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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Indonesia Symposium on Computing IJoICT (International Journal on Information and Communication Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Kebidanan JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Informasi dan Pendidikan Building of Informatics, Technology and Science Jurnal Fisika dan Terapannya JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) International Journal of Clinical Inventions and Medical Sciences (IJCIMS) INFOKUM Jurnal Bumigora Information Technology (BITe) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat Jurnal Pengabdian Masyarakat Indonesia Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Journal of Innovation Research and Knowledge Jurnal Pengabdian Masyarakat Bhinneka eProceedings of Engineering Electrician : Jurnal Rekayasa dan Teknologi Elektro Indonesian Counseling and Psychology Jurnal INFOTEL IJoICT (International Journal on Information and Communication Technology)
Sri Suryani Prasetyowati
Universitas Telkom
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology 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 Medicine & Pharmacology Nursing Physics Public Health Social Sciences

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Big Five Personality Detection Based on Social Media Using Pre-Trained IndoBERT Model and Gaussian Naive Bayes Ni Made Dwipadini Puspitarini; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 1 (2023): Januari 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i1.5439

Abstract

A person's personality offers a thorough understanding of them and has a significant role in how well they perform at work in the future. No wonder it attracted the interest of the researcher to develop a personality detection system. Although much research about personality detection through social media was conducted, this task has been challenging to implement, especially using conventional machine learning. The issue is conventional machine learning still insufficient to make the personality detection system perform better. The purpose of this research is to detect Big Five personalities based on Indonesian tweets and increase its performance by combining machine learning with deep learning, which is Gaussian Naive Bayes and IndoBERT model. The proposed combined model in this research is summing the log probability vector on each model. Gathered 3.342 tweets from 111 Twitter accounts that were used as a dataset. This research also implemented min-max normalization to rescale the data. The result showed that for the entire dataset, the combined model has more accuracy score than Gaussian Naive Bayes by 5.42% and IndoBERT by almost 2%, which indicates the combined model is better than the Gaussian Naive Bayes and IndoBERT models.
Covid-19 Fake News Detection on Twitter Based on Author Credibility Using Information Gain and KNN Methods Nanda Ihwani Saputri; Yuliant Sibaroni; Sri Suryani Prasetiyowati
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 1 (2023): February 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i1.4871

Abstract

Twitter is one of the social media that is used as a tool to share various kinds of information about various kinds of things that are of concern to social media users. One of the information shared is information about COVID-19, which is known that the COVID-19 pandemic is currently spreading throughout the world at a very alarming rate. COVID-19 is an infectious disease caused by SARS-COV-2. The World Health Organization (WHO) claims that the spread of COVID-19 is supported by the spread of false/fake news. So to find out the truth of the news, a COVID-19 fake news detector is needed so that users don't fall for the hoaxes circulating. This study aims to classify COVID-19 news on Twitter based on author credibility. Credibility in question is a person's perception of the validity of information and is a multidimensional concept that is used as a means of receiving information to assess the source of communication. The method used in this research is Information Gain and KNN. KNN (K-Nearest Neighbor) is a supervised learning algorithm that works by classifying a set of data based on classified training data. Information Gain is used to ranking the most influential attributes, and KNN is used to classify data based on learning data taken from the nearest neighbors. The research consists of 6 main stages, namely data collection (crawling data), data preprocessing, feature extraction, feature selection, data split into training data and testing data, KNN stage, and data evaluation stage. The research carried out succeeded in obtaining an accuracy value of 91%, a correlation value between credibility and hoax of 0.115, and a p-value <0.005.
Comparative Analysis of Naive Bayes Model Performance in Hate Speech Detection in Media Social Twitter Muhammad Hadyan Baqi; Yuliant Sibaroni; Sri Suryani Prasetiyowati
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5493

Abstract

Twitter is a popular social media in Indonesia, and for some people, it is a place to find and disseminate information. Hate speech is aggressive behavior against individuals or groups such on race, gender, religion, nationality, ethnicity, sexual orientation, gender identity, or disability. In this study, hate speech is modeled using Naive Bayesian models, which consist of Multinomial, Bernoulli, and Gaussian Naïve Bayes Models. These methods were chosen because Naïve Bayes is a simple method but has good performance in the case of sentiment analysis. This research aims to get the method with the highest accuracy value in analyzing hate speech. Thus, the Naïve Bayes model can provide the best solution for hate speech problems. The process carried out in this study is to process all data which obtained from Twitter social media and then classify it using the Multinomial Naïve Bayes, Gaussian Naïve Bayes, and Bernoulli Naive Bayes models based on the classification of HS and non-HS sentiment categories.  In this study, to get the best accuracy, two different scenarios were used. The result of the analysis of the accuracy is 82.13% of the Multinomial Naïve Bayes model which is the best accuracy rate value compared with other models.
Comparison of Word2Vec with GloVe in Multi-Aspect Sentiment Analysis Classification of Nvidia RTX Products with Naïve Bayes Classifier Wira Abner Sigalingging; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5528

