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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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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.
Analisis Asosiasi Antara Produktivitas Pelajar dan Manajemen Waktu Berdasarkan Algoritma FP-Growth Rabbani, Muhammad Randy; Theonady, Oktavio; Faizah, Haniyah; Satria, Eka Bayu; Meiriza, Alsella; Tania, Ken Ditha
Indonesian Journal Computer Science Vol. 5 No. 1 (2026): April 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcs.v5i1.12327

Abstract

Penelitian ini bertujuan menganalisis hubungan antara manajemen waktu dan produktivitas pelajar menggunakan algoritma FP-Growth. Data yang digunakan berasal dari dataset Ultimate Student Productivity yang terdiri dari 5.000 data dan 21 atribut. Analisis dilakukan melalui tahapan Knowledge Discovery in Databases (KDD) yang meliputi seleksi data, pra-pemrosesan, transformasi, serta pembentukan association rule berdasarkan nilai support, confidence, dan lift ratio. Hasil penelitian menunjukkan bahwa kategori sedang (medium) mendominasi sebagian besar variabel yang dianalisis. Aturan asosiasi memiliki nilai confidence tinggi dan lift ratio lebih dari satu, yang menunjukkan hubungan signifikan antar variabel. Produktivitas kategori sedang berkaitan dengan durasi belajar dan tingkat fokus yang seimbang, sedangkan kategori rendah berkorelasi dengan hasil akademik yang rendah. Temuan ini menunjukkan bahwa keseimbangan dalam pengelolaan waktu belajar berperan penting dalam membentuk pola produktivitas pelajar. Selain itu, pendekatan berbasis data mampu memberikan gambaran objektif mengenai perilaku belajar siswa. Temuan ini dapat dimanfaatkan untuk mengoptimalkan manajemen waktu belajar guna meningkatkan produktivitas dan capaian akademik pelajar, serta sebagai acuan bagi institusi pendidikan dalam menyusun strategi pembelajaran berbasis data.
Klasifikasi Adopsi Berbasis Kecerdasan Buatan pada UMKM di Indonesia Menggunakan Algoritma Random Forest Muhammad Ihsan Dirgantara; Fakhri Sepriansyah; Nulry Izzatul Maula; Farhan Daffazka; Ken Ditha Tania; Alsella Meiriza
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp35-44

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a strategic role in the Indonesian economy; however, digital transformation based on artificial intelligence (AI) remains a significant challenge. This study aims to classify AI adoption among MSMEs in Indonesia using the Random Forest algorithm and to identify the factors that influence it. The dataset was obtained from the Zenodo repository, consisting of questionnaire results regarding AI adoption in MSMEs. The research stages included data cleaning, encoding, splitting the data into training (80%) and testing (20%) sets, implementing the Random Forest algorithm, evaluation, and result analysis. The evaluation results show an accuracy of 80.3% with an ROC-AUC of 0.884. The weighted precision, recall, and F1-score values are 81.2%, 80.3%, and 80.4%, respectively. These evaluation results indicate that the Random Forest algorithm performs well on this dataset. Furthermore, the feature importance analysis revealed several influential variables in AI adoption among MSMEs, including strategic decision-making (10.9%), digital leadership (8.3%), and respondent position (7.8%). In conclusion, the implementation of the Random Forest algorithm demonstrates strong performance in classifying AI adoption among MSMEs in Indonesia and highlights key influential variables such as strategic decision-making, digital leadership, and respondent position.
Perancangan Knowledge Management System Berbasis Website Menggunakan Model SECI untuk Mendukung Knowledge Sharing Guru pada SMP Bina Karya Muhammad Ihsan Dirgantara; Fakhri Sepriansyah; Nurly Izzatul Maula; Farhan Daffazka; Ken Ditha Tania; Zaqqi Yamani
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp112-126

Abstract

The design of a website-based Knowledge management system (KMS) using the SECI model at SMP Bina Karya is motivated by several problems, including knowledge that remains stored individually with each teacher, the unavailability of centralized learning materials and lesson plans (RPP), and the difficulty faced by substitute teachers in delivering lessons when replacing the main teacher who is absent. The Knowledge management system serves as a solution to document, distribute, and prevent the loss of knowledge, while also acting as a medium to enhance the culture of knowledge sharing among teachers. The design method used is a qualitative approach consisting of data collection through observation, interviews, and literature studies, identification of knowledge management using the SECI model, system requirements analysis, system design, and testing using Focus Group Discussion (FGD). This study produces a website-based KMS equipped with features such as user account management, substitute teacher schedule management, learning material management, lesson plan management, and discussion forums. The results of the FGD testing show an average acceptance rate of 94.2% for all developed features, with the substitute teacher schedule management feature serving as the main differentiator that successfully addresses the specific problems at SMP Bina Karya.
Implementasi Knowledge Management Berbasis Model SECI di Perpustakaan Daerah Provinsi Sumsel Ummu Farida Muthmainnah; Putri Salsabilah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Zaqqi Yamani A
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp138-142

