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All Journal Bulletin of Electrical Engineering and Informatics Nuansa Informatika Jurnal Informatika dan Teknik Elektro Terapan Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmiah Universitas Batanghari Jambi JURNAL MEDIA INFORMATIKA BUDIDARMA CogITo Smart Journal Jurnal Informatika Universitas Pamulang JITTER (Jurnal Ilmiah Teknologi Informasi Terapan) ILKOM Jurnal Ilmiah JurTI (JURNAL TEKNOLOGI INFORMASI) Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Technologia: Jurnal Ilmiah Aisyah Journal of Informatics and Electrical Engineering Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Aviation Electronics, Information Technology, Telecommunications, Electricals, Controls (AVITEC) Respati Jurnal Abdi Insani Journal of Computer System and Informatics (JoSYC) Jurnal Graha Pengabdian Infotek : Jurnal Informatika dan Teknologi jurnal syntax admiration TEPIAN Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA SENADA : Semangat Nasional Dalam MengabdI Journal of Electrical Engineering and Computer (JEECOM) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) Jurnal Sisfotek Global Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science Cerdika: Jurnal Ilmiah Indonesia SENADA : Semangat Nasional Dalam Mengabdi Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science SITEKNIK: Sistem Informasi, Teknik dan Teknologi Terapan Jurnal Teknik AMATA Jurnal TAM (Technology Acceptance Model)
Kusnawi Kusnawi
Universitas Amikom Yogyakarta
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Klasifikasi Pengenalan Wajah Siswa Pada Sistem Kehadiran dengan Menggunakan Metode Convolutional Neural Network Henri Kurniawan; Kusrini Kusrini; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 2 (2023): April 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i2.5958

Abstract

The student attendance system is useful for monitoring student attendance. The current technology is technology capable of detecting an object, such as fingerprints, voice, eye retinas, and faces. The author will create a model that can be used to detect student faces. In this study the authors used a modified Convolutional Neural Network (CNN) algorithm. The complexity of the CNN designed is in accordance with the specifications of the hardware and software used. Face data is taken directly from students in class (private dataset). Recording of students' faces using a standard quality webcam camera. The images produced by each student are 126 images with a total of 20 classes (labels). Taking pictures with various angles of the face, namely from above, below, front, left side and right side. The augmentation techniques used are flip, random rotation and affine techniques to enrich the data. Regularization techniques, such as dropout are also used. This is in order to increase accuracy, speed of model training and avoid overfitting of the built model. The evaluation results with the confusion matrix on the modified Convolutional Neural Network (CNN) algorithm produce a faster model training process with 5.31 hours and accuracy reaching 97.78%, the loss value is stable at 0.1177, loss validation with the number 0.0192, with as many iterations (epochs) as 60. The resulting model will be developed on a prototype of the student attendance system.
Sentiment Analysis of Neobank Digital Banking using Support Vector Machine Algorithm in Indonesia Kusnawi Kusnawi; Majid Rahardi; Van Daarten Pandiangan
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1652

Abstract

Currently, in the industrial era 4.0, information and communication technology is very developed, whereas, in this era, there is an increase in complex activities, one of which is in the banking sector. With the ease and efficiency of online finance, people want to switch to using digital banks. Neobank is an online savings and deposit application from Bank Neo Commerce (BCN) that the public can use by using the Internet. One of the online services is mobile banking which can be used by both Android and iOS versions of customers. Users can review Neobank's performance and services through the Google Play Store to improve and evaluate Neobank's performance. Neobank application reviews on the Google Play Store are increasing. Therefore, a review analysis is needed by conducting a sentiment analysis on Neobank's review. The data amounted to 3159 user reviews collected from reviews of the Neobank application on the Google Play Store. This study aims to classify Neobank user review data, including positive or negative sentiments. The method used in this study is an experimental method using the Support Vector Machine algorithm. The accuracy results obtained using the Support Vector Machine algorithm are 82.33%, which is owned by the scenario of 90% training data and 10% test data. The precision results are 82%, and recall is 81%. Future studies can add datasets from various sources so that there are even more datasets so as to increase the accuracy of model classification.
Determining the Minimarket Sales Pattern with the Apriori Algorithm Association Data Mining Method Dewi Kartika; Kusnawi Kusnawi
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol 12, No 1: April 2023
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v12i1.1185

