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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 JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) 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) Jurnal Siteba 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
Agung Ramadhanu
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

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Analisis Kepuasan Masyarakat terhadap Layanan KUA Menggunakan Algoritma K-Means dan C4.5 Nabilah Putri Permana; Agung Ramadhanu; Gunadi Widi Nurcahyo
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9324

Abstract

The Office of Religious Affairs (KUA) is an institution under the Ministry of Religious Affairs that provides religious services to the community, including marriage administration. Improving the quality of public services requires data-driven evaluation to measure the level of public satisfaction with the services provided. This study aims to analyze the level of community satisfaction with the services of the Office of Religious Affairs in Tebing Tinggi District using a combination of the K-Means Clustering and C4.5 algorithms. The research data were obtained from questionnaires distributed to community members who used KUA services. The K-Means algorithm was applied to group community satisfaction data based on the similarity of attribute values, while the C4.5 algorithm was used to build a classification model that generates decision rules to predict the level of community satisfaction. The results show that the proposed methods are able to group satisfaction levels in a structured manner and produce a classification model with high accuracy in analyzing public service satisfaction. The findings of this study are expected to support KUA in evaluating and improving service quality, as well as provide a reference for the application of data mining techniques in analyzing community satisfaction in public service sectors.
Komparatif Metode Convolutional Neural Network, GoogleNet & Transfer Learning pada Klasifikasi Sampah Ariza Ikhlas; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Information System Research (JOSH) Vol 7 No 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i3.9326

Abstract

The waste problem is a very complex global issue, especially in Indonesia. The volume of waste that continues to increase every year is a major challenge for the environment, health, and the economy. so it is necessary to conduct research related to smart waste management, namely, a concept of utilizing artificial intelligence in waste management by adopting image management techniques. Based on this, this study aims to compare the modeling of Covolutional Neural Network (CNN), GoogleNet, and Transfer Learning. The methods used in this study, CNN, GoogleNet, and Transfer Learning by utilizing data augmentation, activation functions, and transfer learning, are able to overcome the problem of limited data and reduce or avoid overfitting problems in modeling. The datasets used in this study are sourced from datasets built by the researcher himself and Kaggle datasets with a total of 300 samples consisting of 6 classes: Cardboard, Glass, Plastic, Metal, Paper, and Other/Trash. The results present that the transfer learning method is superior to other methods with accuracy, precision, recall, and f1-score, 100%. The contribution of this research is to enrich the literature in the field of machine learning and computer vision, develop more efficient models for limited datasets, and become a reference for future researchers who want to develop similar systems.
Hybrid Decision Support System and Image Processing for Classifying Priority Applications in the Padang Government Agung Ramadhanu; Mardison; Halifia Hendri; Febri Hadi; Dodi Guswandi; Deri Marse Putra; Romi Hardianto; Syafrika Deni Rizki
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.178-191

Abstract

The development of e-government has encouraged every Regional Apparatus Organization (OPD) within the Padang City Government to submit various digital applications to improve the quality of public services. However, the large number of applications often creates challenges in determining priorities, primarily due to limited resources and budgets. This research aims to design a Hybrid Decision Support System (DSS) that combines the WASPAS (Weighted Aggregated Sum Product Assessment) method and the development of the K-Means Clustering method to provide a more objective and measurable priority classification. The WASPAS method is used to provide a ranking of alternatives based on predetermined criteria, such as urgency of need, service impact, funding availability, and alignment with the regional strategic plan. Next, the K-Means algorithm is applied to group the calculation results into several priority classes, ranging from the most urgent to the least urgent. As an innovation, this research also utilizes image processing techniques to visualize the K-Means classification results, allowing for a more intuitive and easily understood presentation of priority grouping patterns for decision-makers. In this research, data were collected from 52 OPDs within the Padang City Government as a case study. The test results show that the hybrid DSS approach combining WASPAS and K-Means successfully produces priority scale classification with an accuracy level of 94.75%, which demonstrates consistency and accelerates the application evaluation process at OPDs. Integration with image processing for visualization of clustering results also successfully helps clarify data interpretation and facilitates analysis. Thus, this system is expected to support more effective, transparent decision-making in accordance with the principles of electronic-based governance in Padang City.
Automated Fruit Image Classification Based on HSV Features, Morphological Segmentation, and Extreme Learning Machine Agung Ramadhanu; Halifia Hendri; Wahyu Saptha Negoro; Mardison Mardison; Larissa Navia Rani; Sofika Enggari; Muhammad Reza Putra
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.135-147

