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All Journal Techno.Com: Jurnal Teknologi Informasi Speed - Sentra Penelitian Engineering dan Edukasi Jurnal Teknologi Informasi dan Ilmu Komputer CESS (Journal of Computer Engineering, System and Science) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIV : International Journal on Informatics Visualization Jurnal Informatika Swabumi (Suara Wawasan Sukabumi) : Ilmu Komputer, Manajemen, dan Sosial IJCIT (Indonesian Journal on Computer and Information Technology) Bianglala Informatika : Jurnal Komputer dan Informatika Akademi Bina Sarana Informatika Yogyakarta Paradigma Jurnal Pilar Nusa Mandiri Jurnal Riset Informatika EVOLUSI : Jurnal Sains dan Manajemen JSAI (Journal Scientific and Applied Informatics) Indonesian Journal of Business Intelligence (IJUBI) Jurnal Infortech Abditeknika - Jurnal Pengabdian Kepada Masyarakat Innovative: Journal Of Social Science Research Ladang Artikel Ilmu Komputer

Rizal Amegia Saputra

Universitas Bina Sarana Informatika

Author-ID : 646026

Humanities Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Public Health Social Sciences Other

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Optimasi Pencarian Data Varietas Tanaman Menggunakan Sistem Temu Kembali Pada Aplikasi BALITTRI Sukabumi Aira Elzahra; Rizal Amegia Saputra; Erika Mutiara; Diah Puspitasari; Lis Saumi Ramdhani
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 7, No 1 (2022): IJCIT Mei 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Perkembangan teknologi informasi saat ini relative signifikan dapat mempengaruhi efektivitas operasional pada perusahaan, dimana semua perusahaan sangat membutuhkan teknologi informasi untuk mempermudah suatu pekerjaan. Balai Penelitian Tanaman Industri dan Penyegar (BALITTRI) Sukabumi merupakan instansi pemerintah dibidang pertanian tanaman industri dan penyegar, pada sistem yang berjalan data tanaman industri dan penyegar terdata dalam sebuah arsip, pada arsip tersebut tersimpan berbagai jenis tanaman industri yang sangat penting. Permasalahan yang ada pada BALITTRI ini yaitu Pencarian arsip data setiap peneliti sering kesulitan karena deretan arsip yang terlalu banyak, sehingga peneliti kebingungan dan membutuhkan waktu yang cukup lama agar arsip data tersebut ditemukan. Untuk itu pada penelitian ini akan di bangun sebuah aplikasi Balittri Arsip Varietas (BALAVAS) dimana pada aplikasi tersebut menerapkan sebuah sistem temu kembali informasi atau bisa disebut juga information Retrieval yang dapat memenuhi kebutuhan informasi dari sekumpulan data yang berskala besar pada server komputer local ataupun internet. Metode pengembangan software menggunakan model Rapid Application Development (RAD). Dengan adanya sistem ini maka akan membantu admin dan peneliti untuk menemukan data varietas beserta komponennya yang sudah tersimpan berdasarkan koleksi sumber informasi yang dicari atau dibutuhkan. Sistem ini juga dapat mengurangi dokumen pencarian yang tidak relevan terkait data varietas yang ada di Balai Penelitian Tanaman Industri dan Penyegar. The development of information technology is currently relatively significant can affect the operational effectiveness of the company. Where all companies really need information technology to facilitate a job. The Sukabumi Research Institute for Industrial and Refreshing Plants (BALITTRI) is a government agency in the field of industrial and refreshing plant agriculture. In the running system the data on industrial plants and refreshers is recorded in an archive. In the archive stored various types of very important industrial plants. The problem with BALITTRI is that the search for data archives for each researcher is often difficult because there are too many archives, so researchers are confused and it takes a long time for the data archive to be found. For this reason, this research will build an application Balittri Arsip Varieitas (BALAVAS) where the application implements an information retrieval system or it can also be called information retrieval that can meet the information needs of large-scale data sets on local computer servers or the internet. The software development method uses the Rapid Application Development (RAD) model. With this system it will help administrators and researchers to find data on varieties and their components that have been stored based on the collection of information sources sought or needed. This system can also reduce irrelevant search documents related to varietal data at the Research Institute for Balai Penelitian Tanaman Industri dan Penyegar Sukabumi.