Abstract

By reviewing the various needs of the community in areas that are not covered by national minimarkets, Gemilang minimarket has become one of the providers of a variety of supporting products to the basic needs of residents around the supermarket area. The limitations of the transaction technology used are considered to be prone to causing losses to company management. Renewable data management using algorithms and digital media will build better transaction and organizational patterns. This study applies the Apriori algorithm to determine frequent item sets. The Apriori algorithm is the process of using data mining methods to describe findings related to databases, as well as the application of association analysis techniques to determine item combination rules to improve methods in designing minimarket applications. Thus, business owners can control inventory, sales percentage, and design strategies to minimize losses. The results of the design carried out create a website-based application that can be tailored to user needs, so that it is hoped that it can create innovative strategies to support Minimarket business development.Keywords: Data Mining; Apriori Algorithm; Association Rule AbstrakDengan meninjau beragam kebutuhan masyarakat pada daerah yang tidak terjangkau oleh minimarket nasional, menjadikan minimarket Gemilang menjadi salah satu penyedia beragam produk penunjang hingga kebutuhan pokok warga di sekitar area supermarket. Keterbatasan teknologi transaksi yang digunakan dinilai rawan menimbulkan kerugian bagi manajemen perusahaan. Pengelolaan data yang terbarukan menggunakan algoritme serta media digital, akan membangun pola transaksi dan pengorganisasian dengan lebih baik. Penelitian ini menerapkan algoritme Apriori untuk menentukan frequent item set. Algoritme Apriori merupakan proses penggunaan metode data mining untuk penggambaran terkait penemuan pada database, serta penerapan teknik analisis asosiasi untuk menentukan aturan kombinasi item guna menyempurnakan metode dalam perancangan aplikasi minimarket. Dengan demikian, pemilik usaha dapat mengontrol persediaan, presentase penjualan, hingga merancang strategi guna meminimalkan kerugian. Hasil perancangan yang dilakukan menciptakan sebuah aplikasi berbasis website yang dapat disesuaikan dengan kebutuhan pengguna, sehingga diharapkan dapat menciptakan strategi yang inovatif sebagai penunjang perkembangan bisnis Minimarket.
IMPLEMENTASI MOORA PADA SELEKSI DOSEN TERBAIK BERDASARKAN HASIL PENILAIAN DALAM PEMBELAJARAN KULIAH Hasirun Hasirun; Kusrini Kusrini; Kusnawi Kusnawi
Indonesian Journal of Business Intelligence (IJUBI) Vol 6, No 1 (2023): Indonesian Journal of Business Intelligence (IJUBI)
Publisher : Universitas Alma Ata

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21927/ijubi.v6i1.3331

Abstract

Lecturer performance assessment is one of the activities of monitoring and evaluating performance with the aim of supervising the learning process and ensuring that lecturers carry out their duties in accordance with policies and teaching materials that have been determined. Lecturer performance assessment is carried out by students at the end of each semester by assessing lecturers based on criteria related to lecture learning. The criteria assessed in college learning are learning aspects, technological proficiency, integrity, and inspiration. The results of the student assessment will be reported to the learning development and quality assurance institution, which can later be used to determine the best lecturer performance. In this research, we apply the MOORA method to help determine the best lecturer based on assessment results in lecture reasoning. In its implementation, the MOORA method performs calculations based on criteria and weight values that have been determined and produces a ranking that can be used to determine the best lecturer's performance. In this study, the highest ranking was on the VPB alternative with a final value of 0.138, while the lowest value was on the DAM alternative with a final value of 0.108.
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%.
Analisis Sentimen Publik Terhadap Elektabilitas Ganjar Pranowo di Tahun Politik 2024 di Twitter dengan Algoritma KNN dan Naïve Bayes Dede Sandi; Ema Utami; Kusnawi Kusnawi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6298

Abstract

The political year for 2024 has now increasingly entertained all Indonesian people to hold a democratic party. Various political parties have become quite dramatic in expressing their coalitions and declaring their alignment with several presidential candidates that are known to the whole community. The electability of each presidential candidate that is determined is increasingly interesting and hotly discussed, which often makes anyone take action to voice their partisanship between the pros and cons. One of them is Ganjar Pranowo, who is a political figure for the governor of Central Java. Recently, in the middle of 2023, a political party has proposed him to advance to the seat of head of state as a presidential candidate for the upcoming 2024 election. With the existence of various polemics of opinion from various layers of society, this is the right moment to carry out an analysis as a form of polarization unanimity which is presented from various public opinions as a general description and an outline in sentiment in the form of information on the conclusions of public opinion. The stages in this research began with conducting a literature study and exploring studies related to opinions and alignments with public sentiment regarding the electability of Ganjar Pranowo as a presidential candidate, and then collecting opinion data from Twitter on the electability of Ganjar Pranowo. At the experimental stage, the authors divided the data with a percentage of 80% training data and 20% testing data. The modeling used is K-Nearest Neighbor (KNN) and Naïve Bayes to classify text data as well as make comparisons of the two. In the implementation process, the author uses python as a programming language in building the model. Confusion Matrix is used for every performance evaluation related to model accuracy in each algorithm. The results showed that the division of training data and testing data and the value of k in the K-Nearest Neighbor (KNN) model greatly affect the accuracy of the model. From the test results on the comparison of the two models, the K-Nearest Neighbor model has the best accuracy with an accuracy value of 99% of the K-Nearest Neighbor with an accuracy value of 96%. The percentage of sentiment with a comparison of 96.6% positive sentiment and 3.4% negative sentiment concluded that most people still dominate positive sentiment.
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
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%.
Penggunaan Variabel Event dan Libur Sekolah Dalam Memprediksi Wisatawan Dengan Metode LSTM Candra Rusmana; Kusrini; Kusnawi
JURNAL FASILKOM Vol 13 No 02 (2023): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v13i02.4974

Abstract

Event yang diadakan diberbagai daerah menjadi daya tarik tersendiri bagi wisatawan untuk datang ketempat tersebut. Liburan musiman sekolah juga menjadi agenda tahunan keluarga untuk pergi ke tempat wisata. Naik turunnya jumlah wisatawan yang datang ke Provinsi NTB memberikan dampak kepada pemerintah daerah, masyarakat sekitar tempat wisata dan pelaku usaha bidang pariwisata. Tujuan penelitian ini untuk melakukan pengujian terhadap variabel event tahunan dan libur sekolah. Datasetyang digunakan didapatkan dari website publik Provinsi NTB yaitu data.ntbprov.go.id dataset tersebut berupa histori jumlah kunjungan wisatawan setiap bulan, dari website disbudpar.ntbprov.go.id didapatkan dataset event tahunan dan dari website kalender pendidikan.com didapat dataset kalender akademik untuk liburan sekolah, dataset yang diambil dari setiap sumber diambil mulai dari tahun 2017 sampai tahun 2022. Dari semua dataset yang didapat bisa dimanfaatkan dalam menggali informasi untuk melakukan prediksi. Dalam melakukan prediksi digunakan Algoritma LSTM dengan menggunakan variabel histori wisatwan, event dan libu sekolah. Penggunaan variabel histori, event dan liburan menghasilkan kinerja MAPE sebesar 20.8% dengan penggunaan data training dan data testing 90/10. Hasil kinerja dengan variabel histori dan liburan saja menghasilkan kinerja MAPE sebesar 38,6%. sedangkan hasil dengan variabel histori dan event saja menghasilkan kinerja MAPE sebesar 23,81%. Ini menunjukan bahwa variabel event dan kalender liburan bisa dengan baik digunakan dalam melakukan prediksi terhadap kedatangan wisatawan di waktu berikutnya. Penelitian ini memperkenalkan pendekatan baru dalam memprediksi jumlah wisatawan dengan menggunakan variabel event tahunan dan kalender libur sekolah dengan menggunakan algoritma LSTM sebagai alat prediksi yang lebih canggih, yang sebelumnya belum banyak dieksplorasi dalam konteks prediksi pariwisata di Provinsi NTB.
Co-Authors Abdulloh, Ferian Fauzi Afrig Aminuddin Agung Susanto Agung Susanto Ahmad Fauzi Ahmad Sanusi Mashuri Ahmad Yusuf Ainnur Rafli Ainul Yaqin Ali Mustopa, Ali Alva Hendi Muhammad Andi Sunyoto Andi Sunyoto Anggit Dwi Hartanto, Anggit Dwi Arief Setyanto Arifuddin, Danang Arnila Sandi Aryawijaya Asadulloh, Bima Pramudya Assani, Moh. Yushi Atin Hasanah Atin Hasanah Atmoko, Alfriadi Dwi Aulya, Fiola Utri BAYU SATRIYA, RIYAN Bhahari, Rifqi Hilal Candra Rusmana Cynthia Widodo Cynthia Widodo Dede - Sandi Dede Husen Dede Sandi Dewi Kartika Dimaz Arno Prasetio Elsa Virantika Ema Utami Erna Utami Fachri Ardiansyah Fajar Abdillah, Moh Fajar Aji Prayoga Haris, Ruby Hartatik Haryo, Wasis Hasirun Hasirun Hendrik Hendrik Henri Kurniawan Hidayatunnisa'i Indra Irawanto Joang Ipmawati Kanoena, Melcior Paitin Karisma Septa Kresna Khairullah, Irfan Khalil Khoirunnita, Aulia Khrisna Irham Fadhil Pratama Kusirini Kusrini KUSRINI Kusrini Kusrini Kusrini - - Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini Kusrini, Kusrini Luthfi Nurul Huda M Andika Fadhil Eka Putra M. Nurul Wathani Maehendrayuga, Arief Majid Rahardi Maringka, Raissa Muh. Syarif Hidayatullah Muhammad Firdaus Abdi Muhammad Firdaus Abdi Muhammad Irvan Shandika Muhammad Reza Riansyah Nadhira Triadha Pitaloka Nayoma, Fisan Syafa Neni Firda Wardani Tan Ni’matur Rohim Nurul Zalza Bilal Jannah Nurus Sarifatul Ngaeni Omar Muhammad Altoumi Alsyaibani Pattimura, Yudha Bagas Pebri Antara Pramono, Aldi Yogie Prastyo, Rahmat Prema Adhitya Dharma Kusumah Puji Prabowo, Dwi Qurniaty, Charlen Alta Raffa Nur Listiawan Dhito Eka Santoso Rahayu, Christa Putri Rifda Faticha Alfa Aziza Rita Wati Ritham Tuntun Rizal Khadarusman Rodney Maringka Sabda Sastra Wangsa Saifulloh Saifulloh Salman Alfaris Salman Alfaris, Salman San Sudirman Sekarsih, Fitria Nuraini Sepriadi - Bumbungan Sepriadi Bumbungan Sri Yanto Qodarbaskoro Sry Faslia Hamka Suyatmi Suyatmi Suyatmi Suyatmi Syaiful Huda Syaiful Ramadhan Tamuntuan, Virginia Taryoko Taryoko Teguh Arlovin Thedjo Sentoso triadin, Yusrinnatul Jinana Van Daarten Pandiangan Virginia Tamuntuan Wahyu Pujiharto, Eka Widyanto, Agung Wirawan, Tegar Yusa, Aldo Yuza, Adela Zaenul Amri