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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Neutrino : jurnal fisika dan aplikasinya Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal Ilmiah Kursor Jurnal Mahasiswa Jurusan Arsitektur Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Nasional Teknik Elektro dan Teknologi Informasi Journal of Management Research and Studies

Fitra Abdurrachman Bachtiar

Fakultas Ilmu Komputer - Universitas Brawijaya

Author-ID : 2843479

Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Library & Information Science Physics Social Sciences

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Penilaian Faktor Penerimaan Teknologi Blended learning di PTIIK Universitas Brawijaya dengan Metode Unified Theory of Acceptance and Use of Technology (UTAUT) Pradana, Fajar; Rachmadi, Aditya; Bachtiar, Fitra A.
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 2, No 1 (2015)
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (805.774 KB)

AbstrakBlended learning adalah kolaborasi atau kombinasi antara pembelajaran tradisional (pembelajaran dengan tatap muka secara langsung) dan pembelajaran menggunakan teknologi atau e-learning. Universitas Brawijaya sebagai penyelenggara pendidikan tinggi juga telah memfasilitasi penggunaan teknologi untuk blended learning. Namun pada penerapan blended learning masih terdapat beberapa permasalahan Pada penelitian ini digunakan Unified Theory of Acceptance and Use of Technology (UTAUT) sehingga mampu menutupi kekurangan dari penelitian sebelumnya. Faktor-faktor yang dapat diidentifikasi dengan UTAUT diwakili 2 faktor yaitu perilaku penggunaan (Use Behavior) serta perilaku keinginan dalam menggunakan sistem (Behavioral Intention). Masing-masing dari kedua faktor ini dipengaruhi oleh 4 faktor yaitu harapan kinerja sistem (performance expectancy), harapan usaha yang dikeluarkan untuk mengoperasikan sistem (Effort Expectancy), pengaruh sosial (Social Influence) serta kondisi fasilitas yang mendukung operasional sistem (Facilitating Conditions). Sedang 4 faktor ini ditentukan oleh gender, umur, pengalaman dalam menggunakan sistem kesukarelaan penggunaan sistem dari pengguna. DenganÂ menggunakanÂ UTAUT ternyataÂ didapatkanÂ hasilÂ evaluasi bahwa faktor-faktor yang memiliki pengaruh terhadap penggunaan sistem blended learningÂ di PTIIK adalah H1 : Variabel-variabel PU, JF, RF, EA dan OE tidak berpengaruh terhadap variabel AT, H2 : Variabel-variabel PeoU berkontribusi terhadap variabel AT, H3 : SN, SF dan I tidak berkontribusi terhadap variabel AT, H4 : Variabel-variabel PBC/PB berkontribusi terhadap variabel IM, dan H5 : Variable AT mempengaruhi IM.Kata kunci: Blended Learning,UTAUT, Universitas BrawijayaAbstractBlended learning is a collaboration or a combination of traditional learning (learning by direct face to face) and learning to use the technology or e-learning. UB as a provider of higher education has also facilitated the use of technology for blended learning. However, on the application of blended learning there are still some problems in this research used the Unified Theory of Acceptance and Use of Technology (UTAUT) so as to cover the lack of previous studies. Factors that can be identified by UTAUT represented by two factors, namely the usage behavior (Behavior Use) as well as the desire to use the system behavior (Behavioral Intention). Each of these two factors is influenced by four factors: the expectations of system performance (performance expectancy), the hope of effort expended to operate the system (Effort Expectancy), social influence (Social Influence) and the condition of the facilities that support the operation of the system (Facilitating Conditions). 4 of these factors being determined by gender, age, experience in using the voluntary system of use of the system from the user. By using UTAUT it was found on the evaluation that the factors that have an influence on the use of a system of blended learning in PTIIK is H1: Variables PU, JF, RF, EA and OE does not affect the variable AT, H2: Variables PEOU contribute to AT variables, H3: SN, SF and I do not contribute to the variable AT, H4: Variables PBC / PB contribute to variable IM, and H5: Variable AT affect IMKeywords: Blended Learning,UTAUT, Brawijaya University

KUALITAS DAYA TARIK DUSUN SUMBERWANGI DALAM PENGEMBANGAN UB FOREST SEBAGAI DESTINASI EKOWISATA Fitra Bachtiar; Novi Sunu Sri Giriwati
Jurnal Mahasiswa Jurusan Arsitektur Vol 7, No 2 (2019)
Publisher : Jurnal Mahasiswa Jurusan Arsitektur

