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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Majalah Ilmiah Teknologi Elektro Jurnal INKOM Proceedings of KNASTIK Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING KOMPUTIKA - Jurnal Sistem Komputer Jurnal Abdimasa TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol JMECS (Journal of Measurements, Electronics, Communications, and Systems) Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat Prosiding Konferensi Nasional PKM-CSR Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer (JURITEK) eProceedings of Engineering Majalah Ilmiah Teknologi Elektro
Koredianto Usman
Universitas Telkom
Religion Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Public Health Social Sciences Other

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Deteksi Kelebihan Kadar Kolesterol Melalui Citra Iris Mata Menggunakan Metode Gray Level Co-occurrence Matrix Dan Learning Vector Quantization Putri Marito; Jangkung Raharjo; Koredianto Usman
eProceedings of Engineering Vol 6, No 3 (2019): Desember 2019
Publisher : eProceedings of Engineering

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Abstract

Abstrak Seiring perkembangan zaman, teknologi mengalami perkembangan sangat pesat, begitu juga dengan perkembangan teknologi dalam bidang kesehatan. Saat menjalanin tes kesehatan rutin kita akan menjalani tes kolesterol yang memakan waktu lama dikarenakan pasien harus menjalanin puasa terlebih dahulu, dimana untuk mendapatkan hasil tes pun memakan waktu yang lama. Pada penelitian ini, penulis merancang sistem yang mendeteksi kadar kolesterol dalam tubuh manusia dengan mengidentifikasi citra iris mata lalu diekstraksi ciri dengan metode GLCM dan diklasifikasikan dengan metode LVQ. Citra iris mata diambil menggunakan kamera handphone sebagai data sistem. Data sistem terbagi menjadi data latih dan data uji. Setiap data dikelompokkan menjadi tiga kategori yaitu normal, berpotensi kolesterol dan kolesterol. Data sistem di preprocessing berupa cropping, resize, segmentasi, dan merubah citra RGB menjadi citra grayscale. Citra grayscale diekstraksi ciri dengan metode GLCM kemudian dilakukan proses klasifikasi dengan LVQ. Sistem melakukan proses pelatihan berupa data latih yang di preprocessing kemudian diekstraksi ciri dengan ketentuan parameter fitur, jarak piksel, arah/sudut, dan level kuantisasi. Kemudian, sistem mengklasifikasi data latih tersebut dengan ketentuan parameter epoch, dan hidden layer terhadap data latih kembali. Hasil dari proses pelatihan berupa parameter terbaik. Selanjutnya, sistem melakukan proses pengujian berupa data latih yang di preprocessing kemudian diekstraksi ciri dan diklasifikasi dengan ketentuan parameter terbaik terhadap data uji. Dari hasil pengujian, sistem yang dibangun mampu mendeteksi kadar kelebihan kolesterol melalui citra iris mata dan mengklasifikasikan kedalam tiga kelas yaitu berisiko kolesterol, kolesterol dan nonkolesterol dengan tingkat akurasi sebesar 98,67% dan waktu komputasi 0,039s menggunakan masing-masing 75 data latih dan data uji, dengan parameter orde dua yang digunakan adalah kontras-korelasi-homogenitas, jarak piksel (d) = 1, arah/sudut = 0° level kuantisasi (n) = 8, epoch 200 dan hidden layer 10. Kata Kunci: GLCM, LVQ, Citra iris mata, Kolesterol. Abstract Along with the times, technology has developed very rapidly, as well as technological developments in the health sector. When undergoing routine health tests we will undergo a cholesterol test that takes a long time because the patient must undergo fasting first, where to get the results of the test also takes a long time too. In this study, the authors designed a system that detects cholesterol levels in the human body by identifying the iris image then extracted features by the GLCM method and classified by the LVQ method. The iris image was taken using a cellphone camera as a data system. System data is divided into training data and test data. Each data is grouped into three categories namely normal, cholesterol and cholesterol potential. Preprocessing system data in the form of cropping, resizing, segmenting, and changing the RGB image into grayscale image. Grayscale image is extracted by GLCM method then classification process is done by LVQ. The system performs the training process in the form of training data which is preprocessed then features are extracted with the provisions of feature parameters, pixel spacing, direction / angle, and quantization level. Then, the system classifies the training data with the provisions of the epoch parameter, and the hidden layer of the training data again. The results of the training process are in the form of the best parameters. Furthermore, the system performs the testing process in the form of preprocessing training data then features are extracted and classified with the best parameter provisions of the test data. From the test results, the system that was built was able to detect levels of excess cholesterol through iris images and classify them into three classes namely risk of cholesterol, cholesterol and non-cholesterol with an accuracy rate of 98,67% and computing time of 0.039s using 75 each training data and test data, with secondorder parameters used are contrast-correlation-homogeneity, pixel spacing (d) = 1, direction = 0° quantization level (n) = 8, epoch 200 and hidden layer 10. Keywords: GLCM, LVQ, iris image, Cholesterol.
Klasifikasi Penyakit Paru-paru Berbasis Pengolahan Citra X Ray Menggunakan Convolutional Neural Network (classification Of The Lung Diseases Based On X Ray Image Processing Using Convolutional Neural Network) Razief Moch Diar; R. Yunendah Nur Fu’Adah; Koredianto Usman
eProceedings of Engineering Vol 9, No 2 (2022): April 2022
Publisher : eProceedings of Engineering

