Article Per Year (5 Year)
p-Index From 2021 - 2026
4.468
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Majalah Ilmiah Teknologi Elektro 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 Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Jurnal Rekayasa elektrika Majalah Ilmiah Teknologi Elektro
Mauridhi Hery Purnomo
EEPIS
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Other

Published : 118 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 118 Documents
Search

 8   9   10   11   12 
Cost-Effective Parkinson’s Disease Diagnosis Through IoT-Based Finger Tapping and Real-Time Machine Learning Classification Arraziqi, Dwi; Sardjono, Tri Arief; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.86371

Abstract

Parkinson's disease (PD) is a progressive neurological condition that significantly impacts motor functions, including finger tapping (FT). This study aims to develop a cost-effective, real-time, easily implementable, IoT-enabled electronic health record (EHR)-integrated FT analysis system capable of remotely detecting PD with high accuracy. The study uses peak amplitude, the Internet of Things (IoT), and various machine learning classifiers to detect PD through FT pattern analysis on a smartphone application. K-Nearest Neighbors, Convolutional Neural Networks, Support Vector Machines, and Logistic Regression exhibited 100% accuracy, while Naïve Bayes and Decision Trees (DT) had accuracies ranging from 71% to 92%. All classifiers had an Area Under the Curve (AUC) value of 1, except DT with an AUC value of 0.75. This study introduces a novel IoT system for PD detection that demonstrates high diagnostic accuracy, cost-effectiveness, real-time monitoring capability, easy implementation, scalability for telemedicine, and accessibility to EHR during the COVID-19 pandemic. Future studies will focus on expanding the dataset.
Analyzing User Experience and Satisfaction in the B-Block Game-Based Assessment Husniah, Lailatul; Kholimi, Ali Sofyan; Yuhana, Umi Laili; Yuniarno, Eko Mulyanto; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 1 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i1.92784

Abstract

Game-based assessment (GBA) has developed as an innovative education method, including learning basic arithmetic operations. This study aims to analyze user experience and satisfaction using B-Block, an assessment-based game for basic arithmetic operations. The study involved 94 junior high school students with an age distribution of 12-13 years old and varying levels of gaming experience. The research used descriptive statistical analysis, validity and reliability test, Pearson correlation test, and multiple linear regression to identify factors influencing user satisfaction and continuance usage intention. The analysis showed that B-Block has good usability and educational benefits, with user satisfaction being the most dominant aspect. Validity and reliability tests confirmed that most variables were valid and reliable (Cronbach's Alpha > 0.7), except Errors, which had lower reliability (α = 0.632). Pearson correlation shows that Perceived Usefulness has a strong relationship with satisfaction (r = 0.784), while user satisfaction contributes significantly to continuance intention (r = 0.694). Multiple linear regression revealed that perceived usability and perceived usefulness were the main factors influencing user satisfaction, while confirmation and satisfaction had the most effect on continuance intention. The findings confirm that the gameplay's usability and perceived usefulness are key in increasing user satisfaction while matching the experience with initial expectations, and user satisfaction contributes to continued use.
Improving 3D Human Pose Orientation Recognition Through Weight-Voxel Features And 3D CNNs Riansyah, Moch. Iskandar; Putra, Oddy Virgantara; Rahmanti, Farah Zakiyah; Priyadi, Ardyono; Wulandari, Diah Puspito; Sardjono, Tri Arief; Yuniarno, Eko Mulyanto; Hery Purnomo, Mauridhi
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.
Multi-Label Classification of Bilingual Doctor Responses in Online Medical Consultations Using Deep Learning Juanita, Safitri; Purwitasari, Diana; Purnama, I Ketut Eddy; Raihan, Muhammad; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika: JANAPATI Vol. 14 No. 2 (2025)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v14i2.96980

Abstract

Online health consultations (OHCs) have become an integral component of modern healthcare delivery. However, significant challenges remain in multilingual and low-resource contexts such as Indonesia, where language barriers and digital disparities hinder effective doctor–patient communication. Ensuring the quality of such interactions requires the identification of six key communicative functions: building relationships, gathering and providing information, decision-making, promoting disease- and treatment-related behaviour, and responding to emotions. While existing research has largely focused on English-language OHCs, studies analysing these communicative functions in Indonesian remain limited due to the lack of annotated datasets and linguistic complexity. To address this gap, we propose a deep learning framework for multi-label classification of communicative functions in bilingual (Indonesian/English) doctor response texts. The dataset used in this study was annotated by medical professionals with six predefined communicative function labels. We conducted a comprehensive comparative evaluation of three deep learning architectures namely Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Convolutional Neural Networks (CNN) equipped with cross-language word embedding to improve multilingual generalization. Model performance is evaluated through four complementary perspectives: example-based, label-based, ranking-based, and multifaceted metrics, ensuring a holistic assessment. Result show that the fine-tuned LSTM model achieved the highest precision (0.972) on Indonesian texts, while Bi-LSTM obtained the best results on English texts with 0.890 accuracy and 0.980 precision. The LSTM model also reduced false positives in Indonesian classifications, whereas Bi-LSTM improved diagnostic reliability in English, confirming the models’ cross-lingual adaptability. These findings highlight the potential of deep learning to improve communication effectiveness in bilingual and resource-constrained OHC settings.
Sentiment Analysis Twitter Bahasa Indonesia Berbasis WORD2VEC Menggunakan Deep Convolutional Neural Network Juwiantho, Hans; Setiawan, Esther Irawati; Santoso, Joan; Purnomo, Mauridhi Hery
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 1: Februari 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

