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All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Jurnal Teknik ITS IPTEK The Journal for Technology and Science Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Setrum : Sistem Kendali-Tenaga-elektronika-telekomunikasi-komputer agriTECH Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) EMITTER International Journal of Engineering Technology Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Sains Dan Teknologi (SAINTEKBU) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Inotera Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) CCIT (Creative Communication and Innovative Technology) Journal JAVA Journal of Electrical and Electronics Engineering JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Impresi Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Jurnal Rekayasa elektrika International Journal of Computing Science and Applied Mathematics-IJCSAM
Mauridhi Hery Purnomo
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Pengenalan Entitas Biomedis dalam Teks Konsultasi Kesehatan Online Berbahasa Indonesia Berbasis Arsitektur Transformers Abdillah, Abid Famasya; Purwitasari, Diana; Juanita, Safitri; Purnomo, Mauridhi Hery
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 1: Februari 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023106337

Abstract

Pengenalan entitas biomedis merupakan salah satu tahapan penting dalam ekstraksi informasi pada domain kesehatan. Untuk melakukannya, penelitian terkini banyak menggunakan model ekstraksi biomedis berbasis deep learning yang juga dikenal sebagai Biomedical NER (BioNER). Banyak penelitian menggunakan data sosial media sebagai basis data latih BioNER untuk memenuhi kebutuhan data yang besar. Di sisi lain, banyaknya topik bahasan pada sosial media membuat sumber data ini kurang representatif digunakan dalam pelatihan BioNER seiring dengan melimpahnya bias serta kurangnya data terkait biomedis. Oleh karena itu, penelitian ini mengusulkan suatu model BioNER yang telah dilatih pada situs konsultasi kesehatan online (KKO) agar memiliki representasi data medis lebih baik dibandingkan dengan  penelitian lain yang sejenis. Kontribusi utama penelitian ini adalah terbentuknya model BioNER yang dapat digunakan dalam metode ekstraksi informasi biomedis dalam Bahasa Indonesia. Model ini dibangun menggunakan arsitektur state-of-the-art Transformers sehingga mendapatkan hasil evaluasi F1 score sebesar 0.7691, mengungguli model LSTM sebesar 0.03 poin. Hasil simulasi terhadap data riil juga menunjukkan bahwa model BioNER mampu mengenali entitas biomedis secara umum meskipun dilatih pada data yang terbatas. Selain itu, dengan digunakannya model berbasis XLM-R, maka model juga memiliki kemampuan pengenalan multibahasa sehingga potensi implementasinya tidak terbatas pada entitas Bahasa Indonesia saja. Untuk mendukung penelitian lanjutan, model pengenalan entitas biomedis ini juga dapat diakses secara publik untuk di https://huggingface.co/abid/indonesia-bioner. AbstractBiomedical entity recognition is one of the important stage in the information extraction, particularly in the health domain. Recent research uses a deep learning-based biomedical extraction model known as Biomedical NER (BioNER). Due to extensive data requirement, many studies still use social media data as a BioNER training data. On the other hand, social media data is less representative because it contains a lot of bias and lack of medical representation terms as the impact of many topics discussed. Therefore, this study proposes a BioNER model that has trained on an online health consultation platform to gain a better representation of biomedical data. This model also built using the state-of-the-art Transformers architecture. Hence, its evaluation results show that this model is able to achieve an F1 score of 0.7691, outperforming the LSTM model by 0.03. Simulation results on the real data also indicate that the BioNER model is able to recognize biomedical entities in general cases despite only trained on limited data. In addition, by using an XLM-R-based model, the recognition model also has multilingual recognition capabilities. Therefore, there is a potential implementation to apply the our BioNER model beyond Indonesian biomedical entities. Our biomedical entity recognition model is also accessible at https://huggingface.co/abid/indonesia-bioner.
Recommender System Based on Social Network Analysis of Student Workshop and Event Activities Compared to GPA and Department Setiawan, Esther; Santoso, Joan; Cahyadi, Billy Kelvianto; Afandi, Acxel Derian; Saputra, Daniel Gamaliel; Ferdinandus, FX; Fujisawa, Kimiya; Purnomo, Mauridhi Hery
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2943

