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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Teknik Elektro Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Panrita Abdi - Jurnal Pengabdian pada Masyarakat JIKO (Jurnal Informatika dan Komputer) Techne : Jurnal Ilmiah Elektroteknika JMM (Jurnal Masyarakat Mandiri) JUTEI (Jurnal Terapan Teknologi Informasi) Indonesian Journal of electronics, electromedical engineering, and medical informatics Jurnal Abdi Insani Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Almufi Jurnal Pengabdian kepada Masyarakat KOMMAS: Jurnal Pengabdian Kepada Masyarakat Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII eProceedings of Engineering Community Service Seminar and Community Engagement (COSECANT) Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Jurnal Nasional Sains dan Teknik
Ledya Novamizanti
Telkom University
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Analisis Perbandingan Kompresi Haar Wavelet Transform dengan Embedded Zerotree Wavelet pada Citra NOVAMIZANTI, LEDYA; KURNIA, ADRIAN
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 3, No 2: Published July - December 2015
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v3i2.161

Abstract

ABSTRAKKompresi data merupakan salah satu teknologi pemicu revolusi multimedia. Haar Wavelet mampu merepresentasikan ciri tekstur dan bentuk, sedangkan Embedded Zerotree Wavelet (EZW) mampu menyusun bit-bit menurut tingkat prioritas, sehingga mampu mencapai kompresi maksimal. Pada penelitian ini telah dilakukan perbandingan Haar Wavelet Transform dengan Embendded Zerotree Wavelet untuk kompresi citra. Pengujian menggunakan 4 citra grayscale berformat bitmap (.bmp) dengan resolusi 256x256 dan 512x512. Rasio Kompresi yang diperoleh dengan menggunakan algoritma Embedded Zerotree Wavelet dan Haar Wavelet, yaitu 99.54% dan 95.35% pada threshold 80. Laju bit antara Embedded Zerotree Wavelet lebih rendah dibandingkan Haar Wavelet, yaitu 0.06 bpp dan 0.13 bpp. Algoritma Haar Wavelet memberikan waktu kompresi lebih baik dibandingkan EZW dimana selisih antara keduanya sekitar 8 detik.Kata kunci: kompresi citra,  threshold, Haar Wavelet, Embedded  Zerotree WaveletABSTRACTData compression is one of the triggers of the revolution multimedia technology. Haar Wavelet able to represent the characteristics of texture and shape, while Embedded Zerotree Wavelet (EZW) is able to arrange the bits according to priority level, so as to achieve maximum compression. In this study, we had conducted comparison between Haar Wavelet Transform with Embedded Zerotree Wavelet algorithm for image compression. The tests using 4 image format grayscale bitmap (.bmp) with resolution of 256x256 pixels and 512x512 pixels. Compression ratio obtained using Embedded Zerotree Wavelet and Wavelet Haar algorithm, which are 99.54% and 95.35% respectively, at the threshold of 80. The bit rate on Embedded Zerotree Wavelet is lower than Haar wavelet, that is 0:06 bpp and 0:13 bpp respectively. Haar Wavelet algorithm gives a better compression time than the EZW, with the difference between the two is about 8 seconds.Keywords: image compression, threshold, Haar Wavelet, Embedded  Zerotree Wavelet.
Sistem Inspeksi Cacat pada Permukaan Kayu menggunakan Model Deteksi Obyek YOLOv5 AKHYAR, FITYANUL; NOVAMIZANTI, LEDYA; RIANTIARNI, TITA
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 10, No 4: Published October 2022
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i4.990

Abstract

ABSTRAKPermukaan kayu mengalami berbagai serangan serangga dan jamur, sehingga dapat menyebabkan cacat seperti pembusukan pada kayu yang berpengaruh terhadap kualitas dan harga jual kayu tersebut. Pengujian secara lapangan dengan penglihatan manusia menjadi kurang efektif, karena menghasilkan penilaian yang subjektif dan memerlukan waktu yang lama. Penelitian ini mengusulkan sistem deteksi cacat pada permukaan kayu pinus dan kayu karet menggunakan Convolutional Neural Network (CNN) dengan model YOLOv5. Pengujian sistem dilakukan menggunakan beberapa model dari YOLOv5, serta dua teknik image enhancement, yaitu edge filter dan Real ESRGAN. Hasil mAP terbaik sebesar 94,3% dengan kecepatan 125 FPS pada dataset kayu pinus menggunakan model YOLOv5s tanpa penambahan image enhancement. Sedangkan pada dataset kayu karet yang memiliki jenis cacat yang lebih kompleks, hasil mAP terbaik adalah sebesar 94,7% dengan kecepatan 139 FPS menggunakan model YOLOv5s-Transformer dengan penambahan image enhancement Real ESRGAN.Kata kunci: deteksi, kayu, Convolutional Neural Network (CNN), YOLO ABSTRACTWood surafce is subject to various insect and fungal attacks, which can cause defects such as wood rot. This condition affects the quality as well as the selling price of the wood. Field testing with human eyesight becomes less effective because it produces a subjective assessment and time consuming. This study proposes a defect detection system on the surface of pine wood and rubber wood using the Convolutional Neural Network (CNN) with the YOLOv5 model. System testing was carried out using several models from YOLOv5 and two image enhancement techniques, namely edge filter and Real ESRGAN. The best mAP results were 94.3%, with a speed of 125 FPS on the pine wood dataset using the YOLOv5s model without adding image enhancement. While on the rubber wood dataset with more complex defect problem, the best mAP results were 94.7% with a speed of 139 FPS using the YOLOv5s-Transformer model with the addition of image enhancement Real ESRGAN.Keywords: detection, wood, Convolutional Neural Network (CNN), YOLO
Super Resolution pada Citra Udara menggunakan Convolutional Neural Network ABDULFATTAH, MUHAMMAD EFAN; NOVAMIZANTI, LEDYA; RIZAL, SYAMSUL
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 9, No 1: Published January 2021
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v9i1.71

Abstract

ABSTRAKBencana di Indonesia didominasi oleh bencana hidrometeorologi yang mengakibatkan kerusakan dalam skala besar. Melalui pemetaan, penanganan yang menyeluruh dapat dilakukan guna membantu analisa dan penindakan selanjutnya. Unmanned Aerial Vehicle (UAV) dapat digunakan sebagai alat bantu pemetaan dari udara. Namun, karena faktor kamera maupun perangkat pengolah citra yang tidak memenuhi spesifikasi, hasilnya menjadi kurang informatif. Penelitian ini mengusulkan Super Resolution pada citra udara berbasis Convolutional Neural Network (CNN) dengan model DCSCN. Model terdiri atas Feature Extraction Network untuk mengekstraksi ciri citra, dan Reconstruction Network untuk merekonstruksi citra. Performa DCSCN dibandingkan dengan Super Resolution CNN (SRCNN). Eksperimen dilakukan pada dataset Set5 dengan nilai scale factor 2, 3 dan 4. Secara berurutan SRCNN menghasilkan nilai PSNR dan SSIM sebesar 36.66 dB / 0.9542, 32.75 dB / 0.9090 dan 30.49 dB / 0.8628. Performa DCSCN meningkat menjadi 37.614dB / 0.9588, 33.86 dB / 0.9225 dan 31.48 dB / 0.8851.Kata kunci: citra udara, deep learning, super resolution ABSTRACTDisasters in Indonesia are dominated by hydrometeorological disasters, which cause large-scale damage. Through mapping, comprehensive handling can be done to help the analysis and subsequent action. Unmanned Aerial Vehicle (UAV) can be used as an aerial mapping tool. However, due to the camera and image processing devices that do not meet specifications, the results are less informative. This research proposes Super Resolution on aerial imagery based on Convolutional Neural Network (CNN) with the DCSCN model. The model consists of Feature Extraction Network for extracting image features and Reconstruction Network for reconstructing images. DCSCN's performance is compared to CNN Super Resolution (SRCNN). Experiments were carried out on the Set5 dataset with scale factor values 2, 3, and 4. The SRCNN sequentially produced PSNR and SSIM values of 36.66dB / 0.9542, 32.75dB / 0.9090 and 30.49dB / 0.8628. DCSCN's performance increased to 37,614dB / 0.9588, 33.86dB / 0.9225 and 31.48dB / 0.8851.Keywords: aerial imagery, deep learning, super resolution
Optimasi Sistem Penyembunyian Data pada Audio menggunakan Sub-band Stasioner dan Manipulasi Rata-rata Statistik NOVAMIZANTI, LEDYA; BUDIMAN, GELAR; WIBOWO, BHISMA ADI
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 6, No 2: Published May 2018
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v6i2.165

Abstract

ABSTRAKKasus pelanggaran hak cipta musik atau lagu menjadi masalah dan mendapat perhatian serius oleh industri musik di Indonesia. Teknik audio watermarking merupakan salah satu solusi untuk melindungi hak cipta audio digital dari tindakan ilegal dengan cara menyembunyikan watermark berupa identitas pemilik ke dalam audio tersebut. Pada penelitian ini, audio host diubah menjadi matriks 1-dimensi untuk proses framing. Selanjutnya Stationary Wavelet Transform (SWT) digunakan untuk mendapatkan sub- band stasioner terpilih yang akan disisipkan watermark. Metode Statistical Mean Manipulation (SMM) akan menghitung rata-rata host audio dalam satu frame, dan dilakukan proses penyisipan bit. Optimasi dilakukan dengan melakukan evaluasi parameter yang menghasilkan BER paling tinggi setelah sistem diberikan serangan. Hasil dari optimasi diperoleh suatu sistem audio watermarking yang kuat dan tahan terhadap gangguan signal, dengan rata-rata BER 0.113, SNR 31 dB, ODG -0.6, dan MOS 4.6.Kata kunci: audio watermarking, SWT, SMM, optimasiABSTRACTThe case of copyright infringement of music or song becomes a serious problem in Indonesia. Audio watermarking technique is one solution to protect the music copyright of digital audio from illegal acts by hidding the watermark in the form owner's identity into the audio. The workings of audio watermarking is to embed the watermark in the form owner's identity into the audio. In this study, the audio host is converted into a 1-dimensional matrix for the framing process. Furthermore Stationary Wavelet Transform (SWT) used to obtain stationary sub-bands selected to be inserted watermark. Statistical methods Mean Manipulation (SMM) will calculate the average host audio in one frame, and do bits insertion process. Optimization is done by evaluating the parameters that produce the highest BER after the system is given an attack. The results of the optimization obtained an audio watermarking system that is robust and resistant to signal interference, with the average BER 0.113, SNR 31 dB, ODG -0.6, and MOS 4.6. Keywords: audio watermarking, SWT, SMM, optimization
Deteksi Level Kolesterol melalui Citra Mata Berbasis HOG dan ANN SIDDIK, MUHAMMAD ARSYAD; NOVAMIZANTI, LEDYA; RAMATRYANA, I NYOMAN APRAZ
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 2: Published May 2019
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i2.284

Abstract

ABSTRAKKolesterol merupakan lemak yang berada di dalam darah yang dibutuhkan untuk pembentukan hormon dan sel baru. Kadar kolesterol normal harus kurang dari 200 mg/dL, namun jika di atas 240 mg/dL akan berisiko tinggi terkena penyakit stroke dan jantung koroner. Penelitian ini menghasilkan suatu sistem yang dapat mendeteksi kadar kolesterol seseorang melalui citra mata menggunakan metode iridologi dan image processing. Citra mata diperoleh dari pasien laboratorium klinik sebanyak 120 citra mata. Proses sistem diawali dengan mengolah citra mata dengan metode cropping, resize, dan segmentasi. Metode ekstaksi ciri menggunakan Histogram of Oriented Gradients (HOG), dan klasifikasi menggunakan Artificial Neural Network (ANN). Sistem dapat mendeteksi kadar kolesterol dengan tiga level klasifikasi, yaitu normal, berisiko kolesterol tinggi, dan kolesterol tinggi dengan tingkat akurasi sebesar 93% dan waktu komputasi 0,0862 detik.Kata kunci: citra mata, kadar kolesterol, Histogram of Oriented Gradients, Artificial Neural Network ABSTRACTCholesterol is fat in the blood that is needed for the formation of hormones and new cells. Normal cholesterol levels should be less than 200 mg / dL, but if above 240 mg / dL will be at high risk of stroke and coronary heart disease. This study produced a system that can detect a person's cholesterol levels through eye images using iridology and image processing methods. Eye images obtained from clinical laboratory patients were 120 eye images. The system process begins with processing eye images using the method of cropping, resizing, and segmentation. Feature extraction method uses Histogram of Oriented Gradients (HOG), and classification using Artificial Neural Network (ANN). The system can detect cholesterol levels with three levels of classification, namely normal, at high risk of cholesterol, and high cholesterol with an accuracy rate of 93% and computing time of 0.0862 seconds.Keywords: eye image, cholesterol level, Histogram of Oriented Gradients, Artificial Neural Network
APLIKASI MOBILE UNTUK MENGIDENTIFIKASI KUALITAS IKAN CAKALANG BERBASIS ANDROID Fredigo, Agno; Novamizanti, Ledya; Akhyar, Fityanul
Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Vol. 4 No. 1 (2024): Desember 2024
Publisher : Perpustakaan Universitas Telkom

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Abstract

Dalam era digital yang terus berkembang dengan sangat signifikan ini, pemanfaatan teknologi berbasis deep learning, aplikasi mobile, dan komputasi awan atau sering disebut cloud telah menjadi bagian dari kehidupan sehari-hari. Dalam penelitian ini, makalah ini akan melakukan riset mengenai ketiga teknologi tersebut terutama di bagian mobile application berbasis android untuk mendeteksi kualitas dari ikan cakalang. Ikan cakalang merupakan salah satu jenis ikan yang memiliki nilai ekonomi yang cukup tinggi di pasaran, namun untuk kualitasnya dapat bervariasi. Saat ini proses pemeriksaan kualitas ikan cakalang masih dilakukan secara manual menggunakan indra manusia. Untuk memastikan kualitas ikan cakalang yang optimal, diperlukan sistem yang efisien dan akurat dalam mendeteksi kualitas ikan cakalang. Makalah ini mengusulkan berupa aplikasi mobile berbasis android untuk mendeteksi ikan cakalang. Untuk penamaan mobile application ini yakni Qfish.  Dalam pembuatan aplikasi berbasis android ini, sudah banyak alat bantu berupa software untuk mempermudah developer dalam membuat aplikasi android dengan bermodalkan kemampuan dibidang programming, salah satu dari software itu menggunakan android studio dengan menggunkan Bahasa pemograman Kotlin. Qfish ini bermanfaat untuk meningkatkan proses pendeteksian kualitas ikan cakalang, sehingga dapat memberikan manfaat yang besar bagi industri perikanan. Kata kunci—  Qfish, Mobile Application, Android Studio, Ikan Cakalang
Aplikasi Watermark Robust Reversible dengan Antarmuka Putra, Afi Athallah Syamsulhadi; Novamizanti, Ledya; Budiman, Gelar
Telkatika: Jurnal Telekomunikasi Elektro Komputasi & Informatika Vol. 4 No. 1 (2024): Desember 2024
Publisher : Perpustakaan Universitas Telkom

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Abstract

Dalam era digital, keaslian dan keamanan citra digital menjadi sangat penting mengingat mudahnya konten digital diduplikasi dan dimodifikasi tanpa izin. Penulisan ini bertujuan untuk mengembangkan aplikasi robust reversible watermarking citra serta antarmuka pengguna yang intuitif. Fokus utama pengembangan aplikasi adalah pada kemudahan penggunaan agar dapat diakses oleh berbagai kalangan. Aplikasi yang dikembangkan merupakan aplikasi desktop yang memiliki antarmuka dan fungsi-fungsinya. Hasil pengujian dengan dua perangkat yaitu laptop dan PC menunjukkan bahwa aplikasi Wmarks mampu menunjukkan responsivitas latency yang cukup bagus dengan selisih rata-rata 58.079 ms. Berdasarkan pengujian black box, Wmarks berhasil menjalankan fitur dengan baik. Memiliki performa yang cukup baik dengan memperoleh hasil CPU usage dengan kategori “baik” dengan rata-rata 0-4% pada kondisi idle dan memperoleh kategori “buruk” dengan rata-rata 24-68% pada kondisi fungsi aplikasi tereksekusi. Berdasarkan hasil pengujian system usability yang dilakukan oleh 30 responden, aplikasi Wmarks mendapatkan total nilai SUS (System Usability Scale) sebesar 80.35 dengan grade A yang menunjukkan bahwa aplikasi dapat diterima dan digunakan oleh pengguna dengan sistem operasi desktop. Kata kunci— teknologi watermarking, citra digital, antarmuka pengguna, latency responsiveness, black box, spread spectrum adaptive.
Automated Tuna Freshness Assessment via Gas Sensors and Machine Learning Algorithms Pratama , Nyoman Raflly; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Ensuring the safety and health of fish products is crucial for public health, with tuna being Indonesia's second most popular fishery product. Tuna freshness is a key indicator of seafood safety, directly impacting both nutritional quality and contamination risk. This study compares the K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machine (SVM) algorithms to assess and classify tuna freshness, offering an accurate and efficient approach. A machine learning model categorized Tuna freshness based on the gases emitted, utilizing a dataset of 58,389 records. Gas changes were detected using the MQ-135, MQ-9, and MQ-2 sensors, which are highly sensitive to gases like ammonia, methane, and alcohol, commonly associated with spoilage. The KNN, Naive Bayes, and SVM algorithms were then applied to classify the sensor data. KNN and SVM achieved an accuracy of 99%, while Naive Bayes reached 90%. The high accuracy of these methods highlights their potential as practical tools for the fishing industry, enabling suppliers and retailers to assess tuna freshness more effectively. This method could significantly improve consumer safety by ensuring only high-quality, fresh products reach the market. Additionally, automation offers substantial time savings, facilitates faster decision-making, and reduces reliance on manual inspections prone to human error. Keywords—tuna, classification, gas sensor, machine learning
Comparison of k-NN and Naive Bayes Algorithms for Classifying Mackerel Tuna Freshness For Real-Time Classification Using Gas Sensors Setyagraha , Muhammad Rafi Mahfuz; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

The large production and consumption of mackerel tuna in Indonesia reflect its importance as a local staple and a valuable export product contributing to the nation's economy. Mackerel tuna is prized for its nutritional content and affordability, making it a crucial part of the diet for many Indonesians. Ensuring the freshness and quality of this high-demand product is essential. This study introduces a machine-learning approach to detect fish freshness by analyzing gases emitted during spoilage, utilizing MQ-2, MQ-9, and MQ-135 gas sensors. The data were processed using the k-Nearest Neighbors (k-NN) and Naive Bayes algorithms, both achieving accuracy rates near 100%. These findings highlight the system’s potential to enhance quality control in Indonesia’s fishery industry by offering an efficient and reliable method for assessing fish freshness. Keywords—classification, machine learning, tuna, gas sensor
Crab Quality Detection with Gas Sensors Using a Machine Learning Hermawan, Laksamana Mikhail; Novamizanti, Ledya; Wijaya, Dedy Rahman
eProceedings of Engineering Vol. 12 No. 2 (2025): April 2025
Publisher : eProceedings of Engineering

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Abstract

Crab is a widely recognized and favored seafood product globally. Crab’s delicious taste and high nutritional value, particularly its protein content, make it a desirable food choice. Given the global popularity of seafood, including crabs, maintaining its quality is essential for both economic and consumption purposes. However, seafood products are prone to rapid spoilage due to their high-water content, with spoilage rates varying among different types of seafood. It is crucial for industries to monitor and ensure the quality of their products before they reach the market. Given the high demand for crabs, there is a pressing need for an effective method to assess their quality. This research seeks to establish a method for assessing the freshness and quality of crabs using an electronic nose (e-nose) system, employing machine learning algorithms for classification analyses. Three algorithms will be utilized, along with hyperparameter optimization, to achieve optimal accuracy in evaluating crab quality. These algorithms are K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Naïve Bayes. The highest result is achieved by K-NN methods with 98% accuracy percentage. The proposed method of this research has acquired targets that can contribute to advancing seafoods production for industries. Keywords—crabs, detection, e-nose, machine learning, quality
Co-Authors ABDULFATTAH, MUHAMMAD EFAN Aditya, Ghanes Mahesa ADRIAN KURNIA, ADRIAN Adviatmadja, Sebastian Danny Agnes Gabriela Putri Winata Agung Nugroho Jati Ahmad Akbar Khatami Ahmad Alfi Adz Dzikri Ahmad Fauzan Fauzan Aldra Kasyfil Aziz Amini, Siti Aisyah Andy Ruhendy Putra ANGGUNMEKA LUHUR PRASASTI Annida, Nurafifah Aqilah Mamur Tanjung , Najmi Arindaka, Hafizhan Bhamakerti Armanda Nur Fadhlillah Ashri Dinimaharawati Aulia Wibowo Bambang Hidayat Cindy Angelista Deltika Cucu Alex Zaenudin Danny Adviatmadja, Sebastian David Chandra De Lima, Nadya Viana Dedy Rahman Wijaya Denny Meilika Setiawati Desri Kristina Silalahi Dias Wardana Dick Maryopi Dien Rahmawati Dimitri Mahayana Dine Octavia Kumalasari Eko Susatio Elsa Nur Fitri Astuti Elsa Nur Fitri Astuti Erizka Banuwati Candrasari Fahriansyah, Ardy Fajri, Farhan Ulil FARDAN FARDAN, FARDAN Faris Fadhlur Rachman Fathiyya, Dhiya Faza, Lulu Balqis Zianka Felix Pidha Hilman Fenty Alia Fikri Adhanadi Firdaus, Rifqi Fadhilah Fityanul Aditya Fityanul Akhyar Fredigo, Agno Gelar Budiman Gogi Gautama Al Hadiid HAFIZHANA, YASQI Hakim, Farhan Nur HANNAN HARAHAP, HANNAN Hanum, Mirza Alifia Harist Gymnovriza Hermawan, Laksamana Mikhail Husneni Mukhtar I Gusti Putu Agung Satria Bayu Mahendra I N Apraz Ramatryana I Nyoman Apraz Ramatryana Ilman, Mukhamad Zidni Imansyah Basudewa , Muhammad Indra Aulia Intan Sulviyani Irma Safitri Ivandy Chaniago Ivany Sesa Rehadi Iwan Iwut Iwan Iwut Tritoasmoro Iwut Tritoasmoro, Iwan Jangkung Raharjo Koredianto Usman Kurnia Ramadani Kurniawan Nur Ramadhani Mahanani, Edo Lutfi Mahfuz, Muhammad Rafi Marlindia Ike Sari Maulana , Muhammad Dafa Mertu, Aidi Mirsa Bayu Prasetyo Mochamad Reyhand Landrenzy Zulfikar Mohamad Alfaj’ri Muhammad Alief Hidayah Baso Muhammad Azwar Zulmi Muhammad Biyan Priatama Muhammad Fikri Aufa Muhammad Hablul Barri Muhammad Harits Ibrahim Muhammad Iqbal Rabbani Muhammad Raia Pratama Putra Wibowo Muhammad Rayhan Ghifari Muhammad Rizqy Alfarisi Muhammad Sindu Ramadhan Muhammad Wahyu Setiawan Nabila Setya Utami Novelita Dwi Miranda Novialdy Nugroho Santoso Nur Ibrahim Paradila I., Dela Parjuangan, Sabam Pinasthika Aulia Fadhila Pratama , Nyoman Raflly Prawita, Fat’hah Noor Priyambodo, Afif Putra, Afi Athallah Syamsulhadi Putu Harry Gunawan R Ricki Juniansyah R. Yunendah Nur Fu’adah Rabby Fitriana Adawiyah Rahma Nur Auliasari Rahmawati, Aulya RAMATRYANA, I NYOMAN APRAZ Randy Hamzah Hardianto Ratri Dwi Atmaja Razendra Zahran Firdaus Reyhan Radifan Jordy RIANTIARNI, TITA Rita Magdalena Rita Purnamasari Rita Rismala Rizal, Mochammad Fahru Rochmawati Ruslan , Ramah Rinaldi Ryan Anggara Sa'idah, Sofia Sari, Rina Media Satria Mandala Sa’idah, Sofia Setyagraha , Muhammad Rafi Mahfuz SIDDIK, MUHAMMAD ARSYAD Siti Azizah Suci Aulia Sugondo Hadiyoso Sulistyowati, Syifa Dwi Suryo Adhi Wibowo Susatio, Eko Susi Diriyanti Novalina Syamsul Rizal Syamsul Rizal Tanjung, Najmi Aqilah Mamur Thomhert Suprapto Siadari Thoriq Bayu Aji Tora Fahrudin Wahidin Wahidin WANANDA, PUTU DEBBY WIBOWO, BHISMA ADI Wicaksono, Muhammad Rievnuansyah YUYUN SITI ROHMAH Zahra Zettira Zukhrufuljannah Zaky, Pavel Manaf El