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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan ELKHA : Jurnal Teknik Elektro Mechatronics, Electrical Power, and Vehicular Technology Jurnal Simetris Bulletin of Electrical Engineering and Informatics Jurnal Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics JURNAL NASIONAL TEKNIK ELEKTRO JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Knowledge Engineering and Data Science Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika Pendas : Jurnah Ilmiah Pendidikan Dasar SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah SENTIA 2017 SENTIA 2016 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Lectura : Jurnal Pendidikan Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) PEDULI: Jurnal Imiah Pengabdian Pada Masyarakat Infotekmesin Buletin Ilmiah Sarjana Teknik Elektro International Journal of Visual and Performing Arts Generation Journal Jurnal Mnemonic Frontier Energy System and Power Engineering Masyarakat Berdaya dan Inovasi SOSIOEDUKASI : JURNAL ILMIAH ILMU PENDIDIKAN DAN SOSIAL Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Data and Science Letters in Information Technology Education (LITE) Jurnal Graha Pengabdian Jurnal Abdimas Berdaya : Jurnal Pembelajaran, Pemberdayaan dan Pengabdian Masyarakat Science in Information Technology Letters International Journal of Engineering, Science and Information Technology International Journal of Robotics and Control Systems ALINIER: Journal of Artificial Intelligence & Applications Ilmu Komputer untuk Masyarakat SinarFe7 Jurnal Maklumatika Applied Engineering and Technology Jurnal Ekonomi, Bisnis dan Pendidikan (JEBP) Jurnal Inovasi Teknologi dan Edukasi Teknik PROSIDING SEMINAR NASIONAL PENELITIAN DAN PENGABDIAN KEPADA MASYARAKAT (SNPPM) UNIVERSITAS MUHAMMADIYAH METRO Bulletin of Social Informatics Theory and Application Karunia: Jurnal Hasil Pengabdian Masyarakat Indonesia Jurnal Informatika Polinema (JIP) ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat International Journal of Mechanical, Industrial and Control Systems Engineering Journal of Engineering and Technological Sciences Jurnal ilmiah teknologi informasi Asia Jurnal Elektronika dan Telekomunikasi
Anik Nur Handayani
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Perbandingan Instance Segmentation Image Pada Yolo8 Wulanningrum, Resty; Handayani, Anik Nur; Wibawa, Aji Prasetya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1148288

Abstract

Seorang pejalan kaki sangat rawan terhadap kecelakaan di jalan. Deteksi pejalan kaki merupakan salah satu cara untuk mengidentifikasi atau megklasifikasikan antara orang, jalan atau yang lainnya. Instance segmentation adalah salah satu proses untuk melakukan segmentasi antara orang dan jalan. Instance segmentation dan penggunaan yolov8 merupakan salah satu implementasi dalam deteksi pejalan kaki. Perbandingan segmentasi pada dataset Penn-Fundan Database menggunakan yolov8 dengan model yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg. Penelitian ini menggunakan dataset publik pedestrian atau pejalan kaki dengan objek multi person yang diambil dari dataset Penn-Fudan Database. Dataset mempunyai 2 kelas, yaitu orang dan jalan. Hasil perbandingan penggunaan model yolov8 model segmentasi yang terbaik adalah menggunakan model yolov8l-seg. Hasil penelitian didapatkan Instance segmentation valid box pada data orang, mAP50 tertinggi pada yolov8l-seg dengan nilai 0,828 dan mAP50-95 adalah 0,723. Instance segmentation valid mask pada orang nilai mAP50 tertinggi pada yolov8l-seg dengan nilai 0,825 dan mAP50-95 adalah 0,645. Pada penelitian ini, yolov8l-seg menjadi nilai terbaik dibandingkan versi yang lain, karena berdasarkan nilai mAP tertinggi pada valid mask sebesar 0,825.   Abstract   A pedestrian is very vulnerable to road accidents. Pedestrian detection is one way to identify or classify between people, roads or others. Instance segmentation is one of the processes to segment people and roads. Instance segmentation and the use of yolov8 is one of the implementations in pedestrian detection. Comparison of segmentation on Penn-Fundan Database dataset using yolov8 with yolov8n-seg, yolov8s-seg, yolov8m-seg, yolov8l-seg, yolov8x-seg models. This research uses a public pedestrian dataset with multi-person objects taken from the Penn-Fudan Database dataset. The dataset has 2 classes, namely people and roads. The results of the comparison using the yolov8 model, the best segmentation model is using the yolov8l-seg model. The results obtained Instance segmentation valid box on people data, the highest mAP50 on yolov8l-seg with a value of 0.828 and mAP50-95 is 0.723. Instance segmentation valid mask on people the highest mAP50 value on yolov8l-seg with a value of 0.825 and mAP50-95 is 0.645. In  his study, yolov8l-seg is the best value compared to other versions, because based on the highest mAP value on the valid mask of 0.825.
Exploring the Role of Deep Learning in Forecasting for Sustainable Development Goals: A Systematic Literature Review Utama, Agung Bella Putra; Wibawa, Aji Prasetya; Handayani, Anik Nur; Chuttur, Mohammad Yasser
International Journal of Robotics and Control Systems Vol 4, No 1 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i1.1328

