Article Per Year (5 Year)
p-Index From 2021 - 2026
7.155
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Robotics and Automation (IJRA) Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Teknologi Informasi dan Ilmu Komputer Seminar Nasional Informatika (SEMNASIF) ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology Padjadjaran Journal of Dentistry Briliant: Jurnal Riset dan Konseptual JOIV : International Journal on Informatics Visualization JURNAL INTEGRASI J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Mantik Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Indonesian Journal of Electrical Engineering and Computer Science GUYUB: Journal of Community Engagement Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
Bima Sena Bayu Dewantara
Politeknik Elektronika Negeri Surabaya
Religion Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

Published : 58 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 58 Documents
Search

 2   3   4   5   6 
Deep Learning Models for Dental Conditions Classification Using Intraoral Images Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Bayu Dewantara, Bima Sena; Brahmanta, Arya
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1914

Abstract

This paper presents the digitalization of dentistry medical records to support the dentist in the patient examination process. A dentist uses manual input to fill out the evaluation form by drawing and labeling each patient’s tooth condition based on their observations. Consequently, it takes too long to finish only one examination. For time efficiency, using AI-based digitalization technology can be a promising solution. To address the problem, we made and compared several classification models to recognize human dental conditions to help doctors analyze patient teeth. We apply the YOLOv5, MobileNet V2, and IONet (proposed CNN model) as deep learning models to recognize the five common human dental conditions: normal, filling, caries, gangrene radix, and impaction. We tested the ability of YOLO classification as an object detection model and compared it with classification models. We used a dataset of 3.708 intraoral dental images generated by various augmentation methods from 1.767 original images. We collected and annotated the dataset with the help of dentists. Furthermore, the dataset is divided into three parts: 90% of the total dataset is used as training and validation data, then divided again into 80% training data and 20% validation data. 10% of the total dataset will be used as testing data to compare classification performance. Based on our experiments, YOLOv5, as an object detection model, can classify dental conditions in humans better than the classification model. YOLOv5 produces an 82% accuracy testing value and performs better than the classification model. MobileNet V2 and IONet only get 80% and 70% testing accuracy. Although statistically, there is not much of a difference between the test accuracy values for YOLOv5 and MobileNet v2, the speed in classifying dental objects using YOLOv5 is more efficient, considering that YOLOv5 is an object detection model. There are still challenges with the deep learning technique used in this research, but these can be addressed in further development. A more complex model and the enlargement of more data, ensuring it is varied and balanced, can be used to address the limitations. 
Environmental Monitoring System using Wireless Multi-Node Sensors based Communication System on Volcano Observations Drones Huda, Achmad Torikul; Setiawardhana, Setiawardhana; Dewantara, Bima Sena Bayu; Sigit, Riyanto
JOIV : International Journal on Informatics Visualization Vol 8, No 2 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.1961

Abstract

Indonesia is on the Ring of Fire and has the world's most active volcanoes. Volcanic activity has a significant effect on the landscape and on the people who live there. The difficulty of evacuating and helping victims requires hard work and sometimes even the safety of the rescue team itself. For this reason, high-tech tools are needed. Unmanned aerial vehicles (UAVs), also called drones, have become a hopeful tool for remote environmental monitoring in recent years. The system design has a monitoring platform, gateway, and sensor nodes attached to the UAV, which monitors the content of toxic gas contamination in the air. Using IoT technology, sensor data is sent wirelessly to a central monitoring station for a thorough and accurate volcanic activity study. This system is a flexible and complete way to monitor volcanic activity, learn more about it, and make it easier to respond to disasters. Tests are also done to measure system speed, including latency, and determine network service quality. The results show that data is successfully sent in real-time from the sensor nodes to the monitoring system. The average Round-Trip time for the payload transmission is 446.046226 ms. This shows how well the system works to send data from the sensors connected to the UAV to the monitoring station. The UAV has sensor nodes and a monitoring system platform. These can be used to build and optimize disaster mitigation systems.
Perbandingan Algoritma Pembelajaran Mesin untuk Klasifikasi Wajah Menggunakan Penyematan FaceNet Catoer Ryando; Riyanto Sigit; Setiawardhana; Bima Sena Bayu Dewantara
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4323

