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Kusrini

Universitas Amikom Yogyakarta

Author-ID : 302666

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Klasifikasi Citra Digitalretina Penderita Diabetes Retinopati Menggunakan Metode Euclidean Yusuf Fadlila Rachman; Kusrini Kusrini; Hanif Al Fatta
DoubleClick: Journal of Computer and Information Technology Vol 3, No 2 (2020): Edisi February 2020
Publisher : Universitas PGRI Madiun

Retinopati diabetes merupakan gangguan yang terjadi pada mata, yang disebabkan oleh penyakit diabetes. Diagnosa Retinopati Diabetes dapat dilakukan menggunakan citra digital retina. Pada penelitian ini dilakukan klasifikasi dan analisa tingkat keparahan Retinopati Diabetes berdasarkan ciri atau fitur gambar retina. Terdapat 2 dataset yang digunakan. Dataset pertama merupakan hasil dari preprocessing gambar yaitu konversi gambar ke grayscale. Dataset kedua didapat dengan menghapus optikal disk pada gambar dataset pertama. Metode ekstraksi ciri yang digunakan pada penelitian ini adalah ekstraksi ciri tekstur metode statistik orde pertama menggunakan histogram. Ciri gambar yang didapatkan adalah mean, variance, skewness, curtosis, dan entropy. Klasifikasi dilakukan sebanyak 2 kali pada dataset pertama dan kedua menggunakan metode klasifikasi jarak euclidean. Pengujian dilakukan menggunakan confusion matrix dengan menghitung nilai akurasi, presisi, dan recall. Berdasarkan hasil perngujian didapatkan bahwa dataset kedua memiliki nilai akurasi yang lebih tinggi yaitu 64,81%, nilai precision 69,3%, dan recall sebesar 64,8%

OPTIMASI METODE NAIVE BAYES DENGAN FEATURE SELECTION INFORMATION GAIN UNTUK PREDIKSI KETERLAMBATAN PEMBAYARAN SPP SEKOLAH Muqorobin Muqorobin; Kusrini Kusrini; Emha Taufiq Luthfi
Jurnal Ilmiah SINUS Vol 17, No 1 (2019): Vol. 17 No. 1 Januari 2019
Publisher : STMIK Sinar Nusantara

The cost of education is one component of input that is very important in implementing education. Because costs are the main requirement in an effort to achieve educational goals. SMK Al-Islam Surakarta is a private education institution that requires students to pay school fees in the form of Education Development Donations. Educational Development Donation is a routine school fee that is conducted every month. Based on last year's TU report, many students were late in paying Education Development Donations, around 60%. This is a big problem. The purpose of this study is that researchers will build a predictive system using the Naïve Bayes method. Because the method can classify the class right or late, in the payment of school fees. Data processing was taken from the dapodik data of schools in 2017/2018 with the test dataset taking 30 records. To find out the level of accuracy, this research was conducted with the Naive Bayes Method and the Information Gain Method for feature selection. Accuracy testing is done by the Confusion Matrix method. The results showed that the highest accuracy was obtained by combining the Naive Bayes Method with the Information Gain Method obtained by 90% accuracy.

PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) DALAM MENENTUKAN POSISI DUDUK PESERTA DIDIK Moh Er Kamim; Rendy Bagus Pratama; Wartono Wartono; Kusrini Kusrini
Jurnal Ilmiah SINUS Vol 16, No 2 (2018): Juli 2018
Publisher : STMIK Sinar Nusantara

Learning process there are several factors that influence success rate of learners in receiving lessons. one of the supporting factors is a conducive classroom environment, a conducive class can be created with the ideal seating position arrangement. In the determination of sitting position at Girirejo Elementary School Nganjuk State conducted randomly so as to cause uneven seating position and resulted in the learning activities do not run optimally, there are students who should be in front of sitting behind and vice versa. To overcome these problems then it takes an election to determine the sitting position of learners in order to help guardians determine the sitting position in the class so as to create an ideal sitting position. In determining the sitting position used a method of Simple Additive Weighting (SAW) with the criteria of value, height, and the use of glasses, the method combined alternative search with seating object where seats are sought alternative value difference between the highest value and the lowest score of students, will be divided by the number of students, will be generated the highest value and the lowest value on each seat, so that will appear alternative to the seat to be occupied by students with the weight of the value entered in the value range on the seat. The final result in this research is not maximal in accordance with the planned because there are still vacant seats are not filled by the students, so it needs to be improved in the future to be able to find alternative every seat to be filled by all students so it can help teachers in mapping the ideal student sitting position in the classroom.

Perbandingan Fitur Ekstraksi Untuk Klasifikasi Emosi Pada Sosial Media riska dwi handayani; Kusrini Kusrini; Hanif Al Fatta
Jurnal Ilmiah SINUS Vol 18, No 2 (2020): Vol. 18, No. 2, Juli 2020
Publisher : STMIK Sinar Nusantara

Emotions are complex conscious experiences characterizing mental states, such as excitement, anger, love, fear, and so on, as part of important human nature. Nowadays, many people express themselves as a reflection of their personality using social media. Social Media grows and becomes a method for social interaction and information sharing. Based on that, researchers tried to use social media data to classify someone's emotions. Emotional detection of text from social media is a field of research that is gaining a high interest, especially for the sake of emotional analysis. To be able to classify such emotions, researchers use comparative feature comparison and algorithms classification. The comparison of features in this research is the extraction features TF-IDF and N-gram which are then classified using Naïve bayes algorithm. However, before the extraction feature is applied, there is a pre-processing text technique using several methods: Case Folding, Stopword Removal, and Stemming. Based on this research, techniques of extraction features in this research generating the highest accuracy value after the classification method is the TF-IDF with an accuracy value of 80%, 98% for the highest value of precision in measurement of “pleasure” emotion, 99% for the highest recall value is in “happy” emotion, and 95% for the highest F1-score value in “pleasure” emotion.

