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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Simantec Jurnal Teknologi Informasi dan Ilmu Komputer JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah SENTIA 2017 Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Teknologi Informasi dan Terapan (J-TIT) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Electrical Engineering and Computer Science JAMI: Jurnal Ahli Muda Indonesia International Journal of Engineering, Science and Information Technology Jurnal Pengabdian pada Masyarakat Ilmu Pengetahuan dan Teknologi Terintegrasi Jurnal Teknik Ilmu dan Aplikasi Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Kepada Masyarakat
Rosa Andrie Asmara
Politeknik Negeri Malang
Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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YOLO-based object detection performance evaluation for automatic target aimbot in first-person shooter games Asmara, Rosa Andrie; Rahmat Samudra Anugrah, Muhammad; Wibowo, Dimas Wahyu; Arai, Kohei; Burhanuddin, Mohd Aboobaider; Handayani, Anik Nur; Damayanti, Farradila Ayu
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.6895

Abstract

First-person shooter (FPS) focuses on first-person perspective action gameplay, with gunfights usually giving the player a choice of weapons, significantly impacting how the player approaches or strategies. General military-themed FPS games have realistic models with actual weapons’ shapes and characteristics. This type of game requires high aiming accuracy while using a mouse on a PC. However, not all players have a fast response time in knowing the surrounding situation. New players may need aid when targeting enemies in the FPS world. One popular yet underhanded method is injecting a program code using a dynamic-link library (DLL) to manipulate memory and asset data from the game. Instead of DLL, we promote a novel approach using the player’s real-time game screen, detecting the person without injecting program code into the game. The you only look once (YOLO) algorithm is used as an object detector model since it can process images in real time for up to 45 frames per second. The proposed object detection has an outstanding performance with 65% accuracy, 98% precision, and 61% recall of 51 tests for each game. YOLO’s fastest detection speed produces an average of 35 FPS on the YOLO tiny variant using a mixed precision (half) graphics processing unit (GPU).
An Experimental Study on Deep Learning Technique Implemented on Low Specification OpenMV Cam H7 Device Asmara, Rosa Andrie; Rosiani, Ulla Delfana; Mentari, Mustika; Syulistyo, Arie Rachmad; Shoumi, Milyun Ni'ma; Astiningrum, Mungki
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.8.2.2299

Abstract

This research aims to identify and recognize the OpenMV Camera H7. In this research, all tests were carried out using Deep Machine Learning and applied to several functions, including Face Recognition, Facial Expression Recognition, Detection and Calculation of the Number of Objects, and Object Depth Estimation. Face Expression Recognition was used in the Convolutional Neural Network to recognize five facial expressions: angry, happy, neutral, sad, and surprised. This allowed the use of a primary dataset with a 48MP resolution camera. Some scenarios are prepared to meet environment variability in the implementation, such as indoor and outdoor environments, with different lighting and distance. Most pre-trained models in each identification or recognition used mobileNetV2 since this model allows low computation cost and matches with low hardware specifications. The object detection and counting module compared two methods: the conventional Haar Cascade and the Deep Learning MobileNetV2 model. The training and validation process is not recommended to be carried out on OpenMV devices but on computers with high specifications. This research was trained and validated using selected primary and secondary data, with 1500 image data. The computing time required is around 5 minutes for ten epochs. On average, recognition results on OpenMV devices take around 0.3 - 2 seconds for each frame. The accuracy of the recognition results varies depending on the pre-trained model and the dataset used, but overall, the accuracy levels achieved tend to be very high, exceeding 96.6%.
Convolutional Neural Network in Motion Detection for Physiotherapy Exercise Movement Laistulloh, Dika Fikri; Handayani, Anik Nur; Asmara, Rosa Andrie; Taw, Phillip
Knowledge Engineering and Data Science Vol 7, No 1 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i12024p27-39