Abstract

In recent years, face recognition has grown significantly in importance and popularity. Google created FaceNet, a deep learning system, in 2015, and it performs very well in creating extremely precise and personalised numerical representations of faces, or embeddings. In order to swiftly and effectively identify people, this study evaluates FaceNet's effectiveness in producing face embeddings and applies it to a variety of classification techniques, including support vector machine (SVM), decision tree, random forest, and k-nearest neighbours (KNN). A dataset with a wide range of positions, facial expressions, and lighting settings is used for the assessment. The findings of the experiment demonstrated that SVM with an radial basis function (RBF) kernel outperformed the other assessed classification techniques, achieving the maximum accuracy of 95%. These findings demonstrate the wide range of applications that face recognition technology may be used for, including identity management and security in different settings.
Usability testing of “smart odontogram” application based on user’s experience Brahmanta, Arya; Maharani, Aulia Dwi; Dewantara, Bima Sena Bayu; Sigit, Riyanto; Sukaridhoto, Sritrusta; Fadhillah, Excel Daris
Padjadjaran Journal of Dentistry Vol 34, No 2 (2022): July
Publisher : Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24198/pjd.vol34no2.36566

Abstract

ABSTRACTIntroduction: Collecting dental data for odontogram in medical records is done chiefly conventionally and causes a lot of human errors. Disadvantages of the conventional method can be overcome by developing a server-based system to store medical information equipped with embedded artificial intelligence (AI), which can identify the patient’s dental condition using an intra-oral camera with the help of Deep Learning algorithms. It is essential to evaluate the usability of this application to adapt to user needs. This study aimed to know the user’s experience in using this application and also provide information for improvements of the application. Methods: This is quantitative descriptive research with 15 users (dentists) as the respondent. The questionnaire was used to measure the user’s experience using this application. The user’s experiences measured are effectivity, efficiency, and satisfaction.  Results: The highest scores of respondents on the three variables are extremely efficient, effective, and satisfied (9 people). The lowest score is slightly efficient and neutral on the efficiency and effectiveness variables (0 people). In the satisfaction variable, the lowest score is slightly satisfied (0 people). Conclusions: The Usability Testing of the “Smart Odontogram” Application based on User’s Experience showed a good result in 3 variables: effectiveness, efficiency, and satisfactionKeywords: smart Odontogram; medical record; application; usability testing; user’s experience
Skema Handover pada Multi-kamera dengan Logika Fuzzy untuk Sistem Pemantauan Orang IMANUDDIN, ACHMAD ILHAM; KRISTALINA, PRIMA; DEWANTARA, BIMA SENA BAYU
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.v9i1.58

Abstract

ABSTRAKAdanya berbagai peristiwa yang membahayakan di tempat keramaian menyebabkan diperlukannya sebuah sistem pemantauan aktifitas manusia di sekitarnya untuk pengawasan keamanan. Sistem multi-kamera sangat cocok digunakan untuk pemantauan target pada lingkungan area yang luas. Disaat target meninggalkan jangkauan area kamera menuju lainnya, proses pemantauan target harus tetap bekerja dan diserahkan ke kamera lainnya. Protokol serah terima target dapat berjalan jika terdapat komunikasi antar kamera yang tersedia. Penelitian ini menyajikan skema handover pada sistem multi-kamera dengan menerapkan pengambilan keputusan handover berbasis logika fuzzy. Dengan begitu, target akan selalu ditangani oleh kamera meskipun target bergerak menjauhinya. Berdasarkan hasil simulasi, skema handover ini mampu mereduksi total number of handover sebesar 20% dibandingkan dengan metode AHCS (Active Handover Control Scheme). Selain itu, handover delay pada metode usulan memperoleh waktu 123.72μs dan masih lebih lama dari AHCS.Kata kunci: handover, multi-kamera, pemantauan orang, fuzzy logic ABSTRACTThe existence of various dangerous events in a crowded place causes the need of surveillance system to monitor the human activity continuously in a certain area. Multi-camera systems are used to monitor targets in large areas. When the target leaves the camera’s range for another, the target monitoring process should continue to work and be left to other cameras. The target handover protocol may work if there is communication between the available cameras. This document presents a handover scheme in a multi-camera system by applying a fuzzy logic handover decision. Thus, the target will always be processed by the camera, even if the target is moving away from it. Based on the simulation results, this handover scheme is able to reduce the total number of handovers by 20% compared to the AHCS (Active Handover Control Scheme) method. In addition, the handover delay in the proposed method obtains 123.72 μs and is still longer than the AHCS.Keywords: handover, multi-camera, human monitoring, fuzzy logic
Development of an Omni Directional based Mobile Robot Navigation System using Optimized-Fuzzy Social Force Model WIBISANA, ANUGERAH; DEWANTARA, BIMA SENA BAYU; PRAMADIHANTO, DADET
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.v10i4.961

