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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
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Penerapan Image Processing untuk Identifikasi RAM, SSD, dan Webcam Menggunakan Metode K-Means Clustering Zakiya Hikmi; Agung Ramadhanu
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 Algoritma K-Means Pada Pengolahan Citra Untuk Deteksi Bentuk Dan Material Gelas kamila amaliah putri; 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.v7i4.2267

Abstract

Digital image processing is a branch of computer science that plays a significant role in automating object identification processes. This study presents the implementation of the K-Means Clustering algorithm for detecting the shape and material of drinking glasses based on digital images. The research methodology involves several stages, including image data collection, color space conversion from RGB to Lab, image segmentation using K-Means Clustering, and feature extraction of shape and texture. The K-Means algorithm is employed to cluster image pixels into multiple groups according to color similarity and texture patterns, thereby enabling the classification of glasses based on their material (glass, plastic, or clay) and shape. The experimental results demonstrate that the proposed method achieves a high level of accuracy in object identification and can be effectively implemented within a Matlab-based system. Consequently, this approach offers a potential solution for the automation of drinking container identification in various industrial and research applications.
Implementasi Algoritma K-Means untuk Klasifikasi Citra Biota Laut: Gurita, Lobster, dan Kerang Laut Muhammad Dicky Imansyah; 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.v7i4.2271

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.
Implementasi Metode K-Means Clustering untuk Mengklasterikasikan Perangkat Elektronik dengan Teknik Pengolahan Citra Ryan Firmansyah; Agung Ramadhanu
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.
Clustering Data Penjualan Menggunakan Algoritma K-Medoids sebagai Pendukung Keputusan Penjualan Yusvi Diana; Neni Sri Wahyuni Nengsi; Febri Hadi; Agung Ramadhanu; Halifia Hendri
Jurnal Ekonomika Dan Bisnis (JEBS) Vol. 6 No. 1 (2026): Januari - Februari
Publisher : CV. ITTC INDONESIA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jebs.v6i1.4210

Abstract

Inaccuracies in sales strategies are often caused by the lack of optimal utilization of sales data. Large amounts of available transaction data are generally only used as archives, without further analysis to explore consumer purchasing behavior patterns. This condition makes it difficult to determine product segmentation, stock priorities, and appropriate marketing strategies. This study aims to assist the sales decision-making process by applying sales data clustering techniques using the K-Medoids algorithm. The method used is a data mining approach with the stages of collecting historical sales data, data preprocessing to handle empty data and outliers, data normalization, and the clustering process using the K-Medoids algorithm. The dataset used comes from sales data from a store with attributes such as sales volume, item price, and transaction frequency. The K-Medoids algorithm was chosen because of its ability to produce clusters that are more stable against outliers than other clustering algorithms. The results show that the K-Medoids algorithm is able to group sales data into several clusters that represent product sales levels, such as products with high, medium, and low sales. The information obtained from this clustering can be used as a basis for decision support in determining sales strategies, managing inventory, and planning promotions. Thus, the application of the K-Medoids algorithm has proven effective in supporting data-driven sales decision-making.
Analysis of Strategies for Improving Learning Quality Based on Naive Bayes and Support Vector Machines Fadhila Putri Sani; Syafri Arlis; Agung Rahmadhanu
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.682

Abstract

Islamic educational institutions, particularly Islamic boarding schools, face increasing challenges in improving the quality of learning. The learning quality in Islamic boarding schools should be analyzed in depth to support effective improvement strategies. Based on this background, this study aims to classify strategies for enhancing learning quality using the Naïve Bayes and Support Vector Machine (SVM) algorithms. Naïve Bayes with a Gaussian distribution is widely recognized for its simplicity and accuracy in data classification. Meanwhile, Support Vector Machines (SVM) with a linear kernel are effective for linearly separable and high-dimensional data, enabling stable and efficient modeling in the context of data-driven analysis of learning quality in formal education. The data were collected through questionnaires distributed to 100 female students and 100 teachers. The variables examined include teacher competence, infrastructure, school management, student participation, and learning quality level. The analysis results indicate that the Naïve Bayes algorithm achieved superior performance with an accuracy of 90%, precision of 95.65%, recall of 83.33%, and an F1-score of 86.56%. In contrast, the Support Vector Machine (SVM) obtained an accuracy of 80%, precision of 58.97%, recall of 66.67%, and an F1-score of 62.32%.These findings demonstrate that Naïve Bayes provides more stable classification performance across all learning quality categories. Conversely, the Support Vector Machine (SVM) shows less optimal performance in the low-quality class due to the limited number of data samples. This study contributes effectively to the classification of learning quality levels in Islamic boarding schools
Klasifikasi Buah Kelapa Muda, Kelapa Tua, dan Buah Naga Menggunakan Pendekatan Hybrid PCA-KNN Ridwan Sutri; Agung Ramadhanu
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.
IMPLEMENTASI MECHINE LEARNING PADA HYBRID INTELLIGENCE SISTEM MENGUNAKAN METODE PCA-KNN PADA JENIS BUAH APEL, JERUK, TOMAT Angga Angga; Agung Ramadhanu
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.
IMPLEMENTASI METODE ALGORITMA PRINCIPAL COMPONENT ANALYSIS (PCA) DAN ALGORITMA K-NEAREST NEIGHBOR (KNN) DALAM KLASIFIKASI BUAH JAMBU MADU JAMBU MERAH DAN MANGGIS Muhammad Ikhsan Al-Arrafi; Agung Ramadhanu
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).
KLASTERISASI BUNGA TEROMPET DAN BUNGA KAKI ITIK DENGAN METODE K-MEANS BERBASIS PENGOLAHAN CITRA Taufik Masri; Agung Ramadhanu
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
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