Abstract

The increasing number of gamers has increased the demand for Graphics Processing Unit (GPU) products, one example of which is the Nvidia RTX product. Many users submit their reviews on social media Twitter in the form of tweets. These Tweets can be analyzed to determine the quality of a product. But most of the tweets talking about the product as a whole ignoring the category aspects of the product, making it difficult for both users and companies to pinpoint which aspects need attention. In this research, a multi-aspect based sentiment analysis will be carried out on tweets on Nvidia RTX products based on aspects of the product. The classification method used is Naive Bayes Classifier which will then compare feature extraction using Word2Vec and GloVe. Performance parameters are measured using a confusion matrix to produce values for accuracy, precision, recall, and f1-score. The highest accuracy results obtained were 60.71% on the price aspect, GloVe feature extraction, and classification with Gaussian Naive Bayes.Keywords: naive bayes classifier; Word2Vec; GloVe; confusion matrix; multi-aspect sentiment analysis
Performance of ANN and RNN in Predicting the Classification of Covid-19 Diseases based on Time Series Data Ridho Isral Essa; Sri Suryani Prasetyowati; Yuliant Sibaroni
JURIKOM (Jurnal Riset Komputer) Vol 10, No 1 (2023): Februari 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v10i1.5557

Abstract

Indonesia is one of the countries with the highest confirmed cases of COVID-19. The city of Bandung is an area in Indonesia where the number of confirmed cases have continued to increase from 2021 to 2023. Currently there are around 103,574 cases with a total of deaths of around 1485 people. This is bad news for the city of Bandung because of the increasing number of confirmed cases. Various precautions against factors that might affect the rapid spread of COVID-19 in the city of Bandung have been carried out. But the confirmation cases still can't be stopped. Therefore, in this study we made a classification of the spread of COVID-19 in the city of Bandung with 25 features which will later be expanded using feature expansion techniques. This aims to analyze what factors have a major influence on the spread of COVID-19 in the city of Bandung. The method used are ANN and RNN methods. Where in this study the two methods were compared to determine which model had the best performance. Modeling is done by building models 2, 3, 4, and 5 months then the best model accuracy results from the ANN method are 79% and 81% for the RNN method. The author's contribution in this research is to build 2, 3, 4, and 5 month models, compare the performance results of ANN and RNN models, analyze the results of the confusion matrix, and make conclusions about what features are often used in each modeling.
PERFORMANCE ANALYSIS OF THE IMBALANCED DATA METHOD ON INCREASING THE CLASSIFICATION ACCURACY OF THE MACHINE LEARNING HYBRID METHOD Azmi Aulia Rahman; Sri Suryani Prasetiyowati; Yuliant Sibaroni
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 1 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i1.3286