Penerapan Algoritma C4.5 Pada Sistem Pakar Penyakit Aeromonas Hydrophila Ikan Mas Berbasis Mobile Desi Susilawati - AMIK BSI Sukabumi; Taufik Hidayatulloh - AMIK BSI Jakarta; Rizal Amegia Saputra - AMIK BSI Sukabumi
Speed - Sentra Penelitian Engineering dan Edukasi Accepted Paper Speed 2017
Publisher : APMMI - Asosiasi Profesi Multimedia Indonesia

ABSTRACT - Lack of knowledge on goldfish disease and limited handling of aeromonas disease in carp is often experienced by fish farmers. Therefore it is necessary action / handling to prevent carp infected by aeromonas hydrophila bacteria. Algorithm C4.5 is a decision tree classification algorithm that is widely used because it has the main advantages that can produce decision trees that are easily interpreted, has an acceptable level of accuracy, efficient in dealing with discrete and numeric attributes [5]. For that, in this research will be analyzed data of goldfish disease using data mining classification that is Algoritma C4.5 by using six parameters that is fin, stomach, skin, swimming position, red spots on body and gills. Based on the description, required a system that can represent an expert who has knowledge base and experience of goldfish disease, that is an expert system. The development of android-based smartphone sales compared to mobile phones is amazing, resulting in the rise of android-based mobile apps (Laksono, 2013). Therefore, in order to get the value of information more quickly and flexible, this expert system will be applied in the form of Andorid-based mobile applications. Of 87 cases consisting of 46 goldfish infected by aeromonas hydrophila and 41 bacteria that were not infected by aeromonas hydrophila bacteria obtained from BBPAT Sukabumi. So it can be concluded that research implemented into this mobile application can help users, especially fish farmers in diagnosing aeromonas hydrophila disease in carp.Keywords: Expert system, C4.5 algorithm, mobile application. ABSTRAK - Kurangnya pengetahuan terhadap penyakit ikan mas serta keterbatasan penanganan penyakit aeromonas pada ikan mas sering kali dialami para peternak ikan. Oleh sebab itu perlu adanya tindakan/penanganan untuk mencegah ikan mas yang terinfeksi bakteri aeromonas hydrophila. Algoritma C4.5 merupakan algoritma klasifikasi pohon keputusan yang banyak digunakan karena memiliki kelebihan utama yaitu dapat menghasilkan pohon keputusan yang mudah diinterprestasikan, memiliki tingkat akurasi yang dapat diterima, efisien dalam menangani atribut bertipe diskret dan numerik [5]. Untuk itu, dalam penelitian ini akan dilakukan analisa data penyakit ikan mas menggunakan klasifikasi data mining yakni Algoritma C4.5 dengan menggunakan enam parameter yaitu sirip, perut, kulit, posisi renang, bercak merah pada tubuh dan insang. Berdasarkan uraian tersebut, dibutuhkan sebuah sistem yang dapat mewakili seorang pakar yang memiliki basis pengetahuan dan pengalaman tentang penyakit ikan mas, yaitu sebuah sistem pakar. perkembangan penjualan smartphone berbasis android dibandingkan dengan telepon seluler sangat menakjubkan, yang mengakibatkan meningkatnya aplikasi-aplikasi mobile berbasis android (Laksono, 2013). Oleh karena itu, agar mendapatkan nilai informasi yang lebih cepat dan fleksibel, sistem pakar ini akan diaplikasikan dalam bentuk aplikasi mobile berbasis Andorid. Dari 87 jumlah kasus yang terdiri dari 46 ikan mas yang terinfeksi bakteri aeromonas hydrophila dan 41 yang tidak terinfeksi bakteri aeromonas hydrophila yang didapat dari BBPAT Sukabumi. sehingga dapat disimpulkan bahwa penelitian yang diimplementasikan ke dalam aplikasi mobile ini dapat membantu para pengguna khususnya para peternak ikan dalam mendiagnosa penyakit aeromonas hydrophila pada ikan mas.Kata kunci: Sistem pakar, algoritma C4.5, aplikasi mobile.

