Article Per Year (5 Year)
p-Index From 2021 - 2026
9.62
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) SITEKIN: Jurnal Sains, Teknologi dan Industri Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal POINTER JURNAL PENGABDIAN KEPADA MASYARAKAT Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science SISFO Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) JAST : Jurnal Aplikasi Sains dan Teknologi Indonesian Journal of Electrical Engineering and Computer Science Bulletin of Information Technology (BIT) ALMUJTAMAE: Jurnal Pengabdian Masyarakat Tri Dharma Mandiri: Diseminasi dan Hilirisasi Riset Kepada Masyarakat (Jurnal Pengabdian Kepada Masyarakat) Kohesi: Jurnal Sains dan Teknologi Journal of Information System and Application Development
Lailil Muflikhah
Fakultas Ilmu Komputer, Universita Brawijaya, Malang, Indonesia
Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Public Health Social Sciences Other

Published : 130 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 130 Documents
Search

 9   10   11   12   13 
Analisis Masalah Kulit Wajah Dan Saran Perawatan Pada Klinik Zap Menggunakan Algoritma Support Vector Machines Yudhonugroho, Wirkananda Bagus; Muflikhah, Lailil; Darma Setiawan, Budi
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 3 (2026): Maret 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Perkembangan teknologi informasi mendorong transformasi layanan klinik kecantikan, namun sebagian besar sistem yang ada masih berfokus pada aspek administratif dan belum memanfaatkan kecerdasan buatan untuk klasifikasi otomatis masalah kulit wajah. Penelitian ini mengembangkan sistem analisis masalah kulit wajah dan rekomendasi perawatan berbasis Support Vector Machine (SVM) dengan pendekatan klasifikasi multilabel. Dataset yang digunakan merupakan data primer dari Klinik ZAP yang terdiri dari 10 fitur gejala biner dan 4 label diagnosis, yaitu Dry Skin, Sensitive Skin, Telangiectasis, dan Dermatitis. Model SVM dilatih menggunakan strategi One-vs-Rest (OvR) dengan pembagian data secara stratified ke dalam data latih, validasi, dan data uji dengan rasio 64%, 16%, dan 20%. Optimasi hyperparameter dilakukan menggunakan Grid Search dan Random Search. Hasil pengujian menunjukkan bahwa Grid Search dengan kernel RBF, nilai C = 10, dan γ = 0.1 menghasilkan performa terbaik dengan F1-score sebesar 98.50% pada data uji. Selain itu, sistem rekomendasi perawatan yang diimplementasikan menggunakan pendekatan case-based reasoning dengan metode L1 distance mampu memberikan saran perawatan yang konsisten dengan pola gejala pasien. Hasil penelitian ini menunjukkan bahwa kombinasi SVM multilabel dan optimasi hyperparameter efektif digunakan sebagai dasar pengembangan sistem pendukung keputusan klinis di bidang dermatologi estetika.
Analisis Sentimen Ulasan Aplikasi SP4N LAPOR! dengan IndoBERT dan Koreksi Ejaan Berbasis Levenshtein Distance Kirana, Nareswara Lintang; Muflikhah, Lailil; Indriati
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 4 (2026): April 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Ulasan pengguna pada platform SP4N-LAPOR! memuat sentimen yang penting untuk evaluasi layanan publik, namun teksnya sering tidak terstruktur, mengandung variasi ejaan, singkatan, dan kalimat ambigu sehingga menyulitkan klasifikasi sentimen. Penelitian ini bertujuan mengklasifikasikan sentimen ulasan SP4N-LAPOR! menggunakan IndoBERT serta menganalisis pengaruh normalisasi variasi ejaan berbasis Levenshtein Distance dan penerapan class-weighted loss terhadap kinerja model. Tahap awal meliputi pembersihan data dan normalisasi ejaan, kemudian data dibagi menjadi latih, validasi, dan uji. IndoBERT selanjutnya di-fine-tune dengan pengujian hyperparameter (epoch, learning rate, batch size) dan dievaluasi dalam empat skenario kombinasi penggunaan normalisasi Levenshtein dan class-weighted loss. Hasil uji menunjukkan performa terbaik diperoleh pada kombinasi class-weighted loss dan normalisasi Levenshtein dengan accuracy 0,9272 dan macro F1-score 0,8996. Skenario class-weighted loss tanpa normalisasi menghasilkan macro F1-score 0,8985, sedangkan skenario tanpa class-weighted loss berada pada macro F1-score 0,8901. Temuan ini menegaskan class-weighted loss paling berpengaruh, sementara normalisasi Levenshtein memberi manfaat tambahan saat dikombinasikan.
Pengaruh Kombinasi Indikator Teknikal terhadap Kinerja Model Hybrid CNN–LSTM dalam Prediksi Arah Harga Bitcoin Setyawan, Frederikus; Muflikhah, Lailil; Perdana, Rizal Setya
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 5 (2026): Mei 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pergerakan harga Bitcoin yang sangat volatil menjadikan prediksi arah harga sebagai tantangan penting dalam pasar cryptocurrency. Penelitian ini bertujuan untuk menganalisis pengaruh kombinasi indikator teknikal terhadap kinerja model hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) dalam memprediksi arah harga Bitcoin, serta mengevaluasi kontribusi masing-masing kategori indikator melalui metode ablation study. Model diuji menggunakan data harga Bitcoin periode 2023–2025 yang di-resample ke interval 15 menit dengan delapan indikator teknikal terpilih. Hasil pengujian menunjukkan bahwa Full Model mencapai performa terbaik dengan nilai accuracy sebesar 97,74% dan AUC-ROC 99,78%, meningkat signifikan dibandingkan model Baseline berbasis OHLCV. Analisis ablation menunjukkan bahwa indikator Trend-Following dan Momentum memberikan kontribusi terbesar terhadap peningkatan performa model, diikuti oleh indikator RSI, sementara kategori Oscillator justru menunjukkan dampak negatif. Temuan ini menegaskan bahwa kombinasi CNN untuk ekstraksi pola lokal dan LSTM untuk menangkap dependensi temporal efektif dalam menangani karakteristik pergerakan harga Bitcoin yang fluktuatif, serta berpotensi digunakan sebagai dasar pengembangan sistem pendukung keputusan dalam strategi trading aset kripto.
Perbandingan SVM dan IndoBERT untuk Analisis Sentimen Layanan Akademik Mahasiswa Muhammad Ibnu Sa'ad; Lailil Muflikhah; Fitra Abdurrachman Bachtiar
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2913

