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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 SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan 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 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) Majalah Bisnis & IPTEK
Anita Desiani
Universitas Sriwijaya, Palembang, Indonesia
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Pemanfaatan marketplace shopee sebagai strategi untuk meningkatkan pemasaran kain songket Desiani, Anita; Irmeilyana, Irmeilyana; Putri, Ajeng Islamia; Yuniar, Enyta; Calista, Nur Avisa; Makhalli, Siddiq; Amran, Ali
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 4 No 2 (2021)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v4i2.9222

Abstract

South Sumatera songket woven cloth is one of the cultural assets of South Sumatera Province which is usually used at weddings and other traditional ceremonies. One of the villages which is famous as a producer of songket cloth is a Penyandingan Village. The songket cloth industry in Penyandingan Village experienced a decline in turnover of up to 60% during the Covid-19 pandemic. This is supported by the lack of knowledge of society regarding marketing strategies and technology in marketing products. For this reason, Shopee market management training is needed for songket cloth craftsmen and the Penyandingan Village society through the Sriwijaya University Thematic Community Service team program so that the marketing of songket fabrics can reach a wide market and be able to compete with other products. The method used is the lecture method including data collection planning and implementation of activities. The research analysis uses descriptive analysis to provide a general description of the implementation of the Shopee marketplace training. After the training was carried out,  Penyandingan Village society was able to understand the material and apply it directly using the Shopee application, and could be applied on a sustainable scale so that sales of songket cloth could increase.
Pemanfaatan aplikasi daring untuk peningkatan pemasaran songket dan purun perajin Burai Maiyanti, Sri Indra; Desiani, Anita; Yahdin, Sugandi; Erwin, Erwin; Rodiah, Desty; Rachmatullah, Muhammad Naufal; Geovanni, Dite; Shidqi, Muhammad Akmal; Al-Filambany, Muhammad Gibran
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 5 No 2 (2022)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v5i2.14356

Abstract

Desa Burai merupakan salah satu desa yang terletak di Provinsi Sumatera Selatan. Masyarakat Desa Burai mayoritas memiliki mata pencaharian sebagai pengrajin songket dan pengrajin anyaman purun. Usaha rumah tangga ini mengalami kendala pada masalah pemasaran yang terbatas. Kegiatan pelatihan dan penyuluhan aplikasi daring seperti WhatsApp Business dan marketplace dapat menjadi alternatif dalam meningkatkan hasil kerajinan songket dan purun. Selama ini pemasaran yang dilakukan oleh pengrajin songket dan kerajinan anyaman purun masih dengan cara manual tanpa bantuan aplikasi daring. Tim memberdayaan penggunaan apliaksi daring yaitu media sosial WhatsApp Business dan marketplace Shopee bagi para perajin songket dan anyaman purun. Kegiatan pendampingan dilakukan dengan cara memberikan penyuluhan berupa paparan meteri dan pelatihan. Untuk mengukur keberhasilan kegiatan dilakukan pre-test sebelum kegiatan pendampingan dimulai da post-test setelah kegiatan dilaksanakan. Dari uji yang dilakukan menunjukkan adanya perubahan yang signifikan dari peserta pada saat sebelum diberikan pendampingan dan setelah dilakukan pendampingan. Dari hasil pre-test dan post-test menunjukan adanya peningkatan lebih dari 20% setelah dilakukannya kegiatan. Hal menyimpulkan bahawa para perajin telah mampu menggunakan aplikasi daring untuk memasarkan produk mereka secara lebih luas.
BLOOD VESSEL SEGMENTATION IN RETINAL IMAGES USING CONVOLUTIONAL NEURAL NETWORK VV-NET METHOD Sinta Bella Agustina; Erwin, Erwin; Desiani, Anita; Saputra, Tommy
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1723

