAS) di wilayah Kota Palembang diintegrasikan melalui proses extract, transform, dan load (ETL) data ke dalam arsitektur data warehouse multidimensional, kemudian dianalisis menggunakan Pentaho dashboard visualisasi dan teknik clustering dengan algoritma K-Means. Hasil implementasi menunjukkan bahwa sistem yang dikembangkan mampu mengatasi permasalahan fragmentasi data dan menghasilkan dashboard interaktif yang mendukung pemantauan kondisi kesehatan masyarakat secara multidimensi. Penerapan teknik clustering menghasilkan tiga klaster wilayah kerja puskesmas berdasarkan profil epidemiologis yang berbeda, memberikan wawasan yang sebelumnya tidak dapat diidentifikasi melalui laporan periodik konvensional. Evaluasi melalui user acceptance test (UAT) menunjukkan tingkat penerimaan yang baik dari pengguna sitem. Penelitian ini menegaskan bahwa BI berpotensi meningkatkan transparansi informasi dan mendukung perumusan kebijakan kesehatan yang lebih tepat sasaran di tingkat pemerintah daerah.
Perbandingan Model Regresi Machine Learning untuk Prediksi Skor Tingkat Stres Berdasarkan Pola Screen Time Tahun 2025 Pratama Putra, Daffa; Apriyadi, Apriyadi; Firmansyah, Zikri; Ditha Tania, Ken; Kurniawan, Dedy
Jurnal Pendidikan dan Teknologi Indonesia Vol 6 No 4 (2026): JPTI - April 2026
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.1537

Abstract

Transformasi digital yang masif pada era modern telah mendorong peningkatan signifikan dalam durasi paparan layar (screen time), yang diidentifikasi sebagai salah satu faktor risiko utama terhadap kesehatan mental, khususnya peningkatan prevalensi stres psikologis. Metode diagnosis konvensional yang mengandalkan instrumen kuesioner mandiri dinilai kurang optimal karena rentan terhadap bias pelaporan dan bersifat subjektif. Penelitian ini bertujuan untuk membandingkan performa tiga algoritma machine learning, yaitu Random Forest, Support Vector Regression (SVR), dan XGBoost Regression, dalam memprediksi skor tingkat stres secara kontinu (skala 0–10) berdasarkan pola penggunaan perangkat digital. Tahapan penelitian meliputi akuisisi dataset "Screentime vs Mental Wellness Survey 2025" dari repositori publik, pra-pemrosesan data melalui imputasi statistik, normalisasi Min-Max Scaling, dan One-Hot Encoding, dilanjutkan dengan pembangunan model menggunakan evaluasi 10-fold cross-validation serta interpretasi model berbasis metode SHAP. Hasil evaluasi pada data uji menunjukkan bahwa XGBoost merupakan model dengan performa terbaik, mencapai nilai Mean Absolute Error (MAE) terendah sebesar 0,6502, Root Mean Squared Error (RMSE) sebesar 0,8253, dan koefisien determinasi (R²) sebesar 0,8367. Temuan ini mengindikasikan bahwa model mampu menjelaskan lebih dari 83% variasi tingkat stres pada data yang belum pernah dilatih sebelumnya. Analisis feature importance mengungkapkan bahwa indeks kesejahteraan mental dan produktivitas merupakan prediktor paling dominan, sedangkan durasi screen time berkontribusi relatif kecil, yang menunjukkan bahwa faktor psikologis internal lebih berpengaruh terhadap stres dibandingkan intensitas interaksi digital semata. Penelitian ini menyimpulkan bahwa pendekatan ensemble learning, khususnya XGBoost, efektif dalam memodelkan fenomena stres yang bersifat kompleks dan multidimensional sebagai dasar pengambilan keputusan klinis berbasis data.