Abstract

Digital transformation in higher education has generated an increasing volume of textual data, including student comments, academic service evaluations, and feedback on academic information systems. These data contain valuable information for supporting decision-making; however, their unstructured and contextual nature makes manual analysis inefficient. This study aims to compare the performance of a TF-IDF-based Support Vector Machine (SVM) model and a Transformer-based IndoBERT model for sentiment analysis of academic services from student feedback. The dataset consists of 1,700 text entries, combining template-based synthetic data and real-world data collected from social media, which were classified into positive, negative, and neutral sentiment categories. The research process involved exploratory data analysis, text preprocessing, feature extraction, model development, and evaluation using accuracy, precision, recall, and F1-score metrics. The experimental results showed that both models achieved very high performance on the dataset, with an accuracy of 100% on the test set. These findings indicate that both traditional machine learning and Transformer-based approaches are capable of identifying sentiment patterns within the dataset. Nevertheless, the results should be interpreted cautiously, as the relatively homogeneous nature of the dataset and the inclusion of synthetic data may affect the models’ generalizability. The main contribution of this study lies in the comparative evaluation of SVM and IndoBERT within the context of higher education academic services, as well as the development of a sentiment analysis framework that can support data-driven service quality monitoring. Future studies should employ larger, more diverse datasets derived entirely from real-world sources to further validate the findings.
Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms Lailil Muflikhah; Fitra A. Bachtiar; Dian Eka Ratnawati; Riski Darmawan
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 1 (2024): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i1.28107

