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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 Jurnal Penelitian Sains JST ( Jurnal Sains Terapan ) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat BAREKENG: Jurnal Ilmu Matematika dan Terapan 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) Energy : Jurnal Ilmiah Ilmu-Ilmu Teknik 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 Majalah Bisnis & IPTEK JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Anita Desiani
Universitas Sriwijaya, Palembang, Indonesia
Religion Aerospace Engineering 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 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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Klasifikasi Jamur Beracun Menggunakan Algoritma Naïve Bayes dan K-Nearest Neighbors Batubara, Gracia Mianda Caroline; Desiani, Anita; Amran, Ali
Jurnal Ilmu Komputer dan Informatika Vol 3 No 1 (2023): JIKI - Juni 2023
Publisher : CV Firmos

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54082/jiki.68

Abstract

Jamur adalah salah satu organisme eukariot heterotrof dengan jenis yang sangat banyak, sekitar 1.500.000 di dunia. Namun, pengenalan akan jamur masih sangat kurang, dimana jumlah jamur yang sudah dikenali hanya sebanyak 74.000 jenis. Beragamnya jenis jamur ini membuat pengenalan akan klasifikasi jamur menjadi sangat penting agar manusia tidak mengonsumsi jamur beracun yang akan memberikan dampak negatif. Penelitian ini bertujuan untuk menemukan algoritma terbaik dalam pengklasifikasian jamur beracun dan tidak beracun. Klasifikasi jamur berdasarkan ciri-cirinya dapat dilakukan melalui penerapan algoritma Naïve Bayes dan k-Nearest Neighbors (kNN) pada dataset jamur. Hasilnya, algoritma Naïve Bayes memberikan rata-rata akurasi sebesar 92%, lebih kecil dibanding k-Nearest Neighbors yang memberikan rata-rata akurasi sebesar 98%. Rata-rata presisi algoritma Naïve Bayes dan k-Nearest Neighbors sama, yaitu 92,5%. Rata-rata recall algoritma Naïve bayes sebesar 91,5% dan algoritma k-Nearest Neighbors sebesar 98%. Berdasarkan rata-rata akurasi, presisi, dan recall kedua algoritma tersebut, dapat disimpulkan bahwa algoritma k-Nearest Neighbors lebih baik dibanding algoritma Naïve Bayes dalam klasifikasi jamur beracun. Namun, rata-rata akurasi, presisi, dan recall dari algoritma Naïve Bayes masih tergolong sangat baik karena nilainya berada diatas 90%.
Penerapan Metode Certainty Factor Pada Sistem Pakar Untuk Diagnosa Penyakit Tanaman Padi Agatha, Lucy Chania; Desiani, Anita; Suprihatin, Bambang
JAGROS : Jurnal Agroteknologi dan Sains (Journal of Agrotechnology Science) Vol 7, No 2 (2023): JAGROS : Jurnal Agroteknologi dan Sains (Journal of Agrotechnology Science)
Publisher : Universitas Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52434/jagros.v7i2.2577

Abstract

Padi (Oryza Sativa L) menjadi tanaman pangan yang sangat penting di dunia setelah Padi (Oryza Sativa L) merupakan bahan pangan yang sangat penting di dunia setelah gandum dan jagung, serta sumber protein utama bagi sebagian besar penduduk dunia, terutama di Asia. Di antara jenis-jenis tumbuhan serelia lainnya, padi menjadi jenis tanaman yang paling produktif. Untuk itu, sangat penting untuk mewaspadai faktor-faktor yang mempengaruhi tingkat produksinya. Penyakit merupakan salah satu faktor yang sangat merugikan dalam produksi tanaman padi ini, dimana banyak kerugian yang diakibatkan oleh adanya penyakit. Setiap penyakit tersebut umumnya menunjukan gejala-gejala penyakit yang diderita sebelum mencapai tahap yang lebih parah dan meluas, gejala- gejala tersebut dapat dikenali dengan dilakukannya pendiagnosisan terlebih dahulu. Sebelum melakukan diagnosis, kita bisa mendapatkan informasi melalui sebuah sistem yang dapat menerima inputan berupa gejala penyakit dan juga memberikan informasi yang jelas mengenai penyakit tersebut. Hal ini dapat dilakukan menggunakan sistem pakar. Sistem pakar dalam kasus ini menjadi ahli botani atau orang yang paham menangani penyakit pada tumbuhan. Sistem pakar penyakit tanaman padi pada penelitian ini menggunakan metode Certainty Factor. Certainty Factor adalah metode yang digunakan untuk membuktikan ketidakpastian pemikiran seorang pakar untuk keyakinan pakar terhadap masalah yang sedang dihadapi.
Hepatitis Disease Diagnosis Expert System Using Certainty Factor Method: Hepatitis Disease Diagnosis Expert System Using Certainty Factor Method Sitorus, Dina Suzzete; Desiani, Anita
Jurnal Mahasiswa Teknik Informatika Vol. 3 No. 1 (2024): Jurnal Jamastika Vol.3 No.1 April 2024
Publisher : Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35473/jamastika.v3i1.3064

