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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmu Komputer MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TELKOMNIKA (Telecommunication Computing Electronics and Control) Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Jurnal Ilmiah Kursor Journal of Innovation and Applied Technology International Journal of Local Economic Governance Journal of Environmental Engineering and Sustainable Technology Jurnal Pembangunan dan Alam Lestari Jurnal Teknologi Informasi dan Ilmu Komputer The International Journal of Accounting and Business Society Jurnal Edukasi dan Penelitian Informatika (JEPIN) International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Knowledge Engineering and Data Science Jambura Law Review Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Indonesian Journal of Electrical Engineering and Computer Science International Journal of Engineering, Science and Information Technology Indexia Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Bulletin of Culinary Art and Hospitality Bulletin of Social Informatics Theory and Application Jurnal ilmiah teknologi informasi Asia Signal and Image Processing Letters Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika

Wayan Firdaus Mahmudy

Brawijaya University

Author-ID : 312490

Agriculture, Biological Sciences & Forestry Astronomy 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 Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation

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Optimasi Derajat Keanggotaan Fuzzy Tsukamoto Menggunakan Algoritma Genetika Untuk Diagnosis Penyakit Sapi Potong Kurnianingtyas, Diva; Mahmudy, Wayan Firdaus; Widodo, Agus Wahyu
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 1: Maret 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Sistem inferensi fuzzy bisa digunakan untuk diagnosis penyakit pada sapi potong. Untuk mendapatkan akurasi yang tinggi maka batasan fungsi keanggotaan fuzzy perlu ditentukan secara tepat. Penggunaan metode logika fuzzy untuk memperoleh hasil diagnosis penyakit pada sapi potong sesuai pakar berdasarkan batasan gejala penyakit dan aturan-aturan yang diperoleh dari pakar. Batasan tersebut bisa diperbaiki menggunakan Algoritma Genetika untuk mendapatkan akurasi yang lebih baik. Pengujian yang dilakukan pada 51 data dari beberapa gejala penyakit menghasilkan akurasi sebesar 98,04% dengan menggunakan parameter genetika terbaik antara lain ukuran populasi sebesar 80, ukuran generasi sebesar 15, nilai Crossover rate (Cr) sebesar 0,9, dan nilai Mutation rate (Mr) sebesar 0,06. Akurasi tersebut mengalami peningkatan sebesar 3,54% sesudah dilakukannya optimasi pada metode logika fuzzy.Kata kunci: diagnosis penyakit sapi potong, logika fuzzy, Algoritma GenetikaAbstract Fuzzy inference systems can be used to diagnose cattle disease. Prior to obtaining the most accurate of limitation, fuzzy membership functions must be defined precisely. Thus, the limits will be optimized along with Genetic Algorithm to get more accurate results. The function of fuzzy logic methods in the diagnosis of disease is relied upon the parametres set by experts. Tests that were performed on 51 data from some of the symptoms of the disease resulted in an accuracy of 98.04% using the best genetic parameters with the population size of 80, the size of the generation of 15, crossover rate value of 0.9, and the value of mutation rate of 0.06. The accuracy has increased by 3.54% compare to results before optimization. Keywords: cattle disease diagnosis, fuzzy logic, genetic algorithms

Kombinasi Logika Fuzzy dan Jaringan Syaraf Tiruan untuk Prakiraan Curah Hujan Timeseries di Area Puspo – Jawa Timur Utomo, M. Chandra Cahyo; Mahmudy, Wayan Firdaus; Anam, Syaiful
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 3: September 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakPrakiraan curah hujan merupakan salah satu tanggung jawab penting yang dilakukan oleh layanan meteorologi di seluruh dunia. Permasalahan utama dalam hal analisis dan prakiraan adalah tingkat kesalahan yang semakin meningkat dari waktu ke waktu. Hal ini dapat terjadi karena kondisi ketidakpastian juga meningkat seiring dengan perubahan musim dan iklim. Penelitian ini mencoba mengombinasikan dua metode yaitu Logika Fuzzy untuk menghadapi kondisi-kondisi yang tidak pasti dan Jaringan Syaraf Tiruan multi-layer untuk menghadapi kondisi dengan ketidakpastian yang terus meningkat. Penelitian ini juga menggunakan algoritma Particle Swarm Optimization untuk menentukan kebutuhan secara otomatis. Kebutuhan yang perlu ditentukan secara otomatis adalah bobot-bobot awal dalam Jaringan Syaraf Tiruan multi-layer sebelum akhirnya melakukan proses pelatihan algoritma. Penelitian ini menggunakan studi kasus di empat area Jawa Timur yaitu Puspo, Tutur, Tosari, dan Sumber untuk memprakirakan curah hujan di area Puspo. Data yang digunakan merupakan curah hujan timeseries yang dicatat selama 10 tahun oleh Badan Meteorologi Klimatologi dan Geofisika (BMKG). Hasil penelitian ini menunjukkan bahwa kombinasi dari Logika Fuzzy dengan Jaringan Syaraf Tiruan multi-layer mampu memberikan tingkat RMSE sebesar 2.399 dibandingkan dengan hanya menggunakan regresi linear dengan tingkat RMSE sebesar 7.211.Kata kunci: fuzzy, hujan, hybrid, jaringan syaraf, optimasi, timeseriesAbstractRainfall forecasting is one of the important responsibilities that carried out by meteorological services in the worldwide. The main problem in terms of analysis and forecasting is the error rate is almost increasing from time to time. This caused by the uncertainty conditions are also increasing with the change of seasons and climate. This study tried to combine two methods of Fuzzy Logic for the problem solved of uncertain conditions and multi-layer Artificial Neural Network for the problem solved of the uncertainty that continues to increase. Particle Swarm Optimization algorithm also is used to determine the requirement automatically. The requirement that needs to be determined automatically is initial weights in multi-layer Artificial Neural Networks before the process of algorithm training. This study uses a case study in four areas of East Java that are Puspo, Tutur, Tosari, and Sumber. The data are a time series of rainfall rate that recorded in the 10 years by Badan Meteorologi Klimatologi dan Geofisika (BMKG). The results of this study indicate that the combination of Fuzzy Logic with Multi-Layer Neural Networks is capable of providing an RMSE level of 2,399 compared to only using linear regression with an RMSE level of 7,211.Keywords: fuzzy, hybrid, neural networks, optimization, rainfall, time series

Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia Alfiyatin, Adyan Nur; Mahmudy, Wayan Firdaus; Ananda, Candra Fajri; Anggodo, Yusuf Priyo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 2: April 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.AbstractInflation is one indicator to measure the development of a nation. If inflation is not controlled will give many negative impacts to the people in a country. There are many ways to control inflation, one with forecasting. Forecasting is an activity to know future events based on past data. In this research using artificial intelligence method is extreme learning machine (ELM). The advantages of ELM are fast in the learning process. Based on ELM testing gets obtained an error value of 0.0202008, smaller than the backpropagation method of 1.16035821. It proves that ELM method is very suitable for forecasting.

Sistem Rekomendasi Profesi Berdasarkan Dimensi Big Five Personality Menggunakan Fuzzy Inference System Tsukamoto Mar'i, Farhanna; Mahmudy, Wayan Firdaus; Yusainy, Cleoputri
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 5: Oktober 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Sistem rekomendasi dapat dimanfaatkan sebagai alat bantu untuk pengambilan keputusan. Pada sebuah perusahaan, sistem rekomendasi profesi bisa digunakan untuk menempatkan seorang karyawan pada posisi yang tepat. Pada penelitian ini diusulkan sistem rekomendasi profesi berdasarkan Big Five Personality traits yang meliputi Extraversion, Aggreableness, Conscentiousness, Neuroticm, dan Opennes. Input yang digunakan ialah parameter dimensi Big Five Personality yang dirumuskan oleh John. Metode yang digunakan adalah Fuzzy Inference System (FIS) Tsukamoto. Keakuratan sistem dihitung dengan membandingkan output sistem dengan dengan acuan Top Ranked Personality - Based Work Styles for 22 Job Families yang menghasilkan nilai akurasi sebesar 63%.AbstractRecommendation systems can be used as a tool for decision making. In a company, a professional recommendation system can be used to place an employee in the right position. In this study proposed system of professional recommendation based on Big Five Personality traits which includes Extraversion, Aggreableness, Conscentiousness, Neuroticm, and Opennes. The input used is the Big Five Personality dimension parameter formulated by John. The method used is Fuzzy Inference System (FIS) Tsukamoto. The accuracy of the system is calculated by comparing the output of the system with the reference Top Personality - Based Work Styles for 22 Job Families that produce an accuracy score of 63%.

Klasifikasi Emosi Manusia pada Citra Digital dengan MobileNetV3 dan Spatial Transformer Dhaifullah, Afif Naufal; yudistira, Novanto; Mahmudy, Wayan Firdaus
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 10 (2025): Oktober 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

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Emosi merupakan kondisi psikofisiologis seseorang dengan faktor internal dan/atau lingkungan. Pengenalan akurat dari emosi wajah melalui sistem pengenalan visi komputer akan kondusif untuk kelancaran kemajuan interaksi manusia-komputer. Selain itu, karena dinamika dan heterogenitas layanan seluler, dibutuhkan sistem pengenalan emosi yang dapat memproses data secara akurat dengan waktu komputasi yang minimal. Kami mengusulkan penggunaan MobileNetV3 dengan penjadwalan learning rate serta penambahan attention module guna mendapatkan model dengan performa akurasi baik namun efisien dalam penggunaan sumber daya, seperti ukuran model, jumlah parameter yang rendah, dan waktu komputasi yang cepat, sehingga cocok diterapkan pada perangkat mobile. Adanya SE (Squeeze and Excitation) dan fungsi h-swish memungkinkan MobileNetV3 mempertahankan akurasi sekaligus mengurangi jumlah parameter secara signifikan. Adapun fitur localization dan sampling grid pada spatial transformer membantu model untuk fokus ke bagian yang diperlukan guna meningkatkan akurasi. Pada penelitian ini dibuktikan bahwa metode yang digunakan memberikan hasil yang baik dengan akurasi validasi sebesar 69.89% pada dataset FER2013 dan 84.10% pada FER2013+ dengan ukuran model sebesar 16.36 MB dan parameter 4,235,112 menunjukkan efisiensi memori dan kecepatan inferensi yang signifikan dibandingkan model lain seperti ResNet50 sehingga dapat dinyatakan model memiliki performa akurasi yang baik dengan efisiensi memori dan komputasi yang cukup baik.

Maximizing Profitability: The Lean Way To Sustainable Manufacturing Rachmawati, Christina; Mahmudy, Wayan Firdaus; Moh. Khusaini; Kurniawan, Andi
The International Journal of Accounting and Business Society Vol. 33 No. 1 (2025): IJABS
Publisher : Accounting Department,

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ijabs.2025.33.1.864

Purpose - This quantitative study examines the impact of lean production practices on the financial performance and sustainability of Indonesian FMCG companies. By analyzing waste reduction, efficiency improvements, and eco-design integration, the research demonstrates that lean practices significantly enhance both financial performance and environmental responsibility. These findings offer valuable insights for multinational companies operating in similar contexts. Design/methodology/approach - This research employs a quantitative approach to discern the contribution of lean way to sustainable manufacturing. Through a comprehensive survey and meticulous observation, this work maps the intricate ways in which lean production practices affect economic and business outcomes, providing valuable insights into the real-world application of lean principles. Findings -. This case study demonstrates how lean production methods can substantially benefit a company's financial performance and overall operations. The implementation of lean production methodologies has a proven track record of improving efficiency, reducing waste, and elevating product and process quality, leading to substantial economic gains and enhanced business performance. Practical implications - Implementing lean production requires careful consideration of technological innovation and organizational culture. Neglecting either aspect poses a significant risk of hindering the successful implementation and realization of the myriad benefits that lean production offers. Originality/value - This article examines an Indonesian FMCG company's experience with lean production, providing a rare glimpse into how these methods are applied in a developing economy. This study aims to expand our understanding of lean production and offer useful guidance to multinational companies operating in comparable settings. Paper type -Case study

Systemic framework for coffee roasting decision support in Malang Regency using soft systems methodology Effendi, Mas’ud; Santoso, Imam; Astuti, Retno; Mahmudy, Wayan Firdaus
Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Vol 9, No 2 (2026)
Publisher : Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.afssaae.2026.009.02.4

Malang Regency has many different types of land and coffee varieties in each sub-district. It spans from highland Arabica coffee in Poncokusumo, grown at 900 to 1,400 meters above sea level, to lowland Robusta coffee in Gedangan, grown at less than 300 meters above sea level. These differences make the green coffee beans have very different sensory profiles. However, the way people decide how to roast the beans is still mostly based on experience and cannot be easily shared. In this study, a seven-stage Soft Systems Methodology (SSM) was used to create a conceptual Decision Support System (DSS) framework that can recommend coffee roasting parameters. Data was collected through field observations and interviews with experienced roasters, farmers/farmer cooperative leaders, and staff from the Malang Regency Agriculture Office in all ten sub-districts. The SSM process resulted in several outputs, including a Rich Picture that shows six groups of actors and three main social and technical problems, a root definition using CATWOE that explains how unclassified green beans can be turned into roasting profile recommendations for each origin, a conceptual model with ten activities, and a gap analysis that found the biggest problems are the lack of formal roasting knowledge and the absence of a system to deliver recommendations to users. The proposed framework has a seven-layer DSS design that uses computer vision with edge computing to classify green bean quality and includes a knowledge base for each sub-district. It is made to work offline in rural areas with limited resources. The framework was tested using the Efficacy, Efficiency, Effectiveness, and Elegance (4Es) criteria. The results showed that SSM can be used as a structured and repeatable way to design agroindustrial DSS in tropical coffee-producing regions with many different stakeholders and limited infrastructure.

Performance Evaluation of Machine Learning and Deep Learning for Rainfall Forecasting Arief Andy Soebroto; Lily Montarcih Limantara; Wayan Firdaus Mahmudy; Moh. Sholichin; Nurul Hidayat; Agi Putra Kharisma
International Journal of Engineering, Science and Information Technology Vol 5, No 4 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i4.1179

Climate change is a significant challenge for both humans and the environment, with its impacts increasingly felt across various regions of the world. The most evident consequence is the alteration of extreme weather patterns, which often lead to destructive and life-threatening natural disasters. Among these, extreme rainfall was the most damaging factor, frequently triggering floods. However, the increasing occurrence of related events outlined the urgent need for developing more accurate rainfall forecasting systems as a strategic measure for disaster risk reduction. This research adopted daily rainfall data from Samarinda City, collected between 2004 and 2012, to conduct prediction using both machine and deep learning methods. The implementation of machine learning methods, such as Support Vector Regression (SVR), enabled the model to learn from historical data and uncover complex patterns, resulting in accurate forecasts and improved adaptability to climate variability. Meanwhile, deep learning models, including Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM), enhanced prediction performance by capturing more intricate and abstract data relationships. Performance evaluations conducted using Mean Absolute Error (MAE) and Mean Squared Error (MSE) showed that deep learning outperformed machine learning in accuracy. The LSTM model achieved the best performance, with loss values of 0.0482 and 0.0527 for MSE and MAE, respectively. The advantage of deep learning lies in its ability to build more complex models for handling non-linear problems and to learn data representations at various levels of abstraction, which has led to more accurate results. Furthermore, LSTM surpassed RNN by effectively overcoming the vanishing gradient issue, allowing for more stable and efficient training that led to superior predictive performance.

Hybrid machine learning for imbalanced lettuce disease classification Fazlur Ihzanurahman; Wayan Firdaus Mahmudy
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1783-1789

This study investigates a hybrid machine learning framework combining EfficientNet-B3 feature extraction with classical classifiers for lettuce disease classification under conditions of extreme class imbalance. The system utilizes EfficientNet-B3 to extract high-dimensional feature embeddings from 2,337 images, which are subsequently classified using support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN). Although the proposed SVM-based model achieves a high overall accuracy of 94.01%, experimental results reveal a substantial performance discrepancy compared to the macro F1-score of 37.94%. This critical gap indicates that while the model successfully identifies the majority classes, it fails to recognize rare disease categories with limited samples. Theoretical analysis suggests that while SVM handles high-dimensional feature spaces more effectively than RF and KNN, the deep features extracted are biased toward majority class characteristics. These findings highlight the severe limitations of accuracy-centric evaluation in agricultural diagnostics and demonstrate that deep feature extraction alone is insufficient to guarantee robust detection for minority pathologies. The study concludes that relying on aggregate accuracy can mask diagnostic failures, emphasizing the urgent need for per-class performance analysis and data-level mitigation strategies in future research.

Signature Pattern Recognition using Kohonen Network Sari, Nadia Roosmalita; Sarwani, Mohammad Zoqi; Aulia, Yudha Alif; Mahmudy, Wayan Firdaus
Knowledge Engineering and Data Science
Publisher : citeus

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A signature is a special form of handwriting that used for human identification process. The current identification process is extremely ineffective. People have to manually compare signatures with the previously stored data. This study proposed SOM Kohonen algorithm as the method of signature pattern recognition. This method has able to visualize high-dimensional data. The image processing method is used in this study in pre-processing data phase. The accuracy of SOM Kohonen was 70 %, indicated the method used was good enough for pattern recognition.

Co-Authors A.N. Afandi Abdul Latief Abadi Abdul Latief Abadi Achmad Arwan Achmad Basuki Achmad Ridok Adimoelja, Ariawan Aditama, Gustian Adyan Nur Alfiyatin Agi Putra Kharisma Agung Mustika Rizki Agung Mustika Rizki, Agung Mustika Agung Setia Budi Agung Setia Budi, Agung Setia Agus Naba Agus Wahyu Widodo Agus Wahyu Widodo Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Aji Prasetya Wibawa Al Khuluqi, Mabafasa Alauddin, Mukhammad Wildan Alfiani Fitri Alfita Rakhmandasari Alfiyatin, Adyan Nur Alqorni, Faiz Amalia Kartika Ariyani Amalia Kartika Ariyani Amalia Kartika Ariyani Anantha Yullian Sukmadewa Andi Kurniawan Andi Maulidinnawati A K Parewe Andi Maulidinnawati A. K. Parewe Andreas Nugroho Sihananto Andreas Pardede Andreas Patuan G. Pardede Andrew Nafalski Angga Vidianto Anissa, Retno Dewi Aprilia Nur Fauziyah Aprilia Nur Fauziyah Arief Andy Soebroto Arinda Hapsari Achnas Armanda, Rifki Setya Arviananda Bahtiar Arya, Putu Bagus Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi Asyrofa Rahmi, Asyrofa Aulia, Yudha Alif Bagus Priambodo Bagus Priambodo Bayu Rahayudi Binti Robiyatul Musanah Budi Darma Setiawan Burhan, M.Shochibul Cahya, Reiza Adi Cahyo Prayogo, Cahyo Candra Dewi Candra Fajri Ananda Cleoputri Yusainy Darmawan, Abizard Hashfi Dea Widya Hutami Dhaifullah, Afif Naufal Diah Anggraeni Pitaloka Didik Suprayogo Dinda Novitasari Dinda Novitasari, Dinda Diny Melsye Nurul Fajri Dita Sundarningsih Durrotul Fakhiroh Dyan Putri Mahardika Dzikron, Ahmad Ahdani Edi Satriyanto Edy Santoso Effendi, Mas’ud Eko Widaryanto Elta Sonalitha Ervin Yohannes Evi Nur Azizah Fadhli Almu’iini Ahda Fais Al Huda Fajri, Diny Melsye Nurul Fatchurrochman Fatchurrochman Fatwa Ramdani, Fatwa Fauzi, Muhammad Rifqi Fauziatul Munawaroh Fazlur Ihzanurahman Febriyana, Ria Fendy Yulianto Fitra Abdurrachman Bachtiar Fitri Anggarsari Fitria Dwi Nurhayati Gayatri Dwi Santika Ghozali Maski Grady Davinsyah Gusti Ahmad Fanshuri Alfarisy Gusti Ahmad Fanshuri Alfarisy, Gusti Ahmad Fanshuri Gusti Eka Yuliastuti Hafidz Ubaidillah Hamdianah, Andi Hanggara , Buce Trias Herman Tolle Hernando, Deo Heru Nurwarsito Hidayat, Luthfi Hilman Nuril Hadi Ida Wahyuni Ihsan Akram, Faiz Imada Nur Afifah Imam Cholisoddin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Santoso Indriati Indriati Irvi Oktanisa Ishardita Pambudi Tama Ismiarta Aknuranda Jauhari, Farid Khozaimi, Ach. Kukuh Tejomurti, Kukuh Kuncahyo Setyo Nugroho Kuncahyo Setyo Nugroho Kurnianingtyas, Diva Lailil Muflikhah Lily Montarcih Limantara M Chandra Cahyo Utomo M Fadli Ridhani M Shochibul Burhan, M Shochibul M. Shochibul Burhan M. Zainal Arifin Mabafasa Al Khuluqi Mar'i, Farhanna Marji Marji Mayang Anglingsari Putri, Mayang Anglingsari Mochammad Anshori Moh. Khusaini Moh. Sholichin Moh. Zoqi Sarwani Mohammad Zoqi Sarwani Mohammad Zoqi Sarwani, Mohammad Zoqi Muâ€™asyaroh, Fita Lathifatul Muh Arif Rahman Muh. Arif Rahman Muhammad Ardhian Megatama Muhammad Faris Mas'ud Muhammad Halim Natsir Muhammad Isradi Azhar Muhammad Khaerul Ardi Muhammad Noor Taufiq Muhammad Rivai Muhammad Rofiq Nadia Roosmalita Sari Nadya Oktavia Rahardiani Nashi Widodo Ni Wayan Surya Wardhani Nindynar Rikatsih Novanto Yudistira Novi Nur Putriwijaya Nurizal Dwi Priandani Nurul Hidayat Oakley, Simon Oktanisa, Irvi Pardede, Andreas Philip Faster Eka Adipraja Pratama, Muhammad Fajarivan Prayudi Lestantyo Prima Zulvarina, Prima Purnomo Budi Santoso Putra, Firnanda Al Islama Achyunda Putri Hasan, Vitara Nindya Putu Indah Ciptayani Qoirul Kotimah Rachmansyah, Ghenniy Rachmawati, Christina Rani Kurnia Rayandra Yala Pratama, Rayandra Yala Retno Astuti Retno Dewi Anissa Riani, Garsinia Ely Rifa’i, Muhaimin Rinda Wahyuni Rizal Setya Perdana Rizal Setya Perdana Rizdania, Rizdania Rizki Ramadhan Rody, Rafiuddin Ruth Ema Febrita Ryan Iriany S, M Zaki Samaher . Saputro, Arkaditya Lanang Saragih, Triando Hamonangan Selly Kurnia Sari Setyawan Purnomo Sakti Sudarto Sudarto Suhana, Rizka Sukarmi Sukarmi, Sukarmi Sulistyo, Danang Arbian Sutrisno . Sutrisno Sutrisno Syafrial Syafrial Syafrial Syafrial Syaiful Anam Syandri, Hafrijal Tanjung, Lutfi Mitra Tirana Noor Fatyanosa, Tirana Noor Titiek Yulianti Titiek Yulianti Titiek YULIANTI Tomi Yahya Christyawan Tri Halomoan Simanjuntak Ubaidillah, Hafidz Ullump Pratiwi Utaminingrum, Fitri Utomo, M. Chandra Cahyo Vivi Nur Wijayaningrum Wahyuni, Ida Widdia Lesmawati Windi Artha Setyowati Yeni Herawati Yogi Pinanda Yogie Susdyastama Putra Yudha Alif Aulia Yudha Alif Auliya Yudha Alif Auliya, Yudha Alif Yulia Trianandi Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo Yusuf Priyo Anggodo, Yusuf Priyo

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