Abstract

Nephropathy is a severe diabetic complication affecting the kidneys that presents a substantial risk to patients. It often progresses to renal failure and other critical health issues. Early and accurate prediction of nephropathy is paramount for effective intervention, patient well-being, and healthcare resource optimization. This research used medical records from 500 datasets of diabetic patients with imbalanced classes. The main goal of this study is to get high-performance predictive models for nephropathy. So, this study suggests a new way to deal with the common problem of having too little or too much data when trying to predict nephropathy: adding more data through adaptive synthetic sampling (ADASYN). This technique is particularly pertinent in ensemble machine-learning methods like Random Forest, AdaBoost, and bagging (Adabag). By increasing the number of instances of minority classes, it tries to reduce the bias that comes with imbalanced datasets, which should lead to more accurate and strong predictive models in the long run. The experimental results show an improving 4% rise in performance evaluation such as precision, recall, accuracy, and f1-score, especially for the ensemble methods. Two contributions of this research are highlighted here: first, the utilization of adaptive synthetic sampling data to improve the balance and diversity of the training dataset. The second contribution is incorporating ensemble methods within machine learning algorithms to enhance the accuracy and robustness of diabetic nephropathy detection.
A Hybrid Soft Voting and Stacking-Based Meta-Learning Approach for Sentiment Analysis of Bangkalan Batik Wahyudi, Moh. Imron; Muflikhah, Lailil; Perdana, Rizal Setya
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sentiment analysis is an important field in Natural Language Processing (NLP) that focuses on processing consumer opinions to gain useful insights. The information generated from sentiment analysis can be used as a basis for business decision-making, service quality evaluation, and the formulation of more effective marketing strategies. In the local context, Bangkalan Batik, as one of Madura's distinctive cultural products, has high economic value and cultural identity. However, consumer reviews available online, for example through Google Maps, are still rarely utilized optimally by MSMEs as a source of strategic information. Therefore, this study was conducted to develop a sentiment classification model capable of overcoming the limitations of small data and unbalanced class distribution. The research dataset consisted of 1,000 Bangkalan Batik consumer reviews categorized into three sentiment classes: positive, neutral, and negative. After the preprocessing stage, text representation was performed using TF-IDF. Next, three basic algorithms, Naïve Bayes, Logistic Regression, and Support Vector Machine, were combined through the Soft Voting approach. To improve performance, Logistic Regression meta-classification was used as an additional layer of stack-based Meta-Learning. In addition, the Synthetic Minority Oversampling Technique (SMOTE) was applied to overcome class imbalance so that the model was more sensitive to minority opinions. The results of experiments with stratified k-fold cross-validation show that the proposed model performs better than both single and conventional ensemble models. The developed hybrid model achieves 87% accuracy and an F1 score of 88%, and shows a significant improvement in remembering minority classes. This research contributes to the development of ensemble-based text classification methods for small and imbalanced datasets.
Pemberdayaan Perempuan melalui Koperasi Digital Fatayat NU Kabupaten Kediri Nurul Badriyah; Setyo Tri Wahyudi; Ismatul Khasanah; Anif Fatma Chawa; Lailil Muflikhah; Amalia Rahmawati; Kartika Sari; Fajrin Intan Safitri; Radeetha Radeetha; Rihana Sofie Nabela
ALMUJTAMAE: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): April
Publisher : Universitas Djuanda Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30997/almujtamae.v6i1.22127