Abstract

The liver is the largest visceral organ in the body with important roles such as hormone production, immunity, protein metabolism, and more. This visceral organ can also be affected by various diseases such as hepatitis. Hepatitis is an inflammatory disease of the liver caused by a virus. Hepatitis has five types of disease, namely Hepatitis A, Hepatitis B, Hepatitis C, Hepatitis D, and Hepatitis E. The types of hepatitis that have the most cases in Indonesia are Hepatitis A, Hepatitis B, and Hepatitis C. Hepatitis occurs due to a sedentary lifestyle. Hepatitis occurs due to unhealthy lifestyles and delays in treatment due to the patient's lack of knowledge about hepatitis. If hepatitis is not cured early, it can cause other diseases such as chronic liver and can also result in death, therefore this study aims to design an expert system that can diagnose hepatitis disease. This expert system design uses the certainty factor (CF) method. The certainty factor (CF) method is used because it can help and facilitate diagnosing hepatitis disease with a certainty value. The certainty value can be presented with a range of values from 0 to 100. This research produces an accuracy value of 80%, therefore this expert system is effective for measuring certainty in diagnosing hepatitis disease early.
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 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 Classification Results of SVM, KNN, Decision Tree, and Ensemble Methods in Diabetes Diagnosis Arsyad. H, Muhammad Iqbal; Amran, Ali; Desiani, Anita; Napitu, Michael Jackson
Journal Medical Informatics Technology Volume 2 No. 3, September 2024
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/medinftech.v2i3.62

Abstract

This study aims to determine which algorithms and test techniques are the most optimal in detecting diabetes mellitus and obtaining the best results based on the value of accuracy, precision, and recall. In this study, approaches were used in early diagnosis of diabetes using KNN, SVM, Decision Tree, and Ensemble Majority Voting methods in Percentage Split and K-Fold Cross Validation methods. Diabetes is a disease characterized by high blood sugar (glucose) levels and can cause a variety of disease complications and damage to the body's organs if not treated immediately. Early diagnosis of diabetes is becoming crucial so that people can take immediate action to the hospital for immediate treatment. The data used is Healthcare-Diabetes from Kaggle. The results of this study have found that the K-Fold Cross Validation method is better because it can provide an average improvement in Ensemble accuracy of 13.42% compared to the Percentage Split method which only gives an average increase in Ensamble accuracy of 9.15%. The best algorithm for classifying diabetes disease is the Ensemble Majority Voting algorithm using the K-Fold Cross Validation method with a 98.81% accuracy rate. These excellent research results may contribute to detecting early symptoms of diabetes before it become too severe.
Klasifikasi Penyakit Hati Menggunakan Perbandingan Implementasi Algoritma Naïve Bayes Dan K-Nearest Neighbor Simamora, Valentino; Desiani, Anita; Irmeilyana, Irmeilyana
Jurnal Teknik Elektro dan Komputasi (ELKOM) Vol 6, No 1 (2024): ELKOM
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/elkom.v6i1.18424

