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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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KLASTERISASI KESEHATAN DAUN MENGGUNAKAN K-MEANS CLUSTERING DENGAN TEKNIK PENGOLAHAN CITRA Pratama, Dede; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3087

Abstract

Abstract: Leaves are an important part of plants that greatly influence the health and quality of the plant. Poor leaf conditions, such as yellow or degraded leaves, can indicate problems in plant growth or health. Manual classification of fresh and yellow leaves can be time-consuming and often inconsistent, necessitating a more efficient automated method. The main goal of this research is to implement an image processing-based automatic classification system to classify fresh and yellow leaves based on visual features such as color, texture, and shape, to improve efficiency and consistency in plant health monitoring. To distinguish brightness and color, images are processed by converting the RGB color space to LAB. Using the K-Means Clustering algorithm, images are grouped into two clusters, each consisting of fresh leaves and yellow leaves. The data used in this research consists of eight images, each comprising four images of fresh leaves and four images of yellow leaves. The research results show that this method successfully classified fresh and yellow leaves with an accuracy rate of 100%, with 8 out of 8 images correctly identified. The K-Means Clustering method has been demonstrated as an effective and accurate method for determining leaf health conditions. Keywords: Fresh Leaves, Yellow Leaves, K-Means Clustering, Image Processing,                 Feature Extraction Abstrak: Daun merupakan bagian penting dari tumbuhan yang sangat mempengaruhi kesehatan dan kualitas tanaman. Kondisi daun yang buruk, seperti daun kuning atau daun yang terdegradasi, dapat menunjukkan adanya masalah dalam pertumbuhan atau kesehatan tanaman. Klasifikasi daun segar dan daun kuning secara manual dapat memakan waktu dan sering kali tidak konsisten, sehingga diperlukan metode otomatis yang lebih efisien. Tujuan utama dari penelitian ini adalah untuk mengimplementasikan sistem klasifikasi otomatis berbasis pengolahan citra dalam mengklasifikasikan daun segar dan daun kuning berdasarkan fitur visual seperti warna, tekstur, dan bentuk, guna meningkatkan efisiensi dan konsistensi dalam proses pemantauan kesehatan tanaman. Untuk membedakan kecerahan dan warna, gambar diproses dengan mengubah ruang warna RGB ke LAB. Dengan menggunakan algoritma K-Means Clustering, gambar dikelompokkan ke dalam dua kelompok, masing-masing terdiri dari daun segar dan daun kuning. Data yang digunakan dalam penelitian ini terdiri dari delapan gambar, masing-masing terdiri dari empat gambar daun segar dan empat gambar daun kuning. Hasil penelitian menunjukkan bahwa metode ini berhasil mengklasifikasikan daun segar dan daun kuning dengan tingkat akurasi 100%, dengan 8 dari 8 gambar teridentifikasi dengan benar. Metode K-Means Clustering telah ditunjukkan sebagai metode yang efektif dan akurat untuk menentukan kondisi kesehatan daun. Kata kunci:  Daun Segar, Daun Kuning, K-Means Clustering, Pengolahan Citra,  Ektraksi Fitur.
KLASTERISASI BUNGA TEROMPET DAN BUNGA KAKI ITIK DENGAN METODE K-MEANS BERBASIS PENGOLAHAN CITRA Masri, Taufik; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.2661

