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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) ComEngApp : Computer Engineering and Applications Journal TEKNIK INFORMATIKA Jurnal Pendidikan Matematika Media Informatika JSI: Jurnal Sistem Informasi (E-Journal) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Jurnal Informatika dan Teknik Elektro Terapan POSITIF Annual Research Seminar KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Proceeding of the Electrical Engineering Computer Science and Informatics Science and Technology Indonesia Demography Journal of Sriwijaya Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Penelitian Sains JST ( Jurnal Sains Terapan ) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat InComTech: Jurnal Telekomunikasi dan Komputer BAREKENG: Jurnal Ilmu Matematika dan Terapan JITK (Jurnal Ilmu Pengetahuan dan Komputer) Dinamisia: Jurnal Pengabdian Kepada Masyarakat PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Infomedia KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Riau Journal of Empowerment Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jurnal Kreativitas PKM Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal KOMPUTEK Indonesian Journal of Applied Informatics KOMPUTIKA - Jurnal Sistem Komputer Jurnal Teknologi dan Informasi JKPM (Jurnal Kajian Pendidikan Matematika) Jurnal Teknologi Terapan Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jurnal Vokasi Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal ABDINUS : Jurnal Pengabdian Nusantara Scientific Journal of Informatics Jurnal Teknik Elektro Uniba (JTE Uniba) Square : Journal of Mathematics and Mathematics Education BERNAS: Jurnal Pengabdian Kepada Masyarakat JOINT (Journal of Information Technology Idealis : Indonesia Journal Information System Jurnal Sistem Informasi dan Sistem Komputer Jurnal Teknik Informatika (JUTIF) Jurnal AbdiMas Nusa Mandiri Jurnal Amplifier: Jurnal Ilmiah Bidang Teknik Elektro dan Komputer JAGROS : Jurnal Agroteknologi dan Sains (Journal of Agrotechnology Science) Jurnal Ilmu Komputer dan Informatika Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Rekayasa Elektro Sriwijaya Jurnal Teknologi BAKTI : Jurnal Pengabdian Kepada Masyarakat Pattimura International Journal of Mathematics (PIJMath) Proceeding Applied Business and Engineering Conference Technology and Informatics Insight Journal Electrician : Jurnal Rekayasa dan Teknologi Elektro COREAI: Jurnal Kecerdasan Buatan, Komputasi dan Teknologi Informasi "JAMASTIKA" Jurnal Mahasiswa Teknik Informatika Journal Medical Informatics Technology Journal Of Artificial Intelligence And Software Engineering Jurnal INFOTEL Jurnal Informatika Polinema (JIP) Kreano, Jurnal Matematika Kreatif Inovatif Jurnal Kecerdasan Buatan dan Teknologi Informasi JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Energy: Jurnal Ilmiah Ilmu-ilmu Teknik Majalah Bisnis & IPTEK
Anita Desiani
Universitas Sriwijaya, Palembang, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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Analisis Perbandingan Prediksi Harapan Hidup Hepatitis Menggunakan Algoritma K-Nearest Neighbor dan C4.5 Karina; Herlina Hanum; Anita Desiani
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v8i2.98-111

Abstract

Hepatitis is an inflammatory disease of the liver caused by a virus that causes damage to the cells and function of the liver. This study compares the accuracy, precision, and recall results of the K-Nearest Neighbor (K-NN) and C4.5 algorithms using the Percentage Split and K-fold Cross Validation methods. Of the two algorithms, the best level of accuracy is obtained using the K-fold Cross Validation method. Based on the accuracy and error rate, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-NN algorithm. Based on the special Precision and Recall values ​​on the Recall value to predict class zero the best algorithm is obtained using the C4.5 algorithm. To assess Precision and Recall, the other best algorithm in predicting the fixed response variable is obtained by using the K-NN algorithm. Overall, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-Nearest Neighbor (K-NN) algorithm.
Improving Optic Disc and Optic Cup Segmentation with Flip-Gamma Augmentation and SegFormer Salamah, Fitri; Erwin; Desiani, Anita
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15996