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

Dusun Sumberwangi merupakan salah satu dusun yang terdapat didalam UB Forest, Kabupaten Malang. Dusun Sumberwangi memiliki potensi daya tarik yang melimpah meliputi daya tarik alam dan buatan. Potensi daya tarik yang ada saat ini perlu dikaji ulang sehubung adanya pengembangan UB Forest sebagai destinasi ekowisata, Penelitian ini bertujuan untuk mengetahui kulitas daya tarik Dusun Sumberwangi yang nantinya dapat mendukung pengembangan ekowisata UB Forest. Penilaian kualitas daya tarik didasarkan pada persepsi masyarakat yang didukung dengan kajian teoritik kondisi lapangan. Hasil dari penelitian ini didapatkan aspek – aspek daya tarik yang perlu dikembangkan sehingga dapat menjadi acuan dalam pengembangan Ekowisata UB Forest. Kata kunci: Daya Tarik, Ekowisata, UB Forest

Detecting Emotion in Indonesian Tweets: A Term-Weighting Scheme Study Kuncahyo Setyo Nugroho; Fitra A. Bachtiar; Wayan Firdaus Mahmudy
Journal of Information Systems Engineering and Business Intelligence Vol. 8 No. 1 (2022): April
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20473/jisebi.8.1.61-70

Background: Term-weighting plays a key role in detecting emotion in texts. Studies in term-weighting schemes aim to improve short text classification by distinguishing terms accurately. Objective: This study aims to formulate the best term-weighting schemes and discover the relationship between n-gram combinations and different classification algorithms in detecting emotion in Twitter texts. Methods: The data used was the Indonesian Twitter Emotion Dataset, with features generated through different n-gram combinations. Two approaches assign weights to the features. Tests were carried out using ten-fold cross-validation on three classification algorithms. The performance of the model was measured using accuracy and F1 score. Results: The term-weighting schemes with the highest performance are Term Frequency-Inverse Category Frequency (TF-ICF) and Term Frequency-Relevance Frequency (TF-RF). The scheme with a supervised approach performed better than the unsupervised one. However, we did not find a consistent advantage as some of the experiments found that Term Frequency-Inverse Document Frequency (TF-IDF) also performed exceptionally well. The traditional TF-IDF method remains worth considering as a term-weighting scheme. Conclusion: This study provides recommendations for emotion detection in texts. Future studies can benefit from dealing with imbalances in the dataset to provide better performance. Keywords: Emotion Detection, Feature Engineering, Term-Weighting, Text Mining

ASPECT EXTRACTION IN E-COMMERCE USING LATENT DIRICHLET ALLOCATION (LDA) WITH TERM FREQUENCY-INVERSE DOCUMENT FREQUENCY (TF-IDF) Satyawan Agung Nugroho; Fitra A Bachtiar; Randy Cahya Wihandika
Jurnal Ilmiah Kursor Vol 11 No 2 (2021)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v11i2.247

Social media is a common thing that people use. Posts or comments found on social media describe someone’s feelings and opinions so there have to be important topics that can be extracted from social media. In the e-commerce field, topic is an interesting thing to know because it can describes people’s opinion towards a product. However, the large number of social media users is currently making the process of finding topics from social media difficult, so computer assistance is needed. One method that can be used is Latent Dirichlet Allocation (LDA). LDA is a good method for extracting topics, but the drawback is that sometimes the topics are incomprehensible. To cover up the drawback, TF-IDF feature selection method is used so that less important words can be skipped so LDA can generate a better topic. The best hyperparameter values obtained were 10 iterations, 10 topics, α and β values consecutively 0,1 and 0,01. The best feature selection percentile value is 90. This value is used to find the threshold that can be used as the lower limit of the TF-IDF value of each word so that the word with greater TF-IDF value can be used as feature.

Optimasi Nilai K pada Algoritma KNN untuk Klasifikasi Spam dan Ham Email Eko Laksono; Achmad Basuki; Fitra Bachtiar
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 4 No 2 (2020): April 2020
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Evaluasi Topik Tersembunyi Berdasarkan Aspect Extraction menggunakan Pengembangan Latent Dirichlet Allocation Dinda Adimanggala; Fitra Abdurrachman Bachtiar; Eko Setiawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 5 No 3 (2021): Juni 2021
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Recently, Sentiment Analysis is used for expression detection of products or services. Sentiment Analysis is one category type with a level of aspect focused on extracting product aspects. One of the common methods used for aspect extraction is Latent Dirichlet Allocation (LDA) using random topic identification, but this method has not been able to find an acceptable topic with some aspects having been found. Undeterminable topics are referred to as the hidden topics. This study purpose is to evaluate and compare the suitability of identifying hidden topics between human and computer evaluation. The study is also focused on aspect extraction using a variety of LDA innovations. The data used in this study used case studies on e-Commerce. Data were processed using feature selection and grouped using LDA development. Then the data results are processed using Latent Topic Identification based on subjective and objective evaluations. The identification of hidden topic results was evaluated using several semantic and lexicon tests. The evaluation results indicate the comparison of two hidden topic identification assessment values is quite relevant with the average difference in value reaching 6%. As a result, computer calculations assist humans in determining topics if each topic has a low coherence value.

