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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Agromet IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Veteriner Techno.Com: Jurnal Teknologi Informasi CAUCHY: Jurnal Matematika Murni dan Aplikasi Lingua Jurnal Bahasa dan Sastra PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmu Komputer dan Agri-Informatika Journal of the Indonesian Mathematical Society Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Aplikasi Bisnis dan Manajemen (JABM) E-Journal Seminar Nasional Informatika (SEMNASIF) Widyariset Indonesian Journal of Science and Technology Al-Jabar : Jurnal Pendidikan Matematika JOIV : International Journal on Informatics Visualization Jurnal Matematika: MANTIK MAJALAH ILMIAH GLOBE Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Zero : Jurnal Sains, Matematika, dan Terapan Teorema: Teori dan Riset Matematika Jambura Journal of Mathematics Jambura Geoscience Review SALINGKA Jurnal Matematika UNAND Building of Informatics, Technology and Science Sains, Aplikasi, Komputasi dan Teknologi Informasi Indonesian Journal of Electrical Engineering and Computer Science InPrime: Indonesian Journal Of Pure And Applied Mathematics Widyariset Jambura Journal of Biomathematics (JJBM) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Jurnal Pijar MIPA Jurnal Sains Terapan : Wahana Informasi dan Alih Teknologi Pertanian Journal of Applied Agricultural Science and Technology Milang Journal of Mathematics and Its Applications Jurnal Sintak Jurnal Matematika Integratif Indonesian Journal of Mathematics and Applications Jurnal Pendidikan Progresif Indonesian Journal of Mathematics and Natural Sciences MILANG Journal of Mathematics and Its Applications Majalah Ilmiah Bahasa dan Sastra
Sri Nurdiati
Department Of Mathematics, Faculty Of Science And Mathematics, IPB University, Jl. Meranti, Kampus IPB Dramaga, Bogor 16680
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Neuroscience Physics Social Sciences Transportation Veterinary Other

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Komentar untuk artikel Savitri et al.: Implementasi algoritma genetika dalam mengestimasi kepadatan populasi jackrabbit dan coyote Mohamad Khoirun Najib; Sri Nurdiati
Jambura Journal of Biomathematics (JJBM) Volume 3, Issue 2: December 2022
Publisher : Department of Mathematics, Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34312/jjbm.v3i2.16857

Abstract

This article is a commentary on research conducted by Savitri et al which was published in Jambura Journal of Biomathematics volume 3 number 1 in 2022. It was found that there was an error in the MAPE calculation for the approximation of population density of coyote. The MAPE obtained for coyotes was 66.05% so there was a significant difference from what had been given before. With these results, there is an opportunity to estimate parameters with better accuracy.
Analisis Pembentukan Sebaran Bivariat Berbasis Copula Antara Luas Area Terbakar dan Curah Hujan di Sumatra Bagian Selatan Sri Nurdiati; Mohamad Khoirun Najib; Muhammad Zidane Bayu
Jurnal Matematika Integratif Vol 18, No 2: Oktober 2022
Publisher : Department of Matematics, Universitas Padjadjaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (709.096 KB) | DOI: 10.24198/jmi.v18.n2.42024.217-227

Abstract

Kebakaran hutan dan lahan selalu terjadi setiap tahunnya di Indonesia. Luas area terbakar memiliki hubungan tidak langsung dengan curah hujan. Fungsi copula dapat memodelkan hubungan bivariat curah hujan sebagai iklim global dengan kebakaran hutan, khususnya di Sumatera bagian Selatan. Oleh karena itu studi ini menganalisis dan memodelkan sebaran bivariat berbasis copula antara curah hujan dan luas area terbakar. Data dipilah berdasarkan indikator iklim global El Nino-Southern Oscillation (ENSO) dan Indian Ocean Dipole (IOD). Estimasi parameter model dilakukan menggunakan metode Inference of Function for Margins (IFM). Beberapa fungsi copula digunakan untuk membentuk distribusi bersama, seperti Gaussian, student’s t, Clayton, Gumbel, Frank, Joe, Galambos, BB1, BB6, BB7, dan BB8. Hasil menunjukkan bahwa sebaran bersama antara curah hujan dan luas area terbakar dipengaruhi oleh indeks ENSO dan IOD. Semakin tinggi indeks ENSO dan IOD, semakin tinggi peluang luas area terbakar pada saat curah hujan rendah dan sebaliknya. Pernyataan tersebut diperkuat dengan peluang bersyarat terjadinya luas area terbakar lebih dari 100 ribu hektar ketika curah hujan dalam kondisi rendah, sangat kecil hampir mendekati nol ketika La Nina dan IOD Negatif. Sementara itu, pada kondisi El Nino Moderat Kuat dan IOD Positif, peluang terjadinya luas area terbakar tersebut bernilai tinggi yaitu 62% dan 91%
IMPLEMENTASI PENYELESAIAN PERSAMAAN BURGERS DENGAN METODE BEDA HINGGA DALAM BAHASA PEMROGRAMAN JULIA Fahren Bukhari; Sri Nurdiati; Mochamad Tito Julianto; Mohamad Khoirun Najib; Ruben Harry Valentdio
MILANG Journal of Mathematics and Its Applications Vol. 19 No. 1 (2023): MILANG Journal of Mathematics and Its Applications
Publisher : Dept. of Mathematics, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/milang.19.1.1-9