Abstract

This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Nave Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.
Penggunaan Algoritma Bootstrap Untuk Penentu Selang Kadar Emas Dan Perak Pada Lokasi Penggalian Dengan Metode Simple Kriging Siti Rahmah Madusari; Sri Suryani; Rian Febrian Umbara
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak Salah satu tahap yang sangat penting pada pertambangan adalah ekplorasi. Tugas akhir ini menggunakan komputasi untuk membantu eksplorasi dengan melakukan estimasi kadar pada lokasi titik yang belum diketahui. Tujuan tugas akhir ini adalah untuk mengestimasi nilai selang batas bawah dan batas akhir kadar emas dan perak dengan menggunakan algoritma bootstrap dan mensimulasikan sebaran estimasinya pada titik-titik lokasi yang belum diketahui. Estimasi dilakukan dengan menggunakan metode Simple Kriging dan Algoritma Bootstrap untuk mendapatkan selang kadar estimasi. Simple kriging menggunakan nilai kovariogram dalam pencarian nilai korelasi data spasial. Nilai kovariogram teoritis yang valid diuji dengan menggunakan validasi silang, lalu diubah menjadi data independen yaitu dengan metode dekomposisi cholesky yang digunakan pada algoritma bootstrap. Data yang telah dipanggil oleh algoritma bootstrap kemudian ditransformasi kembali menjadi bentuk data semula dan diproses dengan metode simple kriging untuk mendapatkan nilai estimasi. Dari proses pemanggilan data hingga pencarian hasil estimasi diulangi sebanyak B kali pengulangan dan dicari selang konfidensi dengan tingkat kepercayaan 95%. Pada tugas akhir ini didapatkan sebaran nilai estimasi kadar untuk batas bawah adalah 4,03040-4,09727 gr/ton untuk data emas dan 190,042-190,103 gr/ton untuk data perak, sebaran batas atasnya adalah 4,22531-4,27059 gr/ton untuk data emas dan 190,219- 190,262 gr/ton untuk data perak. Kata kunci : data spasial, kovariogram, validasi silang, simple kriging, dekomposisi cholesky, algoritma bootstrap
Studi Kasus Sistem Antrian Tapping Ktm Berbasis Rfid Over Fiber Di Gedung Tokong Nanas Universitas Telkom Indri Octavellia Wulanissa; Erna Sri Sugesti; Sri Suryani Prasetyowati
eProceedings of Engineering Vol 5, No 2 (2018): Agustus 2018
Publisher : eProceedings of Engineering