Abstract

Fruit image classification plays a crucial role in smart agriculture, particularly in automating sorting and quality control processes. This study proposes a fruit classification system by integrating HSV color space conversion, adaptive thresholding, morphological segmentation, and the Extreme Learning Machine (ELM) algorithm. The dataset consists of three fruit classes—apple, pineapple, and watermelon—with a total of 480 images, divided into 360 training samples and 120 testing samples. Image preprocessing involves resizing, HSV conversion, noise reduction through morphological operations, and feature extraction based on color and shape characteristics. The extracted features are used to train and test an ELM model. To improve classification performance and address potential overfitting in traditional ELM, this study introduces a new development called the Extended Extreme Learning Machine (EELM). The key innovation lies in modifying the calculation of the output weights βj, where a regularization term is introduced using ridge regression to stabilize learning and improve generalization. Experimental results show that the proposed system achieves 100% accuracy on the training data and an average accuracy of 83.3% on the testing data. The system also demonstrates robustness in handling varying lighting conditions and fruit shapes. These improvements enable EELM to better handle noisy or complex data by preventing over-reliance on randomly initialized hidden layer parameters. Consequently, EELM demonstrates improved reliability, making it more suitable for deployment in resourceconstrained real-world environments such as mobile or embedded systems.
GoogLeNetMP: A Development of GoogLeNet Architecture for Multi-Class Microplastic Classification in Subsurface Water Image Halifia Hendri; Yuhandri Yuhandri; Agung Ramadhanu
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1372

Abstract

Microplastic pollution has become a major environmental concern due to its persistence in marine ecosystems and its potential impact on aquatic organisms and human health. Automatic detection of microplastic particles in underwater environments remains challenging because of turbidity, low contrast, light distortion, and the visual similarity between microplastics and natural marine objects. This study proposes GoogLeNetMP, an enhanced GoogLeNet-based deep learning architecture for multi-class classification of subsurface marine images into four categories: primary microplastics, secondary microplastics, non-microplastics, and marine biota. The proposed framework integrates basic image preprocessing (resizing and noise reduction) with a modified GoogLeNetMP architecture designed to intrinsically handle fine-grained feature extraction under degraded conditions, thereby minimizing the reliance on complex external enhancement pipelines. A dataset of underwater images acquired from the coastal waters of Padang, Indonesia, was used for model development and evaluation. Experimental results show that GoogLeNetMP outperformed the standard GoogLeNet model, achieving 95.75% accuracy, 92.80% sensitivity, 97.00% specificity, and an F1-score of 92.06%. The proposed model also demonstrated more stable training convergence and better discrimination of visually challenging classes. The architecture is designed to internalize the robust feature extraction process, thereby minimizing the reliance on extensive external enhancement pipelines while maintaining standard normalization steps for input consistency. These findings indicate that GoogLeNetMP is a promising approach for AI-based marine pollution monitoring and decision support in sustainable coastal management.
Analisis Kepuasan Masyarakat Terhadap Proses Pengurusan Sertipikat Analog Ke Elektronik Menggunakan Metode Naïve Bayes Muhammad Ikhsan Al-Arrafi; Rini Sovia; Agung Ramadhanu
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.758

