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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknologi Dan Industri Pangan Jurnal Pustakawan Indonesia ComEngApp : Computer Engineering and Applications Journal Journal of Tropical Life Science : International Journal of Theoretical, Experimental, and Applied Life Sciences TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmu Komputer dan Agri-Informatika Jurnal Ilmiah Kursor Biogenesis: Jurnal Ilmiah Biologi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Indonesian Journal of Biotechnology Seminar Nasional Informatika (SEMNASIF) Sosio Konsepsia Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Jurnal Teknologi dan Sistem Komputer INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control ILKOM Jurnal Ilmiah Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika Jurnal Jamu Indonesia Journal of Electronics, Electromedical Engineering, and Medical Informatics VISI PUSTAKA: Buletin Jaringan Informasi Antar Perpustakaan JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Indonesian Journal of Electrical Engineering and Computer Science Nusantara Science and Technology Proceedings Bioinformatics and Biomedical Research Journal Jurnal Pustakawan Indonesia Jurnal Nasional Teknik Elektro dan Teknologi Informasi J-Icon : Jurnal Komputer dan Informatika Indonesian Journal of Jamu
Wisnu Ananta Kusuma
Department Of Computer Science, Faculty Of Mathematics And Natural Sciences, IPB University, Jl Meranti Kampus IPB Dramaga, Bogor 16680, Indonesia
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Medicine & Pharmacology Physics Public Health Social Sciences Other

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Algorithm for Predicting Compound Protein Interaction Using Tanimoto Similarity and Klekota-roth Fingerprint Isnan Mulia; Wisnu Ananta Kusuma; Farit Mochamad Afendi
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 4: August 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i4.5916

Abstract

This research aimed to develop a method for predicting interaction between chemical compounds contained in herbs and proteins related to particular disease. The algorithm of this method is based on binary local models algorithm, with protein similarity section is omitted. Klekota-Roth fingerprint is used for the compound's representation. In the development process of the method, three similarity functions are compared: Tanimoto, Cosine, and Dice. Youden’s index is used to evaluate optimum threshold value. The result showed that Tanimoto similarity function yielded higher similarity values and higher AUC value than those of the other two functions. Moreover, the optimum threshold value obtained is 0.65. Therefore, Tanimoto similarity function and threshold value 0.65 are selected to be used on the prediction method. The average evaluation accuracy of the developed algorithm is only about 50%. The low accuracy value is allegedly caused by the only use of compound similarity on the prediction method, without including the protein similarity.
Fuzzy-based Spectral Alignment for Correcting DNA Sequence from Next Generation Sequencer Kana Saputra S; Wisnu Ananta Kusuma; Agus Buono
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2395

Abstract

Next generation sequencing technology is able to generate short read in large numbers and in a relatively short in single running programs. Graph based DNA sequence assembly used to handle these big data in assembly step. The graph based DNA sequence assembly is very sensitive to DNA sequencing error. This problem could be solved by performing an error correction step before the assembly process. This research proposed fuzzy inference system (FIS) model based spectral alignment method which can detect and correct DNA sequencing error. The spectral alignment technique was implemented as a pre-processing step before the DNA sequence assembly process. The evaluation was conducted using Velvet assembler. The number of nodes yielded by the Velvet assembler become a measure of the success of error correction. The results shows that FIS model based spectral alignment created small number of nodes and therefore it successfully corrected the DNA reads.
Identification of Tuna and Mackerel based on DNA Barcodes using Support Vector Machine Mulyati Mulyati; Wisnu Ananta Kusuma; Mala Nurilmala
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 2: June 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i2.2469