Abstract

The retina is susceptible to various diseases that can be fatal if not treated quickly. Image processing is currently very helpful for doctors to detect retinal diseases faster so that retinal diseases can be treated immediately. The first step in image processing is to improve the quality of retinal images affected by noise, aiming to increase accuracy in the process of segmentation and image extraction. accurate segmentation of retinal blood vessels is the first step in disease detection. The process of segmentation and analysis of retinal blood vessels has an important role in assisting medical professionals in identifying the severity of a disease. Image quality improvement steps in preprocessing use grayscale, median filter (denoising), and clahe. The method used for blood vessel segmentation is CNN VV-Net. Evaluation of the results of applying image quality enhancement and segmentation techniques using the VV-Net method was performed on the DRIVE, STARE, and CHASEDB_1 datasets at both stages, training and testing. The measurement results of blood vessel segmentation using the CNN VV-net method on the DRIVE dataset (accuracy 96.27%, sensitivity 84.38%, precission 75.95%, and jaccard score 66.28%), STARE dataset (accuracy 96.58%, sensitivity 82.78%, precission 76.73%, and jaccard score 65.38%), and CHASEDB_1 dataset (accuracy 97.04%, sensitivity 83.55%, precission 76.72%, and jaccard score 66.40%). From the three datasets used, the CHASEDB_1 dataset obtained better results than the DRIVE and STARE datasets.
Penerapan Sistem Pakar Menggunakan Metode Certainty Factor untuk Diagnosis Penyakit pada Tanaman Jagung Fivalianda, Dido; Desiani, Anita
Square : Journal of Mathematics and Mathematics Education Vol. 6 No. 2 (2024)
Publisher : UIN Walisongo Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21580/square.2024.6.2.16042

Abstract

Diseases in corn plants are a major factor contributing to suboptimal corn production and can even lead to crop failure. Common diseases in corn include downy mildew and leaf blight. Downy mildew in corn is characterized by symptoms such as khorotil-colored streaks running parallel to the leaf veins, white spots, inhibited growth, and rolled leaves. These symptoms prevent optimal corn growth and may result in total crop failure. This research focuses on developing an expert system to diagnose corn diseases using the Certainty Factor (CF) method. The CF method is designed to provide a degree of certainty in diagnosis or analysis based on symptoms or evidence, especially in situations with limited absolute certainty. The system developed in this study achieves an accuracy rate of 68.74% for diagnosing leaf blight in corn.Keywords: expert system, CF, corn crop disease, downy mildew, leaf blight.
SISTEM PAKAR DIAGNOSIS PENYAKIT GINEKOLOGI MENGGUNAKAN METODE CERTAINTY FACTOR Azzahra, Nur Devita; Desiani, Anita
Jurnal Informatika dan Teknik Elektro Terapan Vol. 11 No. 3 (2023)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v11i3.3063

Abstract

Ginekologi adalah cabang ilmu kedokteran yang berfokus pada tubuh wanita dan kesehatan reproduksinya mulai dari masa pubertas hingga dewasa. Ginekologi merupakan masalah kewanitaan yang asing ditelinga orang awam. Keterbatasan pengetahuan dan informasi yang dimiliki oleh maryarakat tentang kesehatan ginekologi disebabkan karena barbagai kondisi yang ada, dengan faktor utama yang menjadi permasalahan tersebut adalah rasa enggan ataupun malas untuk berkonsultasi secara langsung dengan seorang pakar atau ahli  dikarenakan merasa malu untuk membahas mengenai kesehatan pribadi apalagi yang berhubungan dengan organ vital. Oleh karena itu, dibuatlah sebuah sistem pakar yang digunakan sebagai alternatif solusi layakya seorang pakar atau ahli dalam mendiagnosis pasien. Penggunaan metode certainty factor dalam membangun sistem pakar diagnosa kanker ginekologi ini diharapkan dapat memberikan informasi yang jelas dengan memunculkan presentase kemungkinan user mengalami masalah kanker ginekologi berdasarkan gejala yang dialami. Sistem pakar ini dibuat dengan tujuan untuk memberikan informasi yang jelas kepada user dengan menampilkan presentase keyakinan bedasarkan seorang pakar. Sistem pakar diagnosa kanker ginekologi dibuat berdasarkan 27 gejala dengan 5 jenis kanker ginekologi yang meliputi kanker serviks, kanker endometrium, kanker vulva, kanker tuba fallopi, dan kanker ovarium, dengan masing-masing tingkat akurasi diagnosa, yaitu 47,8291%, 35,9512%, 58,4773%,45,6657% dan 45,4034%. 
Pemberdayaan Masyarakat Melalui Bimbingan Teknis Pengembangan Budidaya Ikan Lokal Sumatera Selatan di Indralaya Raya, Kabupaten Ogan Ilir Yonarta, Danang; Muslim, Muslim; Desiani, Anita; Syaifudin, Mochamad; Taqwa, Ferdinand Hukama
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 7 No. 1 (2024): Januari 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v7i1.2735