Komparasi Criteria Splitting Pada Algoritma Iterative Dichotomizer 3(ID3) Untuk Klasifikasi Kelayakan Kredit Nandya Ayu Fatmandini; Rizal Amegia Saputra; Resti Yulistria
Paradigma Vol 22, No 1 (2020): Periode Maret 2020
Publisher : LPPM Universitas Bina Sarana Informatika

Credit is a form of business run by various banks. In running credit, banks will always consider the credit that will occur, for that data about creditworthiness decisions will be very necessary to support the business wheels of banking life. Credit analysis is carried out to support credit or historical data. This can reduce credit. Bank Sinarmas Sukabumi is one of the largest finance companies in Indonesia. This company provides financing services for the purchase of new vehicles or used vehicles, some funds and funds provided by Sinarmas Bank in terms of credit, credit assistance, one for analyzing important credit, because one of them, looking for credit, bad things that can be done by companies that are not careful in granting credit. The ID3 algorithm can search all discussions and decisions. The application of the ID3 method by comparing the three criteria for obtaining credit, understanding the assessment criteria, obtaining an accuracy value of 62.67% and an AUC value of 0.800, the highest in accordance with the criteria being compared, is discussed with the criteria. The Strengthening Ratio and Gini Index have the lowest Accuracy and AUC values. Thus, the ID3 method with Gain Information criteria is a good method and criterion in predicting lending to Bank Sinarmas Sukabumi.

Deteksi Kematangan Buah Melon Dengan Algoritma Support Vector Machine Berbasis Ekstraksi Fitur GLCM Rizal Amegia Saputra; Diah Puspitasari; Taufik Baidawi
Jurnal Infortech Vol 4, No 2 (2022): Desember 2022
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/infortech.v4i2.14436

Abstrak - Melon merupakan tanaman buah yang termasuk kedalam suku labu-labuan, banyak petani di negara tropis khususnya indonesia mengembangkan budidaya buah melon. Kematangan buah melon menjadi salahsatu tolak ukur keberhasilan panen pertanian melon, namun terdapat permasalahan dalam menentukan kualitas kematangan buah melon karena panen buah melon lebih awal akan menyebabkan rendahnya kualitas sedangkan panen melebihi waktu panen akan menyebabkan pendeknya umur penyimpanan. Analisa berdasarkan tekstur perlu dilakukan dalam menentukan kematangan buah melon, teknik GLCM akan menjadi solusi dalam Analisa tekstur berdasarkan citra digital, Selain Analisa tektur, pada penelitian ini akan menerapkan algoritma Support Vector Machine (SVM), SVM mampu mengatasi klasifikasi citra digital, ada empat fungsi kernel pada algoritma SVM yang akan digunakan yaitu linear, polynomial, sigmoid dan RBF. Dengan jumlah data sebanyak 650 citra buah melon yang terbagi kedalam 250 citra matang, 200 citra setengah matang dan 200 citra tidak matang dan pengujian data citra dibagi menjadi dua bagian yaitu 80% untuk data tranning sedangkan 20% untuk data testing, didapat hasil pengujian dalam percobaan dua sudut GLCM dan empat fungsi kernel SVM. Didapat nilai akurasi, recall dan precision yang paling baik nilainya yaitu pada fungsi kernel linier dan pada delapan sudut GLCM dengan nilai akurasi 80%, Precision 81% recall 80 %.

Optimasi Algoritma C4.5 Untuk Mengukur Keputusan Pembelajaran Daring Berbasis Particle Swarm Optimization (PSO) Dewi Ayu Nur Wulandari; Siti Masripah; Rizal Amegia Saputra
IJCIT (Indonesian Journal on Computer and Information Technology) Vol 7, No 2 (2022): November 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/ijcit.v7i2.14036