Opinion Spam Classification on Steam Review using Support Vector Machine with Lexicon-Based Features Rafif Taqiuddin; Fitra A. Bachtiar; Welly Purnomo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1323

Steam is a video game digital distribution platform developed by Valve Software. Steam provides a user review feature, where users can write about criticism or comments on games that can contain positive or negative sentiments. Based on the questionnaire that the author conducted to Steam users from all over Indonesia, the user review feature provided by Steam was not sufficient. This is because there are fake reviews that allow biased opinions from certain parties so that a phenomenon called review bombing often occurs where users review only to drop or raise the image of a product, not to review it sincerely. From these problems, a solution design is needed that can classify fake reviews on the Steam service. The Support Vector Machine (SVM) classification method was chosen as the model in combination with lexicon-based feature retrieval and Term Frequency – Inverse Document Frequency (TF-IDF) weighting. Of the 236 classification test data conducted by SVM, it produced 105 reviews which were categorized as Valid Reviews. Meanwhile, those categorized as Opinion Spam by SVM are 131 reviews. The accuracy level of the data classification model using Support Vector Machine method is of 81% by dividing training data by 70% and test data by 30% with a random state level of 109. A dashboard in the form of a web application has also been made that contains the classification model to be used for buying reference for Steam user.

Optimasi Asymmetric City Tour di Kota Kediri Menggunakan Ant Colony System Abidatul Izzah; Benni A. Nugroho; Wayan F. Mahmudy; Fitra A. Bachtiar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 9 No 1: Februari 2020
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Kediri City is a stopover/transit city and has many potentials in the fields of tourism, education, and industry. Thus, the City of Kediri became one of the cities that are very likely to develop and be crowded. Therefore, it is required to model city tours in several primary fields of Kediri City. In the literature, determining the optimum route can be approached as a traveling salesman problem. However, traveling salesman problem model cannot be used to determine the city tour path as the distance among the point may vary. In this study, we used the concept of asymmetric traveling salesman problem to solve the city tour path. Furthermore, we used the ant colony system algorithm to solve this problem. The cases resolved in this study are the location of the tourism center, industrial center, and education center in Kediri City. The results show the ant colony system is capable of providing optimum tour route solutions, namely the city tourist route 34.65 km, the industrial route 21.19 km, and the school route 28 km.

Pengembangan Aplikasi Pembelajaran dengan Menerapkan Model Pembelajaran Teams-Games-Tournament (TGT) Anita Rizky Agustina; Fajar Pradana; Fitra Abdurrachman Bachtiar
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 2: Mei 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

UPT SMP Negeri 6 Gresik is one of the few educational institutions that implement a Teams-Games-Tournament (TGT) learning model in their day-to-day class learning activities. The TGT learning model can assist students in understanding the learning materials by relying on their friends as age-equivalent tutors and via game elements. The current model that was being applied has several issues, where teachers formed the groups conventionally which could decrease the in-class study time. Conventional ways of forming a group leave room for an unfair knowledge distribution among groups, such as a group having only students who have high grades or low grades only. Other than that, the teachers will make a crossword puzzle conventionally. The playing board is made before a learning material is given to the students and the amount of “words” on the puzzle are determined on much material there is and how many groups are formed. The grouping feature is developed using k-means clustering. The development process used the waterfall development process and Codeigniter framework. This application requirement analysis resulted in four actors, 37 functional requirements, and one non-functional requirement. Testing for this research was done by blackbox testing techniques and whitebox testing techniques.