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Abstract

k Penyakit pada paru-paru merupakan gangguan yang cukup serius dimana dapat menyerang sistem pernapasan manusia dan bisa berakibat fatal jika tidak ditangani dengan serius. Pada saat ini deteksi penyakit pada paru-paru masih dilakukan secara manual oleh para dokter ahli, namun proses secara manual memakan waktu lama. Oleh karena itu, dalam tugas akhir ini dibuat sistem yang dapat mendeteksi dan mengklasifikasi penyakit paru-paru dengan otomatis.Pada Tugas Akhir ini merancang sistem otomatis untuk mengklasifikasi kondisi paru-paru berdasarkan citra x-ray paru-paru berbasis Convolutional Neural Network (CNN) menggunakan arsitektur MobileNet. Perancangan pada sistem dibagi menjadi beberapa tahapan dimulai dari menginput data citra x-ray paru-paru, tahap selanjutnya preprocessing, pada penelitian ini menggunakan dua jenis preprocessing, yaitu CLAHE, dan Gaussian filter, lalu dari hasil preprocessing dilakukan tahap pelatihan dengan dua jenis optimizer yang berbeda, yaitu Stochastic Gradient Descent (SGD), dan Adaptive moment (Adam). Tahap terakhir mengkalisifikasikan data citra menjadi empat kelas, yaitu Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal dan Tuberculosis. Hasil akhir dari penelitian ini menunjukan optimizer terbaik yaitu Adam menggunakan preprocessing CLAHE pada epoch 50 dan menghasilkan nilai akurasi sebesar 94,687 dan loss sebesar 0,148. Selain itu juga diperoleh hasil dari performansi sistem berupa presisi 95%, recall 93%, dan F-1 score sebesar 94%. Kata Kunci : CNN, MobileNet, citra x-ray paru-paru, Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal, Tuberculosis. Abstract Diseases of the lungs are quite serious disorders which can attack the human respiratory system and can be fatal if not treated seriously. At this time the detection of disease in the lungs is still check manually by expert doctors, but manual process takes a long time. Therefore, in this final project, a system is made that can detect and classify lung diseases automatically. using MobileNet architecture. The design of the system is divided into several stages starting from inputting lung x-ray image data, the next stage is preprocessing, in this study using two types of preprocessing, namely CLAHE, and Gaussian filters, then from the results of preprocessing, the training phase is carried out with two types of optimizers that different, namely Stochastic Gradient Descent (SGD), and Adaptive moment (Adam). The last stage is to classify image data into four classes, namely Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal and Tuberculosis. The final result of this study shows that the best optimizer is Adam using CLAHE preprocessing on epoch 50 and produces an accuracy value of 94,687 and a loss of 0.148. In addition, the results of the system performance are 95% precision, 93% recall, and an F-1 score of 94%. Keywords: CNN, MobileNet, lung x-ray images, Viral Pneumonia, Coronavirus Disease-19 (Covid-19), Normal, Tuberculosis
Pendampingan Pelatihan Perencanaan, Pengoperasiaan dan Pengolaan PLT Mikrohidro Dalam Mendukung Program Pemerintah Meningkatkan Kompetensi SDM di Bidang Bauran Energi Terbarukan Sofia Saidah; Jangkung Raharjo; Koredianto Usman; Denny Darlis; Aris Hartaman; Tita Haryanti
Jurnal Abdimasa Pengabdian Masyarakat Vol 6 No 2 (2023): Jurnal ABDIMASA Pengabdian Masyarakat
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalabdimasa.v6i2.3739