Abstract

Rendahnya literasi digital dan kelembagaan ekonomi perempuan di tingkat lokal masih menjadi kendala dalam meningkatkan akses terhadap peluang usaha dan sumber daya ekonomi. Kondisi ini mendorong perlunya upaya pemberdayaan yang terintegrasi dengan transformasi digital. Kegiatan pengabdian ini bertujuan untuk memberdayakan perempuan melalui pendirian Koperasi Fatayat NU Kabupaten Kediri berbasis digital guna memperkuat kemandirian ekonomi dan kapasitas organisasi perempuan. Metode pelaksanaan meliputi pelatihan literasi digital, pendampingan kelembagaan koperasi, Focus Group Discussion (FGD) di empat korwil, serta perancangan prototipe aplikasi koperasi digital berbasis web yang disesuaikan dengan kebutuhan anggota. Hasil kegiatan menunjukkan adanya peningkatan pemahaman anggota terkait manajemen koperasi, literasi keuangan, dan pemanfaatan sistem digital. Selain itu, telah tersusun Rencana Bisnis (Renbis) di setiap PAC serta pemetaan empat jenis koperasi potensial, yaitu jasa, simpan pinjam, produksi, dan konsumsi. Pengembangan aplikasi digital juga telah mencapai tahap perancangan struktural dengan fitur keanggotaan daring, pencatatan transaksi otomatis, dan laporan keuangan berbasis cloud. Implikasi dari kegiatan ini menunjukkan bahwa pendekatan berbasis digital efektif dalam memperkuat kapasitas ekonomi perempuan serta mempercepat transformasi koperasi, sehingga perlu ditindaklanjuti melalui finalisasi legalitas, implementasi aplikasi, dan penguatan jejaring kemitraan untuk menjamin keberlanjutan program.
Predicting Heart Disease with Enhanced Genetic Algorithms: The Role of Latin Hypercube Sampling and Hamming Distance-Based Diversity Sifaunnufus Ms, Fi Imanur; Amila Fadhila Rahmaniati; Maulida Khairunisa Argaputri; Yonathan Fanuel Mulyadi; Lailil Muflikhah
Journal of Information Technology and Computer Science Vol. 10 No. 2: August 2025
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2025102946

Abstract

Heart disease remains a predominant cause of mortality globally and in Indonesia, impacting both older and younger demographics due to pervasive unhealthy lifestyles, heredity, and lack of awareness of heart disease. This study addresses the critical problem of necessitating early detection methods to mitigate severe complications and fatalities associated with heart disease. With that, it is crucial to develop a robust and highly accurate prediction model for heart disease by integrating Artificial Neural networks (ANN) with Genetic Algorithms (GA). The model starts by constructing an ANN model utilizing the Keras Framework for streamlined training, followed by hyperparameter optimization through GA. As a result, this research found that the integrated ANN and GA model attains superior predictive accuracy, with the optimal configuration achieving an accuracy of 85.33%, precision of 92.55%,  recall of 81.32%, and f1-score of 86.57% via Latin Hypercube Sampling (LHS). These show that the combination of ANN and GA can significantly increase prediction accuracy and model efficiency, as a solution for more effective heart disease identification at an early stage.
Analisis Komparatif Kinerja Model Long Short-Term Memory dan Gated Recurrent Unit untuk Prediksi Harga Timah Wiguna, Riady; Muflikhah, Lailil; Arum Sari, Yuita
Jurnal Pengembangan Teknologi dan Ilmu Komputer Vol 10 No 6 (2026): Juni 2026
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Harga timah merupakan komoditas logam penting yang digunakan dalam berbagai sektor industri dan memiliki tingkat fluktuasi tinggi akibat pengaruh faktor ekonomi, permintaan pasar, serta kondisi global. Karakteristik tersebut menjadikan peramalan harga timah sebagai tantangan karena diperlukan model yang mampu menangkap pola non-linear pada data deret waktu. Penelitian ini bertujuan menganalisis pengaruh parameter model terhadap kinerja Long Short-Term Memory (LSTM) dan Gated Recurrent Unit (GRU), serta membandingkan performa keduanya dengan model baseline ARIMA. Data yang digunakan berupa harga mingguan timah periode 2008–2025 yang diperoleh dari Investing.com. Evaluasi dilakukan menggunakan Root Mean Squared Error (RMSE), Directional Accuracy (DA), dan uji Diebold-Mariano (DM) pada horizon peramalan 1, 4, 8, dan 13 langkah ke depan. Hasil penelitian menunjukkan bahwa learning rate 0,01 merupakan parameter yang konsisten optimal pada kedua model dan panjang sequence optimal umumnya pendek. Pengoptimalan parameter juga terbukti memberikan pengaruh yang signifikan terhadap kinerja model. Hasil komparasi menunjukkan bahwa tidak terdapat perbedaan akurasi yang signifikan antara LSTM, GRU, dan ARIMA pada horizon 1 dan 4 langkah ke depan. Akan tetapi, pada horizon 8 dan 13 langkah ke depan, ARIMA memberikan performa yang lebih baik.
Multi-Level Ensemble Learning for Facial Expression Recognition on Imbalanced FER2013 Dataset Pratama, Muhammad Fajarivan; Mahmudy, Wayan Firdaus; Muflikhah, Lailil
Journal of Information Technology and Computer Science Vol. 11 No. 2: August 2026
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jitecs.2026112919