Klasifikasi Aktivitas Manusia Menggunakan Extreme Learning Machine dan Seleksi Fitur Information Gain Fitra Bachtiar; Fajar Pradana; Issa Arwani
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 10 No 3: Agustus 2021
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Human activity recognition has various benefits in daily lives. However, research in this area is still facing problems that is, unobtrusive data gathering, high dimensionality features, and the algorithm used to classify human activities. Those problems could impact in the result of the developed model. This research is a preliminary study in human activity recognition. Five common human activity will be recognized that is, walking, walking upstairs, walking downstairs, sitting, and standing. The dataset used in this study consist of 1500 data rows and 561 features. Feature selection is performed prior to the modeling step. Information Gain is used as the feature selection in which percentile method is used to subset the number of features in the dataset. The features are then normalized and will classified using ELM. Number of optimal hidden neuron will be searched to yield high predictive accuracy. The results show 240 feature subsets return the higher accuracy. A number of 100 hidden neuron results in highest predictive classification of human activity recognition. The classification results yield accuracy, precision, recall, and F1-score of 0.85.

Co-Authors AA Sudharmawan, AA Abidatul Izzah Abu Wildan Mucholladin Achmad Arwan Achmad Basuki Achmad Basuki Achmad Fahlevi Achmad Firmansyah Sulaeman Achmad Hanim Nur Wahid Achmad Ridok Adam Hendra Brata Adam Syarif Hidayatullah Adhia, Nabila Nur Fajri Adinugroho, Sigit Aditya Rachmadi Aditya Rachmadi, Aditya Admaja Dwi Herlambang, Admaja Dwi Afida, Latansa Nurry Izza Afifurrijal Afifurrijal Agus Wahyu Widodo Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Afif Supianto Ahmad Fairuzabadi Aisyah Awalina Aisyatul Maulidah Akhmad Lazuardi Al Ikhsan, Mochammad Dearifaldi Alaikal Fajri Nur Alfian Aldi Fianda Putra Aldo, Muhammad Alfi Nur Rusydi Alfian, Kharis Alfin Taufiqurrahman Alfirsa Damasyifa Fauzulhaq Alhasyimi, Dana Mustofa Amadea, Karina Amalia Kusuma Akaresti Amrillah, Muhammad Ifa Andi Alifsyah Dyasham Anggit Chalilur Rahman Anita Rizky Agustina Anita Rizky Agustina Anjasari, Ni Luh Made Beathris Anjumi Kholifatu Rahmatika Annuranda, Ramansyah Eka Apriyanti -, Apriyanti Ardi Wicaksono ari kusyanti Arieftia Wicaksono Arifien, Zainal Aulia Dewi Savitri Aulia Nurrahma Rosanti Paidja Aulia Septi Pertiwi Awalina, Aisyah Azhar Izzannada Elbachtiar Azizah, Rizky Adinda Azzam Syawqi Aziz Azzam, Ja'far Shidqul Baharudin Yusuf Widiyanto Barlian Henryranu Prasetio Bayu Aji Firmansyah Bayu Priyambadha Benni A. Nugroho Bere, Stevania Biabdillah, Fajerin Bianca Pingkan Nevista Bintang Fajrianti Budi Darma Setiawan Budi Setiawan Cahya, Reiza Adi Cinthia Vairra Hudiyanti Dariswan Janweri Perangin-Angin Darmawan, Riski Dary Ardiansyah Haryono Dea Zakia Nathania Dedi Romario Delpiero, Rangga Raditya Desy Setya Rositasari Dewi, Elok Nuraida Kusuma Dian Eka Ratnawati Dika Imantika Dimas Angga Nazaruddin Dinda Adimanggala Dito William Hamonangan Gultom Diva Fardiana Risa Djoko Pramono Dona Adittia Dyah Ayu Wulandari Dyah Ayu Wulandari Dzar Romaita Eka Devi Prasetiya Eka Yuni Darmayanti Eko Laksono Eko Setiawan Fabiansyah Cahyo Kuncoro Pradipta Fahrezy, Ahmad Faizatul Amalia Fajar