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Abstract

ABSTRAK Sistem presensi berbasis ICT yang telah diterapkan di lingkungan Universitas Telkom adalah iGracias. Sistem ini terhubung dengan perangkat Radio Frequency Identity (RFID) Over Fiber yang terpasang pada seluruh ruang kelas di 48 gedung Universitas Telkom. Perangkat RFID yang digunakan bekerja pada layanan lalu lintas data dan security menggunakan protokol Slotted ALOHA dimana protokol ini tidak memiliki anti collision, sehingga memungkinkan data bertabrakan atau hilang saat ditransmisikan. Kemungkinan data yang hilang (drop) lebih besar saat kepadatan trafik terjadi. Dari seluruh gedung di lingkungan Universitas Telkom, Gedung Tokong Nanas (KU3) merupakan gedung dengan trafik terpadat pada jam perkuliahan hari Senin hingga Sabtu, pukul 06:30 - 18:30, karena digunakan bersama oleh 7 Fakultas di Universitas Telkom. Permasalahan saat ini adalah kegagalan tapping sangat sering terjadi ketika trafik pada Gedung KU3 dalam kondisi padat. Untuk mengatasi permasalahan tersebut diusulkan sebuah perancangan model antrian baru dalam bentuk Notasi Kendall guna mengelola kepadatan trafik yang dilakukan berdasarkan Uji Perbaikan Kinerja Sistem yang mengacu pada hasil evaluasi kepadatan trafik terhadap parameter: Laju Kegagalan, utilisasi (ρ), volume trafik (V) serta intensitas trafik (A). Evaluasi diambil dari data per-minggu. Berdasarkan analisis didapatkan model antrian pada kondisi existing adalah (M/M/1):(FCFS/500/256). Setelah melakukan evaluasi, diketahui KU3.06 dan KU3.07 merupakan lantai dengan kepadatan trafik tertinggi. Kemudian dengan skema Uji Perbaikan Kinerja Sistem berupa penambahan 1 unit server penyangga untuk KU3.06 dan KU3.07 yang dinyatakan dengan notasi (M/G/2):(FCFS/500/256), didapatkan rata-rata: penurunan ρ sebesar 68,1 %, penurunan V sebesar 19,912 menit, penurunan A sebesar 41,24 menit, dan peningkatan kapasitas server sebanyak 1559 tapping presensi. Namun Laju Kegagalan tidak dapat ditentukan karena belum diimplementasikan. Kata Kunci: RFID, Sistem Antrian, Rekayasa Trafik, Utilisasi, Notasi Kendall. ABSTRACT Presentation system based on ICT that has been applied in Telkom University is iGracias. This system is connected to Radio Frequency Identity (RFID) Over Fiber device which installed in all classrooms at 48 buildings of Telkom University. The RFID device used works on data traffic and security services based on Slotted ALOHA protocol, where this protocol has no anti collision, so it will allowing data to collide or disappear while transmitted. The possibility of missing data (drop) is greater when traffic density occurs. From all buildings in Telkom University, Tokong Nanas Building (KU3) is the most densely populated building during lecture hours from Monday to Saturday, 06:30 am to 06:30 pm. This because the building is shared by 7 faculties at Telkom University. The current problem, tapping failure is very common when traffic on KU3 Building is in heavy condition. To overcome the problem, this final project proposed a new queuing model design by Kendall Notation to manage traffic density that based on System Performance Improvement Test which refers to the result of evaluation from traffic density against parameters: traffic rate, utilization (ρ), traffic volume (V) and traffic intensity (A). The evaluation is taken by weekly data. Based on analysis, the queuing model in the existing condition is (M/M/1):(FCFS/500/256). After evaluation, it is known that KU3.06 and KU3.07 are the floors with highest traffic density. Then, with System Performance Improvement Test scheme in form of addition 1 unit buffer server for KU3.06 and KU3.07 which expressed with notation (M/G/2):(FCFS/500/256), got the mean: decrease ρ equal to 68 , 1%, V decrease to 19.912 minutes, A decrease to 41.24 minutes, and server capacity increase to 1559 tapping Presence. But Failure Rate can not be determined because it has not been implemented yet. Keywords: RFID, Queueing System, Traffic Engineering, Utilization, Kendal Notation.
Analisis Model Txop Wlan Ieee 802.11e/g Menggunakan Discrete Phase-type Untuk Layanan Rt-cbr Gede Astawa Pradika; Erna Sri Sugesti; Sri Suryani Prasetyowati
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak IEEE 802.11e merupakan Wireless Local Area Network (WLAN) standard yang mendukung jaminan Quality of Service (QoS) sehingga dapat membuat layanan real time berjalan dengan baik, namun standard ini memerlukan IEEE 802.11g sebagai physical layer-nya. Pada penelitian ini, analisis model Transmission Opportunity (TXOP) metastabil terfokus pada protokol HCF Coordinated Channel Access (HCCA). HCCA merupakan protokol yang memberikan perlakuan sama untuk semua jenis traffic, karena karakteristik tersebut, HCCA cocok digunakan untuk menangani layanan real time. Proses yang terjadi pada protokol HCCA selalu melibatkan superframe yang terdiri dari beacon, Contention Free Period (CFP) dan Contention Period (CP), selain itu CFP terbentuk dari PCF Inter Frame Space (PIFS), Short Inter-Frame Space (SIFS), dan TXOP. Bagian yang dianalisis hanya CFP, sedangkan bagian yang dimodelkan hanya TXOP yang durasinya dipengaruhi oleh bitrate. Pemodelan dilakukan menggunakan Discrete Phase-Type (DPH) dengan asumsi satu user hanya mendapatkan satu TXOP dan bitrate yang digunakan adalah bitrate mandatory dengan asumsi constant bitrate. Dengan melakukan pemodelan TXOP, diperoleh komposisi TXOP metastabil untuk satu superframe yang dapat menangani real time service dengan baik. Hal ini terbukti dari waktu tunggu yang diperlukan setiap user memenuhi syarat real time service jika komposisi ini digunakan dengan kondisi user yang terhubung ke Access Point (AP) selalu 100. Kata kunci: IEEE 802.11e/g, real time, HCCA, CFP, TXOP, DPH
Pemodelan Harga Tanah Kota Batam Dengan Menggunakanmetode Universal Kriging Hari Yudha Fanani; Sri Suryani; Yulian Sibaroni
eProceedings of Engineering Vol 2, No 2 (2015): Agustus, 2015
Publisher : eProceedings of Engineering