Prediksi Tingkat Keberhasilan Studi Kinerja Santri Menggunakan Algoritma C 5.0 Achmad Agus Athok Miftachuddin; Kusrini Kusrini; Emha Taufiq Luthfi
SAINTEKBU Vol. 13 No. 01 (2021): Vol. 13 No. 01 Januari 2021
Publisher : KH. A. Wahab Hasbullah University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32764/saintekbu.v13i01.2523

The success of pesantren education institutions can be measured by the success of their students. By predicting the possible outcomes of the learning process based on prediction results can help an Islamic boarding school, by adjusting the factors that contribute and influence the success rate of students' performance studies. And by utilizing data mining techniques that can be used to increase the level of success and reduce the failure of students. this can greatly help pesantren educational institutions to improve their graduates 'skills, because data mining is the best solution to find hidden patterns and can predict the success of students' performance studies. This research presents a model based on decision tree classification algorithm C 5.0 used in this model with alumni tracer study filled by santri alumni. In this study also used the k-folds cross validation test scenario with k values of 2,3,6,10 and 15 with a total of 300 alumni data and 84 data used for validation tests without cross validation. Determination of the criteria for the classification results using a confusion matrix form the measurement of the classification results obtained, namely the highest value in this study is 95% resulting from 15 folds the scenario 1. And form the results of testing the validation data without cross validation, the corresponding results are 73.81%, when compared to the k-folds, there was an increase of 21.19% and it can be ignored that the C 5.0 algorithm is able to classify well. So that pesantren educational institutional can provide a foundation in the arrangement for their students in deciding the right school choice.

Analisis Pengaruh Tingkat Akurasi Klasifikasi Citra Wayang dengan Algoritma Convolitional Neural Network Muhammad Resa Arif Yudianto; Kusrini Kusrini; Hanif Al Fatta
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1319

Abstract – The development of technology is inversely proportional to cultural preservation in Indonesia. One of Indonesia's cultures which creates character through the advice and stories is a puppet. But this culture shows less because the devotees have decreased. This makes young people not knowing the names of puppet figures. The introduction of digital images of puppets through the system is very necessary to introduce to the generation of millennial children, bearing in mind that at this time people are familiar with the technology. This recognition is through the image classification of puppet figures with classification algorithms that have been trained previously with puppet images that have been labeled before. To recognize various puppet figures well, a good model is needed. The quality of the model can be measured by the accuracy, precision, and recall variables in the model testing. Several factors influence the formation of the model, including the rise of the dataset, number of iterations (epoch) in learning, and of course the treatment of data before it is used in the process of forming the model. This study used 400 datasets which are divided into 4 classes which will be trained using CNN (Convolutional Neural Network) algorithm to produce a model. Based on the results of experiments obtained the best accuracy of 97%, 93% precision, and 87% recall by applying a combination of augmentation, changing the image to grayscale in preprocessing stage, the use of 80:20 dataset ratio and 100 epoch is a very significant effect in increasing accuracy.Keywords – Classification, Punakawan Puppets, CNN, Image Processing. Abstract – Semakin berkembangnya teknologi berbanding terbalik dengan perkembangan pelestarian kebudayaan di Indonesia. Salah satu kebudayaan Indonesia yang bermanfaat membentuk karakter melalui nasihat dan cerita di dalamnya adalah wayang. Akan tetapi kebudayaan ini semakin jarang terlihat pertunjukkannya dikarenakan peminatnya telah berkurang. Hal tersebut mengakibatkan anak-anak muda tidak mengenal nama tokoh-tokoh pewayangan. Pengenalan citra digital tokoh pewayangan melalui sistem sangat diperlukan untuk mengenalkan kepada generasi anak milenial, mengingat saat ini masyarakat telah terbiasa dengan teknologi. Proses pengenalan ini melalui proses klasifikasi citra tokoh wayang dengan algoritma klasifikasi yang telah dilatih sebelumnya dengan data-data citra wayang yang telah diberi label sebelumnya. Untuk dapat mengenali berbagai tokoh wayang dengan baik dibutuhkan model yang baik. Kualitas model dapat diukur dengan variabel akurasi, presisi dan recall pada proses pengujian model. Terdapat beberapa faktor yang mempengaruhi pembentukan model, diantaranya adalah raiso pembagian dataset, jumlah perulangan (epoch) dalam pembelajaran dan tentunya perlakuan terhadap data sebelum digunakan dalam proses pembentukan model. Pada penelitian ini digunakan dataset sebanyak 400 data yang terbagi ke dalam 4 kelas yang akan dilatih menggunakan algoritma CNN (Convolutional Neural Network) untuk menghasilkan model. Berdasarkan hasil percobaan yang dilakukan didapatkan akurasi terbaik sebesar 97%, presisi 93% dan recall sebesar 87% dengan menerapkan kombinasi augmentation, mengubah citra menjadi grayscale pada tahap preproccessing, penggunaan rasio dataset 80:20 dan epoch sebesar 100 sangat berpengaruh signifikan dalam meningkatkan nilai akurasi.Kata kunci – Klasifikasi, Wayang Punakawan, CNN, Pengolahan Citra.

SEGMENTASI CITRA IKAN ARWANA SUPER RED BERDASARKAN DETEKSI TEPI MENGGUNAKAN ALGORITMA CANNY Noviyanti P.; Kusrini Kusrini; Mei P. Kurniawan
(JurTI) Jurnal Teknologi Informasi Vol 3, No 2 (2019): DESEMBER 2019
Publisher : Universitas Asahan

Abstract - Ornamental fish is a fish that lived in the aquarium and for lovers of fish have ornamental fish is the most favored. One of the ornamental fish is pretty much in demand and has a fairly high selling price is super red Arowana fish. Super red Arowana fish also called dragon fish because it has scales resembling a dragon. In general, the selling price of super red Arowana fish is influenced by the size of the fish, such as the length and width of the fish. The high prices caused the farmers are very careful in treating the fish, so it is necessary there is a system that if it can measure the length and width of super red Arowana fish. The research groove generally consists of two phases. The initial phase of that region contributes to the best image segmentation and the second stage is to produce a long and wide super red Arowana fish. The current study focuses on the early stages, the best image segmentation. At this early stage, the detection process is carried out using the algorithm Canny edge of the fish so as to produce the best image segmentation. In this study, using three scenarios, the first scenario uses the Canny course, the second scenario using the canny and the median blur, the third scenario using the median blur and canny. Based on all these three scenarios obtained image segmentation fish with the best scenario, the scenario-2 using the canny and the median blur with MSE value of 26494.56, PNSR value of 41.89 dB, and MAPE value of 99.28%.Keywords - Canny Edge Detection, Super Red Arowana Fish, Image Segmentation, Ornamental fish Abstrak - Ikan hias merupakan ikan yang dihidupi didalam aquarium dan bagi para pecinta ikan memiliki ikan hias adalah hal yang paling disenangi. Salah satu ikan hias yang cukup banyak diminati dan memiliki harga jual yang cukup tinggi adalah ikan arwana super red. Ikan arwana jenis super red biasa juga disebut dengan ikan naga karena memiliki sisik yang menyerupai naga. Pada umumnya harga jual ikan arwana super red ini dipengaruhi oleh ukuran ikan, seperti panjang dan lebar ikan. Harga jual yang tinggi tersebut menyebabkan para pembudidaya sangat berhati-hati dalam memperlakukan ikan tersebut, sehingga dirasa perlu terdapat sebuah sistem yang sekiranya dapat mengukur panjang dan lebar ikan arwana super red. Alur penelitian secara umum terdiri dari dua tahap. Tahap awal yaitu meghasilkan segmentasi citra terbaik dan tahap kedua adalah menghasilkan panjang dan lebar ikan arwana super red. Penelitian yang dilakukan saat ini berfokus pada tahap awal, yaitu segmentasi citra terbaik. Pada tahap awal ini dilakukan proses deteksi tepi ikan menggunakan algoritma canny sehingga menghasilkan segmentasi citra terbaik. Pada penelitian ini menggunakan 3 skenario, skenario pertama menggunakan canny saja, skenario kedua menggunakan canny dan median blur, skenario ketiga menggunakan median blur dan canny. Berdasarkan ke-3 skenario tersebut diperoleh segmentasi citra ikan dengan skenario terbaik, yaitu skenario ke-2 menggunakan canny dan median blur dengan nilai MSE sebesar 26494.56, nilai PNSR sebesar 41.89 dB, dan nilai MAPE sebesar 99.28%. Kata kunci - Deteksi Tepi Algoritma Canny, Ikan Arwana Super Red, Segmentasi Citra, Ikan Hias

Kombinasi Metode ABC dan MMSL dalam Pengendalian Stok Obat Siti Hartinah; Kusrini Kusrini; Mei P Kurniawan
(JurTI) Jurnal Teknologi Informasi Vol 4, No 2 (2020): DESEMBER 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i2.1431

Abstract - In hospitals, problems often occur in the management of drug supplies with the condition that the drug runs out due to spending in one year 30% for the investment cost of drugs. In overcoming this problem, the hospital should have good drug data processing, processing can be done by doing good management. The author will use the ABC method combined with MMSL. ABC functions in grouping A, B, and C and is combined with MMSL in calculating Maximum Stock and Minimum stock. From this study, it was found that using the ABC method of drug analysis was grouped based on drug use into three groups, namely group A with a percentage of 21.90%, group B with a percentage of 32.25%, and group C with a percentage of 45.85%. For the results of MMSL, namely combining several variables in the ABC to MMSL algorithm. The results of the MMSL entered into the drug stock control process that the optimal prediction was 17.15% MAPE compared to the MAPE investment value of 19.05% and the MAPE lead time 18.14%.Keywords - MMSL, Classification, Prediction, ABC Analysis Abstrak - Pada rumah sakit sering terjadi permasalahan pada pengelolaan persedian obat dengan kondisi obat habis disebabkan oleh pengeluaran dalam satu tahun 30 % untuk biaya investasi obat. Dalam mengatasi permasalahan ini rumah sakit seharunya mempunyai pengolahan data obat yang baik, cara pengolahan bisa dengan melakukan management yang baik. Penelitian ini menggunakan metode ABC dikombinasikan dengan MMSL. ABC berfungsi dalam pengelompokan A,B dan C serta dikombinasikan dengan MMSL dalam menghitung Maximum Stock dan Minimum stock. Dari penelitian ini mandapatkan hasil bahwa dengan menggunakan metode ABC analisis obat dikelompokan berdasarkan pemakaian obat menjadi tiga kelompok yaitu kelompok A dengan persentase 21.90%, kelompok B dengan persentase 32.25% dan kelompok C dengan persentase 45.85%. Untuk hasil dari MMSL yakni mengkombinasikan beberapa variabel pada algoritma ABC ke MMSL. Hasil dari MMSL masuk kedalam proses pengendalian stock obat bahwa prediksi yang optimal yakni MAPE 17.15% dibandingkan dengan nilai investasi MAPE 19.05% dan Lead time MAPE 18.14%.Kata Kunci - MMSL, Klasifikasi, Prediksi , ABC Analisis..

PENERAPAN ALGORITMA C4.5 DALAM MEMPREDIKSI KETERLAMBATAN PEMBAYARAN UANG SEKOLAH MENGGUNAKAN PYTHON Victor Saputra Ginting; Kusrini Kusrini; Emha Taufiq Luthfi
(JurTI) Jurnal Teknologi Informasi Vol 4, No 1 (2020): JUNI 2020
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v4i1.1101

Abstract – Payment of school fees is an important factor in carrying out education because payment of school fees is one of the important obligations in getting an education. Research conducted by Muqorobin, 2019 with the research title "Optimizing the Naive Bayes Method with Feature Selection Gain for Predicting Late School Payment" produces an accuracy rate of 80%, then optimization is carried out using information gain and Naive Bayes produces an accuracy rate of 90%. The research conducted will be a prediction of late payment of school fees using the C4.5 algorithm and then carried out into the form of programming using Python, so as to produce a prediction result. Information Prediction Results obtained from Python then will try to compare the level of accuracy with the dataset that has been collected using a confusion matrix. The dataset obtained from De Britto Yogyakarta High School using Python produces an accuracy rate of 73%. This research is expected to help the school in making decisions and minimize the level of late payment of school fees.Keywords - Prediction, Algorithm C4.5, Python, and Data Mining.Abstract – Pembayaran yang sekolah merupakan faktor yang cukup penting dalam melangsungkan pendidikan karena pembayaran uang sekolah merupakan salah satu kewajiban penting dalam mendapatkan pendidikan. Penelitian yang telah dilakukan Muqorobin, 2019 dengan judul penelitian “Optimasi Metode Naive Bayes Dengan Feature Selection Gain Untuk Memprediksi Keterlambatan Pembayaran Uang Sekolah” menghasilkan tingkat akurasi sebesar 80%, kemudian dilakukan optimasi dengan menggunakan information gain dan Naive Bayes menghasilkan tingkat akurasi sebesar 90%. Penelitian yang dilakukan akan dilakukan prediksi keterlambatan pembayaran uang sekolah dengan menggunakan algoritma C4.5 dan kemudian dilakukan implementasi kedalam bentuk pemrograman dengan menggunakan Python, sehingga menghasilkan keterangan hasil prediksi. Keterangan Hasil Prediksi yang didapatkan dari Python, kemudian akan coba dilakukan perbandingan tingkat akurasi dengan dataset yang sudah dikumpulkan menggunakan confusion matrix. Dataset yang didapatkan dari Sekolah Menengah Atas De Britto Yogyakarta menggunakan Python menghasilkan tingkat akurasi sebesar 73%. Penelitian ini diharapkan dapat membantu pihak sekolah dalam mengambil keputusan dan meminimalisir tingkat keterlambatan pembayaran uang sekolah.Kata kunci - Prediksi, Algoritma C4.5, Python dan Data Mining.

ANALISIS PERBANDINGAN PREDISKSI OBAT DENGAN MENGGUNAKAN METODE ABC ANALISYS DAN SVR PADA APLIKASI “MORBIS” Tutik Maryana; Kusrini Kusrini; Hanif Al Fatta
(JurTI) Jurnal Teknologi Informasi Vol 3, No 2 (2019): DESEMBER 2019
Publisher : Universitas Asahan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36294/jurti.v3i2.1016

Permasalahan rumah sakit mengenai pengolahan persediaan obat adalah kondisi obat yang habis, dikarenakan rumah sakit dalam satu tahun mengeluarkan 33% dari biaya investasi untuk investasi obat. Untuk mengtasi permasalahan diatas rumah sakit harus memiliki pengeloaan logistic dengan baik, cara pengelolaan adalah dengan melakukan perencanaan yang baik. Penulis akan memakai algoritma ABC Analysis dan SVR. ABC Analysis akan digunakan untuk proses klasifikasi obat yaitu dengan cara membagi obat manjadi tiga kelompok utama berdasarkan kepentingan yaitu kelompok A, B dan C. Penulis akan menggunakan metodo SVR untuk menghitung prediksi obat. Hasil penelitian ini adalah ABC analisys dapat membagi obat. Menjadi tiga bagian yaitu kelompo A 276 item dengan presentase 22,96% dari jumlah item keseluruhan, kelompok B sejumlah 396 item dengan presentase 33,11% dan C sejumlah 528 dengan presenrase 43,94% dengan kesluruhan obat adlah 1202 item obat. Pengujian prediksi dilakukan dengan cara mengambil sample lima obat dari hasil klasifikasi. Proses perhitungan SVR adalah membandingkan metode preprocessing linier scaling dan z normalization. Hasil dari penelitian tersebut adalah MAPE menunjukan bahwa dengan menambahkan preprocessing dengan linier scaling dapat membuat proses SVR lebih optimal dari pada menggunakan z nomrlization dan perhitungan dengan klasifikasi ABC analisys.

Co-Authors - Saifulloh A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA ABADI Abda Abda Abdi Firdaus Abdillah, Yahya Auliya Abdul Haris Abdul Malik Zuhdi Abdul Ra'uf Alfansani Abdul Rokhim Abdullah Sukri, M Iqbal Abdullah, Mochamad Fadillah Achmad Agus Athok Miftachuddin Achmad Wazirul Hidayat Achmad Yusron Arif Adadilaga Arya Priwanegara Ade Pujianto, Ade Adhani, Muhammad Azmi Adhien Kenya Estetikha Adiatma, Biva Candra Lutfi Aditya Hastami Ruger Aflahah Apriliyani Afryansih, Nila Agam Saka Jati Agastya , I Made Artha Agastya I Made Artha Agastya, I Made Artha Agatha Deolika Agatstya, I Made Artha Agianto Syam Halim agung budi Agung Budi Agung Budi Prasetio Agung Budi Prasetyo Agung Jasuma Agung Jasuma Agung Nugroho Agus Harjoko AGUS PURWANTO Agus Sriyanto AGUS SULISTIYONO Agus Susilo Nugroho Agustina Rahmawati Agustina, Nova Ahmad Almas'ud ZD Ahmad Harmain Ahmad Sanusi Mashuri Ahmad Yusuf Ahmad Zarkasih Ahsan, Muhammad Salimy Aidina Ristyawan, Aidina Aiman Ayadi Aisha Alfani Aji Santoso, Bayu Aji Susanto Anom Purnomo Ajie Kusuma Wardhana Akhmad Syarif Akhmad Syarif Akrilvalerat Deainert Wierfi Alfahmi Muhammad Arif Alfatta, Hanif Ali Asgar Zainal Abidin Alva Hendi Muhammad Amanullah, Raynaldi Fatih Amborowati, Armadyahah Amir Bagja Amir Fatah Amir Fatah Sofyan Amrina Rosyada anas, hasni Anastasia Meyliana Andayani Andayani Andhika Wisnu Widyatama Andi Bahtiar Semma Andi Muhammad Irfan Andi Sunyoto Andi Suyoto Andi Syahrul Ramdana Andika, Roy Andri Rachmatullah Andri, Agung Andria Andria Andris Faesal Andriyan Dwi Putra Andriyanto, Rifki Angga Kurniawan Angga Kurniawan Anggi Muhammad Rifa'i Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggit Hartanto Anggraeni, Meita Dwi Aninda Astuti Anjar Anjani Putra Anwar Sadad Aolia Ikhwanudin Aolia Ikhwanudin Apriliyani, Aflahah Aprison Wolla Gole Ardana, Wildan Muhammad Ardana, Wildan Muhammmad Ardiansyah, Fachri Arfiani Nur Khusna Arham Rahim Ari Ari Suhartanto Ari Suhartanto Ari Yuana, Kumara Arief Setyanto Arief Setyanto Arief Setyanto Arief, M Rudyanto Arief, M. Rudyanto Arief, Muh. Rudyanto Arief, Muhammad Rudyanto Arief, Rudyanto Ariel Yonatan Alin Arif Budiman Arif Fajar Solikin Arif Fajar Solikin Arif Fajar Solikin Arifuddin, Danang Arik Sofan Tohir Aris Subadi Arli Aditya Parikesit Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Arnap, Adam Arnila Sandi Arridho, Muhammad Noor Artha Agastya, I Made Arwendra Adi Putra Asnawi, Muhamad Fuat Asri, Saffinah Indah Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasrini Assani, Moh. Yushi Atin Hasanah Awaludin Abid Azi, Amanda Azib Widad Zuhaily Imam Azis Wahyudi Aziz Muslim Aziz Muzani, Ma'ruf Aziz, Moh Abdul Azkar, Azkar B, Isdayani B. R., Endah Bagas Dwi Novianto Bahrun Ghozali Banu Santoso Bayu Setiaji Béjar, Rodrigo Martínez Bentar Candra P Bernadhed, Bernadhed Bimantyoso Hamdikatama Bisono, Hadi Hikmadyo Braeken, An Brenstein Mawene Buana, Yopy Tri Burhanudin Dwi Prakoso Candra Adipradana Candra Rusmana Candra, Kurnia Khoirul Choerun Asnawi Christian Budi Andrianto, Christian Budi Christin Nandari Dengen Christin Nandari Dengen Christin Nandari Dengen Christin Nandari Dengen D. Diffran Nur Cahyo da Silva, Bruno Danang Eko Prayogo Danang Wahyu Widodo Darmawan, Eko Rahmad Darno - David Agustriawan David Junggu Manggala Pasaribu dayat subekti Dede - Sandi Dede Husen Dedi Gunawan Dema Matias L Tobing Dengen, Christin Nandari Desty Yani Destyanto, Febrian Devina Ninosari Dhani Ariatmanto DHANI ARIATMANTO Dhani Ariatmanto Dias Ayu Budi Utami Dibyo Sudarsono Didik Irawan Dimaz Arno Prasetio Dina Maulina Dina Maulina Dina Maulina Dina Maulina Djatmiko, Widdi Doni Briyan Wahyudi Donny Yulianto Dwi Astuti Dwi Cahyani, Atika DWI ISKANDAR Dwi Utami Dwi Yuli Prasetyo Dwinda Etika Profesi Dwinda Etika Profesi Dzalfa Tsalsabila Rhamadiyanti Dzulhijjah, Dwi Ahmad Eddy Nurraharjo Eka Wahyu Pujiharto Eka Wahyu Sholeha Eka Yulia Sari Ekastini Ekastini Ekastini Ekastini Eko Boedijanto Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Purwanto Eko Purwanto Eko Purwanto Eldas Puspitarini, Eldas Elfandry Bayunanda Elfrida Ratnawati Elik Hari Muktafin Elik Hari Muktafin Elik Hari Muktafin Eliza Staviana Elok Noor Farida, Elok Noor Elvis Pawan Elvis Pawan Ema Utami Ema Utami Ema Utami Ema Utami Emha T aufiq Luthfie Emha Taufik Luthf Emha Taufiq Luthfi EmhaTaufiq Lutfi, EmhaTaufiq Enni Lidrawati Erfan Tongalu Eri Sasmita Susanto Erwin Apriliyanto Erwin Apriliyanto Eva Oktaviani Fahma Inti Ilmawati Fahrullah, Fahrullah Fajar Dwi Insani Fajar Sodik Fajar Sudana Putra Fandli Supandi Fandli Supandi Fansuri, Muhammad Febrian Fareza Aditiyanto Nugroho Fathoni Mahardika Fatkhurohman, Ari Fatkhurrochman, Fatkhurrochman Fauji Maulana Ramlan, Fauji Maulana Fauzi, Moch Farid Fauzy, Marwan Noor Febrianti, Winda Febriyanti, Nada Rizki Fendy Nugroho Fendy Prasetyo Nugroho Fendy Prasetyo Nugroho Fernandito, Durand Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Firman Sriyono Firmanda Fasya Firza Septian Fitriani Fitriani Fitriyandhi, Ari fitriyanto, nur Fitrony, Fachri Ayudi Fiyas Mahananing Puri Friden E Neno Fryda Fatmayati Gifari, Okta Ihza Gigih Setyaji Guido Adolfus Suni Gutama, Deden Hardan Hadi Sucipto Hadryan Eddy Hafidz Sanjaya, Hafidz Halimi, Ahmad Hamada Zein Hamdikatama, Bimantyoso Hanafi Hanafi Hanafi Handoko Adji Pangestu Hanif Al Fatta Hari Agustiyo Hari Muktafin, Elik Haris, Ruby hartanto, david budi Hartono, Anggit Dwi Haryo, Wasis Hasan, Nur Fitrianingsih Hasan, Nurul Rahmawati Hasim, Hasim As'ari Hasirun Hasirun Hasirun, Hasirun Hasnan Afif Helmawati, Nita Henderi Henderi . Hendriansyah Santosa Hendrik Hendrik Henri Kurniawan Henri Kurniawan Herawati, Maimi Heri Abijono Heri Abijono, Heri Heri Sismoro Herlinawati, Noor Hery Nurmawan Hery Siswanto Hidayat, Fajar Maula Hidayatunnisa'i Hulvi, Alfajri I Made Adi Purwantara I Made Artha Agastya I Putu Agus Ari Mahendra I Wayan Pandu Ibnu Ickwantoro Ichsan Wasiso Ichsan Wasiso Idris Idris Ikhwan Baidlowi Sumafta Ikhwanudin, Aolia Ikmah Ikmah Ilham Aziz Rosadi Ilkham, Sundari Ilkham, Sundari Ilmawati, Fahma Inti Ilmi, M. Bahril Imam Listiono Indarto, Aan Iqbal Irfan Purwanto Irma Darmayanti Irwan Oyong Jangkung Tri Nugroho Jazi Eko Istiyanto Jeki Kuswanto Jeki Kuswanto Jemi Yohanis Babys Jhoanne Fredricka Jimmy H Moedjahedy Joang Ipmawati JOKO PRAYITNO José Ramón Martínez Salio Julia Kurniasih Jumaris Jumaris, Jumaris Jumiati Jumiati Junaidi Junaidi Junaidi Sabtu Juwari Juwari Juwariyah, Siti Kaharuddin Kanafi Kanafi Kasman, Haris Saktiawan Kelik Sussolaikah Kemal Pasha Khoironi, Khoironi Khoirul Islam Khoirun Nisa Khomsatun Khomsatun Khusnawi Khusnawi Kristian, Taufik Aldri Kristina Vaher Kumara Ari Yuana Kumara Ari Yuana Kurni, Samuel Everth Andrias Kurniadi, Harso Kurniasari, Iin Kusnawi , Kusnawi Kusnawi Kusnawi Kusrini, Mashudi Kusuma Chandra Kirana La Aba Laka, Beatus Mendelson Lasimin Lewu, Retzi Y. Liana Trihardianingsih Lili Kartikawati Linda, Kumara Dewi Lisda Lisda Listyanto, Ahmad Wildan López, Alba Puelles Lukman Bachtiar Lutfan Lazuardi M rudyanto Arief M Rudyanto Arief M Vaizul Rahman M. Afriansyah M. Dedi Iskandar M. Idris Purwanto M. Ilham Arief M. Irwan Ukkas Irwan Ukkas Ukkas M. Nurul Wathani M. Rudiyanto Arief M. Rudyanto Arief M. Rudyanto Arief M. Rudyanto Arief M. RUDYANTO ARIEF M. Suyanto M. Suyanto, M. M. Syukri Mustafa M. Zainal Arifin M.Andi Rohmat Madhika, Yudha Randa Mahendra, Awanda Putra MAHMUDI Mahmudi Mahmudi Majid Rahardi Mangun, Syamsul Syahab Mansur Mansur Maradona, Maradona Mardi Utomo Mardiana Mardiana marhalim, marhalim Marissa Utami Marselina Endah Hiswati Marta Ardiyanto Martínez-Béjar, Rodrigo Marwan Noor Fauzy MASRIYAH Masruri, Nizar Haris Masud, Ibnu Mat Sudir maulana, fahrizal Mawene, Brenstein Maykel Sonobe Maykel Sonobe Megantara, Muhamad Arldi MEI PARWANTO KURNIAWAN Metha, Halifa Sekar Miftachuddin, Achmad Agus Athok Mochamad Fadillah Abdullah Moenawar Kholil Moh Er Kamim Moh Royandi Azka Moh. Badri Tamam Mohamad Firdaus, Mohamad Mohammad Diqi Mohammad Rezza Pahlevi Moningka, Nirwan Muahidin, Zumratul Muchamat Zainal Arifin Muflich, Alwie Mufti Ari Bianto Muh Saerozi Muh. Rudyanto Arief Muh. Rudyanto Arief Muhamad Fatahillah Z Muhamad Iksan, Muhamad Muhamad Kurniawan Muhamad Nanda Utama Muhamad Ryandy Ghonim Asgar Muhamad Yusuf Muhammad Agus Muljanto Muhammad Alfariz Muhammad Anwar Fauzi Muhammad Egy Pratama Muhammad Fahmi Muhammad Fajar Apriyanto Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Koprawi Muhammad Mariko Muhammad Nafi’Uddin Muhammad Resa Arif Yudianto Muhammad Riandi Widiyantoro Muhammad Riza Eko S Muhammad Rudyanto Arief Muhammad Rudyanto Arief Muhammad Rudyanto Arief Arief Muhammad Salimy Ahsan Muhirin Muktafin, Elik Hari Mukti Ali Mulia Sulistiyono Mulyaningtyas, Widya Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin Muqorobin, Muqorobin Musa Hendri Janto Rahanra Muslihah, Isnawati Musthofa Galih Pradana Mutiara Dwi Anggraini Mutiara Dwi Anggraini Muzakir, Muhammad MZ, Reza Rafiq Nabawi, Isnan Nabila Payapo Nadea Cipta Laksmita Nafis Sururi Nanang Prasetiyantara Nasiri, Asro Neno, Friden Elefri Ngaeni, Nurus Sarifatul Ni Nyoman Utami Januhari, Ni Nyoman Nibras Faiq Muhammad Nikko Listio Wicaksono NINIK TRI HARTANTI Noor Abdul Haris Norhikmah Norhikmah Noto Narwanto Nova Agustina Noviyanti P Noviyanti P. Nugroho, Agung Nugroho, Hanantyo Sri Nugroho, Rakhmat Prasetyo Agung Nuk Ghurroh Setyoningrum Nuk Ghurroh Setyoningrum Nulngafan, Nulngafan Nur Hamid Sutanto Nurcahyo, Azriel Christian Nurdianingsih, Neneng Nurita Evitarina Nurmalasari, Maulidya Dwi Nurwijayanti Oktafiqurahman, Andi Olajuwon, Sayyid Muh. Raziq Onde, Mitrakasih La ode Oscar Samaratungga Paiman Paiman Pamekas, Bondan Wahyu Pamoengkas, Muhamad Agoeng Pamungkas, Prima Giri Pamungkas, Sapto Paradise, Paradise Parsiyono Parsiyono Patmawati Hasan Pawit Srentiyono Perdana Putra, Muhammad Ricky Perdana, Muhammad Ricky Prabowo Budi Utomo Pradipta, Dody Prameswari, Sonia Anjani Pramono Pramono Pramudya Insan Prasetio, Agung Budi Prasetio, Dimaz Arno Prasetyo, Adi Prastowo, Wahit Desta Prastyo, Rahmat Pratama, Bayu Trisna Pratama, Muhammad Egy Profesi, Dwinda Etika Profesi, Dwinda Etika Pujianto, Ade Puri, Fiyas Mahananing Purnamasari, Resti Rachmawati Oktaria Mardiyanto Rachmawati Oktaria Mardiyanto Rahmat Saleh Sukur Rajnaparamitha Kusumastuti Rama Ashari Rama Saktriawindarta RAMADHAN, SYAIFUL Ramlan Ramlan Rantung , Tessa Rasyid, Magfirah Reflan Nuari Rendy Bagus Pratama Retantyo Wardoyo Retzi Yosia Lewu Ridlan Ahmad Riduan, Nor Ridwan Joko Purnomo Rifan Ferryawan Rifqi Hammad Rifqi Hammad Rini Wijayanti Rini, Rini Riovan Styx Roring Ripto Sudiyarno Ririn Putri Damaiyanti Riska Dwi Handayani Risna Sari Rita Wati Ritham Tuntun Riyan Abdul Aziz Rizal Khadarusman Rizal Sapta Dwi Harjo Rizaldi Rizkayati, Anisa Rizki Firdaus Mulya Rizki Mawan Rizky Aries Saputra Rizqi Sukma Kharisma Robi Wariyanto Abdullah Rohman, Arif Nur Rona Guines Purnasiwi Rooy Thaniket Rudyanto Arief S, Muhamad Rois S, Muhammad Sabri Safagi, Ardian Yuligar Sahirul Muklis Saifudin, Saifudin Saiful Bahri Saikin Saleh, Robby Febrianur Samponu, Yohakim Benedictus Samponu, Yohakim Benedictus Santosa, Hendriansyah SANTRI SANTRI Saputra, Mahmuda Saputra, Rizky Aries Saputro, Moh. Rizal Bayu Saputro, Uyock Anggoro Sarawan, Tommy Sari, Yayak Kartika Sari, Yunita Sartika Sarji, Sarji Selvy Megira, Selvy Semma, Andi Bahtiar Sentoso, Thedjo Sepriadi - Bumbungan Sepriadi Bumbungan Setianto, Yuni Ambar Setiawan, Moh. Arif Ma'ruf Setyanto, Arif Sewindu Putro Sigit Pambudi Simone Martin Marotta Siswo Utomo, Mardi Siti Delimasari Siti Fatonah Siti Hartinah Siti Maesaroh Siti Nurhayati Siti Rahayu Siti Rihastuti Siti Rokhmah Siti Solekhah Slamet Slamet Slamet Slamet Sobirin, Rahmat Sofyan Pariyasto Solikin, Arif Fajar Sri Handayani Sri Hartati Sri Lestari Rahayu Sri Wahyuningsih Sri Wulandari Sri Wulandari Sri Wulandari Sri Yanto Qodarbaskoro Sriyati, Sriyati Sry Faslia Hamka Sudarmawan Sudarmawan Sudarmawan - Sudarmawan . Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudarsono, Dibyo Sudarto Sudarto Sudiana Sudiana Sugi Harsono Sugihandono, Agus Suhardi Marli Suhendri Sukasna . Sukasna, Sukasna Supraba, Laksmita Dewi Supriantara Supriantara Supriatin Supriatin Supriyati Supriyati Surya Wibowo, Sapta Hary Suyono, Ayu Adelina Swardiana, I Wayan Pandu Swastikawati, Claudia Syafutra, Arif Dwi Syah, Firdiyan Syaiful Huda Syaiful Huda Syaiful Ramadhan Syamsu Hidayat Tala, WD. Syarni Tamaulina Br Sembiring Tampubolon, Jandri Tamuntuan, Virginia Teguh Arlovin Teguh Sri Pamungkas Theofanis Orphanoudakis Thiro Kabul Y, Yudhis Tika Dedy Prastyo Tito Prabowo Toifur, Tubagus Tolla, Yefta Tonny Hidayat TONNY HIDAYAT Tri Andi Tri Anggoro Tri Haryanti Tri Nugroho, Arief Tubagus Toifur Tukan, Ewaldus Ambrosius Turmuzi Tutik Maryana Tutut Dwi Prihatin Ula, M. Izul Umam Faqih Zubaedi Umdatur Rosyidah Utami Ema Uyock Anggoro Saputro Veny Cahya Hardita Vera Wati Victor Saputra Ginting Victor Saputra Ginting Vinnesa Patricia Carolina Wahyu Adie Saputro Wahyu Nugraha Wahyu Nur Alimyaningtias Wahyu Pujiharto, Eka Wahyu Sindu Prasetya Wahyu Wijaya Widiyanto Wahyudi, Alfian Cahyo Walidy Rahman Hakim Wangsa, Sabda Sastra Wariyanto Abdullah, Robi Wartono Wartono wati, Vera Wati, Vera Wauran, Alfrets Septy Wicaksono, Kurniawan Widdi Djatmiko Widiyanto, Wahyu Wijaya Widyanto, Agung Wijaya, Jodi Winarnie Winarto, Hendri Wira Dimuksa Wiwi Widayani Wiwi Widayani, Wiwi Wiwit Supriyanti Wulandari, Sri Y. P., Ryan Dwi Yanuargi, Bayu Yeyen Dwi Atma Yoga Handoko Agustin Yohanes Setyo Prabowo, Yohanes Setyo Yossy Ariyanto Yovita Kinanti Kumarahadi Yuana, Kumara Ari Yudha Chirstianto F Yuli Astuti Yuliana Yuliana, Yuliana Yuliani, Enie Yulita Fatma Andriani Yulius Nahak tetik Yuni Ambar S Yunita, Risca Yunita, Selviana Yusrinnatul Jinana triadin Yustian Servanda Yusuf Fadlila Rachman Yusuf Fadlila Rachman Yusuf Wahyu Setiya Putra Yuza, Adela Zaenul Amri Zakaria Zakaria Zenal Muttaqin Zeni Muhamad Noer Zuhri, Muhammad Rafli Zul Hisyam Zulkarnain, Imam Alfath Zulkarnain, Jefri Zulkipli Zulkipli Zulpan Hadi Zumarni, Zumarni

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