Abstract

Burgers equation is a partial differential equation used to modelling several events related to fluids. Burgers equation was firstly introduced by Harry Bateman in 1915 and later studied by Johannes Martinus Burgers in 1948. This study discusses solving Burgers equations with finite difference method. In this study, several parameters have been known for the Burgers equation and several cases of partitions are used in finite difference method. The result shows that the more partitions used, the numerical result obtained will be closer to the exact values. In this study, calculations are numerically carried out with the help of Julia programming language.
Peningkatan Efisiensi Pembelajaran Mandiri Anatomi Veteriner dengan Bantuan Teknologi Virtual Eragilang Muhammad Hastapatria; Sri Nurdiati; Srihadi Agungpriyono; Hany Savitry
Jurnal Veteriner Vol 24 No 4 (2023)
Publisher : Faculty of Veterinary Medicine, Udayana University and Published in collaboration with the Indonesia Veterinarian Association

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19087/jveteriner.2023.24.4.515

Abstract

Pembelajaran Anatomi Veteriner di pendidikan kedokteran hewan umumnya didukung dengan kegiatan praktikum menggunakan kadaver hewan dan dilakukan di laboratorium khusus. Penggunaan hewan atau kadaver hewan sebagai bahan peraga pendidikan sangat membantu pemahaman mahasiswa terhadap struktur dan anatomi. Di sisi lain, pesatnya perkembangan teknologi memberikan alternatif pengembangan metode pembelajaran seperti penggunaan teknologi berbasis virtual reality (VR). Penggunaan teknologi berbasis VR sekaligus untuk mengatasi kekurangan dalam penggunaan cadaver hewan dan pemenuhan kaidah kesejahteraan hewan (animal walfare). Metode tersebut juga mampu membawa pengguna merasakan pengalaman berada di dunia virtual dan memungkinkan pembelajaran mandiri dilakukan di mana dan kapan saja. Dalam penelitian ini dikembangkan sebuah prototype perangkat ajar dengan teknologi VR yang diterapkan pada model tulang kuda (Equus caballus). Selanjutnya untuk mempermudah akses, VR dipadukan dengan kanal akses melalui website menggunakan metode embedding sehingga pengguna bisa mengakses melalui berbagai perangkat keras seperti komputer, telepon seluler pintar dan kaca mata VR sehingga diharapkan tercipta prototype yang bisa digunakan sebagai media pembelajaran untuk membantu mengurangi penggunaan kadaver hewan dan menjadi pengembangan awal untuk penelitian selanjutnya. Dalam studi ini berhasil dikembangkan prototype sistem dan model tulang kuda yang disematkan dalam bentuk VR dan memenuhi seluruh fungsi yang dibutuhkan. Penggunaan metode User Experience Questionnaire (UEQ) prototype sistem telah berhasil memperoleh nilai melebihi rata-rata dengan komponen berupa daya tarik, ketepatan dan stimulasi yang mampu diterima dengan baik oleh kalangan mahasiswa di samping itu pengembangan prototype sistem anatomi veteriner dapat dijadikan alternatif pembelajaran mahasiswa secara mandiri, namun tidak menggantikan metode pembelajaran secara tradisional.
Life Expectancy Prediction Using Decision Tree, Random Forest, Gradient Boosting, and XGBoost Regressions Chairunisa, Ghevira; Najib, Mohamad Khoirun; Nurdiati, Sri; Imni, Salsabila F.; Sanjaya, Wardah; Andriani, Rizka D.; Henriyansah; Putri, Renda S. P.; Ekaputri, Dhea
JURNAL SINTAK Vol. 2 No. 2 (2024): Vol. 2 No. 2 (2024): MARET 2024
Publisher : LPPM-ITEBA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62375/jsintak.v2i2.249

Abstract

Angka harapan hidup menggambarkan rata-rata lamanya waktu seseorang hidup sejak lahir di dunia. Angka harapan hidup menjadi salah satu aspek dalam menentukan indeks pembangunan manusia. Semakin tinggi Angka harapan hidup maka akan semakin tinggi nilai IPM. Tujuan penelitian ini adalah memprediksi angka harapan hidup melalui model yang paling akurat dengan menggunakan model decision tree regression, random forest regression, gradient boosting regression, dan XGBoost regression, serta analisis variabel penjelas yang paling mempengaruhi angka harapan hidup. Data yang digunakan dalam penelitian ini adalah dataset Global Country Information Dataset 2023. Data diperoleh dari situs Kaggle. Berdasarkan analisis diperoleh bahwa model random forest regression menunjukkan kinerja yang lebih unggul dalam memprediksi hasil, yang ditunjukkan dengan nilai RMSE yang lebih rendah dan nilai R² yang lebih tinggi. Kematian bayi dan rasio kematian ibu secara konsisten diidentifikasi sebagai prediktor yang signifikan di semua model, sedangkan populasi merupakan prediktor yang kurang memprengaruhi angka harapan hidup.
Deteksi Penyakit Jantung Menggunakan Metode Klasifikasi Decision Tree dan Regresi Logistik Bukhari, Fahren; Nurdiati, Sri -; Najib, Mohamad Khoirun; Amalia, Rizki Nurul
Sains, Aplikasi, Komputasi dan Teknologi Informasi Vol 5, No 1 (2023): Sains, Aplikasi, Komputasi dan Teknologi Informasi
Publisher : Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/jsakti.v5i1.10780

Abstract

Penyakit jantung merupakan salah satu penyakit paling umum dan kritis yang membahayakan kehidupan manusia. Selain diagnosis klinis, pembelajaran mesin dan pendekatan berbasis pembelajaran mendalam sangat penting dalam diagnosis penyakit jantung, seperti decision tree dan regresi logistik. Penelitian ini bertujuan membandingkan kedua metode klasifikasi tersebut untuk mendeteksi adanya penyakit jantung berdasarkan beberapa indikator. Data yang digunakan adalah data penyakit jantung yang dikeluarkan oleh University of California, Irvine (UCI) Machine Learning Repository.  Berdasarkan hasil yang diperoleh, model decision tree yang terbentuk menempatkan variabel thal (tipe detak jantung pasien) sebagai simpul akar, dikarenakan nilai entropy yang paling tinggi. Model decision tree memiliki akurasi terhadap data uji sebesar 75%. Sementara itu, model regresi logistik menempatkan variabel sex, cp_3, slope_1, ca, dan thal_2 sebagai variabel-variabel yang berpengaruh nyata. Model regresi logistik memiliki akurasi terhadap data uji sebesar 87%. Dari akurasi dari kedua model tersebut, regresi logistik lebih akurat untuk mendeteksi adanya penyakit jantung dibandingkan model decision tree.
Sensitivity and feature importance of climate factors for predicting fire hotspots using machine learning methods Hasafah Nugrahani, Endar; Nurdiati, Sri; Bukhari, Fahren; Khoirun Najib, Mohamad; Muliawan Sebastian, Denny; Nur Fallahi, Putri Afia
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp2212-2225

Abstract

Every year, Indonesia experiences a national crisis due to forest fires because the resulting impacts and losses are enormous. Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still few research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares machine learning methods to predict hotspots in Kalimantan based on local and global climate factors in 2001-2020. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Four methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, measures of sensitivity and feature importance used are variance, density, and distributionbased sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the machine learning model concluded that the Bayesian linear regression model outperformed other models with an RMSE of 750 hotspots and an explained variance score of 68.96% on testing data. Meanwhile, tree-based models show signs of overfitting, including gradient boosting and random forest. Based on the results of sensitivity analysis and feature importance of the Bayesian linear regression model, the number of dry days is the most important feature in predicting fire hotspots in Kalimantan.
Copula in Wildfire Analysis: A Systematic Literature Review Najib, Mohamad Khoirun; Nurdiati, Sri; Sopaheluwakan, Ardhasena
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol 3, No 2 (2021)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v3i2.22131

Abstract

AbstractCopula model is a method that can be implemented in various study fields, including analyzing wildfires. The copula distribution function gives a simple way to define joint distribution between two or more random variables. This study aims to review the application of copula in the analysis of wildfires using a Systematic Literature Review (SLR) and provide insight into research opportunities related to the application in Indonesia. The results show there are very few articles using the copula model in the analysis of wildfires. However, the increasing number of article citations each year shows the importance of such article research and has contributed to wildfire analysis development. In that article, 50% of studies applied the copula model to direct wildfire analysis (using fire data) in Canada, Portugal, and the US. Meanwhile, the other 50% use the copula model for indirect wildfire analysis (not using fire data) in Canada and the European region. The outcome of the presented review will provide the latest research positions and future research opportunities on the application of copula in the analysis of wildfires in Indonesia.Keywords: copula; wildfire; systematic literature review. AbstrakModel copula merupakan metode yang dapat diimplementasikan pada berbagai bidang penelitian, salah satunya pada analisis kebakaran hutan. Fungsi sebaran copula memberikan cara yang mudah untuk mendefinisikan sebaran peluang bersama antara dua peubah acak atau lebih. Tujuan penelitian ini mengulas penerapan model copula tersebut pada analisis kebakaran hutan dalam studi literatur menggunakan Systematic Literature Review (SLR) serta memberikan peluang riset ke depan terkait implementasinya pada analisis kebakaran hutan di Indonesia. Hasil penelitian menunjukkan bahwa model copula pada analisis kebakaran hutan masih sangat sedikit. Namun, peningkatan jumlah sitasi artikel tiap tahun menunjukkan pentingnya penelitian tersebut dan memiliki kontribusi pada perkembangan analisis kebakaran hutan. Pada artikel tersebut, sebanyak 50% penelitian menerapkan model copula pada analisis kebakaran secara langsung (menggunakan data kebakaran) di Kanada, Portugal, dan Amerika. Sementara, sebanyak 50% lainnya menerapkan model copula pada analisis kebakaran secara tak langsung (tidak menggunakan data kebakaran), yaitu di Kanada dan kawasan Eropa. Hasil tinjauan memberikan posisi riset terkini serta usulan riset ke depan mengenai penerapan model copula untuk analisis kebakaran hutan dan lahan di Indonesia.Kata kunci: copula; kebakaran hutan; studi literatur sistematik. 
Cattle weight prediction model using convolutional neural network and artificial neural network Yulianingsih, Yulianingsih; Nurdiati, Sri; Sukoco, Heru; Sumantri, Cece
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp441-449

Abstract

The weight of livestock is a crucial metric for evaluating management efficacy, informing policy decisions, and determining the market value of animals. In certain scenarios, conventional methods such as physical weighing and measurement calculations can prove challenging, including the absence of livestock health records or weighing equipment. This research aims to develop a predictive model for estimating the live weight of cattle through visual assessments and metadata, including age and pixel count, utilizing a combination of convolutional neural network (CNN) and artificial neural network (ANN) methodologies. A total of 223 data were obtained from a local farm before augmentation. The model's predictive capability was successfully demonstrated, with its performance quantified by an average mean absolute percentage error (MAPE) of 10% on test data. This study demonstrates that through the combination of CNN and ANN, as well as optimal parameter tuning, efficient prediction of cattle weight can be achieved.
Probabilistic Prediction Model Using Bayesian Inference in Climate Field: A Systematic Literature Evi Ardiyani; Sri Nurdiati; Ardhasena Sopaheluwakan; Mohamad Khoirun Najib; Fadillah Rohimahastuti
JTAM (Jurnal Teori dan Aplikasi Matematika) Vol 7, No 3 (2023): July
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jtam.v7i3.13651