Abstract

Tuna and mackerel are important fish in Indonesia that have great demand in the community and contain good nutrients for health. Many of the processed products have been faked including processed fish, by replacing the content of products that have high sales value to other lower price one. For ensuring food safety, fraudulent should be prevented by identifying the content of refined product. In this research, we implemented support vector machine (SVM), one of the popular methods in machine learning, to yield a model for identifying the content of refined product based on DNA barcode sequences. The feature extraction of DNA barcode Sequences was conducted by calculating k-mers frequency of each sequences. In this study, we used trinucleotide (3-mers) and tetranucleotide (4-mers). These features were inputted to SVM to classify and identify whether the DNA barcode sequences belong to the class of tuna, mackerel, or other fish. The evaluation results showed model SVM was able to perform identification with the accuracy 88%.
BEBERAPA PENCIRI BERBASIS SEKUENS UNTUK MENGENALI SIFAT FUNGSIONAL PEPTIDA BIOAKTIF: STUDI EKSPLORASI Badrut Tamam; Dahrul Syah; Hanifah Nuryani Lioe; Maggy T. Suhartono; Wisnu Ananta Kusuma
Jurnal Teknologi dan Industri Pangan Vol. 29 No. 1 (2018): Jurnal Teknologi dan Industri Pangan
Publisher : Departemen Ilmu dan Teknologi Pangan, IPB Indonesia bekerjasama dengan PATPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (280.199 KB) | DOI: 10.6066/jtip.2018.29.1.1

Abstract

Bioactive peptides have important role as functional food ingredients. The sequence patterns of amino acids in peptide fragments may relate to their functional mechanisms. On the contrary, presence of an amino acid in a peptide fragment might not be sufficient to provide a unique identifier toward the bio-active peptide functional properties as antihypertensive (AH), antioxidative (AO) or antimicrobial (AM) agents. The main objective of this study was to explore the identifiers of bioactive peptides based on the sequence-generated properties. This study was performed using meta-analysis by utilizing many data sources and qualified international journal publications. The identifiers of bioactive peptides include sequence length, molecular weight, isoelectric point (pI), net charge and hydrophobicity. Based on the average score of the five identifiers, antimicrobial (AM) peptides were very different from antihypertensive (AH) and antioxidative (AO) peptides. The comparisons of the peptide biofunctional properties based on the identifiers may be determined as follows: AH1<AO1<AM1 (for sequence length); AH1<AO1<AM1 (for molecular weight); AH1=AO1<AM1 (for isoelectric point/pI); AH1=AO1<AM1 (for net charge) and AH1<AM1<AO1 (for hydrophobicity).
Performance Comparison of Data Sampling Techniques to Handle Imbalanced Class on Prediction of Compound-Protein Interaction Akhmad Rezki Purnajaya; Wisnu Ananta Kusuma; Medria Kusuma Dewi Hardhienata
Biogenesis: Jurnal Ilmiah Biologi Vol 8 No 1 (2020)
Publisher : Department of Biology, Faculty of Sci and Tech, Universitas Islam Negeri Alauddin Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/bio.v8i1.12002

Abstract

The prediction of Compound-Protein Interactions (CPI) is an essential step in the drug-target analysis for developing new drugs as well as for drug repositioning. One challenging issue in this field is that commonly there are more numbers of non-interacting compound-protein pairs than interacting pairs. This problem causes bias, which may degrade the prediction of CPI. Besides, currently, there is not much research on CPI prediction that compares data sampling techniques to handle the class imbalance problem. To address this issue, we compare four data sampling techniques, namely Random Under-sampling (RUS), Combination of Over-Under-sampling (COUS), Synthetic Minority Over-sampling Technique (SMOTE), and Tomek Link (T-Link). The benchmark CPI data: Nuclear Receptor and G-Protein Coupled Receptor (GPCR) are used to test these techniques. Area Under Curve (AUC) applied to evaluate the CPI prediction performance of each technique. Results show that the AUC values for RUS, COUS, SMOTE, and T-Link are 0.75, 0.77, 0.85 and 0.79 respectively on Nuclear Receptor data and 0.70, 0.85, 0.91 and 0.72 respectively on GPCR data. These results indicate that SMOTE has the highest AUC values. Furthermore, we found that the SMOTE technique is more capable of handling class imbalance problems on CPI prediction compared to the remaining three other techniques.
Pengkontruksian Bidirected Overlap Graph untuk Perakitan Sekuens DNA Wisnu Ananta Kusuma; Albert Adrianus
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 2: April 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020722070

