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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Jurnal Teknologi dan Manajemen Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Majalah Ilmiah MOMENTUM Jurnal Informatika Upgris Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING International Journal of New Media Technology ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Teknologi Sistem Informasi dan Aplikasi Systemic: Information System and Informatics Journal Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Terapan (J-TIT) Infotekmesin Jurnal Teknologi Dan Sistem Informasi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JTIULM (Jurnal Teknologi Informasi Universitas Lambung Mangkurat) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Teknologi Informasi Cyberku Moneter : Jurnal Keuangan dan Perbankan
Ricardus Anggi Pramunendar
Faculty Of Computer Science, Universitas Dian Nuswantoro
Aerospace Engineering Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset Saputra, Kurniawan Aji; Alzami, Farrikh; Kurniawan, Defri; Naufal, Muhammad; Muslih, Muslih; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15530

Abstract

Retinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity.
Analisis Pengaruh Hyperparameter terhadap Kinerja MobileNetV2 dan InceptionV3 pada Klasifikasi Retakan Beton rozada, akfi; Baroroh, Nurul; Khoirur Rizky, Muhammad Ivan; Pramunendar, Ricardus Anggi
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 7 No. 2 (2025): Desember 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v7i2.9389

Abstract

Deteksi retakan pada permukaan beton merupakan langkah penting dalam menjaga keandalan dan keselamatan struktur infrastruktur. Metode inspeksi visual masih memiliki keterbatasan karena dipengaruhi kondisi lingkungan, subjektivitas operator, serta potensi kesalahan identifikasi. Untuk mengatasi hal tersebut, penelitian ini membandingkan performa dua arsitektur Convolutional Neural Network (CNN), yaitu MobileNetV2 dan InceptionV3, dalam melakukan klasifikasi citra retakan beton. Dataset yang digunakan adalah NYA-Crack-DATA yang terdiri dari dua kelas, yaitu crack dan no-crack, dengan total 5.026 citra. Seluruh citra diproses melalui tahapan pra-pemrosesan dan augmentasi untuk menghasilkan data yang seragam, lebih variatif, serta mendukung proses pelatihan yang stabil pada kedua model modern tersebut.Penelitian ini berfokus pada analisis pengaruh hyperparameter terhadap performa kedua arsitektur CNN tersebut. Empat hyperparameter utama diuji secara bertahap, meliputi learning rate, dropout, batch size, dan epoch. Evaluasi setiap konfigurasi dilakukan menggunakan Stratified 5-Fold Cross-Validation agar hasil yang diperoleh lebih stabil, konsisten, dan tidak bias. MobileNetV2 menunjukkan performa terbaik pada kombinasi learning rate 0.0005, dropout 0.2, batch size 128, dan 30 epoch, dengan akurasi 0.981, presisi 0.979, recall 0.988, dan F1-score 0.984. Sementara itu, InceptionV3 mencapai akurasi tertinggi sebesar 0.966 pada konfigurasi learning rate 0.0003, dropout 0.8, batch size 128, dan 40 epoch.Hasil penelitian menunjukkan bahwa MobileNetV2 lebih unggul dalam akurasi, stabilitas, serta efisiensi komputasi dibandingkan InceptionV3, sehingga lebih sesuai untuk implementasi nyata pada perangkat dengan keterbatasan sumber daya komputasi modern.
Optimized LightGBM Model for Predicting Total Cup Points of Arabica Coffee using Sensory Cupping Data Arya Rezagama Sudrajat; Ricardus Anggi Pramunendar; Mohammad Syaifur Rohman
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16348

