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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 Jurnal Pendidikan: Teori, Penelitian, dan Pengembangan JOIV : International Journal on Informatics Visualization Al Ishlah Jurnal Pendidikan 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 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 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 Journal of Engineering and Technological Sciences Jurnal ilmiah teknologi informasi Asia

Anik Nur Handayani

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Author-ID : 2590928

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Comparative Analysis of OCR Methods Integrated with Fuzzy Matching for Food Ingredient Detection in Japanese Packaged Products Muhammad Zaky Rahmatsyah; Jevri Tri Ardiansah; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.257

Advances in digital technology offer a solution to the challenges faced by foreign consumers in understanding ingredient information on Japanese food packaging, especially due to the use of Kanji, Hiragana, and Katakana characters. This study develops and reveals an allergen detection method based on Optical Character Recognition (OCR) and fuzzy match applied to Japanese food packaging. Three OCR methods—Google Vision OCR, PaddleOCR, and Tesseract OCR—were compared and evaluated using Precision, Recall, F1-Score, and Confusion Matrix metrics.The study began with the collection of food product images from bold sources, followed by text extraction using the three OCR methods. The extracted text was then cleaned and normalized before being matched with ground truth data using fuzzy match. Testing was conducted on 10 product image samples with varying quality and lighting conditions. The results showed that Google Vision OCR outperformed the others, achieving an average F1 score of 1.00, followed by PaddleOCR (0.75), and Tesseract OCR (0.30). Google Vision was the most consistent in detecting allergens such as 乳 (milk), 小麦 (wheat), and 卵 (egg). These findings suggest that the integration of OCR and fuzzy matching is effective in detecting allergens, even in the presence of textual variations and recognition errors. This study contributes to the development of automated food recommendation systems for foreign consumers, especially those who have food preferences due to allergies, religious beliefs, or personal preferences.

Comparation Analysis of Otsu Method for Image Braille Segmentation : Python Approaches Wicaksana, Ardi Anugerah; Handayani, Anik Nur
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.268

Braille plays a crucial role in supporting literacy for individuals with visual impairments. However, converting Braille documents into digital text remains a technical challenge, particularly in accurately segmenting Braille dots from scanned images. This study aims to evaluate and compare the effectiveness of several classical image segmentation techniques—namely Otsu, Otsu Inverse, Otsu Morphology, and Otsu Inverse Morphology—in enhancing Braille image pre-processing. The methods were tested using a set of Braille image datasets and evaluated based on six quantitative image quality metrics: Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), Mean Absolute Error (MAE), Structural Similarity Index (SSIM), Feature Similarity Index (FSIM), and Edge Similarity Index (ESSIM). The results show that the Otsu Morphology method achieved the highest PSNR (27.6798) and SSIM (0.5548), indicating superior image fidelity and structural preservation, while the standard Otsu method yielded the lowest MSE (113.3485).These findings demonstrate that applying morphological operations in combination with thresholding significantly enhances the segmentation quality of Braille images, supporting better accuracy in subsequent recognition tasks. This approach offers a practical and efficient alternative to deep learning models, particularly for resource-constrained systems such as portable Braille readers.

YOLOv8 Implementation on British Sign Language System with Edge Detection Extraction Romadlon, Muhammad Rizqi; Anik Nur Handayani
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.276

This study presents the development and implementation of a deep learning-based system for recognizing static hand gestures in British Sign Language (BSL). The system utilizes the YOLOv8 model in conjunction with edge detection extraction techniques. The objective of this study is to enhance the accuracy of recognition and facilitate communication for individuals with hearing impairments. The dataset was obtained from Kaggle and comprises images of various BSL hand signs captured against a uniform green background under consistent lighting conditions. The preprocessing steps entailed resizing the images to 640 640 pixels, implementing pixel normalization, filtering out low-quality images, and employing data augmentation techniques such as horizontal flipping, rotation, shear, and brightness adjustments to enhance robustness. Edge detection was implemented to accentuate the contours of the hand, thereby facilitating more precise gesture identification. Manual annotation was performed to generate both bounding boxes and segmentation masks, allowing for the training of two model variants: The first is YOLOv8 (non-segmentation), and the second is YOLOv8-seg (segmentation). Both models underwent training over a period of 100 epochs, employing the Adam optimizer and binary cross-entropy loss. The training-to-testing data splits utilized were 50:50, 60:40, 70:30, and 80:20. The evaluation metrics employed included mAP@50, precision, recall, and F1-score. The YOLOv8-seg model with an 80:20 split demonstrated the optimal performance, exhibiting a precision of 0.974, a recall of 0.968, and mAP@50 of 0.979. These metrics signify the model's capacity for robust detection and localization. Despite requiring greater computational resources, the segmentation model offers enhanced contour recognition, rendering it well-suited for high-precision applications. However, the generalizability of the model is constrained due to the employment of static gestures and controlled backgrounds. In the future, researchers should consider incorporating dynamic gestures, varied backgrounds, and uncontrolled lighting to enhance real-world performance.

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

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.

A Generalized Deep Learning Approach for Multi Braille Character (MBC) Recognition Widyadara, Made Ayu Dusea; Handayani, Anik Nur; Herwanto, Heru Wahyu; Yu, Tony
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.v7i3.13891

Automated visual recognition of Multi Braille Characters (MBC) poses significant challenges for assistive reading technologies for the visually impaired. The intricate dot configurations and compact layouts of Braille complicate MBC classification. This study introduces a deep learning approach utilizing Convolutional Neural Networks (CNN) and compares four leading architectures: ResNet-50, ResNet-101, MobileNetV2, and VGG-16. A dataset comprising 105 MBC classes was developed from printed Braille materials and underwent preprocessing that included image cropping, brightness enhancement, character position labeling, and resizing to 89×89 pixels. A 70:20:10 data partitioning strategy was applied for training and evaluation, with variations in batch sizes (8–128) and epochs (50–500). The results demonstrate that ResNet-101 achieved superior performance, attaining an accuracy of 91.46%, an F1-score of 89.48%, and a minimum error rate of 8.5%. ResNet-50 and MobileNetV2 performed competitively under specific conditions, whereas VGG-16 consistently exhibited lower accuracy and training stability. Standard deviation assessments corroborated the stability of residual architectures throughout the training process. These results endorse ResNet-101 as the most effective architecture for Multi Braille Character classification, highlighting its potential for incorporation into automated Braille reading systems, a tool for translating braille into text or sound for future needs.

Mono Background and Multi Background Datasets Comparison Study for Indonesian Sign Language (SIBI) Letters Detection using YOLOv8 Andriyanto, Teguh; Handayani, Anik Nur; Ar Rosyid, Harits; Wiryawan, Muhammad Zaki; Azizah, Desi Fatkhi; Liang, Yeoh Wen
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3462

The research in this paper focuses on the detection of Indonesian Sign Language System (SIBI) letters using the YOLOv8 object detection model. The study compares two datasets, one with mono-background (a simple, uniform background) and another with multi-background (complex and varied backgrounds). The research aims to evaluate how the complexity of image backgrounds affects the performance of the YOLOv8 model in detecting SIBI letters This study uses a dataset consisting of 24 SIBI letters (excluding J and Z due to the complexity of their gestures), sourced from Mendeley. The dataset was processed with and without data augmentation (rotation, brightness adjustments, blur, and noise) to test the model under various conditions. Four models were trained and tested: one using mono-background images, another using augmented mono-background images, a third using multi-background images, and a final model trained on augmented multi-background images. The results showed that the YOLOv8 model performed best with the multi-background dataset, achieving a precision of 0.995, recall of 1.000, F1 score of 0.997, and mAP50 of 0.994Adding to the model made it better at generalizing, but it took longer to train. The study finds that multi-background datasets with augmentation make the model much better at finding SIBI letters in real-world settings. This makes it a promising tool for projects that aim to improve communication for deaf people in Indonesia. The study suggests that more research should be done on augmentation techniques and bigger datasets to make detection more accurate.

Intelligent Weighing Machine untuk Meningkatkan Keakuratan Berat Produk Bubuk Herbal Instan Sujito; Sendari, Siti; Handayani, Anik Nur; Gumilar, Langlang; Utomo, Imam Tree; Fakhruddin, Dhiyaurrahman
ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat Vol. 1 No. 2 (2023): ABDI UNISAP: Jurnal Pengabdian Kepada Masyarakat
Publisher : UPT Publikasi dan Penerbitan Universitas San Pedro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59632/abdiunisap.v1i2.112

Syarimpon merupakan usaha yang didirikan oleh Afiani Fadiana pada tahun 2017 yang berlokasi di Perumahan Persada Bhayangkara No.G3, Pangetan, Kecamatan Singosari, Kabupaten Malang. yang bergerak dalam bidang produksi minuman herbal instan dan sudah mengembangkan minuman herbal instan dengan kemasan yang menarik dan sudah memiliki berbagai perizinan mulai dari NIB ( Nomor Induk Berusaha), PIRT (Produk Industri Rumah Tangga), Perizinan penetapan produk halal dan produk sudah HAKI (Hak Kekayaan Intelektual) namun Syarimpon ini mengalami permasalahan mengenai proses penimbangan berat produk masih menggunakan cara manual dan memakan waktu pada saat proses penimbangan dan kurangnya keakuratan dari timbangan yang digunakan. Dengan adanya program pengabdian ini diharapkan mampu mengatasi masalah dari mitra dengan mentransfer teknologi Intelligent Weighing Machine guna meningkatkan keakuratan berat produk dengan cerdas dan lebih efisien. Tujuan dari program pengabdian kepada masyarakat ini menghasilkan Intelligent Weighing Machine yang diharapkan mampu mengurangi waktu dalam proses penimbangan dan membantu mengatasi permasalahan selama proses produksi mereka dan dapat mempertahankan kualitas dari minuman herbal instan yang mereka produksi. Hasil dari program PKM ini melakukan pengembangan Intelligent Weighing Machine alat ini didesain untuk memberikan kemudahan untuk pengguna sehingga mempercepat proses penimbangan dan meningkatkan keakuratan berat bersih produk sehingga dapat mengurangi waktu yang digunakan untuk menimbang serta memastikan berat bersih produk sesuai standar yang telah disesuaikan oleh UMKM Syarimpon dan memberikan dampak positif untuk penjualan dan produksi mereka.

PERBANDINGAN METODE NAÏVE BAYES DAN C4.5 UNTUK MEMPREDIKSI MORTALITAS PADA PETERNAKAN AYAM BROILER Baihaqi, Dimas Imam; Handayani, Anik Nur; Pujianto, Utomo
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 10, No 1 (2019): JURNAL SIMETRIS VOLUME 10 NO 1 TAHUN 2019
Publisher : Fakultas Teknik Universitas Muria Kudus

Ayam broiler adalah jenis ternak yang paling cepat untuk dipanen. Namun dalam berternak ayam broiler pasti banyak masalah yang dihadapi contohnya adalah tingkat kematian. Untuk menekan kerugian, para peternak sebaiknya memperhatikan faktor-faktor apa saja yang menyebabkan kematian ayam tersebut. Beberapa penelitian yang meneliti tentang ayam broiler menggunakan metode percobaan dan RAL. Namun masih belum ada yang meneliti mortalitas ayam broiler menggunkan komputasi. Untuk mengetahui metode mana yang lebih baik untuk memprediksi mortalitas pada peternakan ayam broiler dilakukan penelitian perbandingan metode Naïve Bayes dan C4.5. Hasil dari perbandingan akan dievaluasi menggunakan confution matrix. Hasil dari pengujian data menggunakan confution matrix menghasilkan nilai akurasi dari metode C4.5 lebih besar dari pada metode Naïve Bayes. Nilai akurasi dari metode C4.5 adalah 93% dan nilai akurasi dari metode Naïve Bayes adalah 88.66%.

Pelatihan Robot Edu Bagi Siswa SDN Sumbersuko di Desa Sumbersuko Kecamatan Wagir Kabupaten Malang Handayani, Anik Nur; Lestari, Dyah; Sendari, Siti; Fadlika, Irham
Ilmu Komputer untuk Masyarakat Vol 1, No 1 (2020)
Publisher : Universitas Muslim Indonesia

Sekolah Dasa Negeri Sumbersuko meyakini bahwa pada hakikatnya setiap anak memiliki beragam kecerdasan (multiple intelligences) yang menunggu untuk diungkap, digali, dilatih dan dikembangkan. Sekolah Dasa Negeri Sumbersuko mengupayakan sebaik-baiknya dan menyediakan beragam kegiatan pembelajaran. Tujuannya adalah untuk mengembangkan kecerdasan majemuk yang ada pada anak didik. Maka dari itu kami membantu dengan mengadakan pengabdian kepada pendidikan khusunya di SDN Sumbersuko dengan menggunakan robot edu untuk proses pembelajaran. robot yang dibuat masih dalam bentuk analog belum bisa diprogram. Kegiatan pelatihan robot edu ini dapat memberikan kesempatan belajar yang lebih dalam mengenai pemrograman dasar. Ilmu pemrograman menjadi keterampilan yang penting bagi anak-anak yang tumbuh di jaman teknologi ini. Diharapkan program Pengabdian pada Masyarakat yang diimplementasikan dapat mendukung proses pembelajaran dengan menggunakan robot di Sekolah Dasa Negeri Sumbersuko.

Forecasting Solar Irradiation on Solar Tubes Using the LSTM Method and Exponential Smoothing Handoko, Wahyu Tri; Muladi, Muladi; Handayani, Anik Nur
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26395

Sunlight is an alternative energy source that can be used as a substitute for fossil fuels. Renewable energy potential has not been widely utilized, especially in Indonesia. Utilization of sunlight, one of which is done indoors to save electricity and the source is not limited. This study aims to predict solar irradiance to determine the value of sunlight intensity in an area as the main source of the utilization of renewable electrical energy through the solar tube system with the LSTM method. This low-cost system offers a renewable way and considers the potential for solar radiation as an energy-efficient alternative based on the intensity of light captured by the solar tube. This research uses two methods. The LSTM method is a recurrent neural network forecasting technique that can study deeply and extract temporal relationships in data because of its large architecture. The exponential smoothing method is part of the time series forecasting technique and is used when the dataset has no cyclic variance and trend. Data collection was carried out in sunny conditions because it represents a stable condition in sunlight. The results obtained from the two methods are evaluated with RMSE and MAE values to choose the optimal approach. Due to lower RMSE and MAE values in this comparison, LSTM performs better than Multiple Repeat and Exponential Smoothing in terms of performance.

Co-Authors A.N. Afandi Abdul Rachman Manga' Abdullah Iskandar Syah Achmad Hamdan Achmad Safi’i Achmad Safi’i Adi Izhar Bin Che Ani Adi Prastowo, Nur Kodrad Adib Nur Sasongko Adim Firmansah Afandi, Farrel Candra Winata Agusta Rakhmat Taufani Ahmad Dardiri Ahmad Munjin Nasih Ahmad Nurdiansyah, Ahmad Ahmad Sahru Romadhon Aji Prasetya Wibawa Amaliya, Sholikhatul Andrew Nafalski Anita Qotrun Nada Anusua Ghosh Ardiansyah, Lucky Arengga, Danang Ari Priharta Ari Priharta Arif Widodo, Baskoro 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 Chuttur, Mohammad Yasser Damanhuri, Nor Salwa Damayanti, Farradila Ayu Damayanti, Masyita Danang Arengga Danang Arengga Wibowo Dedes, Khen Devita Maulina Putri, Devita Maulina Dewi Aprilia Lintang 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 Erwina Nurul Azizah Eva, Nur Evania Yafie F.ti Ayyu Sayyidul Laily Faiz Syaikhoni Aziz Fakhruddin, Dhiyaurrahman Faqih, Kamil Faradhila Saffa Dhamira Farah Nisa’ Salsabila Fauzi, Juwita Annisa Fauzi, Rochmad Felix Andika Dwiyanto Ferina Ayu Pusparani Gianika Roman Sosa Graciello, Manuel Tanbica Gunawan Budi P Guyub Raharjo Gwo-Jiun Horng Haffas Zikri Ariyandi Hakkun Elmunsyah Halimahtus Mukminna, Halimahtus Handoko, Wahyu Tri 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 Imanuel Hitipeuw Ira Kumalasari Irfan Ramadhani Irham Fadlika Jehad A. H. Hammad Jehad A.H. Hammad Jevri Tri Ardiansah Jevri Tri Ardiansah Joumil Aidil Saifuddin 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 Made Ayu Dusea Widyadara - Universitas Nusantara Kediri, Made Ayu Dusea Widyadara Mahamad, Abd Kadir Manga, Abdul Rachman Maqbullah, Afwatul Ming Foey Teng, Ming Foey Moh Zainul Falah Moh. Zainul Falah Mohammad Agung Rizki Mohammad Rizky Kurniawan Mohammad Yussril Asri Mohsen Samadi Mokh Sholihul Hadi Much. Arafat Al Mubarok 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 Mulya, Marga Asta Jaya Mumtaazah, Muhammad Athar Mutiara, Titi Nadindra Dwi Ariyanta Nafalski, Andrew Nandang Mufti Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanani Putri Nastiti Susetyo Fanany Putri Naufal Rizaldi Gunawan Nisa, Khoirotun Nizaar, Roub Norzanah Rosmin Norzanah Rosmin Nugraha, Agil Zaidan Nugraha, Youngga Rega Nunung Nurjanah Nur Halim Nur Rahma, Andika Bagus Nurus Sihab Aminudin Nuzuluddin, M. Osamu Fukuda Praja, Rafli Indar 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 Rahman, Nukleon Jefri Nur Rahmat Samudra Anugrah, Muhammad Raja, Roesman Ridwan Ramadhani, Lolita Resty Wulanningrum Reza Setyawan Rini Nur Hasanah Rismayanti, Nurul Rochmawati, Rochmawati Romadlon, Muhammad Rizqi Rosa Andrie Asmara 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 Srini Suciati, Reski Dwi Suryani, Ani Wilujeng Suti Mega Nur Azizah Suziyani Mohamed Syaad Patmantara Syaad Patmanthara Syaichul Fitrian Akbar Taw, Phillip Teguh Andriyanto, Teguh Timothy John Pattiasina Titaley, Gilberth Valentino Tsukasa Hirashima Urnika Mudhifatul Jannah Utama, Agung Bella Putra Utomo Pujianto Utomo, Imam Tree Veithzal Rivai Zainal Wahyu Arbianda Yudha Pratama Wahyu Irianto Wahyu Primadi Wahyu Sakti Gunawan Irianto Wahyu Styo Pratama Wahyu Tri Handoko Wibawa, Aji Presetya Wibowo, Kusmayanto Hadi Wicaksana, Ardi Anugerah Widiharso, Prasetya Wijaya, Mikel Ega Wiryawan, Muhammad Zaki Yogi Dwi Mahandi Yosi Kristian Yu, Tony Yudha Islami Sulistya Yuliana Melita Pranoto Yuni Rahmawati Zaeni, Ilham Ari Elbaith Zufida Kharirotul Umma Zulkham Umar Rosyidin Zulkham Umar Rosyidin Zulkifli, Shamsul Aizam

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