Abstract

Indralaya Raya memiliki jumlah penduduk laki-laki sebanyak 7.079 jiwa dan perempuan 3.617 jiwa. Masyarakat Indralaya Raya sebagian besar berprofesi sebagai pedagang. Hal ini berdasarkan lokasi Indralaya Raya yang dekat dengan Pasar Indralaya yang hanya berjarak 3 km dengan waktu tempuh ± 5 menit. Indralaya Raya dialiri oleh Sungai Kelekar. Sungai Kelekar banyak dihuni oleh ikan-ikan lokal salah satunya ikan tambakan. Produksi ikan tambakan dari kegiatan budidaya masih sangat terbatas sehingga perlu dorongan agar masyarakat dapat membudidayakan ikan ini. Salah satu solusi yang dapat dilakukan adalah dengan melakukan pendampingan terhadap masyarakat mengenai teknis budidaya ikan tambakan mulai dari pemeliharaan induk hingga pemeliharaan larva hingga ukuran benih. Kegiatan pengabdian terhadap masyarakat dapat menjadi jembatan yang menghubungkan dengan masyarakat. Melalui kegiatan penyuluhan dan pendampingan mengenai produksi ikan tambakan masyarakat dapat mandiri secara ekonomi.
Perbandingan Klasifikasi Penyakit Kanker Paru-Paru menggunakan Support Vector Machine dan K-Nearest Neighbor Desiani, Anita; Indra Maiyanti, Sri; Andriani, Yuli; Suprihatin, Bambang; Amran, Ali; Marselina, Nyanyu Chika; Salsabila, Aulia
Jurnal PROCESSOR Vol 18 No 1 (2023): Jurnal Processor
Publisher : LPPM Universitas Dinamika Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33998/processor.2023.18.1.700

Abstract

Lung cancer is a condition where cells grow uncontrollably in the lungs due to carcinogens. Lung cancer is the first cause of death in men and women’s second cause of death. One way to reduce the death rate due to lung cancer is to carry out early detection, that is classification. The process of identifying and grouping objects with the same characteristics or characteristics into several predetermined classes is called classification. Several algorithms widely used in the classification process are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). SVM has advantages, being able to identify hyperplanes separately to maximize the margin between two or more different classes, but it is difficult to use in large data, while KNN can perform large-scale data separation and is resilient to noise in the data. This study aims to build a model using the SVM and KNN algorithms to classify lung cancer. The lung cancer dataset has a total of 309 data, where data is divided using the percentage split method and k-fold cross validation on each algorithm used. The parameters used in evaluating the model are accuracy, precision, and recall. From the research, the highest accuracy, precision, and recall values were obtained in the SVM algorithm with the percentage split method with consecutive values, namely 95.16%, 88%, and 82.5%. This indicates that the SVM algorithm with the percentage split method performs better in classifying lung cancer than other algorithms and methods,
Pembelajaran Ensemble Voting Tertimbang dari Arsitektur CNN untuk Klasifikasi Retinopati Diabetik Desiani, Anita; Primartha, Rifkie; Hanum, Herlina; Dewi, Siti Rusdiana Puspa; Suprihatin, Bambang; Al-Filambany, Muhammad Gibran; Suedarmin, Muhammad
JURNAL INFOTEL Vol 16 No 1 (2024): February 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i1.999