Abstract

Pembangkit Listrik Tenaga Mikro Hidro (PLTMH) merupakan solusi alternatif yang penting dalam memenuhi kebutuhan energi listrik. PLTMH adalah pembangkit listrik skala kecil yang menggunakan tenaga air sebagai sumber energinya, seperti saluran irigasi, sungai, atau air terjun. Inovasi teknologi, seperti penerapan Internet of Things (IoT), mempermudah monitoring dan meningkatkan efisiensi dalam pengelolaan PLTMH. Pelatihan dan pengabdian kepada masyarakat juga penting untuk meningkatkan kompetensi SDM di bidang energi terbarukan. Implementasi PLTMH akan memberikan manfaat dalam memenuhi kebutuhan energi listrik secara lokal dan mengurangi ketergantungan pada sumber energi konvensional yang tidak ramah lingkungan. Dengan demikian, PLTMH merupakan solusi penting dalam membangun pembangunan berkelanjutan.
Deteksi Kelas Ruangan Berdasarkan Reverberation Time dengan Metode Linear Predictive Coding (LPC) dan K-Nearest Neighbor (KNN) Imanadi, Muhammad Tsabit Imanadi; Usman, Koredianto; Hidayat, Bambang
JOURNAL OF ELECTRICAL AND SYSTEM CONTROL ENGINEERING Vol. 6 No. 2 (2023): Journal of Electrical and System Control Engineering
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jesce.v6i2.8360

Abstract

Identification of the size of the evidence file conversation can be one of the tools for various purposes such as in the police world. Determining the class of the room from the recording can be an additional clue in case processing.  One way for the police to identify the class of a room is by creating a room class detection system. The class of the room can be determined by measuring the reverberation time using the LPC algorithm by extracting the characteristics of the training data in the form of audio. After obtaining the characteristics, the system will store these characteristics in the form of a dataset for testing. Then, the test data for which the room class is not yet known is inputted into the test system. KNN will classify the test data based on the previously trained dataset. The last process of the system will issue the value of accuracy and computational time from system testing. This study uses MATLAB calculation software as a calculation and simulation process, using 63 training data and 18 test data.  The  accuracy of  the  system  test  for detecting room class based on reverberation time using the LPC and KNN methods has resulted in a number with the largest accuracy value of 83.33% and computation time along 4,94657 seconds with a K value of 3, LPC order of 12, number of frames 240, and the Hanning window.
GERAKAN “BEBAS PINJAMAN ONLINE” EDUKASI LITERACY DIGITAL DI DESA PATROLSARI ARJASARI KAB BANDUNG Machfiroh, Runik; Usman, Koredianto
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.2235