Algoritma yang populer dan modern dalam pengolahan data dengan teknik data mining adalah Algoritma C4.5. Algoritma C4.5 banyak digunakan untuk melakukan pengklasifikasian data karena algoritma C4.5 dapat menghasilkan sebuah pohon keputusan yang mudah dipahami dan mudah dimengerti. Pada penelitian ini metode yang digunakan adalah dengan menambahkan teknik optimasi menggunakan algoritma Particle Swarm Optimization (PSO) dengan tujuan meningkatkan nilai akurasi pada information gain algoritma C4.5 untuk mengukur keputusan pembelajaran daring. PSO merupakan salah satu metode dan teknik untuk mengklasifikasi dan meningkatkan akurasi, dimana PSO terdiri dari sekumpulan partikel yang mencari posisi yang terbaik. Hasil penelitian menunjukkan akurasi dan Kappa pada nilai information gain yaitu sebesar 91,76 dan 0,834, akurasi dan Kappa Gain Ratio sebesar 89,41 dan 0,788, akurasi dan Kappa Gini Index sebesar 90,59 dan 0,811. Sehingga diperoleh kesimpulan penerapan algoritma PSO dapat berpengaruh terhadap nilai akurasi pada setiap criteria splitting algoritma C4.5 A popular and modern algorithm in data processing with data mining techniques is the C4.5 Algorithm. The C4.5 algorithm is widely used to classify data because the C4.5 algorithm can produce a decision tree and easy to understand. In this study, the author made a comparison between previous studies using the C4.5 algorithm by adding optimization techniques using the Particle Swarm Optimization (PSO) algorithm with the aim of increasing the accuracy value of the C4.5 algorithm information gain in measuring Online Learning Decisions. PSO Technique is one of the methods and techniques for classifying and improving accuracy, where PSO consists of a set of particles that are looking for the best position. The results of this study showed the results of accuracy and Kappa on the value of information gain, namely 91.76 and 0.834, accuracy and Kappa Gain Ratio of 89.41 and 0.788, accuracy and Kappa Gini Index of 90.59 and 0.811. So that it can be concluded that the application of the PSO algorithm can affect the accuracy value in each criteria splitting the C4.5 algorithm

PENERAPAN ALGORITMA NAÏVE BAYES UNTUK PREDIKSI PENYAKIT TUBERCULOSIS (TB) Rizal Amegia Saputra
Swabumi Vol 1, No 1 (2014): Volume 1 Nomor 1 Tahun 2014
Publisher : Universitas Bina Sarana Informatika Kota Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/swabumi.v1i1.994

The disease Tuberculosis (TB) is a contagious and deadly diseases in the world, even the World Health Organization (WHO) declared as the world's emergency disease (global emergency), some research fields of health including one disease TB has been widely carried out to detect the disease early, but it is not yet known which algorithm is quite good in predicting disease TB. On this research will apply the Algorithm Naïve Bayes, in predicting diagnosis of TB disease to Naïve Bayes algorithm so that the destination is the most accurate algorithm in the prediction of the disease TUBERCULOSIS. The test results using the method of Confusion Matrix and the ROC Curve, the naïve bayes algorithm is known that has a value of 91,61%, accuracy and value of the AUC of 0,995. See the value of AUC, the naïve bayes methods including group classification is very good, because the results of his AUC values between 0.90-1.00.

Implementasi Algoritma Random Forest Untuk Menentukan Penerima Bantuan Raskin Ilham Kurniawan; Duwi Cahya Putri Buani; Abdussomad Abdussomad; Widya Apriliah; Rizal Amegia Saputra
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 2: April 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.20231026225