Abstract

Physiotherapy focuses on movement and optimal utilization of the patient's potential. Exercise Therapy is a physiotherapy procedure that specifically focuses exercises on active and passive movements. Cerebral Palsy (CP) patients are one of the sufferers of motor disorders of the upper extremities. Cerebral Palsy (CP) patients suffer from disorders in motor functions of the upper extremities. Physiotherapy Exercise Movement has 4 categories of movement exercises for the therapy of people with upper extremity body disorders: Elbow flexor strengthening in sitting using free weights, lifting an object up, reaching diagonally in sitting, and reaching from a low surface to a high surface. By taking 4 categories of motion movements in exercise therapy, data were taken using normal child subjects as standard movements, which then became a reference for CP child therapy. The limitations of therapy in physical care prompted researchers to investigate the use of image processing as input to Human Computer Interaction (HCI) in the process of motion detection-based therapy. In research using Deep learning as a classifier, namely using the CNN Model (Inception V3, Resnet152, and VGG16 architectural models). The results obtained by the CNN (Inception V3) model have the best performance with an accuracy percentage of 98%.
Optimizing YOLO-Based Algorithms for Real-Time BISINDO Alphabet Detection Under Varied Lighting and Background Conditions in Computer Vision Systems Hayati, Lilis Nur; Handayani, Anik Nur; Gunawan Irianto, Wahyu Sakti; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
International Journal of Engineering, Science and Information Technology Vol 5, No 3 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i3.948

Abstract

This research explores the optimization of YOLO-based computer vision algorithms for real-time recognition of Indonesian Sign Language (BISINDO) letters under diverse environmental conditions. Motivated by the communication barriers faced by the deaf and hearing communities due to limited sign language literacy, the study aims to enhance inclusivity through advanced visual detection technologies. By implementing the YOLOv5s model, the system is trained to detect and classify correct and incorrect BISINDO hand signs across 52 classes (26 correct and 26 incorrect letters), utilizing a dataset of 3,900 images augmented to 10,920 samples. Performance evaluation employs k-fold cross-validation (k=10) and confusion matrix analysis across varied lighting and background scenarios, both indoor and outdoor. The model achieves a high average precision of 0.9901 and recall of 0.9999, with robust results in indoor settings and slight degradation observed under certain outdoor conditions. These findings demonstrate the potential of YOLOv5 in facilitating real-time, accurate sign language recognition, contributing toward more accessible human-computer interaction systems for the deaf community.
Improving Indonesian Sign Alphabet Recognition for Assistive Learning Robots Using Gamma-Corrected MobileNetV2 Hayati, Lilis Nur; Handayani, Anik Nur; Irianto, Wahyu Sakti Gunawan; Asmara, Rosa Andrie; Indra, Dolly; Damanhuri, Nor Salwa
Buletin Ilmiah Sarjana Teknik Elektro Vol. 7 No. 3 (2025): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v7i3.13300

Abstract

Sign language recognition plays a critical role in promoting inclusive education, particularly for deaf children in Indonesia. However, many existing systems struggle with real-time performance and sensitivity to lighting variations, limiting their applicability in real-world settings. This study addresses these issues by optimizing a BISINDO (Bahasa Isyarat Indonesia) alphabet recognition system using the SSD MobileNetV2 architecture, enhanced with gamma correction as a luminance normalization technique. The research contribution is the integration of gamma correction preprocessing with SSD MobileNetV2, tailored for BISINDO and implemented on a low-cost assistive robot platform. This approach aims to improve robustness under diverse lighting conditions while maintaining real-time capability without the use of specialized sensors or wearables. The proposed method involves data collection, image augmentation, gamma correction (γ = 1.2, 1.5, and 2.0), and training using the SSD MobileNetV2 FPNLite 320x320 model. The dataset consists of 1,820 original images expanded to 5,096 via augmentation, with 26 BISINDO alphabet classes. The system was evaluated under indoor and outdoor conditions. Experimental results showed significant improvements with gamma correction. Indoor accuracy increased from 94.47% to 97.33%, precision from 91.30% to 95.23%, and recall from 97.87% to 99.57%. Outdoor accuracy improved from 93.80% to 97.30%, with precision rising from 90.33% to 94.73%, and recall reaching 100%. In conclusion, the proposed system offers a reliable, real-time solution for BISINDO recognition in low-resource educational environments. Future work includes the recognition of two-handed gestures and integration with natural language processing for enhanced contextual understanding.
Sistem Automasi Perkebunan dan Pemantauan Cuaca Menggunakan AWS Berbasis Raspberry Pi Rohadi, Erfan; Abdurrahman, Raka Admiral; Ekojono, Ekojono; Asmara, Rosa Andrie; Siradjuddin, Indrazno; Ronilaya, Ferdian; Setiawan, Awan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (117.188 KB) | DOI: 10.25126/jtiik.2018561121