Abstract

This research uses social network connections and academic data to create a recommender system that helps students choose seminars and events that suit their interests. The aim is to address the issue of students' hesitation in selecting activities. This project investigates the use of social network analysis (SNA) to provide individualized suggestions by analyzing student involvement in workshops and events, as well as their grade point average (GPA). The materials contain student data gathered between 2018 and 2023 from Institut Sains dan Teknologi Terpadu Surabaya (ISTTS), emphasizing the student's social media interactions and event participation. Metrics like centrality are employed to identify prominent nodes inside the network, and the approach combines graph-based SNA and cosine similarity for event recommendation. The network of student involvement in events was represented by a dataset comprising 2,293 edges and 602 nodes. The results show that the relevance of recommendations is improved when social network data is integrated with GPA, rather than GPA-based systems alone. The system identified key nodes, such as specific lectures, that significantly impacted student involvement and were rated highly in terms of centrality. Future research implications recommend expanding the dataset to encompass a broader range of events and refining the algorithm by including content-based filtering. The system's application is not limited to educational environments; it may also be tailored for career counselling or professional development.
Design of Audio-Based Accident and Crime Detection and Its Optimization Pratama, Afis Asryullah; Sukaridhoto, Sritrusta; Purnomo, Mauridhi Hery; Lystianingrum, Vita; Budiarti, Rizqi Putri Nourma
JOIV : International Journal on Informatics Visualization Vol 7, No 1 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.1.1643

Abstract

The development of transportation technology is increasing every day; it impacts the number of transportation and their users. The increase positively impacts the economy's growth but also has a negative impact, such as accidents and crime on the highway. In 2018, the number of accidents in Indonesia reached 109,215 cases, with a death rate of 29,472 people, which was mostly caused by the late treatment of the casualties. On the other hand, in the same year, there were 8,423 mugs, and 90,757 snitches cases in Indonesia, with only 23.99% of cases reported. This low reporting rate is mostly caused by the lack of awareness and knowledge about where to report. Therefore, a quick response surveillance system is needed. In this study, an audio-based accident and crime detection system was built using a neural network. To improve the system's robustness, we enhance our dataset by mixing it with certain noises which likely to occur on the road. The system was tested with several parameters of segment duration, bandpass filter cut-off frequency, feature extraction, architecture, and threshold values to obtain optimal accuracy and performance. Based on the test, the best accuracy was obtained by convolutional neural network architecture using 200ms segment duration, 0.5 overlap ratio, 100Hz and 12000Hz as bandpass cut-off frequency, and a threshold value of 0.9. By using mentioned parameters, our system gives 93.337% accuracy. In the future, we hope to implement this system in a real environment.
Modeling Portfolio Based on Linear Programming for Bank Business Development Project Plan Shanti Wulansari; Mauridhi Hery Purnomo
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 8 No. 1 (2022)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The bank’s business processes target business plans for the next year. Existing conditions, the business plan is based on the growth asset portfolio every year, so that the purchase of productive assets awaits issuers’ offers. This condition will cause a portfolio not to be measured and the inaccuracy of portfolio selection. Asset Liability Management (ALM) is the management of the structure of assets and liabilities to achieve profit. Banking books and trading books are bank portfolios to earn income. In selecting each portfolio, it contains liquidity risk, market risk and, credit risk. The level of profit is reflected in returns, while returns and risks are a trade-off so that calculations require mathematical and simulation models. Each bank needs an overview of the composition of productive assets, as short-term, medium-term and, long-term assets must be measured risk and target achievement. Linear programming method will allocate productive assets as the bank’s leading source of income, to achieve optimization of profit on the risks received. The problem with this research is that there are 830 variables as banking assets and 19 constraints as indicators of risk. In the seventh iteration of mathematical models, return 1,803 Trillyun from 11 banking book assets.
Stable Algorithm Based On Lax-Friedrichs Scheme for Visual Simulation of Shallow Water Arry Sanjoyo, Bandung; Hariadi, Mochamad; Purnomo, Mauridhi Hery
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.479