Abstract

This study analyzes the implementation of knowledge management based on the SECI model at the Regional Library of South Sumatra Province. The method used is qualitative with a case study design through a systematic literature review and digital content analysis of the website, OPAC INLISLITE, social media, and the DiarySumsel application. The results show that the implementation of the SECI model has been carried out across its four stages. Socialization is realized through direct services and mobile libraries using 4 mobile units. Externalization is evidenced by the documentation of circulation service Standard Operating Procedures (SOPs). The combination is implemented through the integration of INLISLITE and DiarySumsel, which served 23,946 users. Internalization is reflected in the adoption of digital systems by librarians and users. Supporting factors include technology availability, management commitment, and extensive service coverage. The challenges faced are limited trained human resources, suboptimal technology utilization, and low community information literacy.
Penerapan Association Rule Menggunakan Algoritma Apriori untuk Rekomendasi Strategi Penjualan pada UMKM Toko Pempek Putri Salsabilah; Ummu Farida Muthmainnah; Zaskia Aulia Wulandari; Talitha Zafirah; Ken Ditha Tania; Alsella Meiriza
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 10 No. 1 (2026): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol10No1.pp192-197

Abstract

Pempek Ceria SME has a growing number of daily sales transactions; however, these data have not been optimally utilized to support sales strategies. This situation highlights the need for transaction data analysis to understand customer purchasing patterns and develop more effective promotional strategies. Therefore, this study focuses on applying association rules using the Apriori algorithm to provide sales strategy recommendations for Pempek Ceria SME. The analysis was conducted using RapidMiner software on 317 transactions from October to December 2025, with a minimum support of 15% and a minimum confidence of 65%. The results show two association rules that meet these criteria: the combination of Pempek Adaan and Orange Juice, with a support of 28% and confidence of 72%, and the combination of Pempek Kapal Selam and Sweet Iced Tea, with a support of 27% and confidence of 70%. These findings indicate that the association rule method based on the Apriori algorithm can identify relationships between menu items frequently purchased together. By understanding these purchasing patterns, Pempek Ceria SME can optimize bundling strategies and product recommendations to improve promotional effectiveness and sales.
Comparative Evaluation of Machine Learning Algorithms for Diabetes Prediction with SMOTE and Principal Component Analysis Badia Inaya Sazrade; Ken Ditha Tania; Ferdiansyah
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 4 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i4.9903

Abstract

Diabetes mellitus is a chronic disease that requires early detection to reduce the risk of severe complications. However, machine learning-based diabetes prediction is often affected by class imbalance and high-dimensional data. This study investigates the effectiveness of integrating Synthetic Minority Over-sampling Technique (SMOTE) and Principal Component Analysis (PCA) for diabetes prediction. A total of 80,437 records from a Kaggle diabetes dataset were processed using the Knowledge Discovery in Databases (KDD) framework. Six machine learning algorithms, namely Random Forest, XGBoost, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naïve Bayes, and Neural Network, were evaluated using train-test split ratios of 70:30, 80:20, and 90:10. Performance was measured using accuracy, precision, recall, and F1-score. Without oversampling, XGBoost consistently achieved the highest accuracy across all split ratios, peaking at 94.04% at the 80:20 ratio; however, recall for the minority (diabetic) class remained substantially lower than for the majority class, indicating that high overall accuracy masked weaker detection of actual diabetes cases. After applying SMOTE, overall accuracy declined across all models (e.g., XGBoost fell to 87.52% at 80:20), but minority-class recall improved markedly, indicating a more balanced classification between classes at the cost of overall accuracy. Notably, at the 80:20 split, the Neural Network achieved a marginally higher accuracy (87.67%) than XGBoost under SMOTE, although XGBoost remained the top performer at the 70:30 and 90:10 ratios, suggesting that its advantage under class-balanced conditions is not uniform across split ratios. PCA was applied to reduce data dimensionality and did not substantially affect predictive performance; however, the present results do not include quantitative evidence, such as the change in feature count or computation time, needed to substantiate claims about its contribution to efficiency. These findings suggest that XGBoost with an 80:20 split is the most effective configuration when class imbalance is not addressed, while the application of SMOTE narrows the performance gap between models and shifts the trade-off toward more balanced, rather than purely accuracy-maximizing, classification.
Optimasi Strategi Inventory dan Mitigasi Knowledge Loss pada Industri Otomotif Melalui Integrasi Algoritma K-Means Clustering dan Framework SECI Juseia Wulandari; Violin Juneyla Nandita; Khairunnisa’ Almaududy; Rafi Herdian; Ken Ditha Tania; Ahmad Rifai; Dedy Kurniawan
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 3 (2026): Juni 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i3.9650