Abstract

The Cup-to-Disc Ratio (CDR) is widely used as a diagnostic indicator for glaucoma, although variations and irregularities can influence its accuracy in the Optic Disc (OD) and Optic Cup (OC). To overcome this challenge, automated image segmentation is used. However, image segmentation is challenged by image blurriness, noise, and uneven illumination, which can affect segmentation quality and increase the risk of misdiagnosis. To address these challenges, this study applies a combined Flip-Gamma Augmentation and SegFormer approach for OD and OC segmentation. Flip-Gamma augmentation increases image diversity and improves image quality by adjusting brightness and contrast. Meanwhile, the SegFormer uses a Transformer-based backbone and efficiently extracts multi-scale features to enhance segmentation performance. Experimental results on the Drishti-GS dataset show that applying Flip-Gamma (δ = 0.8, 0.9, 1.1, 1.2) is associated with improved segmentation performance across all classes, with sensitivity (90-99%), DSC (90-99%), IoU (82-99%), and ROC (94-99%), indicating consistent segmentation of OD, OC and background regions. Furthermore, a one-sided Mann-Whitney U test indicates differences in performance compared to other augmentation methods. These findings suggest that the proposed augmentation strategy is beneficial for segmentation on the Drishti-GS dataset. However, further validation on larger and more diverse datasets is required to assess generalizability.
Comparison of Adaptive Boosting and Categorical Boosting in Heart Attack Diagnosis Amran, Ali; Suryani, Suryani; Fathinah, Nadiva Azro; Desiani, Anita; Ramayanti, Indri
Journal of Artificial Intelligence and Software Engineering Vol 6, No 1 (2026): Maret
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v6i1.9051

Abstract

Heart disease is one of the leading causes of death worldwide, and therefore, accurate early detection methods are needed to help reduce mortality rates. One approach that can be applied is machine learning using classification techniques based on ensemble boosting algorithms. This study aims to compare the performance of two ensemble algorithms, namely Adaptive Boosting (AdaBoost) and Categorical Boosting (CatBoost), in classifying heart attack disease. The labels used in this study are positive and negative. The evaluation process was conducted using two testing techniques: percentage split with a ratio of 80% training data and 20% testing data, and 10-fold cross-validation. Model performance was evaluated based on accuracy, precision, and recall to comprehensively measure classification capability. The results show that in the percentage split method, CatBoost achieved the highest accuracy of 98.88%, while in k-fold cross-validation it reached 98.43%. Nevertheless, AdaBoost also demonstrated good performance, with all evaluation metrics exceeding 90%. Therefore, the best-performing model in this study is CatBoost with the k-fold cross-validation technique on the heart attack dataset.
DIAGNOSA PENYAKIT PARKINSON DENGAN ALGORITMA K-NEAREST NEIGHTBOR DAN DECISION TREE C4.5 Desiani, Anita; Narti, Narti; Ramayanti, Indri; Arhami, Muhammad; Irmeilyana, Irmeilyana
Jurnal Simantec Vol 12, No 1 (2023): Jurnal Simantec Desember 2023
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v12i1.21167

Abstract

Parkinson kamislot adalah suatu penyakit dimana neurologis mempengaruhi neuron dopaminergik, yang dibuktikan dengan kematian sel-sel otak yang ada, hilangnya pigmentasi substantia nigra, adanya inklusi sitoplasma, dan penurunan kadar dopamin di substantia nigra pars compacta dan corpus striatum. Penyakit parkinson dapat didiagnosa dengan melakukan pengklasifikasian untuk mengukur tingkat akurasi. Tujuan dari penelitian ini adalah untuk melakukan diagnosa penyakit Parkinson dengan dua algoritma yang berbeda, yaitu algoritma K-Nearest Neighbor (KNN) dan algoritma C4.5 dengan metode pelatihan Percentage split dan validasi K-fold cross yang nantinya kan dibandingkan satu sama lain. Dari penelitian ini, nilai presisi yang dimiliki penderita Parkinson's disease algoritma C4.5 split persentasenya adalah 96%. Begitu juga untuk nilai recall yang dimiliki oleh penderita penyakit Parkinson yaitu sebesar 93%. Nilai akurasi algoritma K-Nearest Neighbor (KNN) adalah 82% untuk metode pelatihan pada percentage split dan 76,8% dengan metode validasi K-fold cross dan 89% untuk algoritma C4.5 dengan metode pelatihan pada Percentage split dan 81% dengan metode validasi K-fold cross.Kata kunci: C4.5, K- Fold Cross Validation, K-Nearest Neighbor, Parkinson, Percentage Split
Comparison of K-Nearest Neighbor Algorithm and Logistic Regressionin the Classification of Cervical Cancer Disease Azzahra, Nur Devita; Ambarwati; Desiani, Anita; Maiyanti, Sri Indra; Ramayanti, Indri
ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK Vol. 14 No. 1 (2024): ENERGY: JURNAL ILMIAH ILMU-ILMU TEKNIK
Publisher : Universitas Panca Marga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51747/energy.v14i1.p1-8