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 Bardadi Ali Ibrahim Ali Ibrahim (SCOPUS ID: 57203129436) Allsela Meiriza, Allsela Alsella Meiriza Alsella Meiriza Alvines, Mahendi Alzena Aisha Shakira Amanda Ardhani, Dhita Amelia Amelia Amelia Putri, Shinta Amelia, Rita Anadia, Qothrunnada Wafi Ananda Khoirunnisa Andini Bahri, Cheisya Andini, Meisya Dwi Anggun Ramadina Anindya Putri, Salsa Anisa Basulina, Nur Anissa, Cahya Rahmi Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi, Apriyadi 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 Aurelia, Haaniyah Ayuningtiyas, Pratiwi Azmi Zaky, Muhammad Azra, Muhammad Azyumardi Bahri, Cheisya Andini Baidhawi, Alif Bimmo Fathin Tammam Cahya Aulia, Syifa Cahya Rahmi Anissa Catra, Rafa Nadira 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 faizah, haniyah Fajaria, Mutiara Fakhri Sepriansyah Fakhri Sepriansyah Farhan Daffazka Fathoni - Fatihaturrahmah, Aisyah Fatimah, Aisyah Fauzan, Muhammad Fairuz Fikri, M Fauzan Firmansyah, Zikri Gustiani, Sindy Haidar Afif Mufid, Muhammad Hanggara, Bryan Hendrawan, Deni Agus Hermanto, Muhammad Lucky Hikmahwarani, Fellycia Homausyah, Weli Ratri Ichsan Farel Rachmad, Muhammad Ikhwan Najatafani, Bintang Inayah, Anna Fadilla Indira Nailah Ramadhani Ispahan, Tarisha Izzan Fieldi, Muhammad Jackson Imanuel Manurung Jodi Pratama, Muhammad Jonathan Pakpahan Karima, Dzakiah Aulia Karimsyah Lubis, Muhammad Khoiriyah Harahap, Dayana Kurnia Sari, Winda Lakeisyah, Eka Therina Lifiano Jamot Munthe, Gabriel Lubis, Muhammad Ali M Ihsan Jambak M Luthfi Khailani, Kgs M Naufal Hisyam M. Ilham Fahlevi 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 Meiriza, Alsella Miftahul Falah Mira Afrina Mohd Rizky Putra Pratama Mufidah, Luthfiah Muhammad Adisatya Dwipansy Muhammad Dzaky Alifayoezra Muhammad Idris Muhammad Ihsan Dirgantara Muhammad Luthfi Al-Ghifari Muhammad Luthfi Al-Ghifari Munaspin, Zahra Diva Putri mutia fadhila putri, mutia fadhila 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 Nulry Izzatul Maula Nuraini Kusuma, Aisha Nurly Izzatul Maula Onkky Alexander Pacu Putra Prasetia, Dika Pratama Putra, Daffa Pratiwi, Metti Detricia Purba, Kevin Agustin Putri Ariyanti Putri Casanova, Musdalifa Putri Mutiara Arinie Putri Salsabilah Putri Silpiara Putri, Amelia Rizki Putri, Aulia Najibah Putri, Naila Raihana Putri, Salsa Anindya Rabbani, Muhammad Randy 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, Muhammad Bintang Naufal Risyahputri, Aliyananda 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 Satria, Eka Bayu Sembiring Depari, Alrayssa Davinka Septhia Charenda Putri Sevtiyuni, Putri Eka Shelly Putri Siade, Shalya Yunia Siregar, Richi Nauli Juniarto Siswahyudianto Suci Amalia Suci Fitriani, Suci Sukamto, Ika Sumiyarsi Sukatin, Sukatin Syarief Albani, Muhammad Talitha Zafirah Theonady, Oktavio Theresia Pardede, Eva Theressa Hasioani Sianturi, Claudia Tika Octri Dieni Titiana, Nuke Merisca Tri Zafira, Zahra Triana, Ayu Triputra, Muhamad Meiko Tsabitah, Laila Ummu Farida Muthmainnah Wahyuni Cahnia Sari Wilantara, M Pandu Winda Kurnia Sari Wirnanti, Rintan Wulan Dari, Atikah Yamani, Zaqqi Yasir Alghifari, Muhammad Yasyfi Imran, Athallah Zahran Afif, Muhammad Zaqqi Yamani Zaqqi Yamani Zaqqi Yamani A Zaskia Aulia Wulandari Zidan, Umar Rahman