Abstract

Digital transformation within used automotive industry today demands paradigm shift from intuitive decision-making towards data-driven approach to face increasingly intense market competition dynamics. The primary problem identified in this research is high level of subjectivity in stock management and dependence on individual experience triggering organizational knowledge loss risks or knowledge loss. This study aims to optimize stock management strategy and mitigate these risks through integration of K-Means clustering algorithm and Socialization, Externalization, Combination, Internalization framework. The research method involves in-depth analysis of five hundred fifty-eight thousand eight hundred thirty-seven vehicle transaction data using data mining techniques to discover hidden patterns from automotive market behavior. Research results show that the algorithm successfully classified stock into three optimal clusters, where symbol k represents cluster number of three, with high performance proven by Calinski-Harabasz Index score of 283,364.95. These clusters differentiate assets into medium, high-risk, and premium categories based on physical condition and mileage, which allows companies to determine liquidation or retention strategies accurately. Integration with the framework ensures that data mining findings are transformed into permanently documented organizational knowledge management. The implementation of this model provides a significant impact for companies in improving operational efficiency and reducing dependence on individual memory. This research study provides a real contribution in creating an objective foundation for more measurable, systematic, and sustainable managerial decision-making for national industry sectors and other related complex business environment systems.
ANALISIS KOMPARATIF ALGORITMA RANDOM FOREST, XGBOOST, DAN CATBOOST UNTUK KLASIFIKASI TINGKAT STRES PENGGUNA MEDIA SOSIAL Dila Naila Fahria; Ken Ditha Tania; Rizka Dhini Kurnia
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7449

Abstract

The increasingly intensive use of social media in everyday life has various implications for the psychological condition of users, one of which is an increase in stress levels due to high usage duration and excessive exposure to information. This condition necessitates an analytical approach to understand and predict user stress levels more objectively. This study aims to compare the performance of Random Forest, XGBoost, and CatBoost algorithms in classifying the stress levels of social media users based on digital behavior data, as well as to identify behavioral factors that contribute to these stress levels. This study uses a quantitative approach based on data mining with a dataset consisting of 667 social media user data. The research stages include data collection, preprocessing, modeling using three machine learning algorithms, and model performance evaluation using accuracy, precision, recall, and F1-score metrics, reinforced with confusion matrix and feature importance analysis. The results show that Random Forest produced the best performance with an accuracy value of 0.84, precision of 0.86, recall of 0.83, and F1-score of 0.84, followed by CatBoost with an accuracy of 0.80 and XGBoost with 0.78. Feature importance analysis shows that Daily Screen Time and Happiness Index are the most influential variables in determining the stress level of users, which aligns with previous findings in digital mental health research.
PREDIKSI HARGA PANGAN MENJELANG RAMADAN MENGGUNAKAN SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE DI SUMATERA SELATAN: FOOD PRICE PREDICTIONS AHEAD OF RAMADAN USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE IN SOUTH SUMATRA Cindy Dinata; Azera Pramesty; RA Aliffyaa Ramadhani; Merizka Azzahra; Ken Ditha Tania; Allsela Meiriza
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7787

Abstract

Food price fluctuations ahead of Ramadan in Indonesia are a seasonal phenomenon with a significant impact on regional economic stability. This study aims to develop a price prediction model for five major food commodities, namely medium rice, curly red chili, shallots, chicken eggs, and cooking oil, in South Sumatra Province using the Seasonal Autoregressive Integrated Moving Average (SARIMA) approach. The analysis stages began with stationarity testing of historical data, determining optimal model parameters, and evaluating accuracy using the Mean Absolute Percentage Error (MAPE) method. The results show that the SARIMA model effectively identifies seasonal trends with a very high level of accuracy, evidenced by an error rate of only 1.92% for medium rice. Projections for 2026 indicate that rice and chicken egg prices tend to remain stable, while horticultural products such as red chili show a downward trend. This study concludes that not all food commodities experience price increases before Ramadan; therefore, local government intervention should be conducted selectively based on the characteristics of each commodity. This model can be implemented as an early warning instrument to support economic stability in the South Sumatra region.
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