Abstract

Wildfires occur repeatedly every year and have a negative impact on natural ecosystems. Anticipation of wildfires is very necessary, therefore a prediction model is needed that can produce predictions with a good level of accuracy. One approach to develop probabilistic prediction models is Bayesian inference. The purpose of this research is to review the methods that can be used in developing probabilistic prediction models using the Bayesian approach. The methodology used is Systematic Literature Review (SLR) which can be used to provide a comprehensive review of Bayesian inference research in developing probabilistic prediction models. The research strategy used was the Boolean Technique applied to database sources including Scopus, IEEE Xplore, and ArXiv. The articles used have novelty and ease of explanation of Bayesian methods, especially predictions in the field of climate so that articles are selected based on inclusion and exclusion criteria. The results show that probabilistic models can provide more accurate results than deterministic models. The Bayesian Model Averaging (BMA) method is a widely used method because it is easy to implement and develop so that the prediction results can be more accurate. The development of probabilistic prediction models with a Bayesian approach has great potential to grow as seen from the development of the number of research publications over the past 5 years. The research position of probabilistic prediction models with Bayesian approaches in the field of climate is dominated by the research community in China with the main problems related to hydrology.TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian //  TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//
Co-Authors AA Gede Rai Gunawan Abisha, Nicholas Ade Irawan Ade Irawan Agah D. Garnadi Agung Widyo Utomo Agus Buono Aldri Frinaldi Alifah, Nayla Nur Alifah, Rifdah Nur Amalia, Rizki Nurul Amanah, Ayu Anak Agung Gede Rai Gunawan Andriani, Rizka D. Annisa Permata Sari, Annisa Permata Ardhana, Muhammad Reza Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ardhasena Sopaheluwakan Ayu Amanah Bib Paruhum Silalahi Blante, Trianty Putri Budiarti, Retno Cece Sumantri Chairunisa, Ghevira Deni Suwardhi DEWI RAHMAWATI Edi Santosa Ekaputri, Dhea Elis Khatizah Endar Hasafah Nugrahani Eragilang Muhammad Hastapatria Ester Antika Evi Ardiyani Fadillah Rohimahastuti Fahren Bukhari Fahren Bukhari Fahren Bukhari Faiqul Fikri Fajar Delli Wihartiko Fatmawati, Linda Leni Ginting, Dini Tri Putri Br Hanief, Hafzal Hany Savitry Hasafah Nugrahani, Endar Heliza Rahmania Hatta, Heliza Rahmania Henny Nuraini Henriyansah Herlambang, Karen Hilmi, Kautsar I Wayan Mangku Imni, Salsabila F. Indra Jaya Irman Hermadi Jauhari, Muhammad Fakhri Karlisa Priandana Kasiyah Junus Kasiyah Junus Kautsar Hilmi Khatizah, Elis Komariah . Lana Syakina Linda Leni Fatmawati M. Syamsul Maarif Maman Turjaman Marimin Marimin Mas’oed, Teduh W. Mochamad Tito Julianto Mohamad Khoirun Najib Mohamad Khoirun Najib Mohamad Khoirun Najib Muhamad Syukur Muhammad Adam Tripranoto Muhammad Fikri Isnaini Muhammad Ilyas Muhammad Reza Ardhana Muhammad Tito Julianto Muhammad Zidane Bayu Mukhlis Mukhlis Muliawan Sebastian, Denny Nadiyah, Fadilah Karamun Nisaa Najib, Mohamad K. Najib, Mohamad Khoirun Najib, Mohamad Khoirun Nandika Safiqri NGAKAN KOMANG KUTHA ARDHANA Niswati, Za'imatun Noval Nur Fallahi, Putri Afia Nurwegiono, Muhammad Nuzhatun Nazria Pandu Septiawan Pratama, Yoga Abdi Prihasuti Harsani Putri, Renda S. P. Rachma Fauziah Krismayanti Rafhida, Syukri Arif Redytadevi, Tita Putri REFI REVINA Retno Budiarti Rika Kusumawati Ruben Harry Valentdio Salsabila, Fitra Nuvus Salsabilla Rahmah Salsabilla, Fitra Nuvus Sanjaya, Wardah Septian Dhimas Setyawati, Suci Nur Shelvie Nidya Neyman Sony Hartono Wijaya Sopaheluwakan, Ardhasena Sri Hartati Sri Mulatsih Srihadi Agungpriyono Sriwahyuni, Lilis Sukmana, Ihwan SYAHID AHMAD MUKRIM Sya’adah, Syifa Noer Syukri Arif Rafhida Trianty Putri Blante Valentdio, Ruben Harry Verry Riyanto Vicky Zilvan Wisnu Ananta Kusuma Yandra Arkeman Yasin Yusuf Yoga Abdi Pratama