Abstract

Many game applications require fluid flow visualization of shallow water, especially dam-break flow. A Shallow Water Equation (SWE) is a mathematical model of shallow water flow which can be used to compute the flow depth and velocity. We propose a stable algorithm for visualization of dam-break flow on flat and flat with bumps topography. We choose Lax-Friedrichs scheme as the numerical method for solving the SWE. Then, we investigate the consistency, stability, and convergence of the scheme. Finally, we transform the strategy into a visualization algorithm of SWE and analyze the complexity. The results of this paper are: 1) the Lax-Friedrichs scheme that is consistent and conditionally stable; furthermore, if the stability condition is satisfied, the scheme is convergent; 2) an algorithm to visualize flow depth and velocity which has complexity O(N) in each time iteration. We have applied the algorithm to flat and flat with bumps topography. According to visualization results, the numerical solution is very close to analytical solution in the case of flat topography. In the case of flat with bumps topography, the algorithm can visualize the dam-break flow and after a long time the numerical solution is very close to the analytical steady-state solution. Hence the proposed visualization algorithm is suitable for game applications containing flat with bumps environments.
Automatic Segmentation on Glioblastoma Brain Tumor Magnetic Resonance Imaging Using Modified U-Net Tjahyaningtijas, Hapsari Peni Agustin; Nugroho, Andi Kurniawan; Angkoso, Cucun Very; Purnama, I Ketut Edy; Purnomo, Mauridhi Hery
EMITTER International Journal of Engineering Technology Vol 8 No 1 (2020)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v8i1.505

Abstract

Glioblastoma is listed as a malignant brain tumor. Due to its heterogeneous composition in one area of the tumor, the area of tumor is difficult to segment from healthy tissue. On the other side, the segmentation of brain tumor MRI imaging is also erroneous and takes time because of the large MRI image data. An automated segmentation approach based on fully convolutional architecture was developed to overcome the problem. One of fully convolutional network that used is U-Net framework. U-Net architecture is evaluated base on the number of epochs and drop-out values to achieve the most suitable architecture for the automatic segmentation of glioblastoma brain tumors. Through experimental findings, the most fitting architectural model is mU-Net architecture with an epoch number of 90 and a drop out layer value of 0.5. The results of the segmentation performance are shown by a dice value of 0.909 which is greater than that of the previous research.
Mengkuantifikasi Trade-off Biaya-Kualitas dalam Autoscaling Kubernetes Berbasis Reinforcement Learning Rohmat Rohmat; Mauridhi Hery Purnomo; Feby Artwodini Muqtadiroh
Jurnal Impresi Indonesia Vol. 5 No. 2 (2026): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v5i2.7527