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

Abstract

Media sosial sebagai media informasi dan komunikasi mulai berkembang pesat sejak internet mudah diakses. Orang dengan mudah menyatakan pendapat, ekspresi, opini, dan informasi melalui tulisan pada media sosial. Opini atau informasi pada media sosial dapat digunakan untuk menilai baik atau buruk suatu brand perusahaan. Orang cenderung jujur dalam mengungkapkan perasaan terhadap sesuatu pada media sosial. Dengan menggunakan sentiment analysis terhadap opini dari pelanggan, analisis opini dapat dilakukan secara otomatis. Perusahaan dapat secara langsung mengetahui tingkat kepuasan pelanggan dan digunakan untuk meningkatkan kualitas pelayanan hingga menaikan brand perusahaan. Penggunaan metode classical machine learning yang sudah banyak diterapkan pada sentiment analysis, tetapi metode tersebut tidak memperhatikan pentingnya urutan kata pada suatu kalimat. Metode deep learning dengan algoritme Deep Convolutional Neural Network ditawarkan untuk menjawab permasalahan tersebut dengan melakukan operasi convolution menggunakan filter sebesar ukuran window untuk mendapatkan fitur berdasarkan urutan kata. Model Word2Vec untuk Bahasa Indonesia digunakan sebagai representasi kata dalam bentuk vektor. Penggunaan Word2Vec juga mempercepat proses pelatihan dan meningkatkan akurasi algoritme Deep Convolutional Neural Network. Data yang digunakan dalam makalah ini adalah data Twitter Bahasa Indonesia dengan jumlah 999 tweet. Hasil percobaan yang telah dilakukan dengan algoritme Deep Convolutional Neural Network memiliki nilai akurasi terbaik sebesar 76,40%. AbstractSocial media as information media and communication is growing rapidly since the internet is easily accessible. People easily express opinions, expressions, and information by writing on social media. Opinion or information on social media can be used to assess how good or bad a companies is. People tend to be honest in expressing feelings towards something on social media. With sentiment analysis, analysis of the opinions of customers can be done automatically. The company will know the level of customer satisfaction and can be used to improve the quality of service to raise the company's brand. The use of classical machine learning methods that have been widely applied to sentiment analysis ignoring the importance of the word order in a sentence. Deep Convolutional Neural Network algorithm is offered to answer these problems by carrying out convolution operations using filters as large as window size to get features based on word order. Word2Vec model for Indonesian is used as a word vector representation. The use of Word2Vec also reduce the training time and improve the accuracy of the Deep Convolutional Neural Network algorithm. The data used in this paper is Indonesian Twitter data with 999 tweets. The results of experiments that have been carried out with the Deep Convolutional Neural Network algorithm have the best accuracy value of 76.40%.
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.
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 Al Kindhi, Berlian Alamsyah Alamsyah - Alfiyan Alfiyan, Alfiyan Ali Sofyan Kholimi Amirullah Amirullah Amrul Faruq Ananto Mukti Wibowo Andi Setiawan Andreas Agung Kristanto, Andreas Agung Ardyono Pribadi Ardyono Priyadi Ardyono Priyadi Arham Arham, Arham Arif Muntasa Arifin Arifin Arik Kurniawati Aris Nasuha Aris Widayati Arman Jaya Arraziqi, Dwi Aryo Nugroho Atris Suyantohadi Atris Suyantohadi Atyanta Nika Rumaksari Atyanta. N. Rumaksari Bambang Purwahyudi Bambang Sujanarko Bambang Suprianto . Bandung Arry Sanjoyo 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 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 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 Ontoseno Penangsang Pratama, Afis Asryullah Priambodo, Joko Prima Kristalina Purnawan, I Ketut Adi Purwadi Agus Darwito Putra Wisnu AS R Dimas Adityo Rachmad Setiawan Radi Radi Rafly Azmi Ulya, Amik Rahmat Rahmat Rahmat Syam Raihan, Muhammad Ratna Ika Putri Rika Rokhana Rima Tri Wahyuningrum Rima Tri Wahyuningrum Riris Diana Rachmayanti Rokhana, Rika Rumaisah Hidayatillah Ruri Suko Basuki Rusmono Yulianto Saidah Saidah Saputra, Daniel Gamaliel Sartana, Bruri Trya SATO Yukihiko Setiawan, Esther Setijadi, Eko Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sirait, Rummi Santi Rama 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 Tri Arief Sardjono Tsuyoshi Usagawa, Tsuyoshi Ulla Delfana Rosiani Umar Umar Vita Lystianingrum Widodo Budiharto Wijayanti . Wiratmoko Yuwono Wiwik Anggraeni Wridhasari Hayuningtyas Yani Prabowo Yodik Iwan Herlambang Yosi Kristian Yoyon Kusnendar Suprapto Yuhana, Umi Laili Yulianto Tejo Putranto Yuni Yamasari Yuniarno, Eko M. Yusron rijal Zaimah Permatasari Zaman, Lukman