Abstract

AbstrakInternet of Things (IoT) mengalami perkembangan yang sangat pesat dan menjadi topik yang layak untuk diperbincangkan dan dikembangkan saat ini. IoT merupakan sebuah metode yang bertujuan untuk memaksimalkan manfaat dari konektivitas internet untuk melakukan transfer dan pemrosesan data- data atau informasi melalui sebuah jaringan internet secara nirkabel, virtual dan otonom. Salah satu pemanfaatan IoT adalah sistem automasi. Sistem automasi pada umumnya menggunakan pengatur waktu (timer) untuk proses penyiraman tanaman. Penggunaan timer bertujuan agar penyiraman tanaman berjalan secara rutin tanpa bantuan manusia.Pengembangan sistem automasi ini dimulai dengan pembuatan prototype lahan tanaman cabai rawit di lahan 5 x 2.5 meter, kemudian menyusun komponen-komponen yang dibutuhkan serta cara kerjanya. Selanjutnya dilakukan pemrograman sensor-sensor terhadap Raspberry Pi sebagai pengontrol dalam sistem tersebut berdasarkan kondisi yang telah diatur dan perubahan temperatur yang diterima oleh sensor. Setelah semua dilakukan, maka dilakukan pengujian terhadap sistem tersebut.Berdasarkan pengujian yang telah dilakukan, diketahui telah berhasil dilakukan penyiraman otomatis, baik secara reguler (pukul 06.00 dan 18.00) maupun penyiraman pendinginan. Pendinginan dilakukan jika suhu lebih dari 30 derajat celcius. Sistem automasi yang dikembangkan dengan uji tanaman cabai rawit menjanjikan untuk diterapkan pada pemanfaatan lahan di sekitar rumah. AbstractRecently, The Internet of Things (IoT) has been implemented and become an interesting topic for discussion. IoT is a method that aims to maximize the benefits of Internet connectivity to transfer and process data or information through an internet network wirelessly, virtual and autonomous. One of the IoT's utilization is automation system. The automation system generally uses a timer for the plant watering process. The use of timers aims to water the plants routinely without human assistance.The development of this automation system begins with the making of the prototype of chili land in the field 5 x 2.5 meters, then compile the required components and how it works. Further programming of sensors to Raspberry Pi as a controller in the system based on the conditions that have been set and changes in temperature received by the sensor.As a result, the system has been successfully done automatic watering, both on a regular basis (at 06.00 and 18.00) and cooling watering. Cooling is done if the temperature exceeds more than 30 degrees Celsius. The automation system promises to be applied to the utilization of land around the house.
Sistem Monitoring Budidaya Ikan Lele Berbasis Internet Of Things Menggunakan Raspberry Pi Rohadi, Erfan; Adhitama, Dodik Widya; Ekojono, Ekojono; Ariyanto, Rudy; Asmara, Rosa Andrie; Ronilaya, Ferdian; Siradjuddin, Indrazno; Setiawan, Awan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 6: Desember 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (117.312 KB) | DOI: 10.25126/jtiik.2018561135