Kemiskinan adalah salah satu perhatian mendasar dari setiap pemerintah. Program Beras Keluarga Miskin (Raskin) merupakan salah satu program pemerintah. Skema raskin mempunyai tujuan meminimalisir beban rumah tangga tidak mampu sebagai bentuk bantuan untuk menaikkan ketahanan pangan melalui perlindungan sosial. Tujuan penelitian ini adalah menemukan akurasi tertinggi di antara algoritma klasifikasi prediktif yang diusulkan penerima bantuan raskin menggunakan tools python machine learning dan di implementasikan melalui suatu website. Klasifikasi adalah metode penambangan data yang menentukan kategori pada kelompok data untuk mendukung prediksi dan analisa yang semakin akurat. Beberapa algoritma klasifikasi pembelajaran mesin seperti, SVM, NB dan RF, digunakan pada penelitian ini demi menentukan penerima bantuan raskin. Eksperimen dilakukan menggunakan dataset Raskin Kelurahan Gunungparang, Kota Sukabumi yang bersumber dari Kelurahan Gunungparang. Kinerja algoritma klasifikasi dievaluasi dengan beragam metrik seperti Precision, Accuracy, F-Measure, dan Recall. Akurasi diukur melalui contoh yang dikelompokan dengan benar atau salah. Hasil yang diperoleh menunjukkan algoritma klasifikasi RF memiliki nilai precision, recall, f-measure dengan nilai 97%, nilai accuracy sebesar 97,26% dan nilai ROC 0,970, lebih baik dari algoritma klasifikasi lainnya yaitu perbedaan sebesar 5,11% dengan algoritma klasifikasi support vector machine dan 8,87% dengan algoritma klasifikasi naive bayes. Akurasi sangat baik digunakan sebagai acuan kinerja algoritma apabila jumlah False Negative dan False Positive jumlah nya mendekati. Hasil penelitian ini dibuktikan secara akurat dan sistematis menggunakan Receiver Operating Characteristic (ROC). Abstract The problem of poverty is one of the fundamental concerns of every government. The Raskin program is one of the government's programs. The Raskin scheme has the aim of minimizing the burden on poor households in the form of assistance to improve food security by providing social protection. The purpose of this study is to find the highest accuracy among the predictive classification algorithms proposed by Raskin beneficiaries using python machine learning tools and implemented through a website. Classification is a data mining method that determines categories in data groups to support more accurate predictions and analysis. Therefore, three machine learning classification algorithms such as, support vector machine, naive bayes and random forest, were used in this experiment. to determine recipients of Raskin assistance. The experiment was carried out using the Raskin dataset, Gunungparang Village, Sukabumi City, which was sourced from Gunungparang Village. The performance of the classification algorithm is evaluated by various metrics such as Precision, Accuracy, F-Measure, and Recall. Accuracy is measured by correctly and incorrectly grouped samples. The results obtained show that the random forest classification algorithm has precision, recall, f-measure values with a value of 97%, an accuracy value of 97.26% and an ROC value of 0.970, better than other classification algorithms, namely the difference of 5.11% with the support vector classification algorithm. machine and 8.87% with naive bayes classification algorithm. Very good accuracy is used as a reference for algorithm performance if the number of False Negatives and False Positives is close. These results were proven accurately and systematically using Receiver Operating Characteristics (ROC).

Perbandingan Model Bidirectional-LSTM dan Singular Spectrum Analysis dalam Peramalan Harga Cabai Gumgum Darmawan; Nurul Gusriani; Ruslan Ruslan; Ferdian Agustiana; Rizal Amegia Saputra
Innovative: Journal Of Social Science Research Vol. 3 No. 2 (2023): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v3i2.461

Harga cabai sering berfluktuasi secara ekstrim karena permintaan yang tinggi melebihi produksi petani. Baik pemerintah maupun masyarakat memiliki kepentingan dalam memantau perkembangan harga cabai karena komoditas ini penting untuk konsumsi rumah tangga dan kebutuhan industri. Oleh karena itu, dalam makalah ini, pemilihan model dan pengujian dilakukan untuk menentukan model terbaik dalam meramalkan harga cabai. Model yang digunakan adalah model Bididecrtional-LSTM (Bi-LSTM) dan Analisis Spektrum Tunggal (SSA). Model SSA yang digunakan adalah model Alexandrov Autogrouping SSA, model Alternative Autogrouping SSA, dan model Hybrid SSA-ARIMA. Kesalahan Persentase Rata-rata Absolut (MAPE) digunakan untuk menentukan kebaikan model. Berdasarkan hasil analisis, model terbaik ditemukan adalah model BiLSTM dengan nilai MAPE terkecil sebesar 4,191%, dengan sampel luaran sebesar 10% dari total data.