Abstract

ABSTRAKMembangun sebuah sistem navigasi pada mobile robot yang bergerak di ruang sosial perlu memperhatikan beberapa aspek krusial, seperti menghindari rintangan, menjaga arah hadap robot ke tujuan, dan mencapai tujuan dengan cepat. Penelitian ini bertujuan untuk mengembangkan sistem navigasi pada Omnidirectional mobile robot menggunakan Fuzzy-Social Force Model (FSFM). Social Force Model (SFM) mampu menggerakan robot ke tujuan sambil menghindari rintangan. Fuzzy Inference System (FIS) digunakan untuk menghasilkan gain adaptif sebagai salah satu parameter SFM agar respon SFM sesuai dengan masukan dari sensor lidar. Aturan FIS dioptimasi agar mendapatkan nilai optimal menggunakan Particle Swarm Optimization (PSO). Dari hasil percobaan, mobile robot mencapai tujuan lebih cepat dengan selisih 1.59 s dan nilai error heading robot lebih kecil 0.9261 dibandingkan FSFM tanpa optimasi.Kata kunci: Sistem Navigasi, Mobile Robot, Fuzzy-Social Force Model, Optimasi, Particle Swarm Optimization ABSTRACTBuilding a navigation system on a mobile robot moves in social space needs to consider several crucial aspects, such as avoiding obstacles, keeping the robot facing the destination, and reaching the destination quickly. This study aims to develop a navigation system on an Omnidirectional mobile robot using the Fuzzy-Social Force Model (FSFM). The Social Force Model (SFM) guides the mobile robot to its destination while avoiding obstacles. The Fuzzy Inference System (FIS) produces adaptive gain as one of the SFM parameters so that the response of the SFM matches the data of the lidar sensor. The rule base of FIS is optimized to get the optimal value using Particle Swarm Optimization (PSO). From the experimental results, mobile robots reach the destination faster with a difference of 1.59 s and a minor error in robot heading of 0.9261 compared to FSFM without optimization.Keywords: Navigation System, Mobile Robot, Fuzzy-Social Force Model, Optimization, Particle Swarm Optimization
Simultaneous Localization and Mapping pada Smart Automated Guided Vehicle menggunakan Iterative Closest Point berbasis K-Means Clustering MARTINI, NI PUTU DEVIRA AYU; SUMANTRI, BAMBANG; DEWANTARA, BIMA SENA BAYU
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.v10i4.742

Abstract

ABSTRAKAutomated Guided Vehicle (AGV) merupakan salah satu jenis mobile robot yang digunakan untuk mengangkut barang menuju tempat tujuan. AGV mampu bekerja pada lingkungan yang dinamis tanpa menggunakan garis pemandu. Namun sebelumnya harus mempunyai informasi yang cukup terhadap lingkungan kerjanya. Teknik ini dikenal dengan Simulataneous Localization and Mapping (SLAM) yang digunakan robot untuk menggambar peta sekaligus mengetahui posisi robot di dalam peta. Pada penelitian ini, metode yang digunakan yaitu SLAM berbasis Iterative Closest Point (ICP) dengan algoritma K-Means yang menggunakan kumpulan titik dari sensor laser range finder (LRF) untuk membangun peta lingkungan. Pemetaan SLAM menggunakan algoritma K-Means memiliki error hasil scan jarak 77,69% lebih kecil dan waktu eksekusi 0,18% lebih cepat dibandingkan dengan KD-Tree. Peta yang dihasilkan dengan algoritma KMeans pada ICP-SLAM memberikan hasil yang lebih baik & mendekati keadaan ruangan sebenarnya dibandingkan menggunakan algoritma KD-Tree.Kata kunci: ICP-SLAM, K-Means, Laser Range Finder. ABSTRACTAutomated Guided Vehicle (AGV) is a type of mobile robot that is used to transport goods to destination. AGV is able to work in a dynamic environment without guidelines. However, it must have sufficient information about its working environment beforehand. This technique is known as Simultaneous Localization and Mapping (SLAM) which is used by a robot to be able to draw a map as well as to determine its position on the map. In this research, the method used is SLAM based on Iterative Closest Point (ICP) with the K-Means algorithm that uses a collection of points from the Laser Range Finder (LRF) sensor to build an environmental map. SLAM using the K-Means algorithm has 77,69% smaller distance error and 0,18% faster execution time than KD-Tree. The map generated by the K-Means algorithm on an ICP-SLAM gives better results & closer to the actual state than using the KD-Tree. Keywords: ICP-SLAM, K-Means, Laser Range Finder.
Online Terrain Classification Using Neural Network for Disaster Robot Application Sanusi, Muhammad Anwar; Dewantara, Bima Sena Bayu; Setiawardhana; Sigit, Riyanto
The Indonesian Journal of Computer Science Vol. 12 No. 1 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i1.3132