Abstract

Perkembangan transaksi digital atau FinTech khususnya pada area pinjaman online menjadi polemik tersendiri karena rendahnya literasi digital masyarakat. Di desa Patrolsari Arjasari kabupaten Bandung merupakan salah satu daerah yang masyakaratnya terbelenggu oleh pinjaman online. Tujuan pelatihan/pendampingan adalah meningkatkan pengetahuan dan kesadaran masyarakat terkait pengunaan dan pemanfaat aplikasi FinTech. Metode yang digunakan yaitu pertama; pemetaan data awal terkait digital literacy khususnya terkait aplikasi pinjaman online dan era disruptif kedua; melakukan pelatihan terkait kewaspadaan penggunaan platform-platform pinjaman online dan ketiga; pelatihan dan pendampingan pengelolaan keuangan. Hasil pelatihan dan pendampingan berdasarkan hasil kuesioner dan observasi yaitu sebesar 98% sudah memahami perbedaan pinjaman online abal-abal dan diakui OJK, serta 88% masyarakat sudah mengubah pola pengelolaan keuangan. Implikasi pelatihan/pendampingan ini adalah berubahnya pola pengelolaan keuangan dari yang konsumtif menjadi produktif
PENDETEKSIAN KANKER PARU–PARU DENGAN MENGGUNAKAN TRANSFORMASI WAVELET DAN METODE LINEAR DISCRIMINANT ANALYSIS Hanung Tyas Saksono; Achmad Rizal; Koredianto Usman
Jurnal Teknologi Elektro Vol 9, No 1 (2010): (January - June) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

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Abstract

Kanker merupakan pertumbuhan dan penyebaran sel-sel abnormal yang memiliki karakteristik yang khas. Kanker yang sudah menyebar dan tidak dapat terkontrol lagi, biasanya akan menyebabkan kematian. Kanker paru-paru lebih sering menyebabkan pria meninggal dibanding kanker lain, dimana yang sering menjadi penyebab kanker paru-paru adalah merokok. Cara yang digunakan untuk mendeteksi kanker paru-paru ialah melalui pemeriksaan hasil foto rontgen dada. Penelitian ini bertujuan untuk menghasilkan suatu sistem aplikasi yang dapat mendiagnosa citra paru-parudan mengklasifikasikan paru-paruke dalam tipe kanker, normal atau efusi serta menganalisa performansi sistem yang digunakan dalam proses pengenalan citra paru-paru. Proses pendeteksian diawali dengan pemrosesan awal pada citra paru-paru, proses ekstraksi ciri menggunakan Transformasi Wavelet, dan proses klasifikasi menggunakan Linear Discriminant Analysis (LDA). Pemrosesan awal dilakukan untuk membuang informasi yang tidak dibutuhkan dalam pengolahan citra. Proses ekstraksi ciri dilakukan dengan cara mengurangi dimensi citra paru- paru yang akan menjadi masukan pada proses pengenalan menggunakan LDA. Pada penelitian ini citra latih yang digunakan adalah 60 buah citra, yang terdiri dari 20 kelas citra kondisi normal, 20 kelas citra kondisi kanker, dan 20 kelas citra kondisi efusi. Citra uji yang akan digunakan juga berjumlah 60 buah citra, yang tediri dari 20 citra untuk masing-masing kelas. Akurasi yang dihasilkan sistem pada pendeteksian kanker paru-paru ini sebesar 100% untuk citra latih dan 95% untuk citra uji.
Evaluasi Optimizer pada Residual Network untuk Klasifikasi Klon Teh Seri GMB Berbasis Citra Daun USMAN, KOREDIANTO; PRATIWI, NOR KUMALASARI CAECAR; IBRAHIM, NUR; SYAHRIAN, HERI; RAHADI, VITRIA PUSPITASARI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 4: Published October 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i4.841