Abstract

Facial expression recognition remains a challenging task in computer vision, particularly due to class imbalance in datasets such as FER2013, where the Happy class accounts for 25.05% and Disgust only 1.52%, leading to biased predictions. This study proposes a Multi-Level Ensemble approach that integrates data diversity (bootstrap sampling in ELM), model diversity (CNN and CNN-ELM), and classifier diversity (ELM with different random seeds). The method is evaluated using Stratified 5-Fold Cross-Validation on 35,887 FER2013 images. Results show that pure ELM achieves 36.37% accuracy, CNN baseline 66.90%, CNN-ELM 67.28%, and ELM ensemble 67.34%. The proposed method achieves the best performance at 68.23%, improving the CNN baseline by +1.33%. Diversity analysis reports a disagreement rate of 21.2%, Q-statistic of 0.9313, and double-fault of 25.5%. These results indicate that the proposed framework effectively improves FER performance under class imbalance conditions.
Co-Authors A. Bachtiar , Fitra Abdurrachman Bachtiar, Fitra Achmad Jafar Al Kadafi, Achmad Jafar Addin Sahirah, Rafifa Adinugroho, Sigit Adnawirya Pratama, Cendikia Agung Setiyoaji Agus Ardiansyah Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Nur Royyan Ahmad Wildan Attabi' Akbar Grahadhuita Al Kautsar, Prima Daffa Aldi Bagus Sasmita Aldino Caturrahmanto Amalia Rahmawati Amila Fadhila Rahmaniati Anif Fatma Chawa Anis Zubair, Anis Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiza Dwi Septian Argaputri, Maulida Khairunisa Arief Andy Soebroto Arum Sari, Yuita Ashidiq, Muhammad Fihan Aulia Herdhyanti Bachtiar, Harsya Baharudin B. Baharum Baihaqi, Galih Restu Bajsair, Fath' Hani Sarli Barlian Henryranu Prasetio Bayu Laksana Yudha Bayu Rahayudi Bening Herwijayanti Bintang, Tulistyana Irfany Brillian Ghulam Ash Shidiq Budi Darma Setiawan Candra Dewi Candra Dewi Daneswara Jauhari Daneswara Jauhari, Daneswara Darma Setiawan, Budi Daud, Nathan Dewi, Buana Dhimas Wida Syahputra Dian Eka Ratnawati Dian Eka Ratnawati Dimas Joko Hariyanto Dimas Joko Haryanto Duwi Purnama Sidik Edita Rosana Widasari Edy Santoso Edy Santoso Eni Hartika Harahap Eva Agustina Ompusunggu Fajrin Intan Safitri Faris Dinar Wahyu Gunawan Fatimah Az-Zahra, Adinda Feri Eko Herman Firdaus, Nada Fitra A. Bachtiar Fitra Abdurrachman Bachtiar Fitrotuzzakiyah, Shafira Puspa Gessia Faradiksi Putri Gumelar, Dimas HANA RATNAWATI Hanggar Wahyu Agi Prayogo Haris, Asmuni Haryanto, Dimas Joko Hinandy Nur Anisa Hoar, Wilhelmina Sonya