Abstract

Evaluating coffee quality through sensory cupping is essential but inherently subjective, as scoring depends on the consistency and expertise of professional panelists. To improve objectivity, this study applies the Light Gradient Boosting Machine (LightGBM) algorithm to predict the Total Cup Points of Arabica coffee using sensory evaluation data. The dataset, obtained from the Coffee Quality Institute Arabica Reviews (May 2023), contains 1,509 cupping records assessed according to the Specialty Coffee Association (SCA) protocol. Nine sensory attributes aroma, flavor, aftertaste, acidity, body, balance, uniformity, clean cup, and sweetness were used as predictors. The modeling process included data preprocessing, feature selection, hyperparameter tuning using RandomizedSearchCV, and performance evaluation through 5-Fold and 10 Fold Cross-Validation. The tuned LightGBM model achieved an R² of 0.9634 and an RMSE of 0.4673 under the 10-Fold scheme. Comparative analysis showed that LightGBM produced lower prediction error than XGBoost, Random Forest, and Support Vector Regression (SVR) when evaluated under identical default parameter settings. Feature importance indicated that flavor, balance, clean cup, and aftertaste were the most influential contributors to total cup points. The findings provide a reliable computational framework to support more objective, consistent, and efficient coffee cupping assessments
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
Analisis Hyperparameter Tuning MobileNetV2 dengan Metode Sequential Search dalam Sistem Klasifikasi Penyakit Daun Kentang Khoirur Rizky, Muhammad Ivan; Rozada, Akfi; Baroroh, Nurul; Pramunendar, Ricardus Anggi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8786

Abstract

Indonesia’s national potato production faces significant threats from leaf diseases, while manual classification remains slow, subjective, and prone to error due to the high visual similarity across disease categories. This highlights the need for a precise and reliable automated classification system. However, many previous studies have not applied systematic hyperparameter optimization, leaving the capacity of deep learning architectures underutilized. Addressing this research gap, this study aims to enhance the performance of MobileNetV2 for potato leaf disease classification through a structured hyperparameter optimization process. A Sequential Search strategy validated through 3 fold Stratified Cross Validation is employed to obtain stable performance estimates. Four key hyperparameters are examined: learning rate from 0.001 to 0.009, dropout from 0.1 to 0.9, batch size from 8 to 192, and epochs from 10 to 100. The optimal configuration consists of a learning rate of 0.007, dropout of 0.2, batch size of 32, and 60 epochs, which enables MobileNetV2 to achieve an accuracy of 99 percent. Despite this strong performance, evaluation results reveal a minor limitation in the Young Blight class, where precision is slightly lower due to overlapping visual characteristics. These findings establish a new benchmark for potato leaf disease classification and provide a reproducible optimization framework for future studies. The study offers both methodological and practical contributions to the development of precise and efficient plant disease classification systems within the context of smart agriculture.
Addressing Class Imbalance in Android Backdoor Malware DetectionUsing Ensemble Models Megantara, Rama Aria; Pergiwati, Dewi; Alzami, Farrikh; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Naufal, Muhammad; Brilianto, Rivaldo Mersis
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 25 No. 2 (2026)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v15i2.6198

Abstract

Backdoor malware represents one of the most critical threats in the Android ecosystem due to its capability to enable covert remote access, escalate privileges, and exfiltrate sensitive data without user awareness. Although the CCCS-CIC-AndMal-2020 dataset is publicly available, prior studies have not specifically formulated Backdoor detection as a binary classification problem under extreme class imbalance, nor systematically evaluated the impact of oversampling and cost-sensitive weighting using imbalance-aware performance metrics. This study proposes a comprehensive detection pipeline that integrates ensemble learning, class imbalance handling strategies, and explainability-based analysis to extract behavioral signatures of Backdoor malware. A two-stage feature selection process is employed to reduce the original 9,502-dimensional feature space to 500 informative features. Subsequently, five classification algorithms are evaluated under three imbalance-handling scenarios using a composite ranking criterion based on F1-score, Area Under the Receiver Operating Characteristic Curve (AUC), Geometric Mean (G-Mean), and Matthews Correlation Coefficient (MCC). The experimental results demonstrate that the Random Forest model combined with Synthetic Minority Oversampling Technique (SMOTE) achieves the best performance, with an F1-score of 0.9043, AUC of 0.9909, G-Mean of 0.9422, and MCC of 0.8948. Furthermore, SHAP analysis identifies 39 Android permissions related to account access, covert communication, and privilege escalation as key behavioral signatures, with the permissions feature group contributing 2.31 times higher discriminative importance than nonpermission features. These findings indicate that interpretable ensemble learning not only improves detection performance but also provides actionable insights for static malware analysis.
Semantic segmentation of pendet dance images using multires U-Net architecture Ramdan, Hendri; Soeleman, Moh. Arief; Purwanto, Purwanto; Imran, Bahtiar; Pramunendar, Ricardus Anggi
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1316.329-338

Abstract

As a cultural heritage, traditional dance must be protected and preserved. Pendet dance is a traditional dance from Bali, Indonesia. Dance recognition raises a complex problem for computer vision research because the features representing the dancer must focus on the dancer's entire body. This can be done by performing a segmentation task process. One type of segmentation task in computer vision is the semantic segmentation. Mask R-CNN and U-NET were employed in this task. Since it was first introduced in 2015, semantic segmentation using the U-Net architecture has been widely adopted, developed, and modified. One of the new architectures applied is the MultiRes UNet. This study carries out a semantic segmentation task on the Balinese Pendet dance image using the MultiRes UNet architecture by changing the value of α (alpha) to obtain the best results. This architectural is evaluated by DC score, Jaccard index, and MSE. In this dataset, the alpha value of 1.9 resulted in the best score for DC and the Jaccard index with 98.47% and 99.23% respectively. On the other hand, an alpha value of 1.8 obtained the best score of MSE with 8.20E-04.
Classification of Lombok Pearls using GLCM Feature Extraction and Artificial Neural Networks (ANN) Karim, Muh Nasirudin; Pramunendar, Ricardus Anggi; Soeleman, Moch Arief; Purwanto, Purwanto; Imran, Bahtiar
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1317.209-217

Abstract

This study used the second-order Gray Level Co-occurrence Matrix (GLCM) and pearl image classification using the Artificial Neural Network (ANN). No previous research combines the GLCM method with artificial neural networks in pearl image classification. The number of images used in this study is 360 images with three labels, including 120 A images, 120 AA images, and 120 AAA images. The epochs used in this study were 10, 20, 30, 40, 50, 60, 70, and 80. The test results at epoch 10 got 80.00% accuracy, epoch 20 got 90.00% accuracy, epoch 30 got 93.33% accuracy, and epoch 40 got 94.44% accuracy. In comparison, epoch 50 got 95.55% accuracy, epoch 60 got 96.66% accuracy, epoch 70 got 96.66% accuracy, and epoch 80 got 95.55% accuracy. The combination of the proposed methods can produce accuracy in classifying pearl images, such as the classification test results.
Implementation of RFM Method and K-Means Algorithm for Customer Segmentation in E-Commerce with Streamlit Alzami, Farrikh; Sambasri, Fikri Diva; Nabila, Mira; Megantara, Rama Aria; Akrom, Ahmad; Pramunendar, Ricardus Anggi; Prabowo, Dwi Puji; Sulistiyawati, Puri
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1524.32-44

Abstract

E-commerce is selling and buying goods through an online or online system. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One thing that needs to be considered in the business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers to maintain good relationships with customers and increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature and display it in the form of a dashboard using the Streamlit framework. Several stages of research must be carried out. Firstly, taking data from the open public data site (Kaggle), then merging the data to select some data that needs to be used, understanding data by displaying it in graphic form, and conducting data selection to select features/attributes. The step follows the proposed method, performs data preprocessing, creates a model to get the cluster, and finally displays it as a dashboard using Streamlit. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Co-Authors Abdul Syukur Abu Salam Ade Yusupa Affandy Affandy Agus Winarno, Agus Agustina, Feri Ahmad Akrom Akrom, Ahmad Al-Azies, Harun ALI MUQODDAS Alvin, Fris Alzami, Farrikh Andi Kamaruddin Apriyanto Alhamad Arie Nugroho, Arie Arifin, Zaenal Arya Rezagama Sudrajat Aurelia Monica Sari Azzahra, Tarissa Aura Baroroh, Nurul Bastiaans, Jessica Carmelita Brilianto, Rivaldo Mersis Catur Supriyanto Catur Supriyanto Catur Supriyanto Catur Supriyanto D, Ishak Bintang Danny Oka Ratmana Darmawan, Aditya Aqil De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Diana Aqmala Dibyo Adi Wibowo Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Dzuha Hening Yanuarsari, Dzuha Hening Edi Noersasongko Enrico Irawan Erlin Dolphina Etika Kartikadarma Evanita Evanita, Evanita F. Alzami Fafaza, Safira Alya Fajrian Nur Adnan Fakhrurrozi Fakhrurrozi, Fakhrurrozi Farikh Al Zami Fathorazi Nur Fajri Fatkhuroji Fatkhuroji Fauzi Adi Rafrastara Fikri Diva Sambasri Finki Dona Marleny Firmansyah, Muhammad Ilham Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hamid, Maulana As’an Hartojo, James Harun Al Azies Hasan Asari Haydar, Muhammad Rifqi Fajrul Henry Bastian, Henry I Ketut Eddy Purnama Ifan Rizqa Ika Novita Dewi Imran, Bahtiar Irham Ferdiansyah Katili Iswahyudi Iswahyudi Karim, Muh Nasirudin Karis W. Kartika, Gita khoiriya latifah Khoirunnisa, Emila Khoirur Rizky, Muhammad Ivan Kristhina Evandari Kurnia Prayoga Wicaksono Kurniawan Aji Saputra Kurniawan, Defri Kusumawati, Yupie Lalang Erawan Lesmarna, Salsabila Putri M. Arif Soeleman M. Arif Soleman Mambang Maulana, Isa Iant Megantara, Rama Aria Moch Arief Soeleman Moch Arief Soeleman, Moch Arief Moch. Sjamsul Hidajat Mochamad Arief Soeleman Mochamad Hariadi Moh Yusuf, Moh Moh. Yusuf Mohammad Arif Mohammad Syaifur Rohman Muhammad Alkaff Muhammad Naufal Muhammad Nursandi Muhammad Syaifur Rohman Muhammad Zulfadhilah Muljono, - Muslih Muslih Muslih Muslih Nabila, Mira Noor Wahyudi Nuanza Purinsyira Nugroho, Muhammad Bayu Nur Azise Nurhindarto, Aris Nurhindarto, Aris Paramita, Cinantya Pergiwati, Dewi Prabowo, D.P. Pradana, Rifky Bintang Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Purwanto Purwanto Putu Samuel Prihatmajaya R.A. Megantara Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ramdan, Hendri Ratmana, Danny Oka Riadi, Muhammad Fatah Abiyyu Rifqi Mulya Kiswanto Ritzkal, Ritzkal Rohman, Muhammad Syaifur Rony Wijanarko Rozada, Akfi Ruri Suko Basuki Sambasri, Fikri Diva Santoso, Siane Saputra, Filmada Ocky Saputra, Resha Mahardhika Saraswati, Galuh Wilujeng Sasono Wibowo Sinaga, Daurat Soeleman, M. Arief Soeleman, Moh. Arief Sri Winarno Stefanus Santosa Subhan Panji Cipta Sulistyowati, Tinuk Sunardi, Ph.D., Sunardi Sutini Dharma Oetomo Tamamy, Aries Jehan Teguh Tamrin Ullumudin, D.I.I Usman Sudibyo Vincent Suhartono Vincent Suhartono Vincent Suhartono Wibowo, Gentur Wahyu Nyipto Wijaya, Eka Setya Wildanil Ghozi Winarsih, Nurul Anisa Sri Yudha Tirto Pramonoaji Yuliman Purwanto Yuslena Sari, Yuslena Yuventius Tyas Catur Pramudi Zainal Arifin Hasibuan