Abstract

ABSTRAKKomoditas teh berperan strategis terhadap pertumbuhan perekonomian Indonesia, salah satunya dari teh klon Gambung (GMB). Klon GMB memiliki beberapa karakter khas, dengan tingkat kemiripan morfologi yang sangat tinggi. Hal ini berdampak pada proses pengenalan klon GMB dilakukan melalui pengamatan visual oleh tenaga ahli sangat rentan terhadap kesalahan identifikasi. Sehingga, dalam penelitian ini dirancang suatu sistem identifikasi terhadap 11 klon teh seri GMB (GMB-1 hingga GMB-11) dengan menggunakan arsitektur ResNet101. Evaluasi sistem akan dilakukan dengan membandingkan tujuh algoritma optimizer yang berbeda, yaitu; Adam, SGD, RMSProp, AdaGrad, AdaMax, AdaDelta dan Nadam. Hasil pengujian menunjukkan bahwa Adam dan SGD memberikan nilai rata-rata presisi, recall dan F1-score terbaik. Selain itu, Adam memberikan nilai akurasi yang cenderung stabil sejak iterasi pertama. Metode yang diusulkan memberikan tingkat presisi, recall, F1-score sebesar 96% dan akurasi terbaik sebesar 97%.Kata kunci: klasifikasi daun teh, seri Gambung (GMB), CNN, ResNet101 ABSTRACTGambung (GMB) tea is one of the tea commodities that plays a key role in Indonesia's economic development. GMB clones have a number of distinguishing characteristics, including a high degree of morphological similarities. This has an impact on the process of identifying GMB clones through visual observation by experts who are subject to mistakes. In this study, ResNet101 architecture was used to create an identification scheme for 11 tea clones from the GMB series (GMB-1 to GMB-11). System evaluation will be carried out by comparing seven different optimizer; Adam, SGD, RMSProp, AdaGrad, AdaMax, AdaDelta, and Nadam. The test results indicate that Adam and SGD have the highest average accuracy, recall, and f1-score values. Adam also has an accuracy value that has remained consistent since the first iteration. The proposed method provides highest precision, recall, F1-score of 96% and accuracy of 97%.Keywords: tea leaves classification, GMB series, CNN, ResNet101
SISTEM INSPEKSI PERMUKAAN BAJA BERBASIS DEEP LEARNING MENGGUNAKAN METODE ANCHOR-FREE Yuliyanto, Singgih; Nurinda Fadhilah Amani; Fityanul Akhyar; Koredianto Usman
Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer Vol. 2 No. 3 (2022): November: Jurnal Ilmiah Teknik Mesin, Elektro dan Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/juritek.v2i3.364