Iftinan, Salsa Nabila Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indriati Indriati Indriati Indriati Indriati Ismatul Khasanah Issa Arwani Kartika Sari Kautsar, Ahmad Izzan Khairunnisa, Alifah Kirana, Nareswara Lintang Ksatria Bhuana Kukuh Bhaskara Kukuh Haryobismoko Kurnianingtyas, Diva Laily Putri Rizby Luqyana, Wanda Athira Luthfi Afrizal Ardhani M. Ali Fauzi M. Tanzil Furqon, M. Tanzil Marine Putri Dewi Yuliana Marji . Marji Marji Maulana, Muhammad Taufik Maulida Khairunisa Argaputri Maulidiya, Afifulail Maya Nur Muh Arif Rahman Muh Hamim Fajar Muh. Arif Rahman Muhammad Abduh Muhammad Fajri Muhammad Ferian Rizky Akbari Muhammad Ibnu Sa'ad Muhammad Rafif Al Aziz MUHAMMAD SYAFIQ Muhammad Tanzil Furqon Muhammad Wafiq Mukhrodi, Dillah Lyra Nabil Auliya, Muhammad Hanif Nashi Widodo Navira Rahma Salsabila Nisa, Lisa N. Noor Fatyanosa, Tirana Novanto Yudistira Nurfansepta, Amira Ghina Nurhidayati Desiani Nurul Badriyah Nurul Dyah Mentari Nurul Hidayat Nurul Hidayat Olivia Bonita Pratama, Muhammad Fajarivan Puji Indah Lestari Puspita Sari Putra Pandu Adikara Putri, Rania Aprilia Dwi Setya Rachmad Indrianto Rachmatika, Isnayni Sugma Radeetha Radeetha Rafifah Nawawi, Danisha Ramadhan, Galang Gilang Randi Pratama Nugraha Randy Cahya Wihandika Ratih Kartika Dewi Rekyan Regarsari Mardhi Putri Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Rendi Cahya Wihandika Rheza Raditya Andrianto Ria Ine Pristiyanti Rihana Sofie Nabela Rika Raudhotul Rizqiyah Riski Darmawan Riski Darmawan Riyanarto Sarno Rizal Setya Perdana Rizal Setya Perdana Robbiyatul Munawarah Rowan Rowan Rusydi Hanan, Muhammad Satrio Hadi Wijoyo Setiana, Maya Setya Perdana, Rizal Setyawan, Frederikus Setyo Tri Wahyudi Shalsadilla, Shafatyra Reditha Sholeh, Mahrus Sifaunnufus Ms, Fi Imanur Sukma, Lintang Cahyaning Supraptoa Supraptoa Surya Dermawan Susanto, Dominicus Christian Bagus Sutrisna, Naufal Putra Sutrisno Sutrisno Sutrisno, Sutrisno Syafruddin Agustian Putra Syarif Hidayatulloh Tahtri Nadia Utami Tibyani Tibyani Tirana Noor Fatyanosa, Tirana Noor Tri Fadilah, Ghina Utaminingrum, Fitri Vianti Mala Anggraeni Kusuma Vidya Capristyan Pamungkas Wahyu Rizki Ferdiansyah Wahyudi, Moh. Imron Wardana, Dzaky Ahmadin Berkah Warut, Gregorius Batara De Wayan Firdaus Mahmudy Wibowo, Dhimas Bagus Bimasena Wiguna, Riady Wijaya, Nicholas Yobel Leonardo Tampubolon Yogi Suwandy Yonathan Fanuel Mulyadi Yudhonugroho, Wirkananda Bagus Yulian Ekananta Yunita, W. Lisa Zakiyyah, Rizka Husnun Zanna Annisa Nur Azizah Fareza