Abstract

This paper aims to explore the relationship between deep learning and forecasting within the context of the Sustainable Development Goals (SDGs). The primary objective is to systematically review 38 articles published between 2019 and 2023, following PRISMA guidelines, to understand the current landscape of deep learning forecasting for SDGs. Using data from 2019-2023 allows capturing the latest developments in deep learning forecasting for Sustainable Development Goals (SDGs), while excluding data before 2019 and after 2023 is based on the desire to avoid including potentially less relevant or unpublished research and to maintain focus on the most current and contextually relevant literature. The methodological approach involves analyzing the application of deep learning methods for forecasting within various SDG fields and identifying trends, challenges, and opportunities. The literature review results reveal the popularity of LSTM models, challenges related to data availability, and the interconnected nature of SDGs. Additionally, the study demonstrates that deep learning models enhance forecast accuracy and computational performance, as measured by Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), and R-squared (R2). The findings underscore the importance of advanced data preparation techniques and the integration of deep learning with SDGs to improve forecasting outcomes. The novelty of this research lies in its comprehensive overview of the current landscape and its valuable insights for researchers, policymakers, and stakeholders interested in advancing sustainable development goals through deep learning forecasting. Finally, the paper suggests future research directions, including exploring the potential of hybrid forecasting models and investigating the impact of emerging technologies on SDG forecasting methodologies. Innovative methods for imputing missing values in deep learning forecasting models could be further explored to enhance predictive accuracy and robustness.
Comparative Analysis of Fuzzy Logic Models for Depression Prediction: Python and LabVIEW Approaches Rismayanti, Nurul; Titaley, Gilberth Valentino; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.189

Abstract

Depression is one of the mental disorders with a significant impact on individuals' quality of life and productivity. The diagnostic process for depression, which typically relies on subjective assessment, often encounters challenges of uncertainty and variability in symptoms. This study aims to develop a fuzzy model for predicting depression levels based on five primary symptom variables: worthlessness, concentration, suicidal ideation, sleep disturbance, and hopelessness. The model is implemented on two platforms, Python and LabVIEW, to evaluate the accuracy and consistency of prediction results between these platforms. The analysis process begins with data preprocessing, input variable fuzzification, inference using 243 fuzzy rules, and defuzzification to generate a crisp output value classified into four depression levels: No Depression, Mild, Moderate, and Severe. The study results indicate a very small error margin between the two platforms, with error values below 0.01 in each trial. These findings suggest that both Python and LabVIEW can produce nearly identical and consistent predictions. This conclusion supports the effectiveness of fuzzy logic in addressing uncertainty in clinical data, especially for cases of depression with varying symptoms. Nonetheless, there are limitations related to the subjectivity in selecting membership functions and rules, as well as limitations in the number of variables used. Therefore, this study recommends expanding the developed fuzzy model with additional variables or integrating it with machine learning approaches to improve prediction accuracy. These findings are expected to serve as a foundation for the development of fuzzy-based systems in future mental health diagnostics.
Sugeno Fuzzy Personality Prediction System: An Approach to Overcoming Psychological Measurement Uncertainty Nadindra Dwi Ariyanta; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 5 No. 3 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i3.192

