Article Per Year (5 Year)
p-Index From 2021 - 2026
13.956
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Techno.Com: Jurnal Teknologi Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri JURNAL PENDIDIKAN TAMBUSAI Jurnal Ilmiah Media Sisfo JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOISIE (Journal Of Information Systems And Informatics Engineering) INTI Nusa Mandiri Jurnal Ekonomi Manajemen Sistem Informasi Jurnal Teknologi Dan Sistem Informasi Bisnis JATI (Jurnal Mahasiswa Teknik Informatika) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Pendidikan Guru (JPG) Journal of Applied Data Sciences Bulletin of Computer Science Research JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Ipteks Terapan : research of applied science and education Journal of Education Research Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Ekonomika Dan Bisnis Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Sains dan Teknologi Jurnal Komtekinfo Indonesian Journal Computer Science (ijcs) Intellect : Indonesian Journal of Learning and Technological Innovation SATIN - Sains dan Teknologi Informasi Jurnal Quancom: Jurnal Quantum Komputer Journal of Information System and Education Development Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) The Indonesian Journal of Computer Science CSRID
Ramadhanu, Agung -
Universitas Putra Indonesia “YPTK” Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 132 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 132 Documents
Search

 10   11   12   13   14 
KLASIFIKASI OBJEK PANDA, SINGA, DAN HANDYCAM DENGAN PENGOLAHAN CITRA MENGGUNAKAN K-MEANS PADA MATLAB Yusuf, Muhammad; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 4 (2025): November 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i4.4859

Abstract

Abstract: Digital image processing plays an important role in object classification, both for research and practical applications. This study discusses the implementation of the K-Means Clustering method for identifying and classifying images of pandas, lions, and handycam devices using MATLAB software. The research stages include image acquisition, preprocessing such as image conversion and normalization, feature extraction based on color and texture, and classification using the K-Means algorithm. Experimental results show that the K-Means method is capable of grouping objects into the appropriate classes based on image feature similarity. The systems accuracy is influenced by input image quality, cluster parameters, and the amount of training data used. Therefore, this study demonstrates that K-Means can be applied as a simple yet effective method for object image classification, particularly in distinguishing between animal types and non-living objects such as handycams.Keywords: K-Means, Image Processing, MATLAB, Object Classification, Panda, Lion, HandycamAbstrak: Pengolahan citra digital memiliki peran penting dalam bidang klasifikasi objek, baik untuk penelitian maupun implementasi praktis. Penelitian ini membahas penerapan metode K-Means Clustering dalam proses identifikasi dan klasifikasi citra panda, singa, serta perangkat handycam menggunakan perangkat lunak MATLAB. Tahapan penelitian meliputi akuisisi citra, pra-pemrosesan berupa konversi citra dan normalisasi, ekstraksi fitur warna dan tekstur, serta proses klasifikasi dengan algoritma K-Means. Hasil percobaan menunjukkan bahwa metode K-Means mampu mengelompokkan objek ke dalam kelas yang sesuai berdasarkan kemiripan fitur citra. Tingkat akurasi sistem dipengaruhi oleh kualitas citra masukan, parameter klaster, dan jumlah data latih yang digunakan. Dengan demikian, penelitian ini membuktikan bahwa K-Means dapat dijadikan metode sederhana namun efektif dalam klasifikasi citra objek, khususnya untuk membedakan jenis binatang dan perangkat non-hayati seperti handycam.Kata kunci: K-Means, Pengolahan Citra, MATLAB, Klasifikasi Objek, Panda, Singa, Handycam
Implementasi Image Processing dengan Metode K-Means Clustering untuk Identifikasi Buah Berry: Blackberry, Gojiberry, dan Mulberry Agsera, Nilam; Ramadhanu, Agung
Jurnal Sains dan Teknologi (JSIT) Vol. 5 No. 3 (2025): September-Desember
Publisher : CV. Information Technology Training Center - Indonesia (ITTC)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jsit.v5i3.3625