Abstract

in the face of the globalization era that is increasingly competitive, a company is required to be able to devise competitive in order to survive and win the competition. Because it needed a solution to improve the quality of the product so that it can assist in winning the competition  with its competitors. PT. Indonesia Timber Kutai (ITC) as a company engaged in lumber and plywood production was should be ableto survive in an increasingly tight competition.Plywood is formed by layers of sheets –sheets of wood called veneer. The quality of the veneers will  effect  the  quality  of  plywood  produced  by  PT  Indonesia  Timber  Kutai.  To  get  a  good  veneer  quality  then needed a good combination between the blade with the machine. Researchers do research on the condition of the blade. A place for pengashan blade is subsi GrindeThe cause of the majority of the knife is a blunt knife quickly, so the quality of the producedveneer  would be ugly. Researchers try to find problems –problems  encountered and  immediately  make  the  effort improved  sharpening  results  with  seven  quality  control.  For  the  results  of  his research  is  before  there  is  an  improvement  obtained  22  case  knife quickly  dulled  from  72  blade,  after  no improvement, found only 1 case of only fast knife dulled from 72 blades
PENINGKATAN SKILL SISWA TUNARUNGU MELALUI PELATIHAN PATCHWORK DAN QUILTING BERBASIS APLIKASI SIGN TALK DI SLB-B YPAC PALEMBANG Desiani, Anita; Suprihatin, Bambang; Amran, Ali; Oktariansyah, Yadi; Dewi, Siti Rusdiana Puspa; Jonatan, Jonatan; Prayogo, Slamet; Sinabutar, Lonamonika
Jurnal AbdiMas Nusa Mandiri Vol. 8 No. 2 (2026): Periode April 2026
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/abdimas.v8i2.7965

Abstract

Skill development for deaf students at SLB-B YPAC Palembang still faces significant challenges, particularly due to communication barriers between teachers and students that hinder knowledge transfer. This community service program aims to enhance hard skills (technical sewing skills in patchwork and quilting) as well as soft skills (communication, teamwork, and independence) through the integration of an Artificial Intelligence (AI)-based application called Sign Talk. The implementation method was carried out through a workshop and intensive mentoring scheme over 4 months, which included 3 main training sessions, each lasting 120 minutes, for 15 deaf students. Evaluation was conducted using pre-test and post-test instruments analyzed descriptively. The results of the activity showed an increase in students’ soft skills of 40.5%. For hard skills, specifically sewing competencies, there was a 23.31% increase. Although 90% of participants rated the Sign Talk application as very helpful in understanding instructions during the activity, further development of the Sign Talk vocabulary is needed. Additionally, this activity can be expanded to include other hard skill trainings with Sign Talk serving as the communication medium.
Development of An Expert System for The Diagnosis of Kidney Disease Using the Certainty Factor Method Refky Maulana; Anita Desiani
Majalah Bisnis & IPTEK Vol. 16 No. 1 (2023): Majalah Bisnis & IPTEK
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat (P3M) STIE Pasundan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55208/hrc47w79

Abstract

Kidney disease is a prevalent health issue affecting millions of people globally. Early and accurate diagnosis of kidney diseases can help in the timely and effective management of the condition. Expert systems, such as those using the Certainty Factor (CF) method, can provide doctors with valuable assistance in diagnosing kidney diseases more efficiently and accurately. This study aims to develop a kidney disease diagnosis expert system using the CF method. The developed system consists of data collection, data storage, and data processing components, with the CF method used to calculate diagnostic confidence levels and decision-making based on predetermined rules. The knowledge acquisition process was carried out by interviewing three nephrologists to obtain rules for diagnosing kidney diseases. The expert system's evaluation is conducted by comparing the system's diagnostic accuracy with a specialist doctors. The results show that the developed expert system has an accuracy rate of 85.7% in diagnosing kidney diseases. The system also has a user-friendly interface, which allows doctors to input symptoms and obtain a diagnosis quickly and accurately. The developed system has several advantages over traditional diagnosis methods. It can diagnose multiple kidney diseases simultaneously and provide a differential diagnosis, allowing doctors to choose the most appropriate treatment plan for their patients. The system also has the potential to reduce diagnostic errors and improve patient outcomes.
DIABETIC RETINOPATHY SEVERITY CLASSIFICATION USING GAMMA CORRECTION-BASED IMAGE ENHANCEMENT AND BN-VGG ARCHITECTURE Indri Ramayanti; Karnadi; Septiani Nadra Indawaty; Muhammad Umar Abdussalam; Malika Zilda; Anita Desiani
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.8094