Abstract

Personality prediction is a significant field in psychological measurement, yet it faces challenges due to psychological data's ambiguous and uncertain nature. This study aims to develop a Sugeno-based fuzzy logic system for predicting personality types according to the Myers-Briggs Type Indicator (MBTI). The dataset includes synthetic personality data, incorporating age, introversion, sensing, thinking, and judging. The fuzzification process converts crisp input values into fuzzy variables, which are then processed using predefined fuzzy rules to generate personality predictions. The defuzzification step yields crisp outputs corresponding to MBTI types, demonstrating the system's ability to handle uncertainty and ambiguity effectively. Implementation and evaluation were conducted using Python and LabVIEW, revealing a satisfactory performance with a low error rate of 0.445. This study highlights the potential of fuzzy logic, particularly the Sugeno method, in enhancing accuracy and adaptability in personality prediction, contributing to applications in education, human resource management, and personalized digital services.
Analisis Jaringan Saraf Tiruan Pengenalan Pola Huruf Hiragana dengan Model Jaringan Perceptron Irfan Ramadhani; Selly Handik Pratiwi; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.41

Abstract

Artikel ini membahas mengenai program visual basic untuk mengenali dan menganalisis pola huruf hiraganaMa Mi Mu Me dan Mo Pengenalan pola huruf hiragana tersebut memiliki tujuan untuk pengembangan pengenalan pola huruf hiragana yang dimana dalam pendidikan bahasa jepang sudah banyak masuk ke dalam susunan mata pelajaran muatan lokal dalam tingkat sekolah menengah atas sehingga dapat membantu siswa dalam pengenalan pola-pola huruf hiragana. Selain itu tujuan penelitian ini adalah membandingkan keakuratan perhitunga excel dengan hasil program. Metode yang digunakan dalam penelitian ini adalah model jaringan perceptron. Analisis dilakukan dengan berdasarkan nilai alpha dan threshold dari tiap-tiap pola yang dilakukan menggunakan excel dan implementasinya dalam program visual basicHasil dari penelitian ini adalah keakuratan nilai dari excel dan juga program
Simulasi Kinerja Siswa Dengan Metode Fuzzy Inference Sugeno Menggunakan Aplikasi Matlab Halimahtus Mukminna; Devita Maulina Putri; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.53

Abstract

Tujuan artikel ini adalah membuat simulasi untuk penilaian kinerja siswa menggunakan logika fuzzy untuk mengatasi masalah proses penilaian evaluasi siswa. Disamping itu belum adanya sistem khusus yang dapat mengoptimalkan dalam memberikan dukungan bagi guru dalam melakukan evaluasi yang masih bersifat perhitungan manual. Satu cara penentuan perhitungan hasil evaluasi siswa dapat dipermudah dengan menggunakan bantuan pertimbangan Artifical Intelligence (AI) sebagai optimasinya. Dalam pertimbangan evaluasi kinerja siswa ini menggunakan logika fuzzy dengan metode inference system sugeno. Metode sugeno ini merupakan metode inference fuzzy untuk aturan yang direpresentasikan dalam bentuk IF-THEN, dimana output sistem tidak berupa himpunan fuzzy, melainkan berupa persamaan linier. Kriteria yang digunakan dalam penilaian kinerja siswa meliputi very unsuccesusful, unsuccessful, average, successful, dan very successful. Pada simulasi ini hasil yang ditampilkan dengan perhitungan manual dan perhitungan Matlab sebagai pembandingnya hasil perhitungan secara manual nilai result 45,5 sedangkan pada perhitungan matlab nilai result sebesar 48,5. Sehingga dapat disimpulkan selisih yang disebabkan tingkat akurasi hasil inference rule pada perhitungan manual kurang efektif bahkan terkadang banyak inference rule yang harus disesuaikan.
Analisis Jaringan Saraf Tiruan Model Perceptron Pada Pengenalan Pola Pulau di Indonesia Muhammad Ulinnuha Musthofa; Zufida Kharirotul Umma; Anik Nur Handayani
Jurnal Ilmiah Teknologi Informasi Asia Vol 11 No 1 (2017): Volume 11 Nomor 1 (10)
Publisher : LP2M INSTITUT TEKNOLOGI DAN BISNIS ASIA MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32815/jitika.v11i1.56