Abstract

Hati merupakan organ kelenjar dalam tubuh manusia. Hati manusia memiliki bobot kira-kira mencapai 1200 hingga 1500 gram. Sebagai kelenjar terbesar dalam tubuh manusia, hati dapat terserang berbagai macam penyakit. Kita dapat melakukan klasifikasi mengenai penyakit hati yang bertujuan memperoleh jumlah rata-rata manusia yang terserang penyakit hati. Dengan penelitian ini, kita bisa membandingkan dan menyimpulkan algoritma mana yang paling tepat untuk diterapkan pada proses klasifikasi terhadap penyakit hati. Pada penelitian ini algoritma yang digunakan ialah algoritma pertama Naïve Bayes dan algoritma kedua K-Nearest Neighbor (K-NN). Dari hasil penelitian maka diperoleh bahwa Naïve Bayes memberikan nilai akurasi, presisi dan recall sebesar 85%-85,5% yang mana ini dapat dikatakan cukup baik namun belum baik. Sedangkan K-NN dapat memberikan nilai sempurna pada akurasi, presisi dan recall yaitu 100%. Maka algoritma yang terbaik dan dapat digunakan adalah algoritma K-NN
Multi-Stage CNN: U-Net and Xcep-Dense of Glaucoma Detection in Retinal Images Desiani, Anita; Priyanta, Sigit; Ramayanti, Indri; Suprihatin, Bambang; Rio Halim, Muhammat; Geovani, Dite; Rayani, Ira
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.314

Abstract

Glaucoma is a chronic neurological disease in the human eye where there is damage to the nerves which causes vision loss to blindness. Glaucoma can be detected by classifying retinal images. Several previous studies that classified glaucoma did not perform segmentation beforehand. Segmentation is needed to extract the features of the optic disc and optic cup from retinal images that are used to detect glaucoma. This study proposes two stages in the detection of glaucoma, namely the segmentation and classification stages. Segmentation is carried out using the U-Net architecture. Classification is done using a new architecture, namely Xcep-Dense. The Xcep-Dense architecture is a new architecture which is the result of a combination of the Xception and DenseNet architectures. At the segmentation stage, accuracy, recall, precision, and F1-score values are obtained above 90%. The Cohen’s kappa value has a value above 85% and loss below 20%. At the classification stage, accuracy and specification values were obtained above 85%, sensitivity and F1-score above 80%, and Cohen’s kappa above 70%. The predicted image obtained at the segmentation stage has a very similar appearance to the ground truth. Based on the results of the performance evaluation obtained, it shows that the method proposed in this study is feasible in detecting glaucoma.Glaucoma,
Simple Data Augmentation and U-Net CNN for Neclui Binary Segmentation on Pap Smear Images Desiani, Anita; Irmeilyana; Zayanti, Des Alwine; Utama, Yadi; Arhami, Muhammad; Affandi, Azhar Kholiq; Sasongko, Muhammad Aditya; Ramayanti, Indri
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.442

Abstract

The nuclei and cytoplasm can be detected through Pap smear images. The image consists of cytoplasm and nuclei. In Pap smear image, nuclei are the most critical cell components and undergo significant changes in cervical cancer disorders. To help women avoid cervical cancer, early detection of nuclei abnormalities can be done in various ways, one of which is by separating the nuclei from the non-nucleis part by image segmentation it. In this study, segmentation of the separation of nuclei with other parts of the Pap smear image is carried out by applying the U-Net CNN architecture. The amount of pap smear image data is limited. The limiter data can cause overfitting on U-Net CNN model. Meanwhile, U-Net CNN needs a large amount of training data to get great performance results for classification. One technique to increase data is augmentation. Simple techniques for augmentation are flip and rotation. The result of the application of U-Net CNN architecture and augmentation is a binary image consisting of two parts, namely the background and the nuclei. Performance evaluation of combination U-Net CNN and augmentation technique is accuracy, sensitivity, specificity, and F1-score. The results performance of the method for accuracy, sensitivity, and F1-score values are greater than 90%, while the specificity is still below 80%. From these performance results, it shows that the U-Net CNN combine augmentation technique is excellent to detect nuclei in compared to detect non nuclei cell on pap smear image.
Sistem Pakar Diagnosa Penyakit Katarak dengan Metode Certainty Factor Mortara, Alda Amalia; Anita Desiani
JURNAL AMPLIFIER : JURNAL ILMIAH BIDANG TEKNIK ELEKTRO DAN KOMPUTER Vol. 13 No. 1 (2023): Amplifier Mei Vol. 13, No. 1 2023
Publisher : UNIB Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33369/jamplifier.v13i1.27265