Abstract

Diabetic retinopathy (DR) is a diabetes-related condition that can cause vision impairment or vision loss. Accurately identifying the level of DR from retinal fundus images is crucial for early detection. However, poor image quality often degrades classification performance. This study proposes an approach that integrates gamma correction-based image enhancement with a Batch Normalization–Visual Geometry Group (BN-VGG) architecture for multiclass DR severity classification. Gamma correction is applied to improve image contrast, while BN-VGG enhances training stability and feature representation. The proposed method categorizes DR into five classifications: normal, mild, moderate, severe, and proliferative. The enhanced images achieved PSNR of 30.85 and SSIM above 0.86, indicating improved visual quality. The model achieved accuracy at 0.97, sensitivity at 0.92, specificity at 0.98, F1-score at 0.92, Cohen's Kappa at 0.90, and G-Mean at 0.97. The innovative aspect of this study is the incorporation of gamma correction with BN-VGG architecture, demonstrating that image enhancement can significantly improve multiclass DR classification performance without increasing model complexity. The study's results indicate the proposed method's effectiveness for accurate & reliable DR severity classification
Implementasi Algoritma CART dan Naïve Bayes Untuk Mendeteksi Penyakit Stroke Anita Desiani; Puspa Sari; Indri Ramayanti; Muhammad Arhami
InComTech : Jurnal Telekomunikasi dan Komputer Vol. 16 No. 1 (2026)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v16i1.20209

Abstract

Penyakit stroke merupakan penyakit pembuluh darah yang disebabkan oleh kurangnya sirkulasi oksigen dan darah ke otak sehingga menyebabkan kerusakan pada jaringan otak. Stroke dapat menyebarkan perubahan pada fungsi dan fisiologi anatomi yang letaknya jauh dari kerusakan. Penyakit stroke merupakan penyakit penyebab utama kematian setelah penyakit jantung sehingga diperlukan suatu system untuk mendeteksi penyakit stroke sebagai bentuk pencegahan dini agar tidak terserang penyakit stroke. Sistem deteksi menggunakan kecerdasan buatan dengan teknik klasifikasi. Klasifikasi telah digunakan oleh para peneliti untuk mendeteksi penyakit dengan hasil yang memuaskan. Pada penelitian ini menggunakan teknik klasifikasi menggunakan algoritma CART (Clasiification and Regression Tree) dan algoritma Naïve Bayes. Penelitian ini membagi data menggunakan persentase split sebesar 90% data latih dan sisanya berupa data uji dengan dataset dari Kaggle. Berdasarkan penggunaan kedua algoritma dalam mendeteksi penyakit stroke, algoritma CART menghasilkan akurasi sebesar 95.57% sedangkan naïve bayes memiliki akurasi sebesar 85%. Pada sisi presisi model rata-rata yang dihasilkan oleh algoritma CART yaitu 88% sedangkan naïve bayes memiliki presisi 66%. Recall rata-rata yang dihasilkan oleh algoritma CART yaitu 61% sedangkan naïve bayes memiliki recall sebesar 58%. Dari perbandingan antara akurasi, presisi, dan recall yang dihasilkan algoritma CART dan Naïve bayes dapat disimpulkan jika algoritma CART sangat baik dalam mendeteksi penyakit stroke secara dini.
Penerapan Metode Support Vector Machine Dalam Klasifikasi Bunga Iris Desiani, Anita; Irmeilyana, Irmeilyana; Hanum, Herlina; Andriani, Yuli; Maiyanti, Sri Indra; Uteh, Clarita Margo; Rayyani, Ira
IJAI (Indonesian Journal of Applied Informatics) Vol 7, No 1 (2022)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v7i1.61486