Abstract

Steel is one of the important materials in the industry. Steel may have defects in the production process that can affect the steel products. Therefore, the detection of steel surface defects is an important process to control the quality of steel products. An efficient steel surface detection process is carried out by automating steel images taken using a camera. We use an anchor-free model FoveaBox. FoveaBox is an accurate and flexible model for detecting objects and has a simple architecture. This study uses the NEU-DET dataset consists of six types of steel surface defects, namely crazing, inclusion, patches, pitted surface, rolled-in scale, and scratches, each with a total of 300 data.. The test results on the system show that the method used has a good detection performance with a mean average precision of 0.834 or 83.4% at a learning rate of 0.001, Optimizer SGD, sigma 0.6, and the number of epochs 24. This detection method can detect steel surface defects. This detection method can effectively detect steel surface defects with similar foreground and background characteristics. With an accuracy threshold of 80%, the method used in this study has an adequate precision value.
Frontend Website Implementation for Breast Cancer Classification System Using Machine Learning Wardani , Shania; Wibowo, Suryo Adhi; Usman, Koredianto
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Early detection of breast cancer is essential to improve patient survival rates. One way to be used for such detection is to develop a classification system based on genomic data, which can provide more accurate and efficient results. This study aims to design and implement a Streamlit-based website frontend, which functions as a breast cancer classification system interface using Machine Learning technology. This user interface is designed with ease of use and optimal user experience, allowing medical personnel to quickly access and understand the analysis results. The main features of this website include an educational dashboard about breast cancer, a simple and structured patient data input form, and predictive analysis results displayed in an interactive format and can be downloaded for further documentation purposes. Tests conducted on the front of this website show that the system response time to display the analysis results is no more than 5 minutes, making it an efficient solution in supporting medical decision-making. With an intuitive and easily accessible interface, this website makes it easy for medical personnel to perform breast cancer analysis faster and more accurately, supporting more effective early detection efforts. Keywords: Streamlit, User Interface, Breast Cancer, Website
Implementation of Machine Learning for Breast Cancer Classification Based on Genomic Data: Backend Solution with Supabase and Streamlit Humayra, Tia Hasna; Wibowo, Suryo Adhi; Usman, Koredianto
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Breast cancer remains one of the leading causes of cancer-related deaths worldwide, highlighting the need for accurate and efficient diagnostic tools. This study focuses on implementing machine learning models, particularly Artificial Neural Networks (ANN), to classify breast cancer types based on genomic data. Using the METABRIC RNA Mutation dataset, the system combines a cloud-based backend with Supabase and an intuitive frontend built with Streamlit. To ensure data compatibility with the models, preprocessing steps such as standardization, label encoding, and one-hot encoding are applied. TensorFlow is used to load models saved in .h5 format, with two approaches tested: a 30-feature model achieving 99% accuracy and an average prediction time of 80 milliseconds, and a 6-feature model achieving 100% accuracy with a faster prediction time of 42.25 milliseconds. Prediction results are stored securely in Supabase, complete with timestamps for tracking and exported as PDF reports for easy documentation. Data security is prioritized through the use of API keys, JWT tokens, and Streamlit secret management to safeguard sensitive information. The integration of Supabase for backend processing, Streamlit for real-time visualization, and GitHub for CI/CD automation results in a scalable, reliable, and efficient system. This study presents a robust solution for breast cancer classification, providing real-time predictions, secure data handling, and a user-friendly interface suitable for clinical and research applications. Keywords— breast cancer classification, artificial neural network, genomic data, Supabase, Streamlit, real-time prediction, data security.
Co-Authors Achmad Rizal Achmad Rizal Achmad, Fariz Adam Agus Kurniawan Aditya S.B, I Dewa Agung Akbar Trisnamulya Putra Alfarizi, Muhammad Alfian Andriyan B. Suksmono Andriyan Bayu Suksmono, Andriyan Bayu ARIS HARTAMAN Bambang Hidayat Bedy Purnama Budiyanto, Anggara Denny Darlis Denta Rahmadani Eriel Mar Fiky Y. Suratman Fityanul Akhyar Gryaningrum Widi Pangestuti Gusti Ngurah Rama Putra Atmaja Hanung Tyas Saksono Hendra Gunawan Hendra Gunawan Heri Syahrian, Heri Heroe Wijanto Huljannah, Miftah Humayra, Tia Hasna Imanadi, Muhammad Tsabit Imanadi Ivy Anindhita Hadyningtyas Jangkung Raharjo Julyano , Muhammad Billy Kalfika Yani Ledya Novamizanti Maharani , Kartika Dwi Maharani, Kartika Dwi Marnelius, Chelsya Dwi Muhammad Ary Murti Nasirudin, Akhmad Yusuf Noor Akhmad Gilar Romadhon Nor Kumalasari Caecar Pratiwi Nur Ibrahim Nurinda Fadhilah Amani Nurinda Fadhilah Amani Pellokila, Victor Aji Admaja Priyambodo, Afif Putranto, Devdan Wisesa Putri Marito R. Irawan Satria Putra R. Yunendah Nur Fu’adah Radhibilla, Maulaya Radian Sigit Raditiana Patmasari Rahmayati Alindra Razief Moch Diar Rita Magdalena Runik Machfiroh Rustam Rustam Sigit, Radian Singgih Yuliyanto SOFIA SAIDAH Sugondo Hadiyoso Suryo Adhi Wibowo Susmini Indriani Lestariningati Syahanifa , Nancy Olivia Syahanifa, Nancy Olivia Tita Haryanti Tsabita Al Asshifa Hadi Kusuma Vitria Puspitasari Rahadi Wardani , Shania Yuliyanto, Singgih Zhafar, Mochamad Noverian