Abstract

AbstrakInternet of Things merupakan perkembangan teknologi berbasis internet masa kini yang memiliki konsep untuk memperluas manfaat yang benda yang tersambung dengan koneksi internet secara terus menerus. Sebagai contoh benda elektronik, salah satunya adalah Raspberry Pi. Teknologi ini memiliki kemampuan memberikan informasi secara otomatis dan real time. Salah satu pemanfaatan perkembangan teknologi ini di bidang perikanan adalah sistem pemantauan air kolam. Pada prakteknya, para pembudidaya ikan lele masih melakukan pemantauan tersebut secara konvensional yaitu dengan cara mendatangi kolam ikan. Hal ini berpengaruh terhadap efisiensi waktu dan keefektifan kerja pembudidayaan ikan.Pada penelitian ini dikembangkan alat yang berfungsi untuk membantu memantau dan mengontrol kualitas air kolam ikan lele berbasis Internet of Things. Piranti yang diperlukan adalah sensor keasaman (pH), sensor suhu dan sebuah relay untuk mengatur aerator oksigen air. Data dari sensor-sensor tersebut direkam oleh Raspberry Pi untuk kemudian diolah menjadi informasi sesuai kebutuhan pengguna melalui perantara internet secara otomatis. Selanjutnya data-data tersebut dapat ditampilkan dengan berbagai macam platform, salah satunya dengan model mobile web.  Hasil uji menunjukan bahwa pengembangan teknologi Internet of Things  pada sistem ini dapat membantu pembudidaya untuk melakukan pemantauan terhadap kualitas air secara otomatis. Sistem otomasi yang dikembangkan menjanjikan peningkatan keberhasilan dalam pembudidayaan ikan lele. AbstractFor recent years, the Internet of Things becomes the topic interest of improvement based on technologies that have the concept of extending the benefits of an object that is connected to an internet constantly. This technology has the ability to provide information automatically and real time. One of expansion in the field of fishery is the water ponds monitoring system. In the fact, the catfish farmers are still doing conventional monitoring by coming to the fish pond. This could affects the efficiency of time and effectiveness of fish cultivation work.In this research, the systems that can monitor and control the quality of catfish water ponds based on the Internet of Things is proposed. The necessary tools are acidity sensor (pH), temperature sensor and a relay to adjust water oxygen aerator. The data sensors have been recorded by Raspberry Pi that processed into information according to user needs through internet automatically. Furthermore, these data have been displayed with a variety of platforms, one with a mobile web model.The results shows that the system based on Internet of Things technology can monitor the water quality automatically. The automation system promises the productivity of catfish farming.
Implementasi Video Streaming Lalu Lintas Kendaraan Dengan Server Raspberry Pi Menggunakan Protokol H.264 Rohadi, Erfan; Christine, Anastasia Merry; Prasetyo, Arief; Asmara, Rosa Andrie; Siradjuddin, Indrazno; Ronilaya, Ferdian; Setiawan, Awan
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 5 No 5: Oktober 2018
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (117.037 KB) | DOI: 10.25126/jtiik.2018551138

Abstract

AbstrakTeknologi video surveillance system atau kamera pengawas sudah menjadi alat yang sangat penting karena mayoritas kebutuhan masyarakat sekarang ini menginginkan informasi yang cepat untuk diakses serta praktis dalam penggunaannya. Dalam penelitian ini sebuah protokol H.264 dipergunakan dalam memproses video streamingpada video surveillance systemyang berfungsi sebagai pengirim dan pengontrol paket data streamingdari kamera pengawas ke penerima yaitu sebagai user video surveillance system. Analisis frame videopada protokol H.264 dilakukan pada live streaming server berupa embedded system yang terintegrasi pada video surveillance systemdengan kamera pengawas. Dari hasil uji coba menunjukan bahwa Protokol H.264 memberikan kompresi kualitas videoyang baik, sehingga implementasi Video Streaminglalu lintas kendaraan ini menjanjikan dapat membantu memudahkan masyarakat dalam mendapatkan informasi dan juga mengetahui kondisi lalu lintas secararealtime serta efektif dan efesien. Implementasi Video streamingsecara realtimeini memantau kondisi lalu lintas di suatu Lokasi dengan pendeteksi ketersediaan kamera CCTV (Closed Circuit Television)dan Raspberry pisebagai server.  AbstractTechnology of video surveillance system has become a very important tool because the majority of the needs of today's society want information that is fast to access and practical in its use. In this study an H.264 protocol is used in processing video streaming in video surveillance system that functions as a sender and controller of streaming data packets from surveillance camera to receiver that is as user video surveillance system. The frame video analysis of the H.264 protocol has performed on a live streaming server in the form of embedded systems integrated in video surveillance system with surveillance cameras As a result, the system shows that the H.264 protocol provides good video quality compression, so the implementation of Video Streaming traffic this vehicle promises to help facilitate the public in getting information and also know the real time traffic conditions as well as effective and efficient. Implementation streaming video in real time this monitor traffic conditions in a location with the detection of the availability of CCTV (Closed Circuit Television) and Raspberry Pi cameras as a server.
Pemanfaatan Data PDDIKTI sebagai Pendukung Keputusan Manajemen Perguruan Tinggi Ngatmari, Ngatmari; Musthafa, Muhammad Bisri; Rahmad, Cahya; Asmara, Rosa Andrie; Rahutomo, Faisal
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 3: Juni 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722585

Abstract

Pangkalan Data Pendidikan Tinggi (PDDIKTI) merupakan sebuah sistem penyimpan data yang dikelola Pusat Data dan Informasi (Pusdatin) Kementrian Ristek dan Pendidikan Tinggi. Data yang tersedia di PDDIKTI merupakan data yang akurat, karena proses pelaporan data akademik secara berkala dua kali setiap. Data yang telah berlimpah tersebut, tentu sangat disayangkan jika tidak digunakan untuk keperluan yang lebih bermanfaat, misal untuk mengetahui pola akademik kelulusan mahasiswa dan prestasi akademik mahasiswa. Untuk memperoleh informasi-informasi penting tersebut bisa dilakukan dengan cara penggalian informasi (knowledge discovery). Teknik dalam memberikan solusi masalah tersebut adalah teknik klasifikasi untuk membantu pengambilan keputusan, misalkan Decission Tree (C4.5, ID3, CHAID, rule induction) dan teknik peramalan (forecasting) menggunakan metode simple moving average (SMA). Tujuan dari penambangan data PDDIKTI adalah untuk melakukan deteksi dini terhadap mahasiswa, sehingga dosen bisa memberikan masukan-masukan ketika mahasiswa tersebut telah diklasifikan sebagai mahasiswa yang lulus tidak tepat waktu serta memprediksi jumlah mahasiswa yang akan masuk pada perguran tinggi pada salah satu prodi X, sehingga manajemen baik tingkat program studi maupun universitas bisa melakukan langkah-langkah yang dianggap penting guna meningkatkan jumlah mahasiswa. Pengujian pada 2.601 record akademik mahasiswa dengan atribut ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, pekerjaan_ortu, ket_lulus, rerata_ipk, penghasilan_ayah, untuk klasifikasi kelulusan mahasiswa menghasilkan nilai accuracy 86,54 % nilai precission 93,37% dan nilai recall 89,27% serta pengujian prediksi jumlah peminat program studi  diperoleh nilai accuracy 78,25 % dan MAPE sebesar 21,75 %.Abstract The Higher Education Database (PDDIKTI) is a data storage system managed by the Center for Data and Information (Pusdatin) of the Ministry of Research and Technology and Higher Education. The data available at PDDIKTI is accurate data, because the process of reporting academic data regularly twice each. The abundant data is certainly unfortunate if not used for more useful purposes, for example to find out the academic patterns of student graduation and student academic achievement. To obtain important information can be done by extracting information (knowledge discovery). Techniques in providing solutions to these problems are classification techniques to assist decision making, for example Decission Tree (C4.5, ID3, CHAID, rule induction) and forecasting techniques using simple moving average (SMA) methods. The purpose of PDDIKTI data mining is to conduct early detection of students, so that lecturers can provide input when the students have been classified as students who graduate not on time and predict the number of students who will enter the tertiary institutions in one of the X study programs, so that management both the level of study program and university can take steps that are considered important to increase the number of students. Tests on 2601 student academic records with the attributes ipk_sem1, ipk_sem2, ipk_sem3, ipk_sem4, occupation_ortu, graduated, average_ipk, income_ayah, for the graduation classification of students resulted in an accuracy value of 86.54% a value of 93.37% and a recall value of 89.27% and a test of 89.27% and a test of graduation prediction of the number of study program enthusiasts obtained an accuracy value of 78.25% and MAPE of 21.75%.
PEMBUATAN SISTEM INFORMASI BRANDING DAN MARKETING KAWASAN USAHA MIKRO KECIL MENENGAH YANG BERSINERGI DENGAN KEGIATAN WISATA, PENDIDIKAN KELUARGA (DEWI PELAGA) DI CEMOROKANDANG MALANG Mentari, Mustika; Asmara, Rosa Andrie; Amalia, Eka Larasati; Lestari, Vivin Ayu; Ulfa, Farida; Rahman, Mochamad Faisal; Sabita, Almira Rahma; Ardiansyah, Muhammad Rizqi; Fitriana, Aliza Rizqi
Jurnal Pengabdian kepada Masyarakat Vol. 11 No. 1 (2024): JURNAL PENGABDIAN KEPADA MASYARAKAT 2024
Publisher : P3M Politeknik Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/abdimas.v11i1.4756

Abstract

The geographical area of Cemorokandang village in Malang has a set of potential Small and Medium Enterprises (SMEs) that have been running for a long time but require technological support in branding and marketing. Currently, most SME groups in Cemorokandang village use the traditional method by entrusting the goods produced to shops around the area. The SMEs are in Dewi Pelaga (Desa Wisata Pendidikan Keluarga). Other potentials owned by Cemorokandang village that need to be managed are the potential for sports, tourism, and education potential. This potential needs to be promoted so that many people know it better. Therefore, it is necessary to have a branding and marketing information system that contains pages describing the potential of SMEs and other support such as sports, education, and tourism tracking places. The website that has been created has been tested, and results were obtained on the point that "The community services activities carried out really provide solutions to the problems faced by partners." Some 75% of partners gave a strongly agreed response, while the other 25% gave an agreeing response. From these results, it can be said that this information system is effective in branding and marketing products owned by SMEs in the Dewi Pelaga area.
Co-Authors Abdurrahman, Raka Admiral Adhiati Kusuma Wardani Adhitama, Dodik Widya Adzikirani Adzikirani Adzikirani, Adzikirani Agustina, Reza Alfan Hadi Permana Alviana, Vita Andjani, Bella Sita Angga Aditya Indra Wiratmaka Anik Nur Handayani Ardiansyah, Muhammad Rizqi Ariadi Retno Tri Ariadi Retno Tri Hayati Ririd Arie Rachmad Syulistyo Arief Prasetyo Arief Prasetyo Arinda, Vivid Ichtarosa Astiningrum, Mungki Astuti, Ely Setyo Atiqah Nurul Asri Awan Setiawan Bella Sita Andjani Burhanuddin, Mohd Aboobaider Candra Bella Vista Choirina, Priska Christine, Anastasia Merry Citra Nurina Prabiantissa Citra Nurina Prabiantissa Damanhuri, Nor Salwa Damayanti, Farradila Ayu Deddy Kusbianto P. A Deddy Kusbianto Purwoko Aji Dhika Ainul Luthfi Dika Rizky Yunianto Dimas Wahyu Wibowo Dolly Indra Dwi Puspitasari Dwi Puspitasari DWI PUSPITASARI Eka Larasati Amalia Ekojono, Ekojono Elok Nur Hamdana Era Chalis Kurniangesti Erfan Rohadi Faisal Rahutomo Fitriana Nur’Aini D Fitriana, Aliza Rizqi Galang Audi Pramasha Gunawan Budi P Habibie Ed Dien Hapsari, Ratih Indri Hendrawan, Muhammad Afif Imam Fahrur Rozi Indra Wiratmaka, Angga Aditya Kohei Arai Kohei Arai Kohei Arai Kristinanti Charisma Kurniangesti, Era Chalis Kusbianto P. A, Deddy Kusbianto P. A Kusumaningtyas, Sella Laistulloh, Dika Fikri lilis nurhayati M. Rahmat Samudra M. Unggul Pamenang Muhammad Ainur Ilmy Muhammad Ridwan Musthafa, Muhammad Bisri Mustika Mentari Nadhifatul Laeily Nalendra, Adimas Ketut Ngatmari, Ngatmari Noprianto, Noprianto Nur’Aini D, Fitriana Nurudin Santoso Odhitya Desta Triswidrananta Permana, Alfan Hadi Pramasha, Galang Audi Primadhana, Yoga Andri Qonitatul Hasanah Rahmad, Cahya Rahman, Mochamad Faisal Rahmanto, Anugrah Nur Rahmat Samudra Anugrah, Muhammad Rakhmat Arianto Reza Agustina Robertus Romario Rokhman, Syaiful Ronilaya, Ferdian Rudy Ariyanto Ryan Rifqi Arista Sabita, Almira Rahma Santoso, Nurudin Sari, Irawati Nurmala Sella Kusumaningtyas Shoumi, Milyun Ni’ma Siradjuddin, Indrazno Siska Stevani Siti Romlah Stevani, Siska Syaiful Rokhman Taw, Phillip Tri, Ariadi Retno Triswidrananta, Odhitya Desta Ulfa, Farida Ulla Delfana Rosiani Usman Nurhasan Veithzal Rivai Zainal Vita Alviana Vivin Ayu Lestari Wahyu Sakti Gunawan Irianto Wardani, Adhiati Kusuma Wilda Imama Sabilla Yan Watequlis Syaifudin Yoga Andri Primadhana