Abstract

Tujuan dari penulisan artikel ini adalah menganalisa sistem jaringan saraf tiruan menggunakan model perceptron pada pengenalan pola pulau di Indonesia. Model jaringan perceptron biasa digunakan untuk pengenalan pola, karakter, maupun simbol, termasuk pola pulau-pulau di Indonesia. Analisis dilakukan berdasarkan nilai alpha (α) dan threshold (θ) pada setiap pola masukan pada perhitungan manual dengan excel dan diimplementasikan menggunakan program visual basic. Selanjutnya analisis dilakukan dengan cara membandingkan nilai alpha (α) dan threshold (θ) pada excel maupun visualbasic dan hasil pengenalan pola pulau-pulau besar di Indonesia didapatkan hasil yang sama, sehingga dapat disimpulkan keakurasian antara perhitungan keduanya. Analisis juga dilakukan terhadap laju pemahaman yang dimodifikasi mempengaruhi kecepatan iterasi, hal ini dilihat dari perubahan nilai net pada setiap perubahan nilai alpha (α).Hasil analisis perubahan laju pemahaman pada tabel modifikasi laju pemahaman menunjukkan bahwa semakin besar laju pemahaman semakin besar pula respon unit keluaran sehingga proses pemahaman menjadi lambat, begitu pula sebaliknya.
Comparative Analysis of Yolov-8 Segmentation for Gait Performance in Individuals with Lower Limb Disabilities Wulanningrum, Resty; Handayani, Anik Nur; Herwanto, Heru Wahyu
International Journal of Robotics and Control Systems Vol 5, No 1 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i1.1731

Abstract

This research aims to develop an example of gait pattern segmentation between normal and disabled individuals. Walking is the movement of moving from one place to another, where individuals with physical limitations on the legs have different walking patterns compared to individuals without physical limitations. This study classifies gait into three categories, namely individuals with assistive devices (crutches), individuals without assistive devices, and normal individuals. The study involved 10 subjects, consisting of 2 individuals with assistive devices, 3 individuals without assistive devices, and 5 normal individuals. The research process was conducted through three main stages, namely: image database creation, data annotation, and model training and segmentation using YOLOv8. YOLOv8-seg is the platform used to segment the data. The test results showed that the YOLOv8L-seg model achieved convergence value at the 23rd epoch with the 4th scenario in recognizing the walking patterns of the three categories. However, research on walking patterns of people with disabilities faces several obstacles, such as the lack of confidence or emotion of the subject during the data collection process, which is conducted at the location of the subject's choice. In addition, YOLOv8-seg showed consistent performance across the five models used, obtaining a maximum mAP50 value of 0.995 for mAP50 box and mAP50 mask.
Comparative analysis of decision tree and random forest classifiers for structured data classification in machine learning Kinasih, Agnes Nola Sekar; Handayani, Anik Nur; Ardiansah, Jevri Tri; Damanhuri, Nor Salwa
Science in Information Technology Letters Vol 5, No 2 (2024): November 2024
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v5i2.1746

Abstract

This study explores the application of machine learning techniques, specifically classification, to improve data analysis outcomes. The primary objective is to evaluate and compare the performance of Decision Tree and Random Forest classifiers in the context of a structured dataset. Using the Elbow Method for optimal clustering alongside decision tree and random forest for classification algorithms, this research investigates the effectiveness of each method in accurately categorizing data. The study employs K-Means clustering to segment the data and Decision Trees and Random Forests for classification tasks. Dataset used in this research was obtained from Kaggle consisting of 13 attributes and 1048575 rows, all of which are numeric. The key results show that Random Forest outperforms Decision Trees in terms of classification accuracy, precision, recall, and F1 score, providing a more robust model for data classification. The performance improvement observed in Random Forest, particularly in handling complex datasets, demonstrates its superiority in generalizing across varied classes. The findings suggest that for applications requiring high accuracy and reliability, Random Forest is preferable to Decision Trees, especially when the dataset exhibits high variability. This research contributes to a deeper understanding of how different machine learning models can be applied to real-world classification problems, offering insights into the selection of the most appropriate model based on specific data characteristics.
Development of Embedded System Learning Module Using Project-based Learning Method for Industrial Electronics Department Pratama, Diaz Octa; Handayani, Anik Nur
Lectura : Jurnal Pendidikan Vol. 16 No. 1 (2025): Lectura: Jurnal Pendidikan
Publisher : Fakultas Keguruan dan Ilmu Pendidikan (FKIP), Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/lectura.v16i1.25415