Abstract

Abstrak Data mining adalah proses melatih komputer untuk mengenali suatu pola menggunakan teknik statistika mapun matematika. Salah satu teknik data mining yang sering digunakan adalah klasifikasi, yakni mengelompokkan data ke dalam suatu label menggunakan atribut. Pada klasifikasi, Support Vector Machine (SVM) merupakan salah satu metode yang paling banyak digunakan. Penelitian ini akan memanfaatkan metode SVM dalam melakukan klasifikasi bunga Iris. Data yang diteliti menggunakan sebanyak 150 data dengan menggunakan dua metode data latih, yakni percentage split dan k-fold cross validation. Data diolah melalui tahap pre-processing, lalu diklasifikasi menggunakan metode SVM melalui 2 metode data latih, percentage split sebesar 80% dan k-fold corss validation dengan k=10, perhitungan hasil prediksi menggunakan confusion matrix. Pada metode percentage split diperoleh nilai akurasi sebesar 96,7%, presisi 97,6%, recall sebesar 95,3%, dan F1-score sebesar 96,3%. Pada metode k-fold cross validation diperoleh nilai akurasi sebesar 92,6%, presisi 92,6%, recall sebesar 92,6%, dan F1-score sebesar 92,3%. Dengan demikian metode SVM menggunakan kernel polynomial dengan metode data latih percentage split dapat diimplementasikan ke dalam sistem klasifikasi bunga Iris.AbstractData mining is the process of training a computer to recognize a pattern using statistical and mathematical techniques. One of the data mining techniques that are often used is classification, which is to group data into the label using attributes. In classification, the Support Vector Machine (SVM) is one of the most widely used methods. This research will utilize the SVM method in classifying Iris flowers. The data studied used 150 data using two training data methods, percentage split and k-fold cross validation. The data is processed through the pre-processing stage, then classified using the SVM method through 2 training data methods, percentage split of 80% and k-fold cross validation with k = 10, and calculation of prediction results using a confusion matrix. In the percentage split method, the accuracy is 96.7%, precision is 97.6%, recall is 95.3%, and F1-score is 96.3%. In the k-fold cross validation method, the accuracy is 92.6%, precision is 92.6%, recall is 92.6%, and F1-score is 92.3%. So that the SVM method using a polynomial kernel with the percentage split training data method can be implemented into the iris classification system.
Co-Authors Adi Muzakir Adinda Ayu Lestari, Adinda Ayu Adzra Afiifah Nabila Affandi, Azhar Kholiq Agatha, Lucy Chania Agung Alamsyah Ajeng Islamia Putri Ajeng Islamia Putri Al-Ariq, M Al-Filambany, Muhammad Gibran Alamsyah, Agung albar Pratama Alga Mahida Ali Amran Ali Amran All Fajri, Muhammad Arya Ambarwati Ambarwati Ananda Pratiwi Andhini, Shania Putri Andika Cristian Lubis Andriani, Nur Avisa Calista Anggraini, Jeni Putri Anisa Aulia Kusmareni Annisa Aulia Lestari Annisa Kartikasari Annisa Nabila, Annisa Annisa Nur Fauza Annisa Nurba Iffah’da Apledaria Apledaria Arhami, Muhammad Arhami, Muhammad Arsyad. H, Muhammad Iqbal Arum Setiawan Aulia Salsabila Aulia, Annisa Rizka Ayuputri, Niken Azhar Kholiq Affandi Azzahra, Nur Devita Azzahra, Pasma Bambang Suprihatin Bambang Suprihatin Bambang Suprihatin Bambang Suprihatin Batubara, Gracia Mianda Caroline Bella Agustina, Sinta Betty Aprianah Betty Aprianah Budi Mulyono Calista, Nur Avisa Carolina Rahman Chairu Nisa Apriyani Chaya Gladys Zhafirah A Des Alwine Zayanti Des Alwine Zayanti Des Alwine Zayanti, Des Alwine Desty Rodiah Dewi Lestari Dwi Putri Dewi Lestari Dwi Putri Dewi, Deshinta Arrova Dian Cahyawati Diana Dewi Sartika, Diana Dewi Dien Novita Dina Elly Yanti Dina Elly Yanti Dina Suzzete Sitorus Dite Geovani Dite Geovanni Dwi Ranti Dwi Septiani Dwifa, Dima Echa Alda Melinia Efriliyanti, Filda Endang Sri Kresnawati Endang Sri Kresnawati Endro Setyo Cahyono Endro Setyo Cahyono, Endro Setyo Enyta Yuniar Ermatita - Erwin Erwin Erwin Erwin, Erwin Fadhilah, Nadiyah Fadilah, Nadiyah Faishal Fitra Ramadhan Fathinah, Nadiva Azro Ferdi Setiawan Ferdinand Hukama Taqwa Filda Efriliyanti fildzah daniela, nyayu audy Firdaus Firdaus Fitri Salamah Fivalianda, Dido Geovani, Dite Geovanni, Dite Giovillando Hadi Tanuji Hasibuan, MS Henisaniyya, Nabila Herlina Hanum Herlina Hanum, Herlina Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Husaini Husaini Ilham Tri Wibowo Indah Verdya Alvionita Indra Maiyanti, Sri Indri Ramayanti Ira Rayyani Irmeilyana Irmeilyana Irmeilyana Irvan Andrian Jonatan, Jonatan Kanda Januar Miraswan Karina Karnadi, Karnadi Kartila Kartila Kerenila Agustin Kesuma, Lucky Indra Kurnia, M Kahfi Aldi Kurniawan, Rifki Kusmareni, Anisa Aulia Lizah Framesti Lubis, Andika Cristian Lucy Chania Agatha Makhalli, Siddiq Malika Zilda Manoppo, Sania Marselina, Nyanyu Chika Maya Meilensa Maya Meilensa Mayangsari, Oki Sukma Mega Fatimah Rosana Mega Tiara Putri Mitta Permata Sari Mochamad Syaifudin, Mochamad Mortara, Alda Amalia MS Hasibuan Muchlas, Ally Muhammad Akbar Muhammad Akmal Shidqi Muhammad Arhami Muhammad Awaludin Djohar Muhammad Awaludin Djohar Muhammad Azwar Annas Muhammad Gibran Al-Filambany Muhammad Naufal Rachmatullah Muhammad Nawawi Muhammad Nawawi Muhammad Syariful Irsyad Muhammad Umar Abdussalam Muhammad Wahyu Ilahi Muhammat Rio Halim Muslim Muslim Mustaqima, Dina Mutiara Saviera Muzakir, Adi Muzayyadah, Fathona Nur Nadya Riri Febiyanti Napitu, Michael Jackson Narti Narti, Narti Naturatama, Dicky Naufal Rachmatullah Ngudiantoro . Ning Eliyati Novi Rustiana Dewi Novi Rustiana Dewi Nugrohoputri, Rifa Fadhila NUNI GOFAR Nur Avisa Calista Nur Devita Azzahra Nyayu Chika Marselina Oki Dwipurwani Padhil, Azmi Muhammad Pasma Azzahra Permatasari, Mitta Pertiwi, Citra Prabudifa, Muhammad Yusuf Pranata, Teddi Pratiwi, Ananda Prayogo, Slamet Purwita Sari, Purwita Puspa Sari Puspa Sari Puspa Sari, Puspa Putra Bahtera Jaya Bangun, Putra Bahtera Jaya Putri Bella Nusantara Putri Pratiwi Putri, Ajeng Islamia Putri, Tyara Hestyani Rahmadita, Suristhia Rahmat Dwian Ramadhan, Faishal Fitra Ramadhan, Raihan Ramadhani, Syafira Dian Ramayanti, Indri Rana Sania Ravisha Keyna Anduwi Rayani, Ira Rayyani, Ira Redina An Fadhila Chaniago Redina An Fadhila Chaniago Refky Maulana Rifa Fadhila Nugrohoputri Rifki Kurniawan Rifkie Primartha Rifkie Primartha Rifkie Primartha Rio Halim, Muhammat Rizki, Fatur Rufi'i Salahuddin Salahuddin Salamah, Fitri Salsabila, Aulia Saputra, M Aldi Saputra, Tommy Sari Suryati Sasongko, Muhammad Aditya Savera, Mutiara Saviera, Mutiara Septiani Nadra Indawaty Shania Putri Andhini Shidqi, Muhammad Akmal Shinta Octarina Siddiq Makhalli Sigit Priyanta Simamora, Valentino Sinabutar, Lonamonika Sinta Bella Agustina Siti Husnul Hotimah, Siti Husnul Siti Nurhaliza Siti Rusdiana Puspa Dewi Sitorus, Dina Suzzete Sri Indra Maiyanti Sri Indra Maiyanti Sri Indra Maiyanti Sri Indra Maiyanti Suedarmin, Muhammad Sugandi Yahdin Sugandi Yahdin Sugandi Yahdin Suratama, Bintang Suryani Suryani Susanto Susanto Susanto Susanto Syafrina Lamin, Syafrina Syarifuddin, Fauzi Yusuf Teddi Pranata Titania Jeanni Charisa Titania Jeanni Charissa Tri Febriani Putri tri wahyuni Uteh, Clarita Margo Villando, Gio Waafiyah, Hilmiana Wahyudi, Yogi Yadi Oktariansyah Yadi Utama Yassir Yassir Yogi Wahyudi Yonarta, Danang Yuli Andirani Yuli Andriani Yuli Andriani Yulia Resti Yuniar, Enyta Z, Des Alwine Zulhipni Reno Saputra Els