Pradana Faranisa, Puspa Ayu Fardan Ainul Yaqiin Farhan Setya Dhitama Farhansyah, Brahma Hanif Farid Syauqi Nirwan Fasya Ghassani Hadiyan Fatwa Ramdani, Fatwa Ferdian Maulana Akbar Ferry Ardianto Rismawan Ficry Agam Fathurrachman Fikar Mukamal Gandhi Ramadhona Gembong Edhi Setyawan Giga Setiawan Gregorius Dhanasatya Pudyakinarya Gultom, Dito William Hamonangan Gunawan, Alifi Haikal, Raihan Hanif Prasetyo Maulidina Hanifah Khoirunnisak Hanifah Muslimah Az-Zahra Hanifah Muslimah Az-Zahra, Hanifah Muslimah Haryowinoto Rizqul Aktsar Hasyir Daffa Ibrahim Hayashi, Yusuke Herman Tolle Heryana, Ana Hidayatullah, Adam Syarif Hirashima, Tsukasa Holiyanda Husada Hutamaputra, William Ihza Razan Alghifari Ikhsan Putra Arisandi Ikrom Septian Hadi Ilham Pambudi Imam Cholissodin Imam Cholissodin Indra Kurniawan Syahputra Indriati Indriati Indriati Indriati Indriati Indriati Intan Yusuf Habibie Iqbal Taufiq Ahmad Nur Irfani, Ilham Irma Nurvianti Irwan Suprianto Irwanto, M. Sofyan Issa Arwani Istanto, Raga Saputra Heri Ivqonnada Al Mufarrih Joseph Ananda Sugihdharma Joseph Ananda Sugihdharma Julia Ferlin Kartiko, Erik Yohan Katrina Puspita Kevin Gusti Farras Fari' Utomo Kharis Alfian Khoirullah, Habib Bahari Kresna Hafizh Muhaimin Krisnabayu, Rifky Yunus Krisnandi, Dikdik Kuncahyo Setyo Nugroho Kurnia Fakhrul Izza Kurniawan, Rafi Athallah Kusumo, R. Budiarianto Suryo Lailil Muflikhah Larasati, Sza Sza Amulya Lathania, Laela Salma Ludgerus Darell Perwara Luthfi Afrizal Ardhani M Reza Syahputra A M. Ali Fauzi M. Khusnul Azhari M. Raabith Rifqi Mar'i, Farhanna Marji Marvel Timothy Raphael Manullang Maulidah, Aisyatul Mawarni, Marrisaeka Moch Irfan Prayudha Adhianto Mochamad Chandra Saputra Mochamad Havid Albar Purnomo Mochammad Dearifaldi Al Ikhsan Moh Iqbal Yusron Mufidatun Nuha Muh. Edo Aprillia Andilala Muhammad Ferdyandi Muhammad Tanzil Furqon Muhammad Taufik Dharmawan Muhammad Wafi Muhammad Zulfikarrahman Nabila Leksana Putri Nabila Lubna Irbakanisa Nadifa, Rahajeng Mufti Nainggolan, Cesilia Natasya Nanang Yudi Setiawan Nanang Yudi Setiawan Nanang Yudi Setyawan Nanda Ajeng Kartini Nanda Samsu Dhuha Nasita Ratih Damayanti Nevista, Bianca Pingkan Nourman Hajar Novanto Yudistira Novi Sunu Sri Giriwati Novianti, Siska Nur Wahyu Melliano Hariyanto Nur, Iqbal Taufiq Ahmad Nurafifah Alya Farahisya Nurkhoyri, Ageng Nurul Hidayat Oddy Aulia Rahman Nugroho Okta Dwi Ariska Pamungkas, Gilang Alif Pangestu, Gusti Pradana , Fajar Priyambadha, Bayu Pryono, Muhammad Adam Puras Handharmahua Purnomo, Fawwaz Anrico Putra Pandu Adikara Rafif Taqiuddin Rafif Taqiuddin Rafly, Andi Rahman, Rafli Rahmat Adi Setiawan Ramadhan, Muhammad Fitrah Ramadhianti, Fatiha Randy Cahya Wihandika Randy Cahya Wihandika Ratih Kartika Dewi Refi Fadholi Rekyan Regasari Mardi Putri, Rekyan Regasari Mardi Renavitasari, Ivenulut Rizki Diaz Retno Indah Rokhmawati Retno Indah Rokhmawati, Retno Indah Revanza, Muhammad Nugraha Delta Reza Syahputra Rezka Aditya Nugraha Hasan Rezky Dermawan Rhobith, Muhammad Rian Nugroho Ridwan Adi Setiabudi Risa, Diva Fardiana Riski Darmawan Riza Setiawan Soetedjo Rizal Setya Perdana Rizkey Wijayanto Rizkia Desi Yudiari Rizky Adinda Azizah Rizky Muhammad Faris Prakoso Robi Dwi Setiawan Rochmawanti, Ovy Rona Salsabila Said Atharillah Alifka Alhabsyi Salsabila, Rona Samuel Arthur Satrio Agung Wicaksono Satrio Agung Wicaksono Satrio Hadi Wijoyo Satrio Hadi Wijoyo Satyawan Agung Nugroho Satyawan Agung Nugroho Shinta Aprