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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika INFOKAM Jurnal Inspiration SISFOTENIKA CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang RESEARCH : Computer, Information System & Technology Management DoubleClick : Journal of Computer and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi JURTEKSI Jurnal Riset Informatika EKUITAS (Jurnal Ekonomi dan Keuangan) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal JMAI (Jurnal Multimedia & Artificial Intelligence) METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi SENSITEK Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics KURVATEK Indonesian Journal of Business Intelligence (IJUBI) Jurnal Tecnoscienza Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Jurnal Teknimedia: Teknologi Informasi dan Multimedia International Journal of Computer and Information System (IJCIS) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) EXPLORE Innovative: Journal Of Social Science Research Nusantara Journal of Computers and its Applications Jurnal INFOTEL Explore
Kusrini Kusrini
Universitas AMIKOM Yogyakarta
Aerospace Engineering Humanities Automotive Engineering 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 Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Public Health Social Sciences Transportation Other

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Penerapan Algoritma K-Means Untuk Pemetaan Biodiversity Kayu Bulat Di Indonesia Sugi Harsono; Tutut Dwi Prihatin; Anwar Sadad; Kusrini Kusrini; Dina Maulina
CogITo Smart Journal Vol. 9 No. 1 (2023): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v9i1.402.1-14

Abstract

Indonesia merupakan salah satu negara didunia yang memiliki kawasan hutan luas yang tersebar di pulau sumatera, jawa, kalimantan, sulawesi, bali, nusa tenggara, maluku dan papua. Hutan memiliki fungsi sebagai penghasil oksigen dan habitat terbesar keanekaragaman hayati di Dunia. Kebakaran hutan terjadi hampir di seluruh wilayah Indonesia dan merupakan permasalahan yang terus berulang, dan intensitas akan mulai meningkat pada musim kemarau. kebakaran hutan akan menimbulkan banyak sekali kerugian baik dari segi kesehatan yang akan mengancam keselamatan jiwa maupun material, mempengaruhi kualitas udara, lahan, air, kerusakan fasilitas dan tempat hidup flora dan fauna yang ada, berkurangnya produksi oksigen hingga musnahnya keanekaragaman hayati. Untuk dapat memperkecil kemungkinan terjadinya kebakaran hutan yang terus meningkat diperlukan analisis pemetaan titik penyebaran api, penyebab kebakaran,  dan keanekaragaman hayati hutan. Penelitian ini menggunakan etode pengumpulan data primer dataset persebaran titik kebakaran hutan, data statistik kehutanan, kemudian dilanjutkan dengan pengolahan data Algoritma K-Mean dan visualisasi pengolahan data menggunakan bahasa pemrograman Python dan mapping menggunakan GeoPandas. Hasil yang didapatkan adalah jenis produksi hutan tertinggi terdapat di Pulau Sumatera dengan jenis Kayu Akasia sebesar 28,573,433.84 m3, kemudian Pulau Jawa jenis Kayu Sengon sebesar 5,431,029.46 m3 dan Pulau Kalimantan jenis Kayu Rimba Campuran. Sedangkan luas area kebakaran terluas pada kawasan hutan terdapat di Pulau Kalimantan sebesar 243,013.5 hektar dari jumlah area hutan Kalimantan 53,054,900 hektar atau sebesar 0.458 %. Kmeans 5 klaster antara Pulau Sumatera dan Pulau Jawa adalah Klaster 1 terdapat 82 jenis Kayu, Klaster kedua Kayu Akasia, Klaster ketiga Kayu Ekaliptus, Klaster keempat Kayu Sengon dan Klaster kelima adalah Kayu Rimba Campuran, Mahoni dan Jati.
Prediksi Piutang Biaya Pendidikan Mahasiswa Tak Tertagih menggunakan Algoritma Naïve Bayes di Institut Teknologi dan Bisnis Muhammadiyah Wakatobi Sry Faslia Hamka; Kusrini Kusrini; Kusnawi Kusnawi
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.30711

Abstract

Piutang adalah instrument yang krusial dan memerlukan perhatian yang serius dalam mengelola sebuah peusahaan. Kinerja suatu perusahaan dapat dipengaruhi oleh besarnya nilai piutang yang dimilikinya. Apabila nilai piutang terlalu besar, maka dapat menjadi ancaman bagi kelangsungan hidup perusahaan. Ketika melakukan penagihan, perusahaan seringkali menghadapi kendala, salah satunya adalah keterlambatan pembayaran. Seperti halnya perguruan tinggi Institut Teknologi dan Bisnis Muhammadiyah Wakatobi (ITBMW) yang menetapkan biaya pendidikan yang wajib dibayarkan oleh mahasiswa dalam jangka waktu tertentu atau dilakukan dengan cara mengangsur. Akan tetapi malah semakin banyak mahasiswa yang menunggak karena masih banyak mahasiswa yang belum membayar biaya pendidikan dan sistem angsuran yang diterapkan. Akibatnya, semakin tinggi jumlah piutang mahasiswa, semakin besar kemungkinan bahwa biaya pendidikan mahasiswa tak tertagih.  Penelitian ini bertujuan untuk memprediksi piutang biaya pendidikan mahasiswa tak tertagih di ITBMW menggunakan metode klasifikasi yaitu algoritma Naïve Bayes. Data yang akan dimanfaatkan terdiri dari informasi mahasiswa ITBMW yang didapatkan dari PDDikti selama periode 2020/2021, 2021/2022, dan 2022/2023 selain itu juga akan digunakan data internal Biro Administrasi Keuangan ITBMW untuk tahun anggaran 2021, 2022 dan 2023. Pengolahan data dilakukan untuk memperoleh hasil prediksi yang optimal dengan mengevaluasi kinerja algoritma sehingga memperoleh hasil yang terbaik. Atribut pendukung yang digunakan pada dataset yang tersedia yaitu: NIM, nama mahasiswa, status, perguruan tinggi, program studi, jenjang, alamat kelurahan/desa, alamt kecamatan, pendidikan wali, pekerjaan wali, penghasilan wali, keterangan, jumlah piutang uang kuliah tunggal (UKT) mahasiswa, umur piutang UKT mahasiswa, jumlah piutang biaya sarana dan prasarana pembangunan (BPP), umur piutang BPP, status piutang, program studi, jenjang studi, alamat, pendidikan ayah/ibu/wali, pekerjaan ayah/ibu/wali, penghasilan ayah/ibu/wali, jumlah piutang UKT, umur piutang UKT, jumlah piutang DPP, dan umur piutang DPP. Target dan sasaran dari pengolahan data ini adalah piutang mahasiswa dengan status tertagih dan tidak tertagih, dengan menggunakan dua percobaan yaitu dengan data proporsi data training dan data testing 80:20 dan 90:10. Dari dua kombinasi percobaan tersebut proporsi data training dan data testing 80:20 menunjukkan tingkat akurasi yang tinggi yaitu 92,31% merupakan tingkat akurasi yang terbaik dibandingkan dengan proporsi 90:10 yang menghasilkan tingkat akurasi 88,46%.
Prediksi Tingkat Kesehatan Lingkungan Masyarakat Dalam Program Sustainable Development Goals Menggunakan Algoritma Naive Bayes Zulkipli Zulkipli; Kusrini Kusrini; Sudarmawan Sudarmawan
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.18776

Abstract

Indications of a decrease in the level of public awareness about protecting the environment have a direct effect on the urgent need for integrated environmental planning and management, so that the impact affects other aspects, such as the physical and socio-economic environment. The fact is that environmental damage is closely related to poverty and economic growth. In maintaining community environmental health and implementing the Sustainable Development Goals Program in East Lombok Regency, West Nusa Tenggara. Hamzanwadi University is working with the East Lombok Regency government to conduct sampling of the community with a total data of 4624 residents in ten sub-districts in the East Lombok Regency area. The purpose of this study is to help predict the level of public environmental health in the East Lombok region, the data will be classified and then processed using the Naïve Bayes algorithm with the multinomial naïve Bayes method. The results of testing the naïve Bayes algorithm after splitting the data five times, the best results were obtained, with the dataset divided by 20% testing data and 80% training data, an prediction value of 93.28% was obtained. The population environment in ten sub-districts in East Lombok Regency was classified as healthy
Penerapan Algoritma C4.5 Untuk Memprediksi Blog Yang Memiliki Peluang Juara Rita Wati; Kusrini Kusrini; Kusnawi Kusnawi
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.16791

Abstract

Blogs are currently not only used as online diaries or online journals. Blog content that supports text, images, videos, gifs, animations, pdfs and YouTube makes the use of blogs more than just online dairy. Currently blog is one of the media used by companies and organizations in promoting goods and services. The existence of bloggers is needed by companies and organizations to review their products and services with the aim of attracting online customers. One of the efforts to invite bloggers to be able to review products in an interesting way has led business actors to become interested in holding blog contests. In this research, a model was built to predict blogs that have a chance to win by applying the C4.5 decision tree algorithm. The prediction model was created using 100 blog contest participant data obtained from three blog competitions organized by ASUS which were obtained through the contest participant links found on the committee blog. From the processed dataset, supporting seven variables including Word Count, DA, PA, Image Template, Domain and Champion. The resulting prediction model is a decision tree with 7 attributes which produces 11 leaves and 18 trees with an accuracy of 74% with a precision of 0.735 and a recall of 0.740.
Prediksi Tren Pergerakan Harga Saham PT Bank Central Asia Tbk, Dengan Menggunakan Algoritma Long Shot Term Memory (LSTM) M. Nurul Wathani; Kusrini Kusrini; Kusnawi Kusnawi
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.19824

Abstract

Shares are valuable documents that prove ownership of a company. Stock investment is one of the right choices to get more profit. There are various stocks in Indonesia, one of which is the shares of PT Bank Central Asia Tbk (BBCA). However, in making stock investments, it is necessary to analyze the data of a company that can determine the increase or decrease in a stock price. Very dynamic movements require data modeling to predict stock prices in order to get a high level of accuracy. In this study, modeling using the Long-Short Term Memory (LSTM) algorithm to predict BBCA stock prices. The data used is secondary daily data obtained from securities with a date range of January 3, 2011 to December 30, 2022. The main objective of this research is to analyze the accuracy of the LSTM algorithm in forecasting stock prices and to analyze the number of epochs in the formation of the optimal model. The optimal epoch variation is obtained with the number of epochs of 5 and batch size 1. The resulting values include Mean Absolute Error (MAE) of 96.92, Mean Squared Error (MSE) of 16185.22 and Root Mean Squared Error (RMSE) of 127.22. The results of this study provide further insight into the performance of the LSTM algorithm in stock price prediction and show that with the right parameter settings, it can be a useful tool for investors in making better investment decisions
Pengurangan Dimensi dengan Metode Linear Discriminant Analist (LDA) Winarnie Winarnie; Kusrini Kusrini; Anggit Dwi Hartanto
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.10069

Abstract

The purpose of this study is to reduce the dimensions of the dataset that affect the prediction of breast cancer. The data used in research is very much data or is called high-dimensional data. The use of classification algorithms has weaknesses when used on high-dimensional data, so an appropriate method is needed to reduce the dimensions or variables used. There are several methods that can be used to reduce dimensions. In this study using the method of linear discriminant analysis (LDA). LDA is a supervised machine learning algorithm that is used to classify data into several classes, using a linear technique to determine the best set of linear variables to unify class data. LDA is used to reduce the dataset variables used by retaining information that is important for the classification process. The method used in this research is using LDA in data processing and then using a logistic regression model for the classification process. The conclusion obtained in this study is that LDA can overcome the problem of multiclass classification. The results obtained were 16 wrong cases out of a total of 455 cases so that the results obtained were 0.035% misclassification.
Komparasi Algoritma Naïve Bayes Dan Support Vector Machine (SVM) Untuk Klasifikasi Kelayakan Pemberian Pinjaman Amir Bagja; Kusrini Kusrini; Muhammad Rudyanto Arief
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.20059

Abstract

Cooperatives are social organizations or economic bodies that have a very important role in the growth, development of economic potential and community success. One of the cooperative activities is the provision of credit or loans to community members. Cooperative credit is one of the most important banking activities and serves to provide credit to the community. In practice, errors often arise due to inaccurate credit analysis, or the behavior of the customers themselves. The purpose of this research is to compare the accuracy results between the Naive Bayes algorithm and Support Vector Machine (SVM), where the best accuracy results can later be used as a reference to determine the profitability of lending. The attributes used in this study consist of 11 attributes, namely: Gender, marital status, occupation, relatives, nominal income, income criteria, loan amount, loan term, interest rate, installments and class as income characteristics. The dataset used in this study includes 166 members of the Daru Nahdla Capita Shari'ah cooperative. The results of testing the naive bayes algorithm after dividing the data five times, dividing the data set 70% as test data and 30% as training data, obtained a precision value of 97.00%, recall 100.00%, F1 score 99.00%. and accuracy 98.00%. Thus, the Naive Bayesian algorithm is an algorithm that shows accurate classification and prediction
Analisa Prediksi Kesejahteraan Masyarakat Nelayan Lombok Timur Menggunakan Algoritma Random Forest Arnila Sandi; Kusrini Kusrini; Kusnawi Kusnawi
Infotek: Jurnal Informatika dan Teknologi Vol. 6 No. 2 (2023): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v6i2.10104

Abstract

The economic life of the people on the coast, especially fishermen, is very dependent on the natural resources that are around, for example, marine resources, which still get the top position in the survival of fishing communities which are widely used and are also included as renewable natural resources. One example used as material for this research is the fishing community in East Lombok, West Nusa Tenggara. The Fishermen's Community can be interpreted as a group of people whose main livelihood is fishermen. The characteristics of the life of this community are different from society in general. Natural factors influence their lives a lot, from their lifestyle to the level of their economy and welfare, which is different from other communities. The purpose of this study is to predict the level of welfare of fishing communities in East Lombok, West Nusa Tenggara by using the classification method and the Random Forest algorithm. The dataset used is private data, the data is taken from fishing applications. Data processing is done to get the result or performance of the algorithm as the best result in predicting. From the existing dataset we use five supporting variables including, Education, family members, wells (related to clean water), employment and housing. The results or targets of this data processing are the level of welfare of fishing communities with prosperous and non-prosperous statuses. The final results of this study are seen using the Confusion Matrix, where the end result is the accuracy value. Random Forest has the highest accuracy value with a value of 93.37% and an AUC value of 0.735%.
Pemilihan Alat Pelindung Diri (APD) yang efektif bagi pelajar untuk mencegah penyebaran Covid-19 Menggunakan Metode Simple Additive Weighted (SAW) Moh. Badri Tamam; Aolia Ikhwanudin; Kusrini Kusrini; Supriatin Supriatin
NJCA (Nusantara Journal of Computers and Its Applications) Vol 6, No 1 (2021): Juni 2021
Publisher : Computer Society of Nahdlatul Ulama (CSNU) Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36564/njca.v6i1.196

Abstract

Keadaan  di  luar  prediksi  berupa  wabah  penyakit  covid-19  telah  membawa  perubahan  yang mendesak  pada  berbagai  sektor.  Perkembangan  virus  dengan  cepat  menyebar  luas  di  seluruh dunia.  Setiap  hari  data  di  dunia  mengabarkan  bertambahnya  cakupan  dan  dampak  covid-19. Indonesia  pun  masuk  dalam  keadaan  darurat  nasional.  Angka  kematian  akibat  Corona  terus meningkat sejak diumumkan pertama kali ada masyarakat yang positif terkena virus covid-19 pada awal  Maret  2020. Mau tidak mau, semua pihak mulai Dosen/Guru, orangtua, dan Mahasiswa/murid harus siap menjalani kehidupan baru (new normal) lewat pendekatan belajar menggunakan teknologiinformasi dan media elektronik agar proses pengajaran dapat berlangsung dengan baik. Pada konteks yang lain, semua pihak diharapkan tetap bisa optimal menjalankan peran barunya dalam proses belajar-mengajar di masa pandemi ini. Mendikbud menilai usai pandemi akan terjadi perubahan besar pada dua sektor sosial, yaitu pendidikan dimana kita harus memakai APD. Metode Simple Additive Weighting (Saw) juga pernah dilakukan, dimana perkembangan di lapangan menunjukkan bahwa konsumen dalam memilih rumah di dalam perumahan ada enam aspek setidaknya yang dijadikan sebagai bahan pertimbangan yaitu: harga, luas tanah, waktu tempuh kepusat kota, type bangunan, fasilitas umum dan akses menuju perumahan. Sistem pendukung keputusan dibangun untuk membantu orang dalam menentukan pilihan dalam kasus ini adalah membantu orang untuk memilih perumahan yang diinginkan dari berbagai pilihan perumahan yang ada berdasarkan ke enam aspek tersebut. Sistem ini juga menjanjikan proses penilaian yang lebih baik karena dapat memberikan bobot kepada berbagai aspek penilaian.   Metode   Simple   Additive   Weighting. dari    hasil    perhitungan  yang dilakukan dengan menggunakan metode Simple Additive Weighted (SAW), dengan mengambil nilai 6 nilai teratas dalam perengkingan dihasilkan nilai seperti tabel 4. Terdapat beberapa perbedaan hasil perengkingan yang  dihasilkan,  yaitu  pada pertama perengkingan No. 6, 5, 4 dan  untuk peringkat 1  teratas perhitungan SAW P memiliki perengkingan yang sama, yaitu dengan nilai 0,98
Prediction of Rainfall and Water Discharge in The Jagir River Surabaya with Long-Short-Term Memory (LSTM) Retzi Yosia Lewu; Slamet Slamet; Sri Wulandari; Widdi Djatmiko; Kusrini Kusrini; Mulia Sulistiyono
Jurnal Riset Informatika Vol 5 No 3 (2023): Priode of June 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i3.558

Abstract

Flood disasters can occur at any time when the factors for the amount of river water discharge and rainfall intensity tend to be high, so preparations and ways of handling are needed to anticipate flood disasters quickly, precisely, and accurately for the Surabaya Public Works Service. One of the steps to predict and analyze the status of the flood disaster alert level is by calculating predictions based on rainfall and the amount of river water discharge. This study uses the Long-Short Term Memory (LSTM) algorithm to predict rainfall and river water discharge on the Jagir River in Surabaya. The LSTM method is a model commonly used for predictions based on time series data. The data obtained are rainfall data and water discharge on the Jagir River, Surabaya, which will be used as training and testing data to make predictions. The results of implementing the LSTM method using data training of 70% and data testing of 30% on rainfall data using the best epoch, namely at epoch ten by producing tests on data testing can have a Mean Absolute Error (MAE) performance of 4.5 and Root Mean Square Error (RMSE) of 9.7. Whereas the water discharge variable uses the best epoch, namely at epoch 75, by producing data testing data which can have a Mean Absolute Error (MAE) performance of 11.49 and a Root Mean Square Error (RMSE) of 9.63.
Co-Authors Abdi Firdaus Achmad Wazirul Hidayat Adadilaga Arya Priwanegara Adhien Kenya Estetikha Aditya Hastami Ruger Aflahah Apriliyani Agatha Deolika Agianto Syam Halim Agung Budi Prasetyo Agus Susilo Nugroho Ajie Kusuma Wardhana Akrilvalerat Deainert Wierfi Alfahmi Muhammad Arif Alva Hendi Muhammad Amir Bagja Andi Bahtiar Semma Andi Sunyoto Andi Suyoto Andris Faesal Anggit Dwi Hartanto Anjar Anjani Putra Anwar Sadad Aolia Ikhwanudin Arham Rahim Arief Setyanto Arif Fajar Solikin Arnila Sandi Asro Nasiri Asro Nasrini Ayu Adelina Suyono Aziz Muslim Bimantyoso Hamdikatama Candra Adipradana Dedi Gunawan Devina Ninosari Dimaz Arno Prasetio Dina Maulina Donny Yulianto Dwi Astuti Dwi Utami Dwinda Etika Profesi Eka Wahyu Sholeha Eko Pramono Elik Hari Muktafin Emha Taufiq Luthfi Emha Taufiq Luthfii Erwin Apriliyanto Fandli Supandi Fendy Prasetyo Nugroho Ferry Wahyu Wibowo Fiyas Mahananing Puri Guido Adolfus Suni Hadryan Eddy Hafidz Sanjaya, Hafidz Hanafi Hanafi Hanif Al Fatta Hasirun Hasirun Henderi . Hendrik Hendrik Heri Sismoro Hery Nurmawan Hery Siswanto I Made Artha Agastya I Putu Agus Ari Mahendra Ichsan Wasiso Idris Idris Imam Listiono Irma Darmayanti Irwan Oyong José Ramón Martínez Salio Juwari Juwari Kaharuddin Kanafi Kanafi Khoirun Nisa Khomsatun Khomsatun Kumara Ari Yuana Kusnawi Kusnawi Kusuma Chandra Kirana M rudyanto Arief M. Idris Purwanto M. Nurul Wathani M. Rudiyanto Arief M. Rudyanto Arief M. Zainal Arifin Mahmudi Mahmudi Mansur Mansur Marwan Noor Fauzy Maykel Sonobe Mei P Kurniawan Mei P. Kurniawan MEI PARWANTO KURNIAWAN Moh. Badri Tamam Muahidin, Zumratul Muh Saerozi Muhamad Fatahillah Z Muhamad Yusuf Muhammad Fajrian Noor Muhammad Mariko Muhammad Riandi Widiyantoro Muhammad Riza Eko S Muhammad Rudyanto Arief Mukti Ali Mulia Sulistiyono Muqorobin Muqorobin Muslihah, Isnawati Musthofa Galih Pradana Nanang Prasetiyantara Neno, Friden Elefri Nibras Faiq Muhammad Noor Abdul Haris Noviyanti P. Nur Hamid Sutanto Paradise, Paradise Patmawati Hasan Pawit Srentiyono Prabowo Budi Utomo Pramono Pramono Prasetio, Agung Budi Prasetyo, Adi Prastowo, Wahit Desta Reflan Nuari Retzi Yosia Lewu Ridlan Ahmad Rifan Ferryawan Ripto Sudiyarno Rita Wati Riyan Abdul Aziz Rizki Mawan Robi Wariyanto Abdullah Rona Guines Purnasiwi Rudyanto Arief Saikin Sigit Pambudi Simone Martin Marotta Siti Fatonah Siti Hartinah Siti Rahayu Siti Rokhmah Slamet Slamet Sri Handayani Sri Wulandari Sry Faslia Hamka Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudiana Sudiana Sugi Harsono Supriantara Supriantara Supriatin Supriatin Supriyati Supriyati Syaiful Ramadhan Teguh Sri Pamungkas Tito Prabowo Tri Andi Tri Anggoro Tri Haryanti Tutik Maryana Tutut Dwi Prihatin Umdatur Rosyidah Vera Wati Victor Saputra Ginting Wahyu Adie Saputro Walidy Rahman Hakim Widdi Djatmiko Winarnie Yovita Kinanti Kumarahadi Yudha Chirstianto F Yuliana Yulita Fatma Andriani Yulius Nahak tetik Yuni Ambar S Yusrinnatul Jinana triadin Yusuf Fadlila Rachman Zul Hisyam Zulkipli Zulkipli