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Abstract

Abstrak Tanah merupakan sumber daya yang mempunyai peranan strategis dalam pembangunan perkotaan. Maka diperlukan sumber informasi harga tanah yang dapat mendukung pengelolaannya. Distribusi spasial harga tanah dapat diperoleh melalui pemodelan spasial. Penelitian ini bertujuan untuk mengestimasi model harga tanah perkotaan kota Batam menggunakan pendekatan geostatistik. Titik koordinat sampel sangat berpengaruh dalam mengestimasi harga tanah disebuah daerah. Adapun model yang digunakan dalam penelitian ini yaitu universal kriging dan model semivariogram yang digunakan adalah model spherical. Sampel yang digunakan merupakan data dari Badan Pertanahan Nasional Kota Batam pada tahun 2003 dengan jumlah data sampel 87 buah. Dari hasil perhitungan estimasi yang telah dilakukan, model eksponensial memberikan hasil paling baik dibandingkan dengan ketiga model lainnya yaitu model Gaussian, spherical dan linear . RMSE (Root Mean Square Error) untuk estimasi harga tanah sampel memberikan nilai yang terkecil. Parameter yang digunakan model eksponensial yaitu nugget effect 0.2, range 15 dan sill 0.5. Distribusi spasial harga tanah Kota Batam memperlihatkan harga tertinggi terletak di pusat kota, yaitu daerah sekitar Batam Centre. Menjauhi pusat kota secara umum harga tanah menurun dan mencapai nilai terendah. Berdasarkan percobaan yang dilakukan diperoleh informasi bahwa semakin kecil ukuran grid, tingkat penyebaran semakin tinggi mengakibatkan warna dari peta kontur semakin jelas perbedaannya. Kata kunci : harga tanah, geostatisk, universal kriging, semivariogram..
Co-Authors Abduh Salam Adhe Akram Azhari Adhitya Aldira Hardy Aditya Andar Rahim Aditya Faudzan Aditya Firman Ihsan Aditya Gumilar Afrida, Yenni Akmal Muhamad Faza Aniq A. Rohmawati Aniq Atiqi Rohmawati Aqilla, Livia Naura arief rahman Arnasli Yahya Asdar, Meisya Putri Indrayani Asramanggala, Muhammad Sulthon Aufa, Rizki Nabil Azmi Aulia Rahman Bakara, Stella Maris Chamadani Faisal Amri Christina Natalia Claudia Mei Serin Sitio Damar, Muhammad Dede Tarwidi Derwin Prabangkara Diyas Puspandari Ekaputra, Muhammad Novario Ellisa Ratna Dewi Ellisa Ratna Dewi Elqi Ashok Erna Sri Sugesti Erwin Budi Setiawan Fairuz, Mitha Putrianty Fatha, Rizkialdy Fathin, Muhammad Ammar Fatri Nurul Inayah Gede Astawa Pradika Gilang Brilians Firmanesha Gusti Aji, Raden Aria Gutama, Soni Andika Hari Yudha Fanani Hawa, Iqlima Putri Haziq, Muhammad Raffif Hilda Fahlena Hilman Rama Pratama I Putu Ananda Miarta Utama Ibnu Muzakky M. Noor Ichwanul Muslim Karo Karo Indra Kusuma Yoga Indri Octavellia Wulanissa Irfani Adri Maulana Ita Susanti Jauzy, Muhammad Abdurrahman Al Jeckson, Jeckson Juniardi Nur Fadila Karno Karno Kartika Putri Septiyani Kasim, Jamila Lesmana, Aditya Lie Othman Linda Salma Mahadzir, Shuhaimi Maharani, Anak Agung Istri Arinta Mardha Al Nazhfi Ali Marissa Aflah Syahran Marissa Aflah Syahran Mitha Putrianty Fairuz Muh. Ilham Muh. Kiki Adi Panggayuh Muhammad Alauddin Angka Kurniawan Muhammad Damar Muhammad Gentur WitjaksonoKurniawan Muhammad Gentur WitjaksonoKurniawan Muhammad Ghifari Adrian Muhammad Hadyan Baqi Muhammad Ikram Kaer Sinapoy Muhammad Novario Ekaputra Muldani, Muhamad Dika Nanda Ihwani Saputri Naufal Alvin Chandrasa Nazhrin Nazarudin Achmad Nenny Lisbeth Minarno Ni Made Dwipadini Puspitarini Nur Fadila, Juniardi Nuraena Ramdani Nurul Aliyah Nurul Fajar Riani Oktovina Suka Pernanda Arya Bhagaskara S M Pilar Gautama, Hadid Purwanto, Brian Dimas Putra, Ihsanudin Pradana Putri, Pramaishella Ardiani Regita Rachmadania Irmanita Rafika Salis Rahim, Husnul Fahma Rahmanda, Rayhan Fadhil Ratna Rian Febrian Umbara Ridha Novia Ridho Isral Essa Rifaldy, Fadil Rizky Fauzi Ramadhani Rizky Yudha Pratama Rizky, Muhammad Zacky Faqia Salis, Rafika Salsabila, Syifa Shofura Shofura Sinaga, Astria M P Siti Rahmah Madusari Siti Uswah Hasanah Sri Harini Sri Harini Sri Harini Suhendar, Annisya Hayati Tuti Budiawati Willie Japaries Winico Fazry Wira Abner Sigalingging Yulian Sibaroni Yuliant Sibaroni Zaidan, Muhammad Naufal