Abstract

A disaster robot is used for crucial rescue, observation, and exploration missions. In the case of implementing disaster robots in bad environmental situations, the robot must be equipped with appropriate sensors and good algorithms to carry out the expected movements. In this study, a neural network-based terrain classification that is applied to Raspberry using the IMU sensor as input is developed. Relatively low computational requirements can reduce the power needed to run terrain classification. By comparing data from the Accelerometer, Gyroscope, and combined Accelero-Gyro using the same neural network architecture, the tests were carried out in a not moving position, indoors, on asphalt, loose gravel, grass, and hard ground. In its implementation, the mobile robot runs over the field at a speed of about 0,5 m/s and produces predictive data every 1,12s. The prediction results for online terrain classification are above 93% for each input tested.
Analisis Kinematika Maju dari Tangan Robotik Berjari 4 yang Digunakan pada Robot Humanoid T-FLoW Apriandy, Kevin; Dewantara, Bima Sena Bayu; Dewanto, Raden Sanggar; Pramadihanto, Dadet
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3291

Abstract

Model kinematika merupakan bagian penting dalam pengembangan robot humanoid karena dapat merepresentasikan karakteristik dari robot, membuat pemahaman tentang robot menjadi lebih mudah. Mengingat perkembangan robot humanoid T-FLoW yang saat ini dilengkapi dengan sepasang tangan baru, maka perlu dibangun model kinematika untuk memahami lebih lanjut tentang tangan robot baru tersebut. Oleh karena itu, dalam pekerjaan ini, disajikan sebuah analisis kinematika maju untuk memperoleh model kinematika dari tangan berjari 4 baru robot humanoid T-FLoW. Dengan menggunakan pendekatan matriks transformasi homogen, model kinematika tangan robot diturunkan berdasarkan perkalian beberapa matriks rotasi dan matriks translasi yang tersusun dari frame koordinat pangkal ke frame koordinat tujuan. Model kinematika yang diturunkan disimulasikan dalam tugas gerak dasar tangan: menggenggam sebuah benda, dihitung dengan bantuan MATLAB, dan divisualisasikan menggunakan fitur plot 3D MATLAB. Hasil menunjukkan bahwa model tersebut memberikan berbagai karakteristik tangan robot seperti konfigurasi, posisi sendi, dan posisi end-of-effector, yang kemudian dapat divisualisasikan menjadi kerangka tangan. Kedepannya, pekerjaan kami dapat memfasilitasi pengembang T-FLoW dalam membangun pergerakan tangan dengan sistem umpan balik, yang kemudian dapat digunakan untuk menyelesaikan berbagai permasalahan desain gerakan tangan. Kinematics models are important part of humanoid robot development as they can represent the characteristics of the robot, making understanding the robot easier. Given the development of the T-FLoW humanoid robot which is currently equipped with a new pair of hands, it is necessary to build a kinematics model to understand more about the new robot hands. Therefore, in this work, a forward kinematics analysis is presented to derive the kinematics model of the new 4-fingered T-FLoW humanoid robot hand. Using a homogeneous transformation matrix approach, the kinematics model of the robot hand is derived based on the multiplication of several rotation and translation matrices arranged from the base coordinate frame to the goal coordinate frame. The derived kinematics model is simulated in a basic hand motion task: grasping an object, calculated with the help of MATLAB, and visualized using MATLAB's 3D plot feature. The results show that the model provide various characteristics of the robot hand such as configuration, joint positions, and end-of-effector positions, which then be visualized into a hand skeleton. In the future, our work can facilitate T-FLoW developers in building hand movement and feedback systems, which then can be used to solve various hand motion design problems.
Deteksi Kondisi Gigi Manusia pada Citra Intraoral Menggunakan YOLOv5 Makarim, Ahmad Fauzi; Karlita, Tita; Sigit, Riyanto; Dewantara, Bima Sena Bayu; Brahmanta, Arya
The Indonesian Journal of Computer Science Vol. 12 No. 4 (2023): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i4.3355

Abstract

Proses identifikasi dan pencatatan rekam medis pada praktik kedokteran gigi masih dilakukan secara manual. Akibatnya, proses tersebut memakan waktu yang cukup lama. Pada penelitian ini metode deteksi objek dimanfaatkan untuk membantu dokter melakukan identifikasi pada gigi pasien. YOLOv5 dipilih untuk dilatihkan pada dataset citra intraoral dengan lima kelas kondisi gigi (normal, karies, tumpatan, sisa akar, dan impaksi). Dataset yang digunakan berjumlah 1.767 data citra intraoral yang diambil dan dilabeli oleh dokter gigi. Dataset dibagi menjadi tiga bagian, 10% digunakan untuk data testing dan 90% digunakan untuk data training dan validation. Dilakukan komparasi performa berdasarkan nilai metrik evaluasi terhadap tiga jenis model YOLOv5 (S, M, L). Dari hasil pelatihan, YOLOv5 M sebagai model terbaik mendapatkan nilai mAP sebesar 84%, dan 82% nilai akurasi testing. Penelitian ini telah memenuhi tujuan utama untuk membangun sebuah model deep learning yang robust untuk mendeteksi dan mengklasifikasi beberapa kondisi gigi pada manusia.
Co-Authors Achmad Basuki Achmad Basuki Achmad Basuki Afifah, Izza Nur Agus Indra Gunawan Agus Indra Gunawan Agus Indra Gunawan Ahmad Fauzi Makarim Alfan Rizaldy Pratama Pratama Ali Ridho Barakbah Alif Wicaksana Ramadhan Amang Sudarsono, Amang ANUGERAH WIBISANA Anwar Anwar Apriandy, Kevin APRIANDY, KEVIN ILHAM Arif Hidayah Arif Hidayah Arifin, Muhammad Jainal Arna Fariza Arya Brahmanta Arya Brahmanta, Arya Ashadi, Imam Asmarany, Anja Aulia Dwi Maharani Aulia, Fira Bagus Nugraha Deby Ariyadi Bambang Sumantri Bambang Sumantri Catoer Ryando Chandra Edy Prianto Dadet Pramadihanto Dadet Pramadihanto Dadet Pramadihanto Daffa, Muhammad Fariz Dewanto, Raden Sanggar Dewi Mutiara Sari Djoko Purwanto Edo Bagus Prastika Endra Pitowarno Fadhillah, Excel Daris Fatekha, Rifqi Amalya Ferry Astika Saputra Fikri Aulia Fikri Aulia Fildzah Aure Gehara Zhafirah Fithrotul Irda Amaliah Gunawan, Agus Indra Gunawan, Agus Indra Hamida, Silfiana Nur Hary Oktavianto Hozumi, Naohiro Huda, Achmad Thorikul Huda, Achmad Torikul Husein Aji Pratama Idris Winarno Idris Winarno Ihwan Dwi Wicaksono Ilham Iskandariansyah Imam Ashadi IMANUDDIN, ACHMAD ILHAM Insivitawati, Era iwan Syarif Iwan Syarif Jun Miura Jun Miura, Jun Junaedi Ispianto Kamaluddin, Muhammad Wafiq Kevin Apriandy Kisron Kisron Linda Indrayanti Lusiana Lusiana M Udin Harun Al Rasyid, M Udin Harun Makarim, Ahmad Fauzi MARTINI, NI PUTU DEVIRA AYU Meiyanto, Onie Mohamad Walid Asyhari Mohamad Walid Asyhari Muhammad Abdul Haq Muhammad Anwar Sanusi Muhammad Faiz Naohiro Hozumi Oskar Natan Prastika, Edo Bagus Pratama, Ariesa Editya Prianto, Chandra Edy Prima Kristalina Puspasari Susanti Rabbani, Fahmi Muhammad Rabbani Rachmawati, Oktavia Citra Resmi Raden Sanggar Dewanto Ricky Afiful Maula Rika Rokhana Riyanto Sigit Riyanto Sigit, Riyanto Romadhon, Nur Rizky Rudi Kurniawan Sanusi, Muhammad Anwar Sesulihatien, Wahjoe Tjatur Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana Setiawardhana, Setiawardhana Sholahuddin Muhammad Irsyad Sigit Riyanto Susanti, Puspasari Taufiqurrahman Taufiqurrahman Tessy Badriyah Tessy Badriyah, Tessy Tita Karlita Tita Karlita Titon Dutono Tri Harsono Tri Harsono ULURRASYADI, FAIZ Wahjoe Tjatur Sesulihatien Wahjoe Tjatur Sesulihatien Wibowo, Iwan Kurnianto