Abstract

Abstract: Clustering is one of the methods in data processing that aims to group objects based on certain similarities. This study aims to cluster Trumpet Flower (Brugmansia) and Balsam Flower (Impatiens Balsamina) using the K-Means method based on digital image processing. The image processing begins with a pre-processing stage, including grayscale conversion, noise reduction, and object segmentation. Next, image features are extracted to obtain information on texture, color, and shape. The extracted feature data is then analyzed and grouped using the K-Means algorithm, where the clustering results are evaluated based on grouping accuracy and inter-cluster consistency. The study results show that the K-Means method can effectively cluster Trumpet Flower and Balsam Flower with high accuracy, depending on the input image quality and clustering parameters. This study highlights the great potential of the K-Means algorithm in image processing applications, particularly for visual-based object identification and grouping. Keywords: K-Means; clustering; image processing; Trumpet flower; Balsam flower Abstrak: Klasterisasi merupakan salah satu metode dalam pengolahan data yang bertujuan untuk mengelompokkan objek-objek berdasarkan kemiripan tertentu. Penelitian ini bertujuan untuk melakukan klasterisasi terhadap bunga Terompet ( Brugmansia ) dan bunga Kaki Itik ( Impatiens Balsamina ) menggunakan metode K-Means berbasis pengolahan citra digital. Proses pengolahan citra diawali dengan tahap pra-pengolahan yang meliputi konversi ke skala abu-abu, pengurangan noise, serta segmentasi objek. Selanjutnya, fitur citra diekstraksi untuk mendapatkan informasi tekstur, warna, dan bentuk. Data fitur yang dihasilkan kemudian dianalisis dan dikelompokkan menggunakan algoritma K-Means, di mana hasil klasterisasi dinilai berdasarkan akurasi pengelompokan dan konsistensi antar kelompok. Hasil penelitian menunjukkan bahwa metode K-Means mampu mengelompokkan bunga Terompet dan bunga Kaki Itik dengan tingkat akurasi yang tinggi, tergantung pada kualitas citra masukan dan parameter pengelompokan. Studi ini menunjukkan potensi besar algoritma K-Means dalam aplikasi pengolahan citra khususnya untuk identifikasi dan pengelompokan objek berbasis visual. Kata kunci: K-Means;klasterisasi;pengolahan citra;Bunga terompet ;Bunga kaki itik
IMPLEMENTASI METODE K-MEANS UNTUK MENGKLASTERISASI JENIS BUAH NAGA DENGAN TEKNIK PENGOLAHAN CITRA Idris, Muhammad; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3091

Abstract

Abstract: This research aims to implement the K-Means method for clustering red and yellow dragon fruit types using image processing techniques. Image processing is carried out by utilizing features such as color, texture, and shape from dragon fruit images obtained using a digital camera. The captured images are then processed through preprocessing stages such as conversion to a specific color space  and feature extraction to describe the visual characteristics of the dragon fruit. Afterward, the K-Means algorithm is applied to cluster the images based on the similarity of their features. The clustering results show that the K-Means method is effective in distinguishing between red and yellow dragon fruit types, with a satisfactory accuracy rate. This study contributes to the development of an automated classification system for dragon fruit type identification based on images, which can be applied in agriculture, especially in the processing and distribution of products. Keyword: K-Means Method; Red Dragon Fruit Clustering; Yellow Dragon Fruit; Image Processing. Abstrak: Penelitian ini bertujuan untuk mengimplementasikan metode K-Means dalam mengklasterisasi jenis buah naga merah dan buah naga kuning menggunakan teknik pengolahan citra. Pengolahan citra dilakukan dengan memanfaatkan fitur-fitur warna, tekstur, dan bentuk dari citra buah naga yang diperoleh menggunakan kamera digital. Citra yang diambil kemudian diproses melalui tahap pra-pemrosesan seperti konversi ke ruang warna tertentu dan ekstraksi fitur untuk mendeskripsikan karakteristik visual dari buah naga. Setelah itu, algoritma K-Means diterapkan untuk mengelompokkan citra berdasarkan kemiripan fitur yang dimilikinya. Hasil pengklasteran menunjukkan bahwa metode K-Means efektif dalam memisahkan jenis buah naga merah dan kuning, dengan tingkat akurasi yang memadai. Penelitian ini memberikan kontribusi dalam pengembangan sistem klasifikasi otomatis untuk identifikasi jenis buah naga berdasarkan citra, yang dapat diterapkan dalam bidang pertanian, terutama dalam proses pengolahan dan distribusi produk. Kata kunci:  Metode K-Means; Pengklasteran buah naga merah; Buah naga kuning; Pengolahan citra.
Implementation of Extreme Learning Machine Based on HSV Color Features for Marine Animal Image Classification Hidayati, Dzil; Pertiwi, Yuliana; Ramadhanu, Agung
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13490

Abstract

Recognizing sea animals is a significant challenge in digital image recognition. This is due to the diverse visual characteristics of marine animals, including morphological shapes, body surface colors, and textures displayed in images. Environmental factors also influence image quality, such as underwater lighting conditions, water turbidity, and other external elements. To address these classification challenges, one proposed approach is the use of the Extreme Learning Machine (ELM) method, which can be implemented by utilizing HSV (Hue, Saturation, Value) color features as the main input. The HSV color space is chosen because it more closely resembles the way humans perceive colors. In this model, color is separated into three main components: hue represents the type of color, saturation indicates the intensity or purity of the color, and value refers to its brightness or darkness. The dataset consists of several classes of marine animals such as clams, squids, and shrimp, collected from high-resolution image datasets. Test results show that the ELM model can classify images with competitive accuracy, achieving up to 83% accuracy in a much shorter training time compared to traditional learning methods. This study demonstrates that combining HSV color features with the ELM algorithm can be an efficient approach for classifying marine animal images.   Keywords - Shell, Squid, Shrimp, ELM,HSV
IMPLEMENTASI PRINCIPAL COMPONENT ANALYSIS DAN KNEAREST NEIGHBORS DALAM KLASIFIKASI TANAMAN JAHE, KUNYIT, DAN LENGKUAS Yesi Betriana Roza, yesibetriana_18; Ramadhanu, Agung
INTI Nusa Mandiri Vol. 20 No. 1 (2025): INTI Periode Agustus 2025
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/inti.v20i1.6441