Abstract

Diabetic Retinopathy (DR) is a diabetes disease that attacks the retina of the eye and can be recognized through retinal images. The process of assisting retinal images can be done by applying deep learning-based methods, one of which is the Convolutional Neural Network (CNN). CNN has many architectures that can perform image classification processes, namely ResNet-50, MobileNet, and EfficientNet. Weaknesses of each architecture can be overcome through ensemble learning methods that can add up the performance results of each classification method. The study applies the ensemble learning method to improve the performance of the ResNet-50, MobileNet, and EfficientNet architectures in paying for DR disease on the retina by weighted voting. The data used are the APTOS and EyePACS datasets. The method in this research is data collection, training, testing, and evaluation of each architecture and ensemble learning. The results of the superior ensemble learning performance in the value of accuracy, F1-Score, and Cohens Kappa were obtained respectively 93.3%, 93.42%, and 0.866. The best specificity value was obtained by Resnet-50 at 99.78% and the highest sensitivity value was obtained by EfficientNet at 96.2%. Based on the classification results of each architectural and ensemble learning, it can be interpreted that the proposed ensemble learning method is excellent to perform image classification for Diabetic Retinopathy.
Perbandingan Algoritma Random Forest dan Extreme Gradient Boosting (XGBoost) dalam Klasifikasi Penyakit Gagal Jantung Anggraini, Jeni Putri; Chaya Gladys Zhafirah A; Desiani, Anita
Komputika : Jurnal Sistem Komputer Vol. 14 No. 2 (2025): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v14i2.16618

Abstract

Heart failure is a chronic condition where the heart is unable to pump blood optimally, posing a risk of serious complications and death. Early detection is crucial to reduce these risks and can be performed using classification methods with a data mining approach. This study compares two algorithms, Random Forest and Extreme Gradient Boosting (XGBoost), to determine the best algorithm for classifying heart failure disease using two testing techniques: percentage split (80% training data, 20% testing data) and k-fold cross validation (k = 10, alternating 1 fold as test data and 9 folds as training data). The dataset consists of two classes, where 0 represents heart failure and 1 represents no heart failure. Using percentage split, XGBoost achieved an accuracy of 87.07%, while Random Forest reached 91.95%. Meanwhile, in k-fold cross validation, XGBoost achieved 96.43% accuracy, and Random Forest performed best with 98.17% accuracy. Therefore, Random Forest with k-fold cross validation is highly suitable for heart failure classification, although XGBoost also shows good performance with accuracy above 85%. For future research, it is recommended to test the algorithms on more diverse datasets to evaluate their performance across various data conditions.
Pemanfaatan Limbah Kain Songket Desa Limbang sebagai Produk Bernilai Ekonomi Suprihatin, Bambang; Maiyanti, Sri Indra; Primartha, Rifkie; Amran, Ali; Desiani, Anita; Sari, Puspa
Aksiologiya: Jurnal Pengabdian Kepada Masyarakat Vol 9 No 4 (2025): November
Publisher : Universitas Muhammadiyah Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30651/aks.v9i4.26346