Pencarian Criteria Splitting Terbaik Pada Algoritma C4.5 Untuk Mengukur Pemilihan Pembelajaran Pada Era Pendemi Covid-19 Siti Masripah; Dewi Ayu Nurwulandari; Rizal Amegia Saputra
Jurnal Larik: Ladang Artikel Ilmu Komputer Vol 2 No 1 (2022): Juli 2022
Publisher : LPPM Universitas Bina Sarana Informatika

The condition of the 2022 pandemic is still ongoing and has entered the 2nd year of the learning system that is still not 100% offline and is still being done online. The online learning system certainly makes parents, educators and students have to pay extra and extra understanding because not all of them can overcome these two things. Classification in determining the choice of learning becomes very important because online learning reaps the pros and cons in the community. In this study, the dataset was obtained from the results of a survey of parents, educators, students and students, and as many as 283 respondents had been collected to measure learning choices in the Covid-19 Pandemic Era. Data processing uses the Rapid miner application by applying the C4.5 Data Mining Classification Algorithm method, in the experimental process the split criteria comparison process is carried out on the C4.5 algorithm, namely Information Gain, Gini Index and Gain Ratio. The two highest accuracy values obtained are 85.88% for the Gain Ratio and Information Gain, while the Gini Index is 8.24%, for the AUC value the highest value is 0.80 in the Gain Ratio, followed by the Information Gain of 0.783 and the Gini Index of 0.784. Based on the comparison results, the Split gain ratio criterion is included in the Good classification category, because it has a value between 0.80 - 0.90.

DEVELOPMENT OF MANUFACTURING INVENTORY MANAGEMENT SYSTEM USING MATERIAL REQUIREMENT PLANNING METHOD Ami Rahmawati; Rizal Amegia Saputra; Ita Yulianti
Jurnal Riset Informatika Vol. 4 No. 1 (2021): December 2021
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v4i1.135

Inventory has an important role in business activities. This is because inventory has an effect on changes in the production market and anticipates price changes in the demand for many goods. PT. Barkah Jaya Mandiri is a company engaged in manufacturing where the management of inventory at the company is still done conventionally. This causes various problems such as the occurrence of discrepancies in the stock of goods, discrepancies in data and final reports as well as obstacles in the production process in the event of a shortage or excess of raw materials. (Material Requirement Planning) in order to overcome the problems that occur in the company. The combination of the SDLC model and data collection techniques including observation, interviews and literature study were also carried out in this study in order to achieve the system that will be built to suit the targeted needs. With this system, the management of inventory data at this company can be done easily and accurately and save time compared to the previous system, so that the procurement of manufacturing raw materials is optimal and employee performance is better.

Co-Authors A.Gunawan Abdussomad, Abdussomad Adi Supriyatna Aira Elzahra Ami Rahmawati Ami Rahmawati Andi Riyanto Dasya Arif Firmansaha Dede Firmansyah Saefudin Dede Saefudin Desi Susilawati - AMIK BSI Sukabumi Desi Susilawati, Desi Dewi Ayu Nur Wulandari Dewi Ayu Nurwulandari Diah Puspitasari Dudus Kurnia Duwi Cahya Putri Buani Eka Fitriani Erika Mutiara Erika Mutiara, Erika Fatmawati Fatmawati Ferdian Agustiana Gumgum Darmawan Ilham Kurniawan Ilham Kurniawan Ita Yulianti Jamal Maulana Hudin Julia Rahmah Lis Saumi Ramdhani Medi Firmansyah Muhammad Faittullah Akbar Muhammad Sukrisno Mardiyanto Nandya Ayu Fatmandini Neng Senja Nekida Nita Novianti Novitasari, Dian C Rini Nurul Gusriani Pribadi, Denny Rahmawati, Rahmawati Ramdhan Saepul Rohman, Ramdhan Saepul Resti Yulistria Rimuljo Hendradi Rinda Nariswari Rizki Ramadhan Rosita Rosita Ruslan Ruslan Rusli Nugraha Saeful Bahri Shinta Ayuningtias Siti Masripah Sri Wasiyanti, Sri Supriatman, Supriatman Taufik Baidawi, Taufik Taufik Hidayatulloh Toto Haryanto Tya Septiani Nurfauzia K Wasyianti, Sri Widya Apriliah Yuri Rahayu

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