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All Journal International Journal of Electrical and Computer Engineering Jurnal Rekayasa Proses Pixel : Jurnal Ilmiah Komputer Grafis Jurnal Pekommas Indonesian Journal of Educational Review (IJER) Journal of Environmental Engineering and Sustainable Technology Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Indonesian Journal on Computing (Indo-JC) Jurnal Inspiration JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Creative Information Technology Journal JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JFMR (Journal of Fisheries and Marine Research) JIKO (Jurnal Informatika dan Komputer) Insect (Informatics and Security) : Jurnal Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management Jurnal Rekayasa Proses JURNAL EDUCATION AND DEVELOPMENT MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Indonesian Journal of Applied Informatics JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) CSRID (Computer Science Research and Its Development Journal) Informasi Interaktif Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Journal of Sustainable Engineering: Proceedings Series SENSITEK E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Jurnal Teknologi Informasi dan Multimedia bit-Tech Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Dielektrika : Jurnal Ilmiah Kajian Teori dan Aplikasi Teknik Elektro Respati Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics G-Tech : Jurnal Teknologi Terapan JIKA (Jurnal Informatika) Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Ilmiah Publika Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Buletin Poltanesa Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering INFOSYS (INFORMATION SYSTEM) JOURNAL J-SAKTI (Jurnal Sains Komputer dan Informatika) Research Fair Unisri Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Dinamika Informatika (JDI) Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Teknik Informatika Journal of Comprehensive Science Jurnal Indonesia Sosial Teknologi Ceddi Journal of Information System and Technology (JST) SmartComp Teknomatika: Jurnal Informatika dan Komputer JURNAL MULTIDISIPLIN BHATARA Jurnal Pengabdian Indonesia
Arief Setyanto
Universitas Amikom Yogyakarta
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Nursing Social Sciences Other

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SEBARAN TOTAL SUSPENDED MATTER DAN ARUS UNTUK MELINDUNGI LINGKUNGAN PESISIR DI MUARA SUNGAI GLIDIK, LUMAJANG Andik Isdianto; Muhammad Javier Irsyad; Rarasrum Dyah Kasitowati; Aulia Lanudia Fathah; Arief Setyanto; Berlania Mahardika Putri; Supriyadi Supriyadi
JURNAL EDUCATION AND DEVELOPMENT Vol 12 No 2 (2024): Vol 12 No 2 Mei 2024
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v12i2.5740

Abstract

Total Suspended Matter (TSM) merupakan keseluruhan zat padat atau partikel-partikel yang tersuspensi dalam perairan. Apabila kadarnya berlebih, dapat mengakibatkan perubahan lingkungan dan penurunan kualitas perairan. Muara Sungai Glidik dipengaruhi oleh aktivitas antropogenik dan hasil erupsi Gunung Semeru, di mana kedua hal ini menyebabkan banyaknya TSM yang berada di sekitar muara tersebut. Tujuan penelitian ini untuk mengetahui kondisi arus, kadar dan persebaran TSM, serta melihat korelasi kedua variabel tersebut. Metode purposive random sampling dengan mengambil data TSM dan kecepatan arus secara primer dan sekunder pada 4 stasiun pengamatan di Muara Sungai Glidik. Hasil Kecepatan arus tertinggi di badan sungai yaitu berkisar 1,28 m/s dan kecepatan arus terendah di laut bagian timur yaitu berkisar 0,56 m/s. Perkembangan TSM dari Tahun 2000-2020 mengalami fluktuasi setiap 5 tahunnya secara berturut-turut tahun 2000-2005 terjadi kenaikan konsentrasi sebesar 269,91%, tahun 2005-2010 terjadi penurunan konsentrasi sebesar 47,85%, Tahun 2010-2020 terjadi kenaikan konsetrasi tertinggi sebesar 292,12%. Fluktuasi ini disebabkan oleh faktor manusia dan alam. Korelasi antara kecepatan arus terhadap TSM sebesar 0.94 yang menandakan hubungan yang sangat kuat dan positif. Terdapat hubungan yang sangat kuat antara kecepatan arus terhadap TSM, yang berarti semakin tingginya kecepatan arus maka nilai TSM juga akan meningkat.
Evaluasi Trade-off Akurasi dan Kecepatan YOLOv5 dalam Deteksi Kebakaran pada Edge Devices Rahmad Arif Setiawan; Arief Setyanto
Jurnal Syntax Admiration Vol. 5 No. 11 (2024): Jurnal Syntax Admiration
Publisher : Syntax Corporation Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46799/jsa.v5i11.1626

Abstract

Real-time object detection using the YOLO (You Only Look Once) algorithm has shown promising performance in various computer vision applications. However, its application on devices with limited resources is still a challenge due to its high computational requirements. This study aims to optimize the YOLOv5 model for fire and smoke detection on Orange Pi Zero 3 devices using quantization techniques. Using a dataset of 2247 fire and smoke images, this study applies static quantization techniques to improve model efficiency. The methodology includes training of standard YOLOv5 models, conversion to ONNX format, and application of static quantization. Results show a significant improvement in computational efficiency, with a 42.2% reduction in model size and a 65.21% increase in inference speed. Despite a decrease in the mAP value by 25.6%, the optimized model was still able to perform object detection at a significantly higher speed. In conclusion, the quantization technique is effective in optimizing the YOLOv5 model for deployment on edge computing devices, despite the trade-off between speed and accuracy.
Klasifikasi Penyakit Daun Apel Menggunakan Arsitektur CNN dengan Transfer Learning Rahman, Aulia Tegar; Setyanto, Arief; Fatta, Hanif Al
Jurnal SENOPATI : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Vol 6, No 1 (2024): Jurnal SENOPATI Vol 6, No 1
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.senopati.2024.v6i1.6574

Abstract

Salah satu hasil produk pertanian subtropis yang dapat ditanam di Indonesia adalah apel. Dalam budidaya apel, pengendalian hama dan penyakit merupakan salah satu faktor kunci dalam perkembangan tanaman apel, karena dapat mempengaruhi hasil apel. Salah satu teknologi yang berkembang pesat dalam pendeteksian atau diagnosis penyakit tanaman dapat menyederhanakan proses klasifikasi penyakit tanaman khususnya penyakit daun apel dan membantu dalam diagnose dini adalah deep learning. Terdapat salah satu arsitektur deep learning yang dapat digunakan dalam klasifikasi citra, salah satunya Convolutional Neural Networks (CNN). Arsitektur CNN dengan transfer learning yang menghasilkan nilai akurasi yang masih bisa diterima, waktu yang diperlukan pendek pada klasifikasi penyakit daun apel. Hasil dari klasifikasi penyakit daun apel dengan VGG16 mendapatkan akurasi sebesar 99,31 %.
Analisa Prediksi Turnover Karyawan menggunakan Machine Learning Maehendrayuga, Arief; Setyanto, Arief; Kusnawi
bit-Tech Vol. 7 No. 2 (2024): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v7i2.1999

Abstract

Penelitian ini membahas penerapan machine learning untuk memprediksi turnover karyawan, yang merupakan tantangan utama dalam manajemen Sumber Daya Manusia (SDM). Turnover karyawan sering kali disebabkan oleh berbagai faktor, termasuk ketidakseimbangan kehidupan kerja, ketidakpuasan kerja, dan minimnya peluang pengembangan karier. Dalam penelitian ini, digunakan dataset IBM HR Analytics untuk menganalisis faktor-faktor yang memengaruhi turnover karyawan. Algoritma yang diterapkan meliputi Support Vector Machine (SVM) dan Random Forest. Proses penelitian dimulai dengan pengumpulan data, eksplorasi awal, praproses data, seleksi fitur, dan penyeimbangan data menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi kinerja model dilakukan menggunakan confusion matrix untuk mengukur akurasi, presisi, recall, dan f1-score. Hasil analisis menunjukkan bahwa algoritma Random Forest memberikan kinerja yang lebih baik dibandingkan SVM. Random Forest mencapai akurasi 97,72%, sedangkan SVM memperoleh akurasi 92,51%. Setelah menerapkan SMOTE, akurasi meningkat menjadi 97% untuk Random Forest dan 93% untuk SVM. Selain akurasi, Random Forest juga unggul dalam metrik presisi, recall, dan f1-score, membuktikan keandalannya dalam memprediksi turnover karyawan. Temuan ini menegaskan bahwa pendekatan machine learning dapat digunakan untuk memahami pola turnover secara lebih mendalam. Dengan prediksi yang lebih akurat, perusahaan dapat merancang strategi retensi karyawan yang lebih efektif dan berbasis data, menciptakan lingkungan kerja yang mendukung produktivitas serta meningkatkan stabilitas tenaga kerja secara keseluruhan.
Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression Yusuf, Muhammad; Setyanto, Arief; Aryasa, Komang
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.455

Abstract

Currently, climate change in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by temperature, air pressure, wind speed, humidity and rainfall. Rainfall is a climate parameter that has a high level of diversity due to climate anomalies. There are several factors that influence the characteristics of the diversity of rainfall, namely geographical, orographic, topographical, orientation and structure of the islands. These factors cause the distribution pattern of rainfall to be uneven between one area and another. For that we need a method that can solve the problem of predicting rainfall both daily, monthly and yearly. Prediction of rainfall with a statistical approach can be done through the Multiple Linear Regression method. Where in this study, rainfall is the dependent variable, while temperature and humidity are independent variables. The results obtained from the WEKA Application with a total of 60 data from 2017 to 2021, the correlation coefficient value is 0.8175, and from the evaluation results using Linear Regression, the MAE error rate is 78.8695 and the RMSE is 95.1982. It can be concluded that the effect of temperature and air on the occurrence of rainfall is 81.75%
Characterizing Hardware Utilization on Edge Devices when Inferring Compressed Deep Learning Models Nabhaan, Ahmad Naufal Labiib; Rachmanto, Rakandhiya Daanii; Setyanto, Arief
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol 24 No 1 (2024)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i1.3938

Abstract

Implementing edge AI involves running AI algorithms near the sensors. Deep Learning (DL) Model has successfully tackled image classification tasks with remarkable performance. However, their requirements for huge computing resources hinder the implementation of edge devices. Compressing the model is an essential task to allow the implementation of the DL model on edge devices. Post-training quantization (PTQ) is a compression technique that reduces the bit representation of the model weight parameters. This study looks at the impact of memory allocation on the latency of compressed DL models on Raspberry Pi 4 Model B (RPi4B) and NVIDIA Jetson Nano (J. Nano). This research aims to understand hardware utilization in central processing units (CPU), graphics processing units (GPU),and memory. This study focused on the quantitative method, which controls memory allocation and measures warm-up time, latency, CPU, and GPU utilization. Speed comparison among inference of DL models on RPi4B and J. Nano. This paper observes the correlation between hardware utilization versus the various DL inference latencies. According to our experiment, we concluded that smaller memory allocation led to high latency on both RPi4B and J. Nano. CPU utilization on RPi4B. CPU utilization in RPi4B increases along with the memory allocation; however, the opposite is shown on J. Nano since the GPU carries out the main computation on the device. Regarding computation, thesmaller DL Size and smaller bit representation lead to faster inference (low latency), while bigger bit representation on the same DL model leads to higher latency.
Analisis Prediksi Curah Hujan Bulanan Wilayah Kota Sorong Menggunakan Metode Multiple Regression Yusuf, Muhammad; Setyanto, Arief; Aryasa, Komang
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 1 (2022): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i1.455

Abstract

Currently, climate change in Indonesia, which is a tropical region, is always uncertain and makes it difficult to predict weather conditions. Weather conditions can be influenced by temperature, air pressure, wind speed, humidity and rainfall. Rainfall is a climate parameter that has a high level of diversity due to climate anomalies. There are several factors that influence the characteristics of the diversity of rainfall, namely geographical, orographic, topographical, orientation and structure of the islands. These factors cause the distribution pattern of rainfall to be uneven between one area and another. For that we need a method that can solve the problem of predicting rainfall both daily, monthly and yearly. Prediction of rainfall with a statistical approach can be done through the Multiple Linear Regression method. Where in this study, rainfall is the dependent variable, while temperature and humidity are independent variables. The results obtained from the WEKA Application with a total of 60 data from 2017 to 2021, the correlation coefficient value is 0.8175, and from the evaluation results using Linear Regression, the MAE error rate is 78.8695 and the RMSE is 95.1982. It can be concluded that the effect of temperature and air on the occurrence of rainfall is 81.75%
Optimalisasi Akurasi Algoritma Naïve Bayes Dengan Metode Syntetic Minority Oversampling Technique (Smote) Pada Data Numerik Hizbul Izzi; Arief Setyanto; Anggit Dwi Hartanto
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 1 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i1.28340

Abstract

This research will classify numerical data, namely loan data taken from Kaggle. The data used amounted to 9578 datasets which included data classes with borrowers able to complete credit as many as 8045 records and loans that could not complete credit as many as 1533 records. From the amount of data there is an imbalance of classes so it is necessary to do balancing in order to get more accurate classification results. The purpose of this research is to improve the accuracy of the Naïve Bayes algorithm in classifying numerical data. Fraud in financial transactions is an example of a case of imbalanced data, where the number of legitimate transactions is much greater than those that are fraudulent. Optimizing accuracy in minority (fraud) classes is very important to avoid losses. The method used to improve the accuracy of the algorithm is the Synthetic Minority Oversampling Technique (SMOTE) by over sampling the minority of the dataset. In addition, it also uses the K-Fold Cross Validation method to evaluate the performance of the algorithm process used. Data preprocessing is done to clean the data from missing and invalid values and normalize the data so that all features are on the same scale and suitable for classification analysis. Based on the results of the analysis conducted, before the application of SMOTE the model's ability to recognize minority classes was 16.1%, while after the application of SMOTE the model's ability to recognize minority classes became 48.8%. besides that, before the application of SMOTE the model was able to predict the minority class correctly in 10 cases while after the application of SMOTE, the model was able to predict the minority class correctly in 102 cases. So it can be concluded that the SMOTE technique is able to improve the ability of the model
ESTIMATION OF BIOLOGICAL PARAMETERS OF SQUID (LOLIGO SPP) CAUGHT IN THE WATERS OF TUBAN REGENCY Setyohadi, Daduk; Kartikasari, Wahida; Setyanto, Arief; Wiadnya, Dewa Gede Raka; Harlyan, Ledhyane Eka; Sunardi, Sunardi; Nabilla, Azma Salma
Journal of Environmental Engineering and Sustainable Technology Vol 11, No 02 (2024)
Publisher : Directorate of Research and Community Service (DRPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.jeest.2024.011.02.8

Abstract

Squid (Loligo spp.) belongs to the Cephalopoda group (squid, cuttlefish, octopus) and is one of the primary export commodities in the fisheries sector. National squid production increased by 5.5%, from 193,583.82 tons in 2020 to 204,156.28 tons in 2021. However, the potential sustainable catch in the Java Sea (WPPNRI 712, including the Madura Strait) has experienced an average annual decline of 1.9% from 2017 to 2022, dropping to 66,608 tons in 2022. This study aims to identify the species composition of squid, analyze length-weight relationships, and determine the mantle length at first gonad maturity (Lm). Data were obtained from fixed lift-net catches and analyzed in the Fisheries Exploitation Laboratory of Universitas Brawijaya. The results identified two main species: Photololigo duvaucelli (Indian squid) and Sepioteuthis lessoniana (bigfin reef squid). The composition of squid catches was 1.29% in purse seine operations and 2.91% in payang (seine net) operations. The length-weight relationship of both species exhibited a negative allometric growth pattern, where length growth outpaces weight gain. The sex ratio between males and females was balanced for both species. The mantle length at first gonad maturity (Lm) was greater than the mantle length at first capture (Lc), indicating that the catch was dominated by immature squid. These findings highlight the need for minimum catch size regulations to ensure the sustainability of squid resources in the Tuban waters.
Comparison of The Performance of SVR, KNN and Decision Tree Methods in Predicting Rice Production Hamdikatama, Bimantyoso; Kusrini, Kusrini; Setyanto, Arief
JATISI Vol 12 No 1 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i1.10133

Abstract

Rice holds importance in Indonesia as a commodity driving the economy and improving societal well-being, however, its production encounters obstacles attributed to the effects of drastic climate variations. This study sought to evaluate how Support Vector Regression (SVR) k Nearest Neighbors (KNN) and Decision Tree models perform in forecasting rice yields while considering variables related to climate change. The research process included stages such, as gathering and cleaning the information before exploring and analyzing it to apply metrics and implement algorithms like Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) and R² Score, for evaluation purposes. The findings obtained from the study indicate that the Decision Tree technique is efficient, achieving a minimal deviation rate of 0%. This outcome implies that the model effectively grasped the core patterns within the dataset while reducing errors effectively. The KNN model displayed performance levels and suggested room, for enhancement with parameter adjustments; however, SVM Regression was deemed fitting for the datasets needs. The results emphasize the significance of choosing the algorithm for modeling in agriculture and stress the necessity, for additional research to confirm these findings in various datasets.
Co-Authors (Menunda Publikasi) Abdillah, M A Agastya, I Made Artha Agung, Kris Agus Sukarno Agus Tumulyadi Agustina Rahmawati Ahmad Afief Amrullah Ahmad Afief Amrullah Ahmad Naufal Labiib Nabhaan Ahmad Tantoni Ainul Yaqin Akhmad Fadjeri Al Maky, Nuril Huda Aliyah, Nada Rahma Amanda Rifan Fathoni Amir Fatah Sofyan Amiruddin Khairul Huda Ammara, Laya Amrullah, Ahmad Afief Anam, M. Choirul Anang Anang Andi Kriswantono Andik Isdianto Anggit Dwi Hartanto Anggit Hartanto Annisa Gatri Zakinah annisa gatri zakinah Anthon Andrimida, Anthon Anton, Tri Arbiansyah, Moh Junit Ariefandi, Muhammad Fikri Asadi, M. Arif Askar, Muhammad Ichfan Asmirijal, Amrey Syahnur Asro Nasiri Asro Nasiri Asro Nasiri Astika Wulansari Astuti , Septiana Sri Atmaja, Albertus Aldo Danar Atminenggar, Alinda Najma Aulia Lanudia Fathah Basit, Muhammad Abdul Bawan, Sarah Bunda Desi Béjar, Rodrigo Martínez Berlania Mahardika Putri Constantin Menteng Daduk Setyohadi Darmawan Ockto Sutjipto Dedi Tri Hermanto Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya Dewa Gede Raka Wiadnya, Dewa Gede DHANI ARIATMANTO Dhea, Luthfia Ayu Dhiana Puspitawati Diah, M. Dian Rusvinasari Dinar Mustofa Dwi Satrio Anurogo Eko Pramono Eko Pramono Eko Pramono Ema Utami Emha Emha Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi F Purwanto Fathah, Aulia Lanudia Fazlul Rahman Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fiqih Akbari Gatut Bintoro Gibran, Ibrahim El Gibran, Khalil Ginting, Meliani Ananda Br. Gunawan Wicahyono Hadiyah, Lisa Nur Hafidz Sanjaya, Hafidz Hamdallah, Dika Puja Hamdikatama, Bimantyoso Hamka Suyuti Hamzah Hamzah HANIF AL FATTA Hanif Al Fatta Hanif Al Fatta Hanif Al Fattah Hanifa Ramadhani Hari Susanto Harlyan, Ledhyane Eka Henderi . Hendi Muhammad, Alva Heri Sismoro Hidayat, Aji Said Wahyudi Hidayat, Kardilah Rohmat Hizbul Izzi I Made Artha Agastya Ilham Mubarog Imam Syafii Imam Syafii Imam Thoib Irianies Cahya Gozali Irwan Jatmiko Ishaq, Syafrial Yanuar Jamilah Karaman Jimmy H Moedjahedy José Ramón Martínez Salio Kamila, Firda Nikmatul Kartikasari, Wahida Khairan marzuki Khasanah, Nabiila Rizqi Kholida Zia Abidin Komang Aryasa Kris Agung Kudrati, Amelinda Vivian Kumara Ari Yuana Kumoro, Danang Tejo Kurniawan, Mei P Kusnawi Kusnawi KUSRINI Kusrini Kusrini Kusrini, Kusrini La Ariandi, Hadin López, Alba Puelles M. Diah M. RUDYANTO ARIEF M. Rudyanto Arief Maehendrayuga, Arief Mardya Hayati Marsela, Kristina Martiani, Evi Martínez-Béjar, Rodrigo Mei P Kurniawan Mei P. Kurniawan Mohamad Syafri Lamato Morita Puspita Sari Muchamad Zainul Muhamad Maksum Hidayat Muhammad Arif Asadi, Muhammad Arif Muhammad Arif Rahman Muhammad Azmi Muhammad Ghozaly Salim Muhammad Javier Irsyad Muhammad Reza Muhammad Reza Riansyah Muhammad Yusuf Munandar, Arief Muqorobin Muqorobin Nabhaan, Ahmad Naufal Labiib Nabilla, Azma Salma Nadea Cipta Laksmita Nasiri, Asro Naufal Hilda Bahtiar nfn Sarip Nggego, Dedy Abdianto Ni Nyoman Utami Januhari, Ni Nyoman Nico Rahman Caesar Nila Feby Puspitasari, Nila Feby Nina Kurnia Hikmawati Nisrina, Aliyya Nizery, Sefhanissa Puspa Retno Nuddin Harahab Nugroho, Agung Nur Khamidah oktiyas muzaky Luthfi, oktiyas muzaky Pahlawan, Muammar Reza Pangestu, Wanda Suryani Pattisahusiwa, Annisa Shafira P. Prastyo, Agung Budi Prayoghi, M. Lukman Publikasi), (Menunda Putra, Muhammad Naufal Eka Putri, Berlania Mahardika Rachmanto, Rakandhiya Daanii Rafif Zul Fahmi Rahmad Arif Setiawan Rahman, Aulia Tegar Rahmat Taufik R.L Bau Rakandhiya Daanii Rachmanto Ramdhani, Mohamad Dhicy Rarasrum Dyah Kasitowati Ratno Kustiawan Ria Andriani Ripto Sudiyarno Rismayani Rismayani Roni Sasongko Rudyanto Arief Sadikin, Moh. Fal Samuel, Pratama Diffi San Sudirman Saputra, Tedy Eko Sarah Bunda Desi Bawan Sarip, nfn Seniwati, Erni Septiansyah, Moch. Rafli Shahruri, Rifandi Annas Simone Martin Marotta Siswo Utomo, Mardi Siti Alvi Sholikhatin Siti Halimah Soejono, Ajie Wibowo Sriyati Sriyati Stephan Adriansyah Hulukati Suardi, Heri Sucianingsih, Ni Komang Diah Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Suhardi Aras Sukoco Sunardi Sunardi Supriyadi Supriyadi Supriyadi Supriyadi Suwanto Raharjo Suyadi Suyadi Suyuti, Hamka Syarief, Salsabila Nazmie Putri TONNY HIDAYAT Totok Wahyu Caturiyanto Tri Djoko Lelono Tumulyadi, Agus Tyas, Herlin Widi Aning Utama, Andria Ansri Veithzal Rivai Zainal Wahyu Nugroho Widhiarta, Widhiarta Wijaya, Sony Yasmin, Delviega Aisyah Yeni Kartika Sari, Yeni Kartika Yorarizka, Putri Devi Yuliana Yuliana Yumna, Orryza Nayla Zul Hisyam