Abstract

Penelitian autoscaling Kubernetes berbasis reinforcement learning saat ini kurang memiliki evaluasi statistik yang ketat, dengan sebagian besar hanya mengandalkan eksperimen skenario tunggal yang tidak dapat membedakan keunggulan algoritmik yang sebenarnya dari variasi acak. Kesenjangan metodologis ini melemahkan validitas dan reprodusibilitas peningkatan kinerja yang dilaporkan dalam literatur manajemen sumber daya cloud. Studi ini mengembangkan kerangka evaluasi berbasis simulasi yang kuat secara statistik untuk membandingkan secara ketat algoritma autoscaling reinforcement learning terhadap Horizontal Pod Autoscaler (HPA) Kubernetes standar, membangun metodologi benchmarking yang dapat direproduksi dengan pengujian signifikansi statistik yang tepat dan kuantifikasi ukuran efek. Sebuah simulator kejadian diskrit Python yang mengemulasi komponen control-plane Kubernetes (Metrics Server, Controller Manager, Scheduler) dengan dinamika siklus hidup pod yang realistis telah dikembangkan. Autoscaler Hybrid DQN-PPO dan HPA dievaluasi menggunakan desain eksperimen berpasangan di 30 skenario lalu lintas sintetis independen selama 24 jam. Analisis statistik menggunakan uji normalitas Shapiro-Wilk, koreksi Holm-Bonferroni untuk perbandingan berganda, ukuran efek Cohen’s d, dan interval kepercayaan bootstrap. Hasil mengungkapkan trade-off fundamental antara biaya dan kualitas: Hybrid DQN-PPO mencapai kualitas layanan superior dengan 60,58% lebih sedikit pelanggaran SLA, 19,61% lebih cepat latensi P95, dan 4,83% lebih cepat waktu respons rata-rata (semua p < 0, 001). Namun, peningkatan kualitas ini memerlukan premi biaya 8,92% ($6,87 per skenario) dibandingkan dengan HPA, yang mempertahankan utilisasi CPU 7,96% lebih tinggi melalui efisiensi sumber daya yang agresif (p < 0, 001). Perbedaan kinerja berasal dari strategi kontrol yang sangat berbeda: HPA menggunakan kontrol reaktif (menunggu pelanggaran sebelum scaling), mengoptimalkan biaya; Hybrid menggunakan kontrol prediktif (mencegah pelanggaran secara proaktif).
Improving 3D Human Pose Orientation Recognition Through Weight-Voxel Features And 3D CNNs Moch. Iskandar Riansyah; Oddy Virgantara Putra; Farah Zakiyah Rahmanti; Ardyono Priyadi; Diah Puspito Wulandari; Tri Arief Sardjono; Eko Mulyanto Yuniarno; Mauridhi Hery Purnomo
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.847

Abstract

Preprocessing is a widely used process in deep learning applications, and it has been applied in both 2D and 3D computer vision applications. In this research, we propose a preprocessing technique involving weighting to enhance classification performance, incorporated with a 3D CNN architecture. Unlike regular voxel preprocessing, which uses a zero-one (binary) approach, adding weighting incorporates stronger structural information into the voxels. This method is tested with 3D data represented in the form of voxels, followed by weighting preprocessing before entering the core 3D CNN architecture. We evaluate our approach using both public datasets, such as the KITTI dataset, and self-collected 3D human orientation data with four classes. Subsequently, we tested it with five 3D CNN architectures, including VGG16, ResNet50, ResNet50v2, DenseNet121, and VoxNet. Based on experiments conducted with this data, preprocessing with the 3D VGG16 architecture, among the five architectures tested, demonstrates an improvement in accuracy and a reduction in errors in 3D human orientation classification compared to using no preprocessing or other preprocessing methods on the 3D voxel data. The results show that the accuracy and loss in 3D object classification exhibit superior performance compared to specific preprocessing methods, such as binary processing within each voxel.
Optimizing Small-Scale Pumped Hydro Storage Operation for Off-Grid PV: Maximizing Stored Energy Under LPSP and Carbon Footprint Constraints Akhmad Musafa; Ardyono Priyadi; Vita Lystianingrum; Mauridhi Hery Purnomo
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.102327

Abstract

This study explores the utilization of Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and Puzzle Optimization Algorithms (POA) methodologies to improve the performance of a small-scale PHS integrated in an off-grid PV system in a building. The optimization objective function is to minimize the LPSP value, with a carbon footprint of less than 10 kg/kWh. The LPSP value is related to the total energy deficit and total load, and the carbon footprint value is related to the total heat generated by the PHS pump, generator, and carbon intensity value. The optimization setup uses a multi-objective function that has been simplified into a single weighted objective function with normalized and justified weights. The case study is conducted on a 5 kW PV system in a building with a water level of 24 meters and a PHS reservoir of 5 m3. The system is tested under two conditions, namely during the rainy season (January) and the dry season (August). The PSO and GWO algorithms, based on testing results in January and August, demonstrated better performance than POA. This is based on the higher average total PHS energy compared to POA, as well as lower LPSP, LOLE, and EENS values. Meanwhile, for the average stored energy and carbon footprint values, the POA algorithm performs better than PSO and GWO, as indicated by the higher average stored energy and lower carbon footprint values.
Hybrid Balancing and Super-Ensemble Learning With BiomedBERT for Imbalanced Medical Abstract Classification Ghulam Asrofi Buntoro; Oddy Virgantara Putra; Mauridhi Hery Purnomo
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 3 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i3.103811

Abstract

The exponential growth of biomedical textual data presents new challenges for efficient classification, particularly under severe class imbalance where minority disease categories are underrepresented. This study proposes a novel framework that integrates Biomedical Bidirectional Encoder Representations from Transformers (BioMedBERT), a domain-specific transformer pretrained on biomedical corpora, with hybrid balancing strategies and a super-ensemble learning approach for medical abstract classification. The preprocessing pipeline includes label normalization, BioMedBERT-based tokenization, and fixed-length sequence alignment. To mitigate imbalance, oversampling, undersampling, and hybrid balancing are applied, and their outputs are combined in a probabilistic super-ensemble. Evaluation was conducted on a medical abstract dataset comprising 14.438 abstracts across five disease categories, using stratified 3-fold cross-validation. Experimental results demonstrate that the super-ensemble consistently outperforms individual balancing strategies, achieving an accuracy of 64.25% and a macro-F1 score of 64.12%. These results indicate improved robustness and sensitivity to minority classes compared to baseline transformer-based methods. The findings highlight that integrating domain-specific pretrained models with advanced resampling and ensemble techniques provides a promising solution for biomedical Natural Language Processing (NLP) applications, offering enhanced reliability for literature retrieval, clinical decision support, and biomedical research.
Co-Authors Abdillah, Abid Famasya Adhi Dharma Wibawa Adhi Dharma Wibawa Adhi Dharma Wibawa, Adhi Dharma Adhi Kusmantoro Adi Soeprijanto Adi Soeprijanto Adi Soepriyanto Adi Sutanto Adri Gabriel Sooai Adriel Ferdianto Afandi, Acxel Derian Affan, Lazuardi Yaqub Agung Dewa Bagus Soetiono Agung Mega Iswara Agung Wicaksono Agus Dharma Agustinus Bimo Gumelar Ahmad Muslich Akhmad Musafa Alamsyah Alamsyah - Alfiyan Alfiyan, Alfiyan Ali Sofyan Kholimi Amirullah Amirullah Amrul Faruq Ananto Mukti Wibowo Andi Kurniawan Nugroho Andi Setiawan Andreas Agung Kristanto, Andreas Agung Angkoso, Cucun Very Ardyono Pribadi Ardyono Priyadi Ardyono Priyadi Ardyono Priyadi Arham Arham, Arham Arif Muntasa Arifin Arifin Arik Kurniawati Aris Nasuha Aris Widayati Arman Jaya Arraziqi, Dwi Arry Sanjoyo, Bandung Aryo Nugroho Atris Suyantohadi Atris Suyantohadi Atyanta Nika Rumaksari Bambang Purwahyudi Bambang Sujanarko Bambang Suprianto . Basuki, Setio Berlian Al Kindhi Bernaridho Hutabarat, Bernaridho Budi Setiyono Budiarti, Rizqi Putri Nourma Cahyadi, Billy Kelvianto Chastine Fatichah Choirina, Priska Darma Setiawan Putra Dedid Cahya Happyanto Dewi Nurdiyah Diah Puspito Wulandari Diana Purwitasari Djoko Purwanto Dwi F. Suyatno Eddy Satriyanto Effendy Hadi Sutanto Eka Dwi Nurcahya Eko M. Yuniarno Eko Mulyanto Eko Mulyanto Yuniarno Eko Mulyanto Yuniarno Elly Purwanti Endang Setyati Endang Sri Rahayu Endi Permata Era Purwanto Esther Irawati Setiawan Evi Septiana Pane Evi Septiana Pane, Evi Septiana F.X. Ferdinandus Fahmi Amiq Fanani, Nurul Zainal Farah Zakiyah Rahmanti Fath, Nifty Feby Artwodini Muqtadiroh Fendik Eko P Fujisawa, Kimiya Ghulam Asrofi Buntoro Gigih Prabowo Glanny M.Christiaan Mangindaan Gregorius Satio Budhi Gunawan Gunawan Gunawan Gunawan H. Hammad, Jehad A. Hans Juwiantho Hardianto Wibowo Hasti Afianti Hendra Kusuma Hermawan, Norma Herti Miawarni Hidayatillah, Rumaisah Hindarto Husna, Farida Amila Hutama Harsono, Nathanael I Ketut Adi Purnawan I Ketut Eddy Purnama I Ketut Edy Purnama I Made Gede Sunarya I Made Ginarsa I Nyoman Budiastra Ima Kurniastuti Imam Robandi Iman Fahruzi Indah Agustien Sirajudin Indar Sugiarto Ingrid Nurtanio Isa Hafidz Iwan Setiawan Jehad A. H. Hammad Joan Santoso Joko Pitono Joko Priambodo Juanita, Safitri Ketut Eddy Purnama Khairuddin Karim Khamid Khamid Khamid Khamid Kristian, Yosi Lailatul Husniah Laksana, Eka Purwa Lie Jasa Lilik Anifah Lukman Zaman Lystianingrum, Vita Makoto Chiba Margareta Rinastiti Margo Pujiantara Marselin Jamlaay Marsetio Pramono Meidhy Panginda Saputra Moch Hariadi Moch. Hariadi Moch. Iskandar Riansyah Mochamad Ashari Mochamad Hariadi Mochammad Facta Mochammad Hariadi Moh. Aries Syufagi Mohammad Arie Reza Muhamad Ashari Muhamad Haddin Muhammad Nur Alamsyah Muhammad Reza Pahlawan Muhammad Rivai Muhtadin Mukhammad Aris Muldi Yuhendri Mulyanto, Edy Nazarrudin, Ahmad Ricky Nova Eka Budiyanta Nova Rijati Nugroho, Supeno Nugroho, Supeno Mardi S. Nur Kasan, Nur Nurul Fadillah Nurul Zainal Fanani Oddy Virgantara Putra Oddy Virgantara Putra Ontoseno Penangsang Pratama, Afis Asryullah Priambodo, Joko Prima Kristalina Purnama, I Ketut Edy Purwadi Agus Darwito Putra Wisnu AS R Dimas Adityo Rachmad Setiawan Radi Radi Rafly Azmi Ulya, Amik Rahmat Rahmat Rahmat Syam Raihan, Muhammad Rama Sirait, Rummi Santi Ratna Ika Putri Rika Rokhana Rima Tri Wahyuningrum Rima Tri Wahyuningrum Riris Diana Rachmayanti Rohmat Rohmat Rokhana, Rika Rumaisah Hidayatillah Ruri Suko Basuki Rusmono Yulianto Saidah Saidah Saputra, Daniel Gamaliel Sartana, Bruri Trya SATO Yukihiko Setiawan, Esther Setijadi, Eko Shanti Wulansari Sidharta, Bayu Adjie Sihombing, Drigo Alexander Siti Rochimah Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soebagio Soetiono, Agung Dewa Bagus Subagio subagio Subuh Isnur Haryudo Sugiyanto - Sujono Sujono Sujono Sulistyono, Marcelinus Yosep Teguh Sumadi, Fauzi Dwi Setiawan Supeno M. S. Nugroho Supeno Mardi Supeno Mardi S. Nugroho Supeno Mardi Susiki Nugroho, Supeno Mardi Surya Sumpeno Sutedjo Sutedjo Syafaah, Lailis Syaiful Imron Tita Karlita Tita Karlita Tjahyaningtijas, Hapsari Peni Agustin Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Ulla Delfana Rosiani Umar Umar Umi Laili Yuhana Vita Lystianingrum Vita Lystianingrum Widodo Budiharto Wijayanti . Wiratmoko Yuwono Wiwik Anggraeni Wridhasari Hayuningtyas Yani Prabowo Yodik Iwan Herlambang Yosi Kristian Yoyon Kusnendar Suprapto Yulianto Tejo Putranto Yuni Yamasari Yuniarno, Eko M. Yusron rijal Zaimah Permatasari Zaman, Lukman