Abstract

Desa Limbang Jaya merupakan desa pengerajin kain songket di Sumatera Selatan. Mayoritas perempuan di desa ini bekerja sebagai pengerajin songket. Seorang pengerajin di Desa Limbang Jaya dapat menenun 3 hingga 4 helai kain berukuran 50-80 cm setiap bulannya. Setiap kain melalui proses pemotongan yang menyisakan berupa potongan kecil kain. Limbah songket biasanya dibuang atau dibakar, padahal limbah ini dapat diolah menjadi produk kerajinan yang memiliki nilai jual dengan menggunakan teknik menjahit seperti patchwork dan quilting. Kegiatan yang dilakukan berupa pelatihan dalam pengolahan limbah kain songket dengan menerapkan teknik patchwork dan quilting untuk menghasilkan produk dengan nilai jual. Kegiatan ini ditujukan pada penduduk perempuan khususnya pengrajin songket untuk meningkatkan keterampilan dalam mengolah limbah kain songket. Kegiatan pengolahan limbah sampah di desa Limbang Jaya belum pernah dilakukan. Tahapan kegiatan ini terdiri dari observasi, persiapan kegiatan, penyampaian materi, pelatihan, pendampingan, dan evaluasi. Kegiatan ini berhasil meningkatkan secara signifikan pemahaman dan keterampilan praktis peserta, yang diindikasikan oleh kenaikan nilai rata-rata post-test sebesar 38% dan dihasilkannya produk prototipe yang memiliki nilai jual. Hal ini mengindikasikan peningkatan pemahaman dan keterampilan peserta dalam pengolahan limbah kain songket dengan menerapkan teknik patchwork dan quilting yang menandakan keberhasilan dari kegiatan ini. Kegiatan ini dapat mendorong pembentukan usaha kreatif berbasis limbang songet yang dapat meningkatkan perekonomian masyarakat di Desa Limbang Jaya dalam jangka panjang.
Co-Authors Adi Muzakir Adinda Ayu Lestari Adzra Afiifah Nabila Affandi, Azhar Kholiq Agatha, Lucy Chania Agung Alamsyah Ajeng Islamia Putri Ajeng Islamia Putri Al-Filambany, Muhammad Gibran Alamsyah, Agung albar Pratama Alga Mahida Ali Amran Ali Amran All Fajri, Muhammad Arya Ally Muchlas Ambarwati Ananda Pratiwi Andhini, Shania Putri Andi Tenri Ajeng Nur 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 Arsyad. H, Muhammad Iqbal Arum Setiawan Aulia Salsabila Aulia, Annisa Rizka Ayuputri, Niken Azhar Kholiq Affandi Azhar Kholiq Affandi Azmi Muhammad Padhil Azzahra, Nur Devita Bambang Suprihatin 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 Diah Suci Ramadhani Dian Cahyawati Diana Dewi Sartika Dicky Naturatama 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 Gio Villando Giovillando Hadi Tanuji Hasibuan, MS Henisaniyya, Nabila Herlina Hanum Herlina Hanum, Herlina Hermansyah Hermansyah Hermansyah Hermansyah Hermansyah Ilham Tri Wibowo Indah Verdya Alvionita Indra Maiyanti, Sri Indri Ramayanti Ira Rayyani Irmeilyana Irmeilyana Irmeilyana Irvan Andrian Jessica Joseph Sen Jonatan Jonatan Kanda Januar Miraswan Karina Karnadi Kartila Kartila Kerenila Agustin Kurnia, M Kahfi Aldi Kurniawan, Rifki Kusmareni, Anisa Aulia Lizah Framesti Lonamonika Sinabutar Lubis, Andika Cristian Lucky Indra Kesuma Lucky Indra Kesuma Lucy Chania Agatha M Al-Ariq M Aldi Saputra Makhalli, Siddiq Malika Zilda Manoppo, Sania Marisa - 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 Muhammad Akbar Muhammad Akmal Shidqi Muhammad Arhami Muhammad Arhami Muhammad Arya All Fajri Muhammad Awaludin Djohar Muhammad Awaludin Djohar Muhammad Azwar Annas Muhammad Gibran Al-Filambany Muhammad Iqbal Arsyad. H Muhammad Naufal Rachmatullah Muhammad Nawawi Muhammad Nawawi Muhammad Syariful Irsyad Muhammad Umar Abdussalam Muhammad Wahyu Ilahi Muhammad Yusuf Prabudifa Muhammat Rio Halim Muslim Muslim Mustaqima, Dina Mutiara Saviera Muzakir, Adi Muzayyadah, Fathona Nur Nadya Riri Febiyanti Napitu, Michael Jackson Narti Narti Narti Narti, Narti 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 Pasma Azzahra Pasma Azzahra Permatasari, Mitta Pertiwi, Citra Prabudifa, Muhammad Yusuf Pranata, Teddi Pratiwi, Ananda Puspa Sari Puspa Sari Puspa Sari, Puspa Putra Bahtera Jaya Bangun, Putra Bahtera Jaya Putri Bella Nusantara Putri Pratiwi Putri, Ajeng Islamia Rahmadita, Suristhia Rahmat Dwian Ramadhan, Faishal Fitra Ramadhan, Raihan Ramadhani, Syafira Dian 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 Rosalinda Hizkia Amelia Manurung 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 Sinta Bella Agustina Siti Husnul Hotimah, Siti Husnul Siti Nurhaliza Siti Rusdiana Puspa Dewi Siti Rusdiana Puspa Dewi Sitorus, Dina Suzzete Slamet Prayogo Soya Febeauty Yama Otantia Pradini 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 Tiara Valentina Pakpahan Titania Jeanni Charisa Titania Jeanni Charissa Tri Febriani Putri tri wahyuni Tyara Hestyani Putri 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