Abstract

The certificate media conversion program from analog to electronic implemented by the Ministry of ATR/BPN in Sejati Village requires evaluation to ensure its effectiveness. The main problem faced is the limited use of quantitative, data-driven analysis in identifying the factors that influence public satisfaction. This study aims to analyze the level of public satisfaction using the Naïve Bayes method to classify and predict the influence of related variables. Data were obtained from 250 respondents through questionnaires based on digital public service indicators, covering demographic variables, perceived benefits, obstacles, support, service speed, and procedural simplicity. The results show that the level of public satisfaction is in the high category, with procedural simplicity and service speed proven to be the most significant variables influencing satisfaction prediction. The Naïve Bayes model achieved an accuracy of 94%, demonstrating its effectiveness in predicting satisfaction levels. These findings serve as a basis for improving policies and strategies to enhance the quality of digital public services, particularly in the implementation of electronic certificate media conversion in the future.
Identifikasi Varietas Kopi Berdasarkan Analisis Warna dan Tekstur Menggunakan Metode Convolutional Neural Network Kharisma Utama Putra; Agung Ramadhanu; Syafri Arlis
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.759

Abstract

Coffee is a plantation commodity with high economic value in Indonesia, with various varieties such as Arabica, Robusta, and Liberica. Differences in coffee varieties can generally be identified through the physical characteristics of the beans, especially color and texture. Based on this, this study aims to develop a digital image-based coffee variety identification system using the Convolutional Neural Network (CNN) method with color and texture analysis as the main features. The research stages include coffee bean image acquisition, pre-processing including color segmentation and image conversion to grayscale, and color and texture feature extraction. This research dataset comes from images of unroasted coffee beans, commonly called green beans, taken using a high-resolution smartphone camera and also using secondary data taken from the Kaggle site. Both types of datasets have the same characteristics and resolution to maintain data consistency. The image dataset is divided into training data and test data, then used to train and test the Convolutional Neural Network (CNN) model. Based on this study, the Convolutional Neural Network (CNN) method can identify coffee varieties based on color and texture analysis. By using 210 training data and 90 test data of coffee bean images, the CNN method can produce an accuracy rate of 94,44%. This research contribution has the potential to be a supporting solution in the process of identifying coffee varieties quickly, accurately, and consistently, so that it can help the coffee industry in the sorting and quality control process.
Klasifikasi Aksesori Fashion Berdasarkan Fitur Citra Menggunakan K-Means Clustering Zebbil Billian Tomi; Agung Ramadhanu
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.
Klasifikasi Otomatis Citra Buah Menggunakan Ekstraksi Fitur HSV, Segmentasi Morfologi, Dan Extreme Learning Machine Devi Maryuni; Helda Andriany Darwis; Agung Ramadhanu
JURNAL SITEBA Vol. 4 No. 1 (2026): Jurnal Sistem Informasi ITEBA
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/5w1hn437