Abstract

The problem faced in the Industrial Electronics Department of Vocational High School (Sekolah Menengah Kejuruan/SMK) PGRI 3 Malang is the unavailability of systematic teaching materials in the learning process of embedded systems. The module is one of the teaching materials used to improve students’ quality and produce independent and creative students. This research aims to develop Embedded System Learning Module using Project-based Learning method to overcome the urgent need for teaching materials. The research methodology used is R&D with the ADDIE model, which consists of Analysis, Design, Development, Implementation, and Evaluation. The steps of developing this module include: 1) Analysis: identifying the needs of teaching materials; 2) Design: designing the elements needed in the learning module; 3) Development: making and validating the module by material and media experts; 4) Implementation: Small and large group trials in class XI of the Industrial Electronics department of SMK PGRI 3 Malang; 5) Evaluate: module feasibility analysis. Data collection in this study used interviews, observations, and questionnaire instruments. Product validation results were obtained from media experts, with a percentage of 87.5% (very valid), and material experts, with a rate of 95% (very valid). After the developed product received feasible criteria from the experts, a small group trial (10 students) and a large group trial (24 students) were conducted. The results of the small group trial were 85.7% (very valid), and the results of the large group trial were 87.8% (very valid). Therefore, this module is feasible for teaching material in learning embedded systems.
Co-Authors A.N. Afandi Abdullah Iskandar Syah Achmad Hamdan Achmad Safii Achmad Safi’i Achmad Safi’i Adi Izhar Bin Che Ani Adi Prastowo, Nur Kodrad Adib Nur Sasongko Adim Firmansah Afandi, Farrel Candra Winata AFIF, ACHMAD Agung Bella Putra Utama Agusta Rakhmat Taufani Ahmad Dardiri Ahmad Kholish Fauzan Shobiry Ahmad Munjin Nasih Ahmad Nurdiansyah Ahmad Sahru Romadhon Aji Prasetya Wibawa Alifia Fitri Wahyudi Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Aqdam, Yutsabitul Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro Aripriharta Aripriharta - Ariyanta, Nadindra Dwi Asfani, Khoirudin Atmaja, Muhammad Bayu Setya Wahyu Ayu Puspita Azhryl Assagaf Aziz, Faiz Syaikhoni Azizah, Desi Fatkhi Bagaskoro, Muhammad Cahyo Baihaqi, Dimas Imam Baihaqi, Dimas Imam Baskoro Arif Widodo Bayu Prasetyo Bayu Prasetyo, Bayu Bin Che Ani, Adi Izhar Burhanuddin, Mohd Aboobaider Chalista Yulia Hazizah Chandrika, Katya Lindi Chuttur, Mohammad Yasser Damanhuri, Nor Salwa Damayanti, Farradila Ayu Damayanti, Masyita Danang Arengga Danang Arengga Wibowo Dedes, Khen Desi Fatkhi Azizah Devita Maulina Putri, Devita Maulina Dewi Aprilia Lintang Dhiyaurrahman Fakhruddin Didik Dwi Prasetya Difa Hananta Firdaus Am Dika Fikri L Dimas Wahyu Wibowo Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Dolly Indra Dwi Prihanto Dyah Lestari Dyah Rosita Anggraeni Edinar Valiant Hawali Edwin Meinardi Trianto Eka Rahayu Setyaningsih Eko Noerhayati Erwina Nurul Azizah Evania Yafie F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Faqih, Kamil Faradhila Saffa Dhamira Farah Nisa’ Salsabila Fauzi, Juwita Annisa Fauzi, Rochmad Felix Andika Dwiyanto Ferina Ayu Pusparani Fidyah Ajeng Wulandari Fukuda, Osamu Gavyn Rafael Davasco Gianika Roman Sosa Graciello, Manuel Tanbica Gunawan Budi P Guyub Raharjo Gwo-Jiun Horng Haffas Zikri Ariyandi Hakkun Elmunsyah Halimahtus Mukminna, Halimahtus Harits Ar Rasyid Harits Ar Rosyid Hariyono Hariyono Hartarto Junaedi Hary Suswanto Heru Herwanto Heru Wahyu Herwanto Hirashima, Tsukasa Hitipeuw, Emanuel Hosen, Moh I Made Wirawan Ida Ayu Putu Sri Widnyani Ihsan Al-Fikri Imam Tree Utomo Imanuel Hitipeuw Ira Kumalasari Irfan Ramadhani Irham Fadlika Jehad A. H. Hammad Jehad A.H. Hammad Jevri Tri Ardiansah Jevri Tri Ardiansah Julfikar Mawansyah Kamil Faqih Kartika Candra Kirana Kartika Kirana Kasmira, Kasmira Katya Lindi Chandrika Khurin Nabila Kinasih, Agnes Nola Sekar Kirom, M Kohei Arai Kohei Arai Kohei Arai Kohei Arai Korba, Petr Kurniawan, Wendy Cahya Kusumawardana, Arya Laili, Mery Nur Laily, F.ti Ayyu Sayyidul Laistulloh, Dika Fikri Lalu Ganda Rady Putra Langlang Gumilar Larasati, Jade Rosida Leonel Hernandez, Leonel Lestari , Widya Liang, Yeoh Wen Liang, Yoeh Wen lilis nurhayati M. Adib Nursasongko M. Nuzuluddin M. Rodhi Faiz M. Rodhi Faiz Machumu, Paul Igunda Mahamad, Abd Kadir Manga, Abdul Rachman Maqbullah, Afwatul Marga Asta Jaya Mulya Maula Zikri Renaldi Ming Foey Teng, Ming Foey Moch Haris Purwanto Moh Zainul Falah Moh. Zainul Falah Mohammad Agung Rizki Mohammad Muzayyin Amrulloh Mohammad Rizky Kurniawan Mohammad Yussril Asri Mohsen Samadi Mokh Sholihul Hadi Much. Arafat Al Mubarok Muchamad Wahyu Prasetyo Muchamad Wahyu Prasetyo Muhamad Arifin Muhamad Arifin, Muhamad Muhammad Alfan Muhammad Arifin Muhammad Hafiizh Muhammad Holqi Rizki Azhari Muhammad Iqbal Akbar Muhammad Jauharul Fuady Muhammad Ridwan Muhammad Ulinnuha Musthofa Muhammad Younas Darvish Muhammad Zaki Wiryawan Muhammad Zaky Rahmatsyah Muladi Mumtaazah, Muhammad Athar Mutiara, Titi Nadindra Dwi Ariyanta Nailah Aliya Putri Nandang Mufti Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Naufal Rizaldi Gunawan Nisa, Khoirotun Nizaar, Roub Nor Salwa Damanhuri Norma Mustika, Soraya Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Eva Nur Halim Nur Rahma, Andika Bagus Nurul Rismayanti Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda Panji Ageng Timor Pamungkas Prasetya Widiharso Prasetya Widiharso Prasojo, Fadillah Pratama, Awanda Setya Sanfajar Pratama, Diaz Octa Priharta, Ari Primadi, Wahyu Purnomo, Purnomo Putra Utama, Agung Bella Putri Galuh Ningtiaz Qomaria, Ulfa Rafli Indar Praja Rahman, Nukleon Jefri Nur Rahmat Samudra Anugrah, Muhammad Ramadhan, Aslan Poetra Ramadhani, Lolita Resty Wulanningrum Reza Setyawan Ria Febrianti Rini Nur Hasanah Rochmawati Rochmawati Rochmawati Rochmawati Romadlon, Muhammad Rizqi Rosa Andrie Asmara Rosyidin, Zulkham Umar Rusdha Aulia Salah Abdullah Khalil Abdulrahman Salsabila, Reni Fatrisna Saodah Omar Selly Handik Pratiwi Seno Isbiyantoro Setyaningsih, Eka Rahayu Sevilla, Felix Rafael Segundo Siti Sendari Slamet Wahyudi Slamet Wibawanto Soraya Norma Mustika Soubin Sisavath Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Suti Mega Nur Azizah Suziyani Mohamed Syaad Patmantara Syaad Patmanthara Syaghlu Natsalam Saputra Syaichul Fitrian Akbar Syamsul Bahri Taiga Haruta Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tony Yu Tran Thi Hao Triyanna Widiyaningtyas Tsukasa Hirashima Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Nur Hidayat Wahyu Primadi Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wibawa, Aji Presetya Wibowo, Kusmayanto Hadi Wicaksana, Ardi Anugerah Widiharso, Prasetya Widyadara , Made Ayu Dusea Wijaya, Mikel Ega Wirawan, Muhammad Zaki Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Yosi Kristian Yu, Tony Yudha Islami Sulistya Yuliana Melita Pranoto Yuni Rahmawati Yusuf Tri Hadi Mulyana Zaeni, Ilham Ari Elbaith Zufida Kharirotul Umma Zulkham Umar Rosyidin Zulkham Umar Rosyidin Zulkifli, Shamsul Aizam