Abstract

De novo DNA (Deoxyribonucleic Acid) sequence assembly atau perakitan sekuens DNA secara De novo adalah tahapan yang sangat penting dalam analisis sekuens DNA. Tahapan ini diperlukan untuk merakit atau menyambungkan kembali fragmen-fragmen DNA (reads) yang dihasilkan oleh Next Generation Sequencing menjadi genom yang utuh. Masalah perakitan DNA ini dapat direpresentasikan sebagai masalah Shortest Common Superstring (SCS). Perakitan ini memerlukan bantuan perangkat lunak untuk mendeteksi daerah yang sama pada reads DNA (overlap), mengkonstruksi overlap graph, dan kemudian mencari shortest path dari graf yang terbentuk. Metode ini dinamakan Overlap Layout Consensus (OLC). Hal yang penting dalam metode OLC adalah pendeteksian overlap dari masing-masing reads. Pada penelitian ini dikembangkan suatu teknik untuk membuat bidirected overlap graph. Suffix array digunakan untuk menentukan bagian overlap dari setiap reads dengan melakukan pengindeksan setiap suffix dari reads. Proses perakitan sekuens DNA merupakan suatu proses komputasi yang intensif. Untuk mengefisiensikan  proses dilakukan perubahan masing-masing suffix dan prefix menjadi suatu nilai tertentu yang bersifat tunggal dan mencari overlap dengan membandingkan angka yang merupakan representasi dari setiap reads. Cara ini lebih efisien dibandingkan melakukan pendeteksian overlap dengan metode pencocokan  string. Hasil perbandingan menunjukkan bahwa waktu yang diperlukan untuk mengeksekusi metode yang diusulkan (perbandingan angka) jauh lebih singkat dibandingkan dengan menggunakan metode pencocokan string. Untuk jumlah reads 2000 dan 5000 reads teknik yang diusulkan ini dapat menghasilkan overlap graph yang 100% akurat di mana semua reads dapat direpresentasikan ke dalam node yang dikonrtruksi dan semua overlap dapat direpresentasikan ke dalam edge. AbstractDe novo DNA sequence assembly is the important step in DNA sequence analysis. This step is required for assembling fragments or reads produced by Next Generation Sequencing to yield a whole genome. The problem of DNA assembly could be represented as the Shortest Common Superstring (SCS) problem. The assembly requires a software for detecting the overlap region among reads, constructing an overlap graph, and finding the shortest path from the overlap graph.. This method is popular as The Overlap Layout Consensus (OLC). The most important step in OLC is detecting overlaps among reads. This study develop a new approach to construct bidirected overlap graph. Suffis array is used for detecting overlap region from each reads by indexing suffix of each reads. DNA assembly process is computational intensive. To reduce the execution time suffix and prefix was converted into the single value so that the detection of overlap could be done by comparing the values. This method is much more efficient compared to that of using string matching. Using 2000 and 5000 reads, the proposed method (value comparison) could yield the perfect overlap graph, in which all reads and overlap could be represented as nodes and edges, respectively.   
Optimasi Data Tidak Seimbang pada Interaksi Drug Target dengan Sampling dan Ensemble Support Vector Machine Nabila Sekar Ramadhanti; Wisnu Ananta Kusuma; Annisa Annisa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 6: Desember 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020762857

Abstract

Data tidak seimbang menjadi salah satu masalah yang muncul pada masalah prediksi atau klasifikasi. Penelitian ini memfokuskan untuk mengatasi masalah data tidak seimbang pada prediksi drug-target interaction (interaksi senyawa-protein). Ada banyak protein target dan senyawa obat yang terdapat pada basis data interaksi senyawa-protein yang belum divalidasi interaksinya secara eksperimen. Belum diketahuinya interaksi antar senyawa dan target tersebut membuat proporsi antara data yang diketahui interaksinya dan yang belum dikethui menjadi tidak seimbang. Data interaksi yang sangat tidak seimbang dapat menyebabkan hasil prediksi menjadi bias. Terdapat banyak cara untuk mengatasi data tidak seimbang ini, namun pada penelitian ini diimplementasikan metode yang menggabungkan Biased Support Vector Machine (BSVM), oversampling, dan undersampling dengan Ensemble Support Vector Machine (SVM). Penelitian ini mengeksplorasi efek sampling yang digabungkan dalam metode tersebut pada data interaksi senyawa-protein. Metode ini sudah diuji pada dataset Nuclear Receptor, G-Protein Coupled Receptor dan Ion Channel dengan rasio ketidakseimbangannya sebesar 14.6%, 32.36%, dan 28.2%. Hasil pengujian dengan menggunakan ketiga dataset tersebut menunjukkan nilai area under curve (AUC) secara berturut-turut sebesar 63.4%, 71.4%, 61.3% dan F-measure sebesar 54%, 60.7% dan 39%. Nilai akurasi dari metode yang digunakan masih terbilang cukup baik, walaupun nilai tersebut lebih kecil dari metode SVM tanpa perlakuan apapun. Nilai tersebut bias karena nilai AUC dan F-measure ternyata lebih kecil. Hal ini membuktikan bahwa metode yang diusulkan dapat menurunkan tingkat bias pada data tidak seimbang yang diuji dan meningkatkan nilai AUC dan f-measure sekitar 5%-20%. AbstractImbalanced data has been one of the problems that arise in processing data. This research is focusing on handling imbalanced data problem for drug-target (compound-protein) interaction data. There are many target protein and drug compound existed in compound-protein interaction databases, which many interactions are not validated yet by experiment. This unknown interaction led drug target interaction to become imbalanced data. A really imbalanced data may cause bias to prediction result. There are many ways of handling imbalanced data, but this research implemented some methods such as BSVM, oversampling, undersampling with SVM ensemble. These method already solve the imbalanced data problem on other kind of data like image data. This research is focusing on exploration of effect on the sampling that used in these method for compound-protein interaction data. This method had been tested on compound-protein interaction Nuclear Receptor, GPCR and Ion Channel with 14.6%, 32.36% and 28.2% of imbalance ratio. The evaluation result using these three dataset show the value of AUC respectively 63.4%, 71.4%, 61.3% and F-measure of 54%, 60.7% and 39%. The score from this method is quite good, even though the score of accuracy and precision is smaller than the SVM. The value is bias because the AUC and F-measure score is smaller. This proves that the proposed method could reduce the bias rate in the evaluated imbalanced data and increase AUC and f-measure score from 5% to 20%.
Model Prediksi Interaksi Senyawa dan Protein untuk Drug Repositioning menggunakan Deep Semi-Supervised Learning Larasati Larasati; Wisnu Ananta Kusuma; Annisa Annisa
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 7 No 4: Agustus 2020
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2020742236

Abstract

Drug repositioning adalah penggunaan senyawa obat yang sudah lolos uji sebelumnya untuk mengatasi penyakit baru selain penyakit awal obat tersebut ditujukan. Drug repositioning dapat dilakukan dengan memprediksi interaksi senyawa obat dengan protein penyakit yang bereaksi positif. Salah satu tantangan dalam prediksi interaksi senyawa dan protein adalah masalah ketidakseimbangan data. Deep semi-supervised learning dapat menjadi alternatif untuk menangani model prediksi dengan data yang tidak seimbang. Proses pre-training berbasis unsupervised learning pada deep semi-supervised learning dapat merepresentasikan input dari unlabeled data (data mayoritas) dengan baik dan mengoptimasi inisialisasi bobot pada classifier. Penelitian ini mengimplementasikan Deep Belief Network (DBN) sebagai pre-training dan Deep Neural Network (DNN) sebagai classifier. Data yang digunakan pada penelitian ini adalah dataset ion channel, GPCR, dan nuclear receptor yang bersumber dari pangkalan data KEGG BRITE, BRENDA, SuperTarget, dan DrugBank. Hasil penelitian ini menunjukkan pada dataset tersebut, pre-training berupa ekstraksi fitur memberikan efek optimasi dilihat dari peningkatan performa model DNN pada akurasi (3-4.5%), AUC (4.5%), precision (5.9-6%), dan F-measure (3.8%). AbstractDrug repositioning is the reuse of an existing drug to treat a new disease other than its original medical indication. Drug repositioning can be done by predicting the interaction of drug compounds with disease proteins that react positively. One of the challenges in predicting the interaction of compounds and proteins is imbalanced data. Deep semi-supervised learning can be an alternative to handle prediction models with imbalanced data. The unsupervised learning based pre-training process in deep semi-supervised learning can represent input from unlabeled data (majority data) properly and optimize initialization of weights on the classifier. This study implements the Deep Belief Network (DBN) as a pre-training with Deep Neural Network (DNN) as a classifier. The data used in this study are ion channel, GPCR, and nuclear receptor dataset sourced from KEGG BRITE, BRENDA, SuperTarget, and DrugBank databases. The results of this study indicate that pre-training as feature extraction had an optimization effect. This can be seen from DNN performance improvement in accuracy (3-4.5%), AUC (4.5%), precision (5.9-6%), and F-measure (3.8%).
Analysis using top‐k skyline query of protein‐protein interaction reveals alpha‐synuclein as the most important protein in Parkinson’s disease Mohammad Romano Diansyah; Annisa Annisa; Wisnu Ananta Kusuma
Indonesian Journal of Biotechnology Vol 26, No 4 (2021)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijbiotech.63023

Abstract

Parkinson’s disease is the second‐most‐common neurodegenerative disorder and can reduce patients’ quality of life. The disease is caused by abnormalities in dopaminergic neurons, such as reactive oxygen species (ROS) imbalance leading to programmed cell death, protein misfolding, and vesicle trafficking. Protein‐protein interaction (PPI) analysis has been demonstrated to understand better candidate proteins that might contribute to multifactorial neurodegenerative diseases, particularly in Parkinson’s disease. PPI analysis can be obtained from experiments and computational predictions. However, experiment data is often limited in interactome coverage. Therefore, additional computational prediction methods are required to provide more comprehensive PPI information. PPI can be represented as protein‐protein networks and analyzed based on centrality measures. The previous study has shown that top‐k skyline query, a method using dominance rule‐based centrality measures, reveals important protein candidates in Parkinson’s diseases. This study applied the top‐k skyline query to PPIs containing experiment and prediction data to find important proteins in Parkinson’s disease. The result shows that alpha‐synuclein (SNCA) is the most important protein and is expected to be a potential biomarker candidate for Parkinson’s disease.
PENGEMBANGAN VISUAL INTERACTIVE SIMULATION DALAM SISTEM PENUNJANG KEPUTUSAN DENGAN PENDEKATAN AGEN (Studi Kasus Investasi pada Industri Biodisel Kelapa Sawit) Prihasuti Harsani; Sri Nurdiati; Wisnu \ Ananta Kusuma
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 3 (2008): Intelligent System dan Application
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Sistem Penunjang Keputusan dengan simulasi dapat menjadi efektif dengan pemanfaatan Visual Interactive Simulation (VIS). Melalui VIS pengambil keputusan mengontrol sepenuhnya jalannya simulasi dengan penentuan skenario dan modifikasi parameter simulasinya.  Pembangunan sistem dengan VIS dilakukan dengan metodologi gaia untuk memodelkan sistem agen. Melalui gaia, dihasilkan lima model utama sebagai dasar pembentukan arsitektur sistem, yaitu model interaksi, model role, model service, model acquitance dan model agen. Pendefinisian arsitktur sistem yang lebih konkret dilakukan melalui Agent Unified Modelling Languagae (AUML)
Co-Authors Abdul Aziz Abdul Rahman Saleh Agus Buono Ahmad, Tarmizi Aini Fazriani Aisah Rini Susanti Alami, Tegar Albert Adrianus Ali Djamhuri Annisa Annisa Annisa , Annisa Annisa Annisa Annisa Annisa Annisa Annisa Annisa Annisa Anton Suryatama Arini Aha Pekuwali Arini Pekuwali Arwan Subakti Ary Prabowo Auliatifani, Reza Auliya Ilmiawati Auriza Rahmad Akbar Badollahi Mustafa Badrut Tamam Bahrul Ulum BUDI TJAHJONO Budi Tjahjono Dahrul Syah Diah Handayani Dian Indah Savitri Dian Kartika Utami Essy Harnelly Fadli , Aulia Fahrury Romdendine, Muhammad Farhan Ramadhani , Hilmi Farit Mochamad Afendi Farohaji Kurniawan Fatriani, Rizka Fazriani, Aini Firman Ardiansyah Halida Ernita Handayani, Vitri Aprilla Hanifah Nuryani Lioe Hardi, Wishnu Hasibuan, Lailan Sahrina Hendra Rahmawan Hendra Rahmawan Hera Dwi Novita Heru Sukoco Imas Sukaesih Sitanggang Indra Astuti Ira Maryati Irfan Wahyudin Irma Herawati Suparto Irman Hermadi Irmanida Batubara Irvan Lewenusa ISKANDAR ZULKARNAEN SIREGAR Isnan Mulia Janti G. Sudjana Jaya Sena Turana Joni Prasetyo Kana Saputra S Kangko, Danang Dwijo Karlisa Priandana Khaydanur Khaydanur Khaydanur, Khaydanur Laela Wulansari Larasati Larasati Lina Herlina Tresnawati Listina Setyarini Lusi Agus Setiani Maggy T. Suhartono Mala Nurilmala Medria Kusuma Dewi Hardhienata Mohamad Rafi Mohamad Rafi Mohamad Rafi Mohammad Romano Diansyah Mohammad Romano Diansyah Muchlishah Rosyadah Muh Fadhil Al-Haaq Ginoga Muhammad Asyhar Agmalaro Muhammad Subianto Mulyati Mulyati Mushthofa Mushthofa Mushthofa Muttaqin, Muhammad Rafi Nabila Sekar Ramadhanti Nasution, Tegar Alami Nengsih, Nunuk Kurniati Norma Nur Azizah Nunuk Kurniati Nengsih Nur Choiriyati Nurdevi Noviana Ovi Sofia Pramita Andarwati Prihasuti Harsani Priyo Raharjo Pudji Muljono Purnajaya, Akhmad Rezki Purnomo, Tsania Firqin Ramdan Satra Ratu Mutiara Siregar Refianto Damai Darmawan Refianto Damai Darmawan Resnawati Reza Auliatifani Rif’ati, Lutfah Rizky Maulidya Afifa Ronald Marseno Rosy Aldina Rudi Heryanto SATRIYAS ILYAS Septaningsih, Dewi Anggraini Siti Syahidatul Helma Sony Hartono Wijaya Sri Nurdiati Sulistyo Basuki Sulistyo Basuki Supriyanto, Arif Syahid Abdullah Syarifah Aini Syarifah Fathimah Azzahra Syukriyansyah Taufik Djatna Toni Afandi Tsania Firqin Purnomo Usman, Muhammad Syafiuddin Wa Ode Rahma Agus Udaya Manarfa Wahjuni, Sri Widya Sari Wijaya, Eko Praja Hamid Wina Yulianti Wishnu Hardi Wulansari, Laela Yandra Arkeman Yessy Yanitasari Yudhi Trisna Atmajaya Yulianah Yulianah Yunita Fauzia Achmad Zulkarnaen, Silvia Alviani