Abstract

Pengolahan citra digital telah menjadi pendekatan yang efektif dalam sistem klasifikasi objek visual, termasuk klasifikasi buah. Penelitian ini bertujuan mengembangkan sistem klasifikasi otomatis citra buah menggunakan metode Extreme Learning Machine (ELM) dengan ekstraksi fitur warna berbasis ruang warna HSV dan segmentasi citra melalui operasi morfologi. ELM dipilih karena keunggulannya dalam kecepatan pelatihan dan kemampuannya menangani data non-linear secara efisien. Proses dimulai dengan akuisisi citra buah dari dataset yang terdiri atas tiga kelas: semangka, nanas, dan apel. Citra kemudian dipra-proses dan dikonversi dari ruang warna RGB ke HSV. Segmentasi dilakukan menggunakan metode thresholding dan operasi morfologi untuk memisahkan objek utama dari latar belakang dan mengurangi noise. Fitur warna yang diekstraksi dari komponen H, S, dan V berupa nilai statistik seperti rata-rata dan standar deviasi, yang selanjutnya dijadikan input untuk model ELM. Hasil pengujian menunjukkan bahwa kombinasi metode ini mampu menghasilkan akurasi klasifikasi yang tinggi dengan waktu pelatihan yang lebih singkat dibanding metode konvensional. Pendekatan ini menunjukkan potensi besar untuk diterapkan dalam sistem identifikasi buah otomatis berbasis citra, baik untuk kebutuhan industri maupun aplikasi mobile cerdas.
Klasifikasi Citra Alat Musik Marakas, Gitar, dan Drum Menggunakan Metode K-Means dan GLCM alfajri salim; Agung Ramadhanu
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
Co-Authors ., Ulfa Aditya Wiratama Afriadi Afriadi Afriadi, A Agsera, Nilam Agus Salim, David Agusty, Dhia Fadhila Ahmad Syarif Ahmad Syarif ahmad yani Akbar, Syifa Chairunnissa Deliva Al-arrafi, Muhammad Ikhsan alfajri salim Angga Angga Angga Angga Anggara Putra, Febri Antoni Antoni Ariza Ikhlas Arsyah Arsyah atiqah, sri Avezrima Rahmamuthi Ayu Mahessya, Raja Bayuputra, Ramdani Berta Agus Petra Betriana Roza, Yesi Betriana, Yesi Chairunnissa Deliva Akbar, Syifa Chan, Fajri Rinaldi Charisman Fajri Saputra Charisman Fajri Saputra Delvi, Syerlin Aprilia Deri Marse Putra Desi Permata Sari Devi Maryuni Dhia Fadhila Agusty Dicky Imansyah, Muhammad Dila, Rahmah Dinantia, Triend Dodi Guswandi Enggari, Sofika Erlanda, Hadrian Eva Rianti Fadhila Putri Sani Fadila Cahyani Putri Fajri Saputra, Charisman Fajrul Islami Febri Hadi Febri Hadi Fiki Pratama Firmansyah, Ryan Firna Yenila Fitri Yeni, Fitri Gafari, Abuzar Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Hadi Syahputra Hadi Syahputra Halifia Hendri Halifia Hendri Hanna Pratiwi Harnaranda, Jefri Hasmaynelis Fitri Helda Andriany Darwis 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 kamila amaliah putri Kareem, Shahab Wahhab Karseno, Doni Kharisma Utama Putra Kharisma Utama Putra Khomsi, Ahmad Larissa Navia Rani, Larissa M.Iqbal, M.Iqbal Maharani, Filsha Rifi Majid, Mazlina Abdul Mardison Mardison Mardison Mardison Mardison Mardison Mardison Mardison Marfalino, Hari Masri, Taufik Mokti Isra Mokti Isra Muhammad Dicky Imansyah Muhammad Idris muhammad idris Muhammad Idris Muhammad Ikhsan Al-Arrafi Muhammad Ikhsan Al-Arrafi Muhammad Raihan Zaky Muhammad Raihan Zaky Muhammad Reza Putra MUHAMMAD YUSUF Muhammad Yusuf Nabila Frisca Oktavia Nabilah Putri Permana Nadia, Nadia Aini Hafizhah Nasution, Amir Salim Khairul Rijal Nasution, Annio Indah Lestari Negoro, Wahyu Saptha Nengsi, Neni Sri Wahyuni Neni Sri Wahyuni Nengsi Neni Sri Wahyuni Nengsih Neni Sri Wahyuni Nengsih Neni Sri Wayuni Ningsih Neni Sri Wayuni Ningsih Ningsih, Neni Sri Wayuni Novrianto, Andry 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 Repelita Witri Retno Devita Rheza Thresya Riati, Itin Ridwan Sutri Rindy Citra Dewi Rini Sovia Riyan Saputra, Riyan Rizky Gusrianto Romi Hardianto Rosa, Imelda Rosda Syelly Ryan Firmansyah Sajida, Mayang salim, alfajri Saputra, Charisman Fajri Saputra, Randy Sarjon Defit Selvia, Dina Silfia Andini Sisi Hendriani Sofika Enggari Sofika Enggari Sofika Enggari Sofika Enggari Sovia, Rini Suci Wahyuni Sularno Sularno Sumijan, S Sutri, Ridwan Syafri Arlis Syafri Arlis Syafrika Deni Rizki Syafril Syafril Syafril, S Syalsabilla, Adinda Taufik Masri Teri Ade Putra Tesa Vausia Sandiva Tomi, Zebbil Billian Utama Putra, Kharisma Utari, Utari Armila Vidyanti, Angela Citra Windra Yosfand 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 Yusvi Diana Zakiya Hikmi Zebbil Billian Tomi Zubaidah, Rima Puti