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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Innovation and Applied Technology Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Habitat Jurnal Edukasi dan Penelitian Informatika (JEPIN) SMATIKA Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer JAST : Jurnal Aplikasi Sains dan Teknologi International Journal of Engineering, Science and Information Technology J-Intech (Journal of Information and Technology)

Arief Andy Soebroto

Universitas Brawijaya

Author-ID : 6133477

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 Industrial & Manufacturing Engineering Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

Published : 110 Documents Claim Missing Document

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Perancangan Antarmuka Pada Sistem Informasi Investasi dan Pemasaran Domba Digital Masyarakat 5.0 Menggunakan Metode Human Centered Design (HCD) (Desa Bumirejo, Kecamatan Dampit, Kabupaten Malang) Wijaya, Aldi Rahman; Soebroto, Arief Andy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 1 (2025): Januari 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Pertumbuhan teknologi informasi yang semakin pesat, terutama dalam bidang peternakan. Salah satu inovasi terbaru dalam peternakan adalah peternakan domba digital yang menggunakan teknologi digital untuk meningkatkan efisiensi dan produktivitas peternakan. Berdasarkan permasalahan yang ada pada Desa Bumirejo, Kecamatan Dampit, Kabupaten Malang, masih banyak warga yang kesulitan dalam memasarkan hasil ternak domba. Selain itu, kemudahan antarmuka dalam perancangan aplikasi menjadi hal yang sangat berpengaruh terhadap pengalaman pengguna yang baik. Oleh karena itu, perlu dihadirkan sebuah solusi antarmuka yang memiliki fleksibilitas dan dapat diakses di berbagai tempat. Penelitian ini merancang suatu antarmuka pada Sistem Informasi Investasi Dan Pemasaran Domba Digital menggunakan metode Human Centered Design (HCD). Dalam perancangan antarmuka didapatkan hasil penelitian berupa wireframe, high fidelity, serta prototype design. Hasil evaluasi pengujian usabilitas mendapatkan hasil dari aspek efektivitas menggunakan metrik success rate sebesar 99,56%, hasil dari aspek efisiensi menggunakan metrik Overall Relative Efficiency sebesar 100% dan hasil dari aspek kepuasan menggunakan metode System Usability Scale (SUS) rata-rata sebesar 89,1 dengan kategori excellent Hasil excellent dan High Acceptable evaluasi pengalaman pengguna menggunakan User Experience Questionnaire (UEQ) hasil skala daya tarik mendapat kategori excellent, skala efisiensi mendapat kategori excellent, skala ketepatan mendapat kategori excellent, skala stimulasi mendapat kategori Excellent, dan skala kebaruan mendapat kategori good. Kedepannya untuk penelitian selanjutnya perancangan antarmuka aplikasi dilanjutkan kedalam tahapan pengkodean program.

Audit System Development for Government Institution Documents Using Stream Deep Learning to Support Smart Governance Cholissodin, Imam; Soebroto, Arief Andy; Sutrisno, Sutrisno
Journal of Information Technology and Computer Science Vol. 4 No. 1: June 2019
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Document audit system is a means of evaluating documents on the results of delivering information, administrative documentary evidence in the form of texts or others. Currently, these activities become easier with the presence of computer technology, smartphones, and the internet. One of the examples is the documents created by various government institutions whether local, city and central government. The instance is online-published documents that are shaded by certain government institutions. Before the documents are published or used as an archive or authentic evidence for reporting or auditing activities, the documents must go through the editing stage to correct if there are errors and deficiencies such as spelling errors or incomplete information. In the editing process, however, a person may not be able to escape from making mistakes that result in the existence of writing errors after the editing process before the submission. Word spelling mistakes can change the meaning of the conveyed knowledge and cause misunderstanding of information to the readers, especially for assessors or the audit team. Based on the problem, the researcher intends to assist the work of the audit preparation team in document analysis by proposing a system capable of detecting word spelling errors using the Dictionary Lookup method from Information Retrieval (IR) and Natural Language Processing (NLP) science combined with Stream Deep Learning algorithms. Dictionary Lookup method is considered effective in determining the spelling of words that are true or false based on Lexical Resource. In addition, String Matching method that has been developed can correct word-writing errors correctly and quickly.Keywords: spelling mistake detection, dictionary lookup, audit of government institution documents, stream deep learning

Comparison of Regression, Support Vector Regression (SVR), and SVR-Particle Swarm Optimization (PSO) for Rainfall Forecasting Yulianto, Fendy; Mahmudy, Wayan Firdaus; Soebroto, Arief Andy
Journal of Information Technology and Computer Science Vol. 5 No. 3: Desember 2020
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Rainfall is one of the factors that influence climate change in an area and is very difficult to predict, while rainfall information is very important for the community. Forecasting can be done using existing historical data with the help of mathematical computing in modeling. The Support Vector Regression (SVR) method is one method that can be used to predict non-linear rainfall data using a regression function. In calculations using the regression function, choosing the right SVR parameters is needed to produce forecasting with high accuracy. Particle Swarm Optimization (PSO) method is one method that can be used to optimize the parameters of the existing SVR method, so that it will produce SVR parameter values with high accuracy. Forecasting with rainfall data in Poncokusumo region using SVR-PSO has a performance evaluation value that refers to the value of Root Mean Square Error (RMSE). There are several Kernels that will be used in predicting rainfall using Regression, SVR, and SVR-PSO with Linear Kernels, Gaussian RBF Kernels, ANOVA RBF Kernels. The results of the performance evaluation values obtained by referring to the RMSE value for Regression is 56,098, SVR is 88,426, SVR-PSO method with Linear Kernel is 7.998, SVR-PSO method with Gaussian RBF Kernel is 27.172, and SVR-PSO method with ANOVA RBF Kernel is 2.193. Based on research that has been done, ANOVA RBF Kernel is a good Kernel on the SVR-PSO method for use in rainfall forecasting, because it has the best forecasting accuracy with the smallest RMSE value.

Sistem Informasi Profil Kelompok Pertanian Terpadu Berbasis Web dengan Integrated Farming (Studi Kasus: Desa Dawuhan, Malang) Soebroto, Arief Andy; Hidayat, Nurul; Perdana, Rizal Setya; Indriati, Indriati; Darmawan, Hendra; Brilliansyach, Raihan Fikri; Ibnu, Mohammad; Nurannisa, Nadhira; Vasya, M Azka Obila
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1501

Dawuhan Village in Poncokusumo District, Malang Regency, is an evolving village with significant potential in the livestock sector. However, livestock data management in this village is still done manually, facing various challenges such as limited access, data integrity issues, and time-consuming processes. To address these issues, this research aims to develop a Web-Based Integrated Livestock Group Profile Information System. The primary objectives of this study are to improve accessibility, streamline the livestock data management process, and enhance data accuracy and security. The system is designed using the Next.js framework, chosen for its ease of use and security in implementing authentication and authorization, as well as its capability for future integration. The research results show that the developed system functions according to the requirements, providing a more efficient platform, reducing errors, and enhancing the user experience for farmers involved in data management. The implementation of this system is expected to improve operational efficiency and livestock data management in Dawuhan Village comprehensively.

Optimalisasi Prediksi Kasus COVID-19 di Indonesia: Perbandingan Teknik Validasi 80-20 Split dan Walk-Forward dengan ARIMA Asrul, Divanda Arya Inasta; Soebroto, Arief Andy
J-INTECH (Journal of Information and Technology) Vol 12 No 02 (2024): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v12i02.1373

This study presents a comparative analysis of the 80-20 split and walk-forward validation techniques for forecasting daily COVID-19 cases in Indonesia using the ARIMA model. Building on previous research, the ARIMA model has proven effective in various epidemiological contexts; however, this study highlights the critical importance of selecting the appropriate validation technique. The study uses data from January 3, 2020, to October 18, 2023, to develop a predictive model evaluated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings indicate that the walk-forward validation technique outperforms the 80-20 split, with MAE of 137.32 and RMSE of 198.23, compared to the 80-20 split MAE of 4190.92 and RMSE of 4479.15. These results suggest that walk-forward validation provides more accurate and reliable predictions, particularly for dynamic and non-stationary data scenarios. This study underscores the significant impact of validation technique selection on ARIMA model performance, contributing new insights into forecasting methodologies in epidemiology.

Prediksi Tinggi Muka Air (TMA) Untuk Deteksi Dini Bencana Banjir Menggunakan SVR-TVIWPSO Soebroto, Arief Andy; Cholissodin, Imam; Wihandika, Randy Cahya; Frestantiya, Maria Tenika; Arief, Ziya El
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2 No 2: Oktober 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Abstrak Banjir merupakan salah satu jenis bencana alam yang tidak dapat diprediksi kedatangannya, salah satu penyebabnya adalah adanya hujan yang terus – menerus(dari peristiwa alam). Faktor penyebab banjir dari segi meteorologi yaitu curah hujan yang tinggi dan air laut yang sedang pasang sehingga mengakibatkan tinggi permukaan air meningkat. Analisis terhadap data curah hujan serta tinggi permukaan air setiap periodenya dirasa masih belum dapat menyelesaikan permasalahan yang ada. Oleh karena itu, pada penelitian ini diusulkan teknik integrasi metode Time Variant Inertia Weight Particle Swarm Optimization(TVIWPSO) dan Support Vector Regression(SVR). Implementasi memadukan metode Regresi yaitu SVR untuk forecasting TMA, sedangkan TVIWPSO digunakan untuk mengoptimalisasi parameter – parameter yang digunakan di dalam SVR untuk memperoleh kinerja yang maksimal dan hasil yang akurat. Harapannya sistem ini akan dapat membantu mengatasi permasalahan untuk pendeteksian dini bencana banjir karena faktor cuaca yang tidak menentu. Hasil pengujian yang didapat dari 10 data bulanan yang berbeda menunjukkan bahwa didapatkan nilai error terkecil sebesar 0.00755 dengan menggunakan Mean Absolute Error untuk data Juni 2007 dengan menggunakan integrasi metode SVR-TVIWPSO. Kata Kunci : Support Vector Regression, Tinggi Muka Air, Time Variant Inertia Weight Particle Swarm Optimization. Abstract Flood is one type of natural disaster that can not be predicted its arrival, one reason is the rain that constantly occurs (from natural events). Factors that cause flooding in terms of meteorology are high rainfall and sea water was high, resulting in high water level increases. Analysis of rainfall data and water level in each period it is still not able to solve existing problems. Therefore, in this study the method proposed integration techniques Time Variant Inertia Weight Particle Swarm Optimization (TVIWPSO) and Support Vector Regression (SVR). Implementation combines regression method for forecasting TMA is SVR, while TVIWPSO used to optimize parameters that used in the SVR to obtain maximum performance and accurate results. Hope this system will be able to help solve the problems for the early detection of floods due to erratic weather. The result of forecasting experiment in water level forecasting from 10 monthly different data show that the smallest error rate is amount to 0.00755 using Mean Absolute Error for June 2007 with the integration method SVR-TVIWPSO. Keywords: Support Vector Regression, water level, Time Variant Inertia Weight Particle Swarm Optimization.

Integrasi Metode Fuzzy Additive SVM (FASVM) Menggunakan Model Warna YUV-CMY-HSV Untuk Klasifikasi Bibit Unggul Sapi Bali Melalui Citra Digital Cholissodin, Imam; Soebroto, Arief Andy; Hidayat, Nurul
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2 No 2: Oktober 2015
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakBudidaya sapi sangat identik dengan pemilihan bibit unggul, namun permasalahan yang sering muncul adalah cara mengenali bibit unggul tersebut yang relatif tidak mudah, cenderung membutuhkan waktu cukup lama. Peternak masih sering mengamati warna kulit dengan mata secara langsung, yang cenderung kurang teliti. Sehingga dalam penelitian ini, diusulkan metode dengan menggunakan beberapa model warna yang nantinya sebagai rekomendasi untuk fitur yang optimal dalam sistem. Kemudian metode klasifikasi yang digunakan adalah Fuzzy Additive Support Vector Machine (FASVM). Data yang digunakan didapatkan dari Balai Pembibitan Ternak Unggul (BPTU) Sapi Bali. Dari hasil pengujian didapatkan model warna yang paling optimal dan rata-rata akurasi pada Sapi Betina dan Jantan dengan ukuran citra tertentu. Model warna tersebut sangat dipengaruhi oleh kondisi data citra dan juga banyaknya kelas data.Kata kunci: Sapi Bali, Model warna , Intersection kernel, Fuzzy additive SVM, Sequential training SVM AbstractCattle farming is identical with the selection of seeds, but the problems that often arises is how to recognize quality seeds are relatively easy, tend to take a long time. Breeders still often observe skin color with eyes directly, which tend to be less rigorous. Thus, in this study, the proposed method by using several color models that will be voted on features that are optimal in the system. Then the classification method used is Additive Fuzzy Support Vector Machine (FASVM). The data used was obtained from Livestock Breeding Center for Excellence (BPTU) Bali cattle. From the test results obtained the most optimal color models and average accuracy on Cow Females and Males with a particular image size. The color model is highly influenced by the condition of the image data and also the amount of class data. Keywords: Bali cattle, Color model , Intersection kernel, Fuzzy additive SVM, Sequential training SVM

Optimasi Kandungan Gizi Susu Kambing Peranakan Etawa (PE) Menggunakan ELM-PSO Di UPT Pembibitan Ternak Dan Hijauan Makanan Ternak Singosari-Malang Cholissodin, Imam; Sutrisno, Sutrisno; Soebroto, Arief Andy; Hanum, Latifah; Caesar, Canny Amerilyse
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 4 No 1: Maret 2017
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

AbstrakSusu merupakan salah satu sumber protein hewani yang mengandung semua zat yang dibutuhkan tubuh. Ternak penghasil susu utama di Indonesia yaitu sapi perah, namun produksi susunya belum dapat mencukupi kebutuhan masyarakat. Alternatifnya adalah kambing peranakan etawa (PE). Tingginya kualitas kandungan gizi susu sangat dipengaruhi oleh beberapa faktor salah satunya, yaitu faktor pakan. Bagian peternakan kambing PE di UPT Pembibitan Ternak dan Hijauan Makanan Ternak Singosari-Malang masih menghadapi permasalahan, yaitu rendahnya kemampuan dalam memberikan komposisi pakan terhadap kambing PE. Kekurangan tersebut berpengaruh terhadap kualitas susu yang dihasilkan. Diperlukan pengetahuan rekayasa kandungan gizi susu untuk menentukan komposisi pakan dalam menghasilkan susu premium dengan kandungan gizi optimal. Penulis menggunakan metode Extreme Learning Machine (ELM)dan Particle Swarm Optimization (PSO) untuk membuat pemodelan pakan kambing dalam mengoptimasi kandungan gizi susu kambing. Dalam analisa pengujian konvergensi menggunakan metode ELM-PSO yang dilakukan dengan kasus untuk berat badan kambing 32 kg, serta jenis pakan yang digunakan yaitu rumput Odot 70% dan rumput Raja 30% menghasilkan sistem mencapai kestabilan dalam konvergensi pada iterasi ke-20 dengan fitness terbaik yaitu 16.2712.Kata Kunci: Susu Kambing, Optimasi, Artificial Neural Network (ANN), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Kandungan Nutrisi Pakan.AbstractMilk is one of the animal protein sources which it contains all of the substances needed by human body. The main milk producer cattle in Indonesia is dairy cow, however its milk production has not fulfilled the society needs. The alternative is the goat, the Etawa crossbreed (PE). The high quality of milk nutrients content is greatly influenced by some factors one of them, is the food factor. The PE goat livestock division of the UPT Cattle Breeding and the Cattle Food Greenery in Singosari-Malang still faces the problem, it is the low ability in giving the food composition for PE goat. This flaw affects the quality of the produced milk. It needs the artificial science of the milk nutrients contains in order to determine the food composition to produce premium milk with the optimum nutrients contain. The writer uses the method of the Extreme Learning Machine (ELM) and the Particle Swarm Optimization (PSO) to make the modeling of goat food in optimizing the content of goat milk nutrients. In the analysis of the convergence that is done with the case of 32 kg goat weight, also the food type used is the 70 % Odot grass and 30% Raja grass that system get a stability on the 20th iteration with a fitness value is 16.2712.Keywords: Goat Milk, Optimization, Extreme Learning Machine (ELM), Particle Swarm Optimization (PSO), The Food Nutrients Contain.

Klasifikasi Penyakit Tumor Otak berdasarkan Citra MRI Menggunakan Metode Convolutional Neural Network EfficientNetV2-S Nainggolan, Yohana Beatrice; Perdana, Rizal Setya; Soebroto, Arief Andy
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 9 No 10 (2025): Oktober 2025
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Tumor otak merupakan salah satu penyakit yang menjadi ancaman bagi otak manusia. Diagnosis yang cepat dan akurat merupakan kunci utama untuk menentukan rencana pengobatan yang efektif dan meningkatkan prognosis pasien. Magnetic Resonance Imaging (MRI) adalah salah satu teknik pencitraan medis non-invasif untuk mendeteksi dan mendiagnosis tumor otak. Interpretasi citra MRI secara manual oleh ahli radiologi memiliki kekurangan seperti bersifat subjektif, memakan waktu, dan rentan terhadap kelelahan, yang dapat menyebabkan kesalahan diagnosis. Untuk mengatasi tantangan tersebut, penelitian ini melakukan klasifikasi tumor otak menggunakan arsitektur deep learning modern, EfficientNetV2-S, yang unggul dalam efisiensi dan kecepatan. Model ini dilatih dan divalidasi menggunakan dataset MRI yang mencakup kelas tumor glioma, meningioma, pituitary, serta citra tanpa tumor. Tahapan penelitian meliputi pra-pemrosesan citra seperti resizing, augmentasi data, diikuti dengan proses fine-tuning pada arsitektur EfficientNetV2-S. Kinerja model dievaluasi secara komprehensif menggunakan metrik akurasi, presisi, sensitivitas, dan F1 score. Hasil pengujian kinerja model menunjukkan bahwa model yang diusulkan mampu mencapai akurasi klasifikasi yang sangat tinggi dan andal terutama pada pembekuan layers transfer learning sebesar 70% untuk 4 kelas dengan akurasi validasi mencapai 99,81%. Pendekatan ini menegaskan potensi EfficientNetV2-S sebagai metode yang robust untuk dikembangkan menjadi sistem pendukung keputusan klinis, yang dapat membantu ahli radiologi dalam mempercepat dan meningkatkan keakuratan diagnosis tumor otak. Kata kunci: efficientnetv2-s, brain tumor, magnetic resonance imaging, transfer learning

Performance Evaluation of Machine Learning and Deep Learning for Rainfall Forecasting Soebroto, Arief Andy; Limantara, Lily Montarcih; Mahmudy, Wayan Firdaus; Sholichin, Moh.; Hidayat, Nurul; Kharisma, Agi Putra
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.

Co-Authors Achmad Arwan Achmad Ridok Adam Hendra Brata Ade Wija Nugraha Adi Setyo Nugroho Admaja Dwi Herlambang Agi Putra Kharisma Agi Putra Kharisma, Agi Putra Agus Wahyu Widodo, Agus Wahyu Ahmad Afif Supianto Ahmad Mustafirudin Ahmad Shofi Nurur Rizal Aizul Faiz Iswafaza Alfarisi, Muhammad Asnin Ali Akbar Alysha Ghea Arliana Amira Ibtisama Ana Kusuma Ardani Andreas Tommy Christiawan Andri Wijaya Kusuma Asrul Syawal Asrul, Divanda Arya Inasta Asus Maizar Suryanto H Austenita Pasca Aisyah Baghaz, Renanda DSP Bambang Gunadi Brilliansyach, Raihan Fikri Caesar, Canny Amerilyse Candra Dewi Candra Dewi Catur Ari Setianto Dama Yuliana Deby Putri Indraswari Denny Sagita Rusdianto Destyana Ellingga Pratiwi Destyana Ellingga Pratiwi Dhea Azahria Mawarni Dian Eka Ratnawati Djoko Pramono Dwi Cindy Herta Turnip Dwi Puri Cemani Dzikrullah, Muhammad Aulia Fachruz Edy Santoso Eka Miyahil Uyun Eko Ari Setijono Marhendraputro Eko Arisetijono Elza Fadli Hadimulyo Enggar Septrinas Enggarsita Auliasin Eugenius Yosep Korsan N Evi Irhamillah Azza Faisal Roufa Rohman Faizatul Amalia Fajar Pradana Fauziah Mayasari Iskandar Febrianita Indah Perwitasari Fendy Yulianto Ferdy Wahyurianto Fildzah Amalia Galuh Mazenda Guruh Prayogi Willis Putra Habib Yafi Ardi Hanafi, Andy Hastian Bayu Hendra Darmawan Herman Syantoso Himawan Sutanto I Gede Adi Brahman Nugraha I Putu Bagus Arya Pradnyana Ibnu, Mohammad Ibrahim Kusuma Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Imam Cholissodin Indra Ekaristio P Indriana Candra Dewi Indriati Indriati Indriati Indriati Ishak Panangian Sinaga Ismiarta Aknuranda Issa Arwani Issa Arwani Karmia Larissa Br Pandia Khoifah Inda Maula Khrisna Widhi Dewanto Krisna Wahyu Aji Kusuma Lailatul Rizqi Ramadhani Lailil Muflikhah Laode Muhamad Fauzan Latifah Hanum Lily Montarcih Limantara Mahdi Fiqia Hafis Maria Tenika Frestantiya Maria Tenika Frestantiya, Maria Tenika Maya Febrianita Moh. Sholichin Mohammad Imron Maulana Muh. Arif Rahman Muhammad Iqbal Kurniawan Muhammad Rois Al Haqq Muhammad Rouzikin Annur Muhammad Tanzil Furqon Muhammad Taruna Praja Utama Mutia Ayu Sabrina Nadya Rahmasari Nadya Sylviani Nainggolan, Yohana Beatrice Niftah Fatiha Armin Niken Hendrakusma Wardani Nizar Rahman Kusworo Nurannisa, Nadhira Nuriya Fadilah Nurudin Santoso Nurul Faizah Nurul Faridah, Nurul Nurul Hidayat Nurul Hidayat Nurul Hidayat Odhia Yustika Putri Priyambadha, Bayu Randy Cahya Wihandika Raymond Gunito Farandy Junior Rekyan Regasari Restia Dwi Oktavianing Tyas Reynald Daffa Pahlevi Ridwan Fajar Widodo Rio Andika Dwiki Adhi Putra Rio Arifando Risda Nur Ainum Riski Ida Agustiyan Risqi Nur Ifansyah Rivaldy Raihan Syams Rizal Setya Perdana Rizal Setya Perdana Saiful Kirom, Muhammad Ihsan Santoso, Nurudin Sativandi Putra Satrio Agung Wicaksono Sitepu, Yosua Christiansen Stefan Levianto Sukamto, Anjas Pramono Surya Wirawan SUTRISNO Sutrisno Sutrisno Sutrisno, Sutrisno Teddy Syach Pratama Thareq Ibrahim Tiara Rossa Diassananda Tryse Rezza Biantong Vasya, M Azka Obila Vicky Virdus Vivien Fathuroya, Vivien Wayan Firdaus Mahmudy Welly Purnomo Wijaya, Aldi Rahman Wildan Ziaulhaq Wildan Ziaulhaq Wildansyah Maulana Rahmat Yearra Taufan Ardy Rinaldy Yusril Iszha Eginata Zaien Bin Umar Alaydrus Ziya El Arief Ziya El Arief, Ziya El

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