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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
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Klasifikasi Citra Dalam Identifikasi Jeruk Nipis dan Jeruk Mandarin Menggunakan Convolutional Neural Network (CNN) Dan Optimalisasi Median Filter Erlanda, Hadrian; Saputra, Randy; Ramadhanu, Agung
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.546

Abstract

With the rapid advancement of technology in the field of image processing, farmers are increasingly supported in identifying types of citrus fruits. This study aims to differentiate between lime (Citrus aurantiifolia) and mandarin orange (Citrus reticulata) using image processing methods and morphological analysis. Image processing is employed to examine visual differences based on factors such as color, texture, and size of the fruit. Additionally, chemical analysis is conducted to distinguish the composition of compounds found in both types of citrus. The results of the study show that this approach is effective in identifying the differences between lime and mandarin orange with high accuracy, and can be applied in various industries, including agriculture and food processing
Optimalisasi Kentang Merah dan Kentang Biasa Secara Otomatis Menggunakan Median Filter dan Segmentasi Gambar Berbasis Warna dan Analisis Tekstur: Pendekatan K-Means Clustering Permata, Edo; Khomsi, Ahmad; Ramadhanu, Agung
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 6, No 1 (2025): Edisi Januari
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v6i1.551

Abstract

In this study, we propose an automated system to identify potato varieties (red and regular potatoes) using color-based image segmentation, median filter, and texture analysis. The system uses K-Means Clustering for color segmentation in Lab color space, followed by the application of median filter to reduce noise in the image, as well as texture feature extraction using Gray-Level Co-occurrence Matrix (GLCM) to distinguish potato types. Experimental results show that the proposed method achieves more than 90% accuracy in identifying potato varieties, demonstrating its potential for industrial applications in tuber processing. Our findings show that the system is robust under various lighting conditions and can significantly reduce human error in the potato sorting process.
PENERAPAN HYBRID INTELIGENTS SYSTEM UNTUK KLASIFIKASI BUAH ANGGUR DAN PEPAYA MENGGUNAKAN ALGORITMA PCA DAN KNN Afriadi, Afriadi; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2749

Abstract

Abstract: This study utilizes a hybrid intelligence system for classifying grape and papaya fruits, combining the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) algorithms. PCA is employed to reduce data dimensions by extracting key features from fruit images, while KNN performs classification based on the reduced feature set. The dataset consists of images of grapes and papayas captured under various lighting conditions. Experimental results indicate that this hybrid approach achieves 90% accuracy in fruit classification, improves computational efficiency, and enhances classification performance compared to using KNN without feature selection. This system demonstrates great potential for image-based fruit classification and can be implemented to support agricultural product processing technologies. Keywords: Hybrid Intelligence System; Grape and Papaya Classification; PCA; KNN.Abstrak: Penelitian ini menerapkan hybrid intelligence system untuk klasifikasi buah anggur dan pepaya menggunakan algoritma Principal Component Analysis (PCA) dan K-Nearest Neighbor (KNN). Algoritma PCA digunakan untuk mereduksi dimensi data dengan mengekstraksi fitur-fitur utama dari citra buah, sedangkan KNN digunakan untuk melakukan klasifikasi berdasarkan fitur yang telah direduksi. Dataset yang digunakan berupa gambar buah anggur dan pepaya dalam berbagai kondisi pencahayaan. Hasil eksperimen menunjukkan bahwa pendekatan hybrid ini mampu meningkatkan akurasi klasifikasi dan mengurangi waktu komputasi dibandingkan penggunaan KNN tanpa seleksi fitur. Sistem ini menunjukkan akurasi 90% dalam pengelompokan buah berbasis citra dan dapat diimplementasikan untuk mendukung teknologi pengolahan hasil pertanian. Kata kunci: Hybrid Intelligence system; Klasifikasi Buah; PCA; KNN. 
IMPLEMENTASI METODE ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DALAM KLASIFIKASI BUAH JAMBU MADU JAMBU MERAH DAN MANGGIS Al-Arrafi, Muhammad Ikhsan; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2584