Abstract

The eye is one of the most vital parts of the human body, which of course must be maintained. Unhealthy eyes will have a bad impact on the sufferer because it can interfere with the sufferer's activity process. One of the simplest and most common eye diseases suffered by humans is cataracts. Symptoms of cataracts occur painlessly so that many sufferers do not realize that they have cataracts. In Indonesia, the main cause of visual impairment and blindness is cataracts. Cases of blindness caused by cataracts occur due to a condition where the lens of the eye becomes cloudy and cataracts do not only occur at an early age but at all ages even though cataracts are a type of blindness that can be avoided and can be cured through treatment. Therefore an expert system is needed to help diagnose cataracts so that prevention can be done as much as possible. One method that can be used by expert systems is the assurance factor method. The advantage of the assurance factor method is that it can provide settlement solutions with the value of disease symptoms given by experts. With the superiority of the assurance factor method, this study will discuss the application of the assurance factor in the diagnosis of cataracts. To diagnose cataracts, there are 18 symptoms with 3 types of disease, namely congenital, juvenile, and traumatic cataracts. This study used 5 data tests based on the symptoms felt by people with cataracts to produce accurate predictions for each type of cataract where 86.0762% congenital cataracts in the first test data, 94.0595% juvenile cataracts in the second test data, 92.5128% traumatic cataracts in the third test data, 92.2440 % juvenile cataracts in the fourth test data, and 90.2080% juvenile cataracts in the fifth test data.
BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING RESVNET ARCHITECTURE Ramadhani, Syafira Dian; Erwin, Erwin; Desiani, Anita; Bella Agustina, Sinta
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2637

Abstract

The U-Net architecture is often used in medical blood vessel segmentation due to its ability to produce good segmentation. However, U-Net has high complexity due to the presence of the bridge part, which increases the parameters and training time. To overcome this, this research modifies U-Net by removing the bridge part, resulting in V-Net architecture. V-Net architecture faces challenges in capturing deep and complex features. This research proposes modifying V-Net with ResNet architecture in the encoder part, resulting in ResVNet architecture. ResNet, with residual connections, enables the training of very deep networks with more stability and effectiveness in capturing complex features. At the encoder, ResNet is used for more effective training of deep networks and capturing complex features. While at the decoder, U-Net is used to preserve the high resolution and spatial information of the image in segmentation. This study aims to determine the performance evaluation results of the ResVNet architecture. The evaluation measures used are accuracy, sensitivity, precision and Jaccard score. Tests were conducted on the DRIVE and STARE datasets. The measurement results of blood vessel segmentation using ResVNet on the DRIVE dataset resulted in accuracy 96.57%, sensitivity 82.28%, precision 79.57%, and Jaccard score 67.61%. On the STARE dataset, the accuracy results are 96.71%, sensitivity 79.44%, precission 79.44%, and Jaccard score 65.05%. The sensitivity results on the STARE dataset as well as the precision and Jaccard score values on the two datasets produced are still low, in the future this research will make improvements to the ResVNet architecture used.
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 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 Clarita Margo Uteh 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 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 Kanda Januar Miraswan Karina 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 Manoppo, Sania Marselina, Nyanyu Chika Maya Meilensa Maya Meilensa Mayangsari, Oki Sukma Mega Tiara Putri Mitta Permata Sari Mochamad Syaifudin, Mochamad Mortara, Alda Amalia MS Hasibuan Muchlas, Ally Muhammad Akbar Muhammad Akmal Shidqi Muhammad Awaludin Djohar Muhammad Awaludin Djohar Muhammad Azwar Annas Muhammad Gibran Al-Filambany Muhammad Ihsan Muhammad Naufal Rachmatullah Muhammad Nawawi Muhammad Nawawi Muhammad Syariful Irsyad Muhammad Wahyu Ilahi Muhammat Rio Halim Muslim Muslim 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 Purwita Sari, Purwita 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 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 Salahuddin Salahuddin Salamah, Fitri Salsabila, Aulia Saputra, M Aldi Saputra, Tommy Sari Suryati Sasongko, Muhammad Aditya Savera, Mutiara Saviera, Mutiara Shania Putri Andhini Shidqi, Muhammad Akmal Shinta Octarina Siddiq Makhalli Sigit Priyanta Simamora, Valentino 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 Villando, Gio Waafiyah, Hilmiana Wahyudi, Yogi 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