Abstract

Berries are known as "super fruits" because they are rich in nutrients and antioxidants. Despite their abundant health benefits and significant economic potential in Indonesia, classifying berries like blackberries, goji berries, and mulberries is often challenging due to their visual similarities. This challenge hinders the sorting process in the agricultural and trade industries. This research proposes an automated classification model based on image processing to identify these three types of berries this study aims to develop and test effectiveness of an automatic classification model using image processing to accurately differentiate between blackberry, goji berry, and mulberry, in order to address the difficulties of manual sorting in the industry. The study implements K-Means Clustering as the primary technique for image segmentation, aiming to accurately separate the fruit from its background. The workflow begins with collecting 30 images of each berry type, followed by a color space transformation from RGB to Lab to separate color and brightness components. After segmentation, shape and texture feature extraction is performed to obtain the unique characteristics of each fruit. The analysis results show that feature extraction successfully captured significant differences between the three fruits. Blackberries tend to be rounder (metric: 0.56934; eccentricity: 0.56594), whereas goji berries (metric: 0.15132; eccentricity: 0.92832) and mulberries (metric: 0.097072; eccentricity: 0.87125) are oblong. Texture analysis also shows that mulberries have the smoothest surface. These quantitative differences are key to distinguishing the three fruits. Overall, this method provides an effective and accurate identification solution that can be implemented in automated fruit sorting systems to improve the production quality and economic value of berries in Indonesia.
Penerapan Image Processing untuk Identifikasi RAM, SSD, dan Webcam Menggunakan Metode K-Means Clustering Hikmi, Zakiya; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 8 No 1 (2026): Januari 2026
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v8i1.2266

Abstract

The development of computer hardware requires appropriate automatic identification methods to assist in inventory, maintenance, and learning processes. Manual identification methods for hardware such as RAM, SSD, and webcams are often ineffective due to the difficulty of distinguishing their visual forms, especially for those who are unfamiliar with them. This study aims to apply image processing techniques using the K-Means clustering method to identify these three types of devices. The system was created using MATLAB with a graphical user interface (GUI) for ease of use. The process begins by capturing images in RGB format, which are then converted to Lab* color space. Segmentation is performed using the K-Means clustering method, which divides objects from the background into two clusters. The segmentation results are then refined using morphological operations. Next, shape features and texture features are extracted using Gray Level Co-occurrence Matrix (GLCM), which includes contrast, correlation, energy, and homogeneity. The features obtained are compared with the database using Euclidean distance to determine the type of hardware. The test results show that the system is able to accurately distinguish between RAM, SSD, and webcams. In conclusion, the use of K-Means clustering, GLCM, and distance-based classification can be an effective solution in identifying computer hardware through images.
Implementasi Metode K-Means Clustering untuk Mengklasterikasikan Perangkat Elektronik dengan Teknik Pengolahan Citra Firmansyah, Ryan; Ramadhanu, Agung
Jurnal Penelitian Dan Pengkajian Ilmiah Eksakta Vol 5 No 1 (2026): Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE
Publisher : LPPM Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jppie.v5i1.2270

Abstract

Grouping electronic devices such as computers, laptops, and smartphones will be very useful in situations where there are a large number of devices to manage, for example in companies, schools, or service centers. This study uses the k-means clustering method with image processing techniques through the Matlab application. The test data used was taken from the internet, consisting of 30 samples comprising 10 computers, 10 laptops, and 10 smartphones. In accordance with the existing dataset, clustering will be performed on three types of electronic devices, namely computers, laptops, and smartphones. After conducting various tests and model designs, the overall accuracy of the model is 100%. This research can cluster 30 samples consisting of 10 computer images, 10 laptop images, and 10 smartphone images. All samples used were taken from the internet.
ANALISIS SENTIMEN MASYARAKAT MENGGUNAKAN ALGORITMA NAÏVE BAYES DAN SUPPORT VECTOR MACHINE TERHADAP PROGRAM BPJS Saputra, Charisman Fajri; Sovia, Rini; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 9, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v9i1.5660

Abstract

Abstract: BPJS Kesehatan is a national health insurance program that plays a vital role in providing public health services in Indonesia; however, its implementation has generated diverse public perceptions reflected on social media. This study analyzes public sentiment toward the BPJS Kesehatan program based on Instagram comments using a text mining and machine learning approach. The research methodology includes Indonesian text preprocessing, feature weighting using Term Frequency–Inverse Document Frequency (TF–IDF), and three-class sentiment classification (positive, negative, and neutral) using Multinomial Naïve Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,461 Instagram comments, which are divided into training and testing data with an 80:20 ratio. The experimental results show that Multinomial Naïve Bayes achieves an accuracy of 80.55%, while SVM yields a higher accuracy of 86.35%. These results indicate that SVM performs better in separating sentiment classes within short and imbalanced Instagram comment data. This study contributes to Indonesian-language sentiment analysis research and provides insights for evaluating public health services through social media data. Keyword: sentiment analysis; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine. Abstrak: BPJS Kesehatan merupakan program strategis nasional yang berperan penting dalam menjamin akses layanan kesehatan bagi masyarakat Indonesia, namun implementasinya masih memunculkan beragam persepsi publik yang tercermin pada media sosial. Penelitian ini mengkaji analisis sentimen masyarakat terhadap program BPJS Kesehatan berdasarkan komentar pada platform Instagram menggunakan pendekatan text mining dan pembelajaran mesin. Metode penelitian meliputi pra-pemrosesan teks berbahasa Indonesia, pembobotan fitur menggunakan Term Frequency–Inverse Document Frequency (TF–IDF), serta klasifikasi sentimen tiga kelas (positif, negatif, dan netral) menggunakan algoritma Multinomial Naïve Bayes dan Support Vector Machine (SVM). Dataset yang digunakan terdiri dari 1.461 komentar Instagram yang dibagi menjadi data latih dan data uji dengan rasio 80:20. Hasil pengujian menunjukkan bahwa Multinomial Naïve Bayes menghasilkan akurasi sebesar 80,55%, sedangkan SVM mencapai akurasi yang lebih tinggi yaitu 86,35%. Temuan ini menunjukkan bahwa SVM memiliki kemampuan yang lebih baik dalam memisahkan kelas sentimen pada data komentar Instagram yang bersifat pendek dan tidak seimbang. Penelitian ini diharapkan dapat memberikan kontribusi dalam pengembangan analisis sentimen berbahasa Indonesia serta menjadi masukan awal bagi evaluasi layanan publik berbasis media sosial. Kata kunci: analisis sentimen; BPJS Kesehatan; Instagram; Naïve Bayes; Support Vector Machine.
Pengenalan Sayuran Slada Hidroponik dan Non Hidroponik Berdasarkan Bentuk dan Tekstur Menggunakan Metode KNN M.Iqbal, M.Iqbal; Utari, Utari Armila; Agung, Agung Ramadhanu
The Indonesian Journal of Computer Science Vol. 12 No. 5 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i5.3371

Abstract

Selada merupakan sayuran yang banyak tumbuh di daerah yang beriklim sedang maupun tropis. Untuk pemenuhan kebutuhan, para petani banyak membudidayakan sayuran selada hidroponik dengan keunggulan lebih higenis, tahan lama dan bebas dari zat kimia yang sesuai dengan tren gaya hidup sehat masyarakat. Terdapat perbedaan antara selada hidroponik dan non hidroponik diantaranya dari segi warna, bentuk, tekstur dan ukuran. Penelitian ini melakukan pengenalan sayuran selada hidroponik dan non hidroponik berdasarkan bentuk dan tekstur menggunakan metode KNN dengan bantuan aplikasi matlab untuk pengenalan citra berdasarkan image processing ekstraksi ciri bentuk dan tekstur dengan parameter matric, eccentricity, contrast, energy, homogeneity dan perhitungan KNN dengan nilai K = 3, diperoleh hasil akurasi kebenaran lebih dari 80% dan hasil identifikasi citra sesuai.
Implementasi Pengolahan Citra Digital dalam Pengenalan Wajah Menggunakan Contrast Stretching dan Algoritma Viola Jones M.Iqbal, M.Iqbal; Imrah, Imrah Sari; Agung, Agung Ramadhanu
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3660

Abstract

Wajah manusia merupakan salah satu bagian penting pada tubuh yang mempunyai ciri khusus yang dapat membedakan seseorang. Perbedaan ciri dari wajah seseorang dapat diidentifikasi dengan sistem pengenalan wajah (face Recognition). Kemajuan teknologi dalam pengenalan wajah memberikan dampak dalam berbagai sektor seperti keamanan, keuangan, kesehatan dan hiburan. Penelitian ini melakukan Pengenalan wajah dengan mengimplentasikan pengolahan citra digital menggunakan contrast stretching dan algoritma viola jones untuk mendapatkan nilai akurasi yang baik dalam pengenalan wajah dengan bantuan aplikasi Matlab. Dari hasil penelitian dalam pengenalan wajah diperoleh hasil akurasi yang akurat yaitu mencapai 91,89% dan hasil identifikasi pengenalan wajah pada citra sesuai.
Identifikasi Pengolahan Citra Pada Face Detection Menggunakan Metode Median Filtering dan Viola-Jones Sandiva, Tesa Vausia; Yemi, Leonardo; Ramadhanu, Agung
The Indonesian Journal of Computer Science Vol. 13 No. 2 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i2.3675

Abstract

Penelitian ini bertujuan untuk mengidentifikasi pengolahan citra pada sistem deteksi wajah (Face Detection) dengan memanfaatkan metode Median Filtering dan Viola-Jones. Metode Median Filtering digunakan dalam tahap preprocessing untuk mengurangi noise dan meningkatkan kualitas citra, khususnya dalam mengatasi noise seperti salt & pepper. Selanjutnya, metode Viola-Jones diterapkan sebagai metode utama untuk mendeteksi wajah, memanfaatkan Haar Like Feature, Integral Image, Adaboost Learning, dan Cascade Classifier. Penelitian ini mencapai tingkat akurasi keberhasilan deteksi wajah sebesar 90%, menunjukkan efektivitas kombinasi kedua metode dalam meningkatkan performa sistem. Hasil penelitian ini diharapkan dapat memberikan kontribusi positif terhadap perkembangan teknologi pengolahan citra, khususnya dalam aplikasi pengenalan wajah dengan tingkat akurasi yang tinggi.
Klasifikasi Aksesori Fashion Berdasarkan Fitur Citra Menggunakan K-Means Clustering Tomi, Zebbil Billian; Ramadhanu, Agung
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 4 No. 02 (2025): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57255/intellect.v4i02.1476

Abstract

The rapid development of computer vision and machine learning has enabled new applications in the fashion industry, particularly in image-based product classification and recommendation systems. This study aims to classify fashion accessories, namely wallets, bags, and belts, based on image features using the K-Means clustering algorithm. The dataset consists of 30 images acquired under controlled conditions with uniform lighting, resolution, and background. Although the dataset size is relatively limited, this study is designed as an initial baseline to evaluate the effectiveness of K-Means clustering on small and homogeneous datasets, which are commonly encountered in early-stage image classification research. The research workflow includes image preprocessing (resizing, color space conversion, and noise reduction), object segmentation, and feature extraction focusing on color, texture, and shape characteristics. The extracted features include Local Binary Pattern (LBP), entropy, edge density, eccentricity, extent, and area ratio. The results demonstrate that K-Means clustering is capable of grouping fashion accessories into distinct categories according to their visual characteristics. From a practical perspective, the proposed approach can be applied to automated fashion product cataloging to support inventory management, image-based product search, and recommendation systems in e-commerce platforms. This study provides a simple and interpretable baseline for fashion accessory classification and serves as a foundation for future work involving larger datasets, advanced feature descriptors, or deep learning-based methods. Abstrak Perkembangan computer vision dan machine learning memungkinkan penerapan baru dalam industri fesyen, khususnya pada sistem klasifikasi dan rekomendasi produk berbasis citra. Penelitian ini bertujuan mengklasifikasikan aksesori fesyen berupa dompet, tas, dan ikat pinggang berdasarkan fitur citra menggunakan algoritme K-Means clustering. Dataset yang digunakan terdiri dari 30 citra yang dikumpulkan dalam kondisi terkontrol dengan pencahayaan, resolusi, dan latar belakang seragam. Meskipun jumlah dataset relatif terbatas, pendekatan ini dirancang sebagai studi awal (baseline) untuk mengevaluasi efektivitas K-Means pada dataset kecil dan homogen yang umum dijumpai pada tahap awal pengembangan sistem klasifikasi berbasis citra. Tahapan penelitian meliputi preprocessing (penyeragaman ukuran, konversi warna, dan reduksi noise), segmentasi objek, serta ekstraksi fitur warna, tekstur, dan bentuk. Fitur yang digunakan meliputi Local Binary Pattern (LBP), entropi, kerapatan tepi, eksentrisitas, extent, dan rasio area. Hasil penelitian menunjukkan bahwa algoritme K-Means mampu mengelompokkan aksesori fesyen ke dalam kategori yang berbeda berdasarkan karakteristik visualnya. Secara praktis, hasil penelitian ini berpotensi diterapkan sebagai sistem klasifikasi otomatis pada katalog produk fesyen digital untuk mendukung manajemen inventori, pencarian produk berbasis citra, serta sistem rekomendasi pada platform e-commerce. Penelitian ini diharapkan dapat menjadi baseline sederhana dan interpretatif dalam klasifikasi aksesori fesyen, serta menjadi pijakan untuk pengembangan lanjutan menggunakan dataset yang lebih besar, deskriptor fitur modern, maupun metode berbasis deep learning.
Development of New Identification Formula to Extract Organic Fertilizer Content Based on Organic Fertilizer Image Agung Ramadhanu; Mardison Mardison; Halifia Hendri; Febri Hadi; Larissa Navia Rani; Yuhandri Yuhandri
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1300

Abstract

Traditional laboratory techniques for examining the nutrient content of organic fertilizers, specifically nitrogen (N), phosphorus (P), and potassium (K), are expensive, time-intensive, and pose environmental hazards. To address these issues, this paper presents a novel, non-destructive, image-based classification algorithm to identify fertilizer nutrient content. The proposed technique integrates color space conversion, unsupervised clustering, texture extraction, and an adapted New Identification Weighting (NIW) method. The NIW is derived from prior probability-based distance measurements and optimized with a balancing weighting factor to improve analytical stability across heterogeneous agricultural images. First, RGB images of fertilizers are converted into the perceptually uniform CIE L*a*b color space, which enhances color distinction under varying lighting conditions. Next, the images are segmented using K-Means clustering, followed by Gray-Level Co-occurrence Matrix (GLCM) extraction to capture textural and structural features. A key innovation of this research is the NIW method, functioning as an adaptive feature prioritization tool that assesses each features contribution to nutrient classification, effectively overcoming the limitations of previous a priori approaches. The system was tested on a dataset of 500 organic fertilizer images, achieving an overall classification accuracy of 97%, demonstrating its effectiveness and robustness. This approach offers a highly accurate and interpretable alternative to conventional chemical testing, making it a feasible, scalable, and affordable field tool for smart farming. By enabling on-site nutrient analysis, it strongly supports sustainable agricultural practices. Future work will focus on enhancing the systems flexibility to varying environmental conditions and integrating this approach into mobile-based diagnostic devices to facilitate real-time decision-making in agriculture.
Co-Authors ., Ulfa Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan Andry Novrianto Angga Angga Anggara Putra, Febri Antoni Antoni Arsyah Arsyah atiqah, sri Avezrima Rahmamuthi Bayuputra, Ramdani Berta Agus Petra Betriana Roza, Yesi Betriana, Yesi Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Delvi, Syerlin Aprilia Desi Permata Sari Desi Permata Sari Desi Permata Sari Desi Permata Sari Devi Maryuni Devita, Retno Dhia Fadhila Agusty Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Dodi Guswandi Enggari, Sofika Erlanda, Hadrian Fadila Cahyani Putri Fajri Saputra, Charisman Fajrul Islami Febri Hadi Fiki Pratama Firmansyah, Ryan Firna Yenila Fitri Yeni Gafari, Abuzar Gunadi Widi Nurcahyo Hadi Syahputra Hadi Syahputra Hadi Syahputra Putra Halifia Hendri Hanna Pratiwi Harnaranda, Jefri Hasmaynelis Fitri Hendri, Hallifia Hidayati, Dzil Hidayattullah, Hafis Hikmi, Zakiya Honestya, Gabriela Husna Arsyah, Rahmatul Ilmawan, Fachrul Imrah, Imrah Sari Irfan Rizki Nur Irsyad, As'Ary Sahlul Jehan Harka Johan Harlan Jufriadif Na`am, Jufriadif Kareem, Shahab Wahhab Karseno, Doni Khomsi, Ahmad Larissa Navia Rani M.Iqbal, M.Iqbal Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Mardison Mardison Mardison Marfalino, Hari Masri, Taufik Mokti Isra Mokti Isra Muhammad Idris Muhammad Raihan Zaky Muhammad Raihan Zaky Muhammad Yusuf Nabila Frisca Oktavia Nadia, Nadia Aini Hafizhah Nasution, Amir Salim Khairul Rijal Nasution, Annio Indah Lestari Negoro, Wahyu Saptha Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsih Neni Sri Wahyuni Nengsih Neni Sri Wayuni Ningsih Neni Sri Wayuni Ningsih Ningsih, Neni Sri Wayuni Nurdiansyah, Ali Nurhaliza Nurhaliza Nurjannah, Farah Permata, Edo Pertiwi, Yuliana Pratama, Dede Putra, Kharisma Utama Putra, Ramdani Bayu putri, kamila amaliah Rahmad Rahmad Rahmad, R Raja Ayu Mahessya Rani, Larissa Navia Repelita Witri Rheza Thresya Rianti, Eva Riati, Itin Rindy Citra Dewi Riyan Saputra, Riyan Rizky Gusrianto Rosa, Imelda Rosda Syelly Sajida, Mayang salim, alfajri Saputra, Charisman Fajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini, Silfia Sisi Hendriani Sofika Enggari Sofika Enggari Sofika Enggari Sovia, Rini Suci Wahyuni Sularno Sularno Sumijan, S Sutri, Ridwan Syafri Arlis Syafrika Deni Rizki Syafrika Deni Rizki, Syafrika Deni Syafril Syafril Syafril, S Syalsabilla, Adinda Teri Ade Putra Tesa Vausia Sandiva Tomi, Zebbil Billian Utama Putra, Kharisma Utari, Utari Armila Vidyanti, Angela Citra Wiratama, Aditya Wirdawati, Wira Witri, Repelita Yagus Valentino Harefa Yanti, Rahma Yasmin, Nabila Yasmin, Nabilla Yemi, Leonardo Yesi Betriana Roza, yesibetriana_18 Yogi Wiyandra Yolanda Yolanda, Yolanda Yosfand, Windra Yuhandri Yuhandri Yuhandri Yulihartati, Sandra Zubaidah, Rima Puti