Abstract

Ginger (Zingiber offivinale), turmeric (curcuma longa), and galangal (Alpinia galanga) plants are the result of Indonesia's wealth which has high economic and health value. This type of plant has high economic and health value, so its accurate identification is very important in the agricultural and pharmaceutical fields. By combining image classification methods, PCA, KNN, this research aims to develop a system that can identify ginger, turmeric, and galangal automatically and accurately. It is hoped that this system can not only provide a solution for efficient plant identification, but can also contribute to the management of natural resources and the development of herbal plant-based products in Indonesia. Data collected by taking pictures and then processed using MATLAB. This research aims to identify ginger, turmeric and galangal plants using euclidean distance and extract shape and texture characteristics. Shape feature extraction using RGB, HVS, and Area. This research implements the PCA and K-Nearest Neighbor methods in classifying data. Meanwhile, the KNN method is applied by measuring the closest distance between the test data and the training data. In this research there are labels and attributes, labels taken from the level of fruit maturity and attributes obtained from the results of image feature extraction. These attributes are R(red), G(green), B(blue), H(hue), S(saturation), V(value), Area. The accuracy results obtained from the classification of ginger, turmeric and galangal plants using the KNN method were 80% with a K=3 value obtained from 8 test data with accurate classification, and 20% from 2 test data with inaccurate classification.
IMPLEMENTASI METODE EXTREME LEARNING MACHINE UNTUK KLASIFIKASI JENIS MOBIL idris, muhammad; Fajri Saputra, Charisman; Ramadhanu, Agung
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3714

Abstract

Abstract: The rapid development of automotive technology has led to a wide variety of car types with increasingly complex features and characteristics. Alongside this advancement, the need for fast and accurate automatic classification systems has become essential, particularly in the field of vehicle image recognition. This study aims to implement the Extreme Learning Machine (ELM) method to classify vehicle types based on digital images. The research focuses on classifying three types of vehicles (Bus, Pickup, and SUV), using a dataset of 10 images per class. The test results show that ELM is capable of classifying vehicle types with competitive accuracy compared to conventional methods, while offering significantly more efficient computation time. This research demonstrates the potential of ELM as a practical solution to support intelligent vehicle recognition systems in the modern automotive industry. The classification achieved an accuracy rate of 91.67%. Keywords: Extreme Learning Machine (ELM), Vehicle Type Classification, Image Processing, Pattern Recognition. Abstrak: Perkembangan teknologi otomotif yang pesat telah menghasilkan beragam jenis mobil dengan fitur dan karakteristik yang semakin kompleks. Seiring dengan kemajuan ini, kebutuhan akan sistem klasifikasi otomatis yang cepat dan akurat menjadi sangat penting, terutama dalam bidang pengenalan citra kendaraan. Tujuan penelitian ini untuk mengimplementasikan metode Extreme Learning Machine (ELM) dalam mengklasikasi jenis mobil berdasarkan citra digital. Penelitian ini mengklasifikasikan 3 jenis mobil (BUS, Pickup dan SUV) dengan dataset masing masing 10 gambar. Hasil pengujian menunjukkan bahwa ELM mampu melakukan klasifikasi jenis mobil dengan akurasi yang kompetitif dibandingkan metode konvensional, dengan waktu komputasi yang jauh lebih efisien. Penelitian ini menunjukkan potensi ELM sebagai solusi praktis dalam mendukung sistem cerdas berbasis pengenalan kendaraan dalam dunia otomotif modern. Hasil yang didapat dari penelitian ini adalah sebesar 91,67% untuk tingkat akurasinya. Kata kunci: Extreme Learning Machine (ELM), Klasifikasi Jenis Kendaraan, Pemrosesan Citra, Pengenalan Pola.
Identification of Signature Authenticity Using Binary Extraction and K-nearest Neighbor Feature Methods Vidyanti, Angela Citra; Riati, Itin; Ramadhanu, Agung
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2063

Abstract

This research focuses on identifying the authenticity of signatures, which is an important part of the field of biometrics. Identification of signature authenticity has wide applications, including in document security, financial transactions, and identity verification in general. The problem to be resolved is the lack of an effective and efficient method for identifying signature authenticity. The method used is the binary extraction method and the K-nearest Neighbor feature. The main contribution of this research is to propose a new approach in identifying signature authenticity by combining binary extraction methods and K-nearest Neighbor features. This approach is expected to increase the accuracy and efficiency of the signature authenticity identification process. The results of this research are the development of a new model or algorithm for identifying the authenticity of signatures. After testing and validation, the accuracy level of the results of identifying the authenticity of this signature is 75%.
Implementasi Algoritma K-Means untuk Klasifikasi Citra Biota Laut: Gurita, Lobster, dan Kerang Laut Dicky Imansyah, Muhammad; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2271

Abstract

Advances in digital image processing technology and machine learning, such as clustering, have contributed to increased efficiency in various sectors, including marine and fisheries. Octopus, lobsters, and shellfish are high-value fishery commodities that have traditionally been classified manually, with the potential for subjectivity and inefficiency. This study aims to develop a digital image classification model for marine biota using the K-Means Clustering method equipped with image processing techniques. The methods applied include converting the RGB color space to L*a*b, segmentation with K-means, shape feature extraction (metric, eccentricity) and GLCM texture (contrast, correlation, energy, homogeneity). The results show that this method is effective in identifying the three types of marine biota with an average accuracy of 95% based on testing on 30 images. The implementation of K-means Clustering has been proven to be accurate and consistent in the automation of marine biota classification.
Klasifikasi Citra Alat Musik Marakas, Gitar, dan Drum Menggunakan Metode K-Means dan GLCM salim, alfajri; Ramadhanu, Agung
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 7 No 4 (2025): Oktober 2025
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v7i4.2265

Abstract

The development of digital image processing technology enables automatic object identification with high accuracy. This study aims to classify images of musical instruments, namely maracas, guitars, and drums, using a combination of K-Means-based color segmentation and Gray Level Co-Occurrence Matrix (GLCM) feature extraction. The process begins with converting RGB images into the Lab color space, followed by object segmentation using the K-Means clustering algorithm to separate the main object from the background. Subsequently, shape features (metric, eccentricity) and texture features (contrast, correlation, energy, homogeneity) are extracted using GLCM. The extracted features are then compared with a feature database using a distance-based approach to determine the object class. Experimental results show that the system can successfully recognize maracas, guitar, and drum images with a satisfactory accuracy level. This research demonstrates that the combination of K-Means and GLCM methods can serve as an effective approach for musical instrument image classification and has the potential to be further developed for object recognition in other fields
PENINGKATAN METODE MEDIAN FILTER UNTUK IDENTIFIKASI DAN AKURASI JENIS PISANG EMAS DAN PISANG KAPAS Chan, Fajri Rinaldi; Yanti, Rahma; Ramadhanu, Agung
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 8 No 2 (2024)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v8i2.4767

Abstract

Pengembangan teknologi dalam bidang pertanian telah membawa dampak signifikan, terutama dalam proses identifikasi dan klasifikasi hasil pertanian. Salah satu inovasi yang berpotensi meningkatkan efisiensi ini adalah teknologi pengolahan citra digital. Pisang, sebagai komoditas pertanian yang penting di Indonesia, memerlukan akurasi tinggi dalam klasifikasi, khususnya dalam membedakan jenis-jenis seperti Pisang Emas dan Pisang Kapas yang memiliki karakteristik visual mirip. Untuk itu, penelitian ini fokus pada peningkatan metode pengolahan citra untuk membedakan kedua jenis pisang tersebut. Metode yang digunakan adalah Median Filter, yang efektif mengurangi noise pada citra, namun terbukti kurang akurat dalam kasus dengan kemiripan visual tinggi. Penelitian ini bertujuan untuk mengembangkan dan menguji metode Median Filter yang ditingkatkan untuk meningkatkan akurasi dalam identifikasi jenis Pisang Emas dan Pisang Kapas. Hasil penelitian menunjukkan bahwa dengan peningkatan tersebut, tingkat akurasi identifikasi meningkat secara signifikan, mencapai 98% pada 35 citra yang diuji. Temuan ini membuka potensi untuk penerapan teknologi pengolahan citra dalam sistem klasifikasi otomatis di sektor pertanian, terutama dalam memastikan kualitas dan efisiensi distribusi produk pertanian.
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