Abstract

Abstract: Hybrid Intelligent Systems are systems that combine more than one artificial intelligence (AI) technique or computational approach to leverage their respective strengths and overcome their individual weaknesses. HIS are usually designed to handle complex tasks that are difficult to solve with a single approach. These systems combine techniques such as fuzzy logic, artificial neural networks, evolutionary algorithms, and rule-based methods, resulting in more flexible, adaptive, and intelligent solutions. The method used in the classification is the Principal Component Analysis (PCA) Algorithm, which is a statistical analysis method that aims to reduce data dimensions while maintaining significant information. PCA works by transforming the initial variables into a set of uncorrelated principal components. This technique is widely used in various fields such as image processing, pattern recognition, data compression, and exploratory data analysis. The PCA process involves decomposing the covariance or correlation matrix of the data to find the eigenvectors and eigenvalues that represent the principal components. By reducing dimensions, PCA helps overcome data redundancy problems, improves computational efficiency, and enables data visualization in lower dimensions. This study reviews the basic concept of PCA, its mathematical implementation, and its practical application in multidimensional data analysis. The K-Nearest Neighbor (KNN) algorithm is a machine learning method used for classification and regression with a simple principle, namely determining the class or value of a data based on its k nearest neighbors in the feature space. KNN works by calculating the distance between the test data and the training data using metrics such as Euclidean Distance, Manhattan Distance, or Minkowski Distance, then determining the prediction results based on the majority of classes or the average value of the nearest neighbors. Keywords: Hybrid Intelligent Systems, Principal Component Analysis (PCA), K-Nearest Neighbor (KNN). Abstrak: Hybrid Intelligent Systems merupakan sistem yang menggabungkan lebih dari satu teknik kecerdasan buatan (AI) atau pendekatan komputasi untuk memanfaatkan kekuatan masing-masing dan mengatasi kelemahan individu. HIS biasanya dirancang untuk menangani tugas-tugas yang kompleks, yang sulit diselesaikan dengan pendekatan tunggal. Sistem ini memadukan teknik-teknik seperti logika fuzzy, jaringan saraf tiruan, algoritma evolusioner, dan metode berbasis aturan, sehingga menghasilkan solusi yang lebih fleksibel, adaptif, dan cerdas. Adapun metode yang digunakan dalam klasifikasi yaitu Algoritma Principal Component Analysis (PCA) merupakan salah satu metode analisis statistik yang bertujuan untuk mereduksi dimensi data sambil mempertahankan informasi yang signifikan. PCA bekerja dengan mentransformasikan variabel awal menjadi sekumpulan komponen utama (principal components) yang tidak saling berkorelasi. Teknik ini banyak digunakan dalam berbagai bidang seperti pengolahan citra, pengenalan pola, kompresi data, dan analisis data eksploratif. Proses PCA melibatkan dekomposisi matriks kovarians atau korelasi data untuk menemukan vektor eigen dan nilai eigen yang merepresentasikan komponen utama. Dengan mereduksi dimensi, PCA membantu mengatasi masalah redundansi data, meningkatkan efisiensi komputasi, dan memungkinkan visualisasi data dalam dimensi yang lebih rendah. Studi ini mengulas konsep dasar PCA, implementasi matematisnya, serta aplikasi praktisnya dalam analisis data multidimensi. Algoritma K-Nearest Neighbor (KNN) adalah metode pembelajaran mesin yang digunakan untuk klasifikasi dan regresi dengan prinsip sederhana, yaitu menentukan kelas atau nilai suatu data berdasarkan k tetangga terdekatnya dalam ruang fitur. KNN bekerja dengan menghitung jarak antara data uji dengan data pelatihan menggunakan metrik seperti Euclidean Distance, Manhattan Distance, atau Minkowski Distance, kemudian menentukan hasil prediksi berdasarkan mayoritas kelas atau rata-rata nilai tetangga terdekat. Kata kunci: Hybrid Intelligent Systems, Principal Component Analysis (PCA), K-Nearest Neighbor (KNN).
Klasifikasi Buah Kelapa Muda, Kelapa Tua, dan Buah Naga Menggunakan Pendekatan Hybrid PCA-KNN Sutri, Ridwan; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2581

Abstract

 Abstract: This study discusses the classification method of young coconuts, old coconuts, and dragon fruits using a hybrid Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN) approach. This approach aims to improve the accuracy and efficiency of fruit classification based on visual and texture features. The research data were taken from fruit images processed using PCA for dimension reduction, followed by the KNN algorithm for classification. The test results showed that the combination of PCA and KNN was able to provide high accuracy, with an average accuracy value reaching 96%. Keyword: fruit classification, PCA, KNN, image processing.Abstrak: Penelitian ini membahas metode klasifikasi buah kelapa muda, kelapa tua, dan buah naga menggunakan pendekatan hybrid Principal Component Analysis (PCA) dan K-Nearest Neighbors (KNN). Pendekatan ini bertujuan untuk meningkatkan akurasi dan efisiensi dalam klasifikasi buah berdasarkan fitur visual dan tekstur. Data penelitian diambil dari citra buah yang diproses menggunakan PCA untuk reduksi dimensi, dilanjutkan dengan algoritma KNN untuk klasifikasi. Hasil pengujian menunjukkan bahwa kombinasi PCA dan KNN mampu memberikan akurasi tinggi, dengan nilai rata-rata akurasi mencapai 96%. Kata kunci: klasifikasi buah, PCA, KNN, pengolahan citra.
PENGGUNAAN PRINCIPAL COMPONENT ANALYSIS (PCA) DAN K-NEAREST NEIGHBORS (KNN) DALAM KLASIFIKASI SAYURAN MENTIMUN, PARE, DAN TERONG Syarif, Ahmad; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2751

Abstract

Abstract: The classification of vegetable types is an important aspect of the agricultural industry to improve the efficiency of product management, packaging, and distribution. This study aims to implement the Principal Component Analysis (PCA) and K-Nearest Neighbors (KNN) methods in the vegetable classification process, especially for cucumbers, squash, and eggplants. PCA is used to reduce the data dimensions and extract the key significant features that distinguish vegetable categories. Meanwhile, KNN is applied as a classification algorithm based on the proximity of these key features. The research dataset consists of digital images of vegetables extracted into color, texture, and shape attributes. The results show that the combination of PCA and KNN is able to significantly improve the classification accuracy by minimizing computational complexity. Experiments are carried out with various numbers of main components in PCA and variations in kkk parameter values in KNN to determine the optimal configuration. In the best configuration, this method achieves a classification accuracy of 90%, with PCA effectively reducing the data dimension by 95% without losing important information. In conclusion, this approach has great potential to be implemented in vegetable classification automation systems to support efficiency in the agricultural supply chain. Keywords: Vegetables classification, Principal Component Analysis (PCA), K-Nearest                Neighbors(KNN), cucumber, bitter melon, eggplant.Abstrak: Klasifikasi jenis sayuran merupakan salah satu aspek penting dalam industri pertanian untuk meningkatkan efisiensi pengelolaan produk, pengemasan, serta distribusi. Penelitian ini bertujuan untuk mengimplementasikan metode Principal Component Analysis (PCA) dan K-Nearest Neighbors (KNN) dalam proses klasifikasi sayuran, khususnya mentimun, pare, dan terong. PCA digunakan untuk mengurangi dimensi data dan mengekstraksi fitur utama yang signifikan dalam membedakan kategori sayuran. Sementara itu, KNN diterapkan sebagai algoritma klasifikasi berdasarkan kedekatan fitur-fitur utama tersebut. Dataset penelitian terdiri atas citra digital sayuran yang diekstraksi menjadi atribut warna, tekstur, dan bentuk. Hasil penelitian menunjukkan bahwa kombinasi PCA dan KNN mampu meningkatkan akurasi klasifikasi secara signifikan dengan meminimalkan kompleksitas komputasi. Eksperimen dilakukan dengan berbagai jumlah komponen utama pada PCA dan variasi nilai parameter kkk pada KNN untuk menentukan konfigurasi optimal. Pada konfigurasi terbaik, metode ini mencapai akurasi klasifikasi sebesar 90%, dengan PCA secara efektif mereduksi dimensi data hingga 95% tanpa kehilangan informasi penting. Kesimpulannya, pendekatan ini memiliki potensi yang besar untuk diimplementasikan dalam sistem otomatisasi klasifikasi sayuran guna mendukung efisiensi dalam rantai pasok agrikultur. Kata kunci: Klasifikasi sayuran, Principal Component Analysis (PCA), K-Nearest Neighbors (KNN),  mentimun, pare, terong.
IMPLEMENTASI MECHINE LEARNING PADA HYBRID INTELLIGENCE SISTEM MENGUNAKAN METODE PCA-KNN PADA JENIS BUAH APEL, JERUK, TOMAT Angga, Angga; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2583

Abstract

Abstract: This research aims to implement the Principal Component Analysis (PCA) and K-Nearest Neighbor (KNN) methods in a digital image-based classification system for apples, oranges and tomatoes. PCA is used to reduce data dimensions to increase computational efficiency without losing important information, while KNN is applied for the classification process of extracted data. This research includes several stages, starting from image data collection, preprocessing, segmentation, feature extraction, to accuracy testing. The research results show that the combination of PCA and KNN methods is able to provide a high level of accuracy, with an average accuracy of 90%. In detail, the classification of apples achieved 100% accuracy, oranges 90%, and tomatoes 100%. PCA successfully eliminates redundant features, thereby increasing the efficiency of the classification process, while KNN shows reliability in handling reduced data. Keywords: Principal Component Analysis, K-Nearest Neighbor, classification, image processing, machine learning. Abstrak: Penelitian ini bertujuan untuk mengimplementasikan metode Principal Component Analysis (PCA) dan K-Nearest Neighbor (KNN) dalam sistem klasifikasi buah apel, jeruk, dan tomat berbasis citra digital. PCA digunakan untuk mereduksi dimensi data guna meningkatkan efisiensi komputasi tanpa kehilangan informasi penting, sementara KNN diterapkan untuk proses klasifikasi data hasil ekstraksi. Penelitian ini mencakup beberapa tahapan, mulai dari pengumpulan data citra, preprocessing, segmentasi, ekstraksi fitur, hingga pengujian akurasi. Hasil penelitian menunjukkan bahwa kombinasi metode PCA dan KNN mampu memberikan tingkat akurasi yang tinggi, dengan rata-rata akurasi sebesar 90%. Secara rinci, klasifikasi apel mencapai akurasi 100%, jeruk 90%, dan tomat 100%. PCA berhasil mengeliminasi fitur redundan, sehingga meningkatkan efisiensi proses klasifikasi, sedangkan KNN menunjukkan keandalan dalam menangani data yang telah direduksi. Kata Kunci: Principal Component Analysis, K-Nearest Neighbor, klasifikasi, pengolahan citra, machine learning.
PENINGKATAN AKURASI KLASIFIKASI KEMATANGAN PEPAYA BERDASARKAN WARNA DENGAN MEDIAN FILTER, K-MEANS PADA CONVOLUTIONAL NEURAL NETWORK Yosfand, Windra; Putra, Kharisma Utama; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 1 (2025): February 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i1.2781

Abstract

Abstract: The development of technology in the field of digital image processing has become an attraction in itself to make human life easier and has given rise to many applications that can apply it in various fields. Digital image processing methods can transform input images into output images that can be used to identify and classify objects in life. To minimize damage to digital images which is known as noise. Also to reduce the impact of degradation or decrease in image quality caused by noise, colors that are too contrasty or blurry. So a method is needed. One method is the median filter which is used in this research.Keyword: edian Filter, Convolutional Neural Network, Papaya, Classification Abstrak: Dalam perkembangan teknologi di bidang pengolahan citra digital (digital image processing) menjadi daya tarik tersendiri untuk mempermudah kehidupan manusia dan memunculkan banyak aplikasi yang dapat menerapkannya dalam berbagai bidang. Metode digital image processing dapat mentransformasikan citra masukan menjadi citra keluaran yang dapat dimanfaatkan untuk mengidentifikasi dan mengklasifikasi objek dalam kehidupan. Untuk mengurangi meminimalisir kerusakan pada citra digital yang disebut sebagai noise. Juga untuk mengurangi dampak degradasi atau penurunan kualitas citra yang disebabkan oleh derau / noise, warna yang terlalu kontras atau buram. Maka dibutuhkan suatu metode. Salah satu metodenya adalah median filter yang digunakan pada penelitian ini.Kata kunci: Median Filter, Convolutional Neural Network, Pepaya, Klasifikasi
IMPLEMENTASI METODE K-MEANS UNTUK KLASTERISASI VARIETAS PARPIKA DENGAN MENGGUNAKAN TEKNIK PENGOLAHAN CITRA DIGITAL Ilmawan, Fachrul; Ramadhanu, Agung
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 1 (2025): EDISI 23
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i1.5426

Abstract

Dengan menggunakan kombinasi segmentasi objek, ekstraksi bentuk, dan ekstraksi tekstur, penelitian ini bertujuan untuk melakukan klasterisasi pada varietas paprika melalui penggunaan K-Means Clustering dan Gray-Level Co-Occurrence Matrix (GLCM).  Segmentasi objek dilakukan menggunakan algoritma K-Means Clustering untuk membedakan objek dari latar belakangnya. Selanjutnya, proses ekstraksi tekstur dan bentuk dilakukan menggunakan Matriks Co-Occurrence Level Gray (GLCM) untuk membedakan jenis varietas paprika. Hasil kalsterisasi dicapai melalui penggunaan aplikasi matlab, yang mencakup import data, konversi RBG ke L*a*b, segmentasi objek dengan latar belakang menggunakan K-Means Clustering, dan kemudian menggunakan metode Gray-Level Co-Occurrence Matrix (GLCM) untuk ekstraksi bentuk dan tekstur. Hasil penelitian tentang klasterisasi varietas paprika juga menunjukkan bahwa proses itu berhasil. Model berhasil mengidentifikasi setiap sampel gambar dengan akurat seratus persen dengan menggunakan sampel delapan gambar paprika merah dan hijau. Penggunaan algoritma clusteriang K-means dan Matriks Co-Occurrence Level Gray (GLCM) menunjukkan hasil yang sangat baik; ini membuktikan efektivitasnya dalam melakukan klasterisasi pada varietas paprika.
OPTIMASI HYBRID INTELLIGENT SYSTEM UNTUK IDENTIFIKASI BUAH: STUDI KASUS PISANG DAN APEL Yanti, Rahma; Agung Ramadhanu
INTI Nusa Mandiri Vol. 19 No. 2 (2025): INTI Periode Februari 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v19i2.6382

Abstract

Image processing-based fruit classification is one of the rapidly developing technology applications in the field of digital agriculture. This study aims to develop a fruit identification system, especially yellow bananas, green bananas, and apples, by utilizing the K-Nearest Neighbors (KNN) and Principal Component Analysis (PCA) methods. The background of this study is the need for an accurate automatic system to distinguish fruit types based on visual characteristics, such as color, texture, and shape, to support the distribution and management of agricultural products. The method used in this study involves four main stages: image loading, segmentation, feature extraction, and classification. PCA is used to reduce data dimensions by maintaining relevant main features, while KNN functions for classification based on the closest distance between test data and training data. The dataset used consists of 130 images, with 120 images as training data and 10 images as test data. The results of the study show that the developed system is able to classify all test data with 90% accuracy. This success proves that the combination of PCA and KNN methods is effective in identifying fruit types based on extracted visual characteristics. This system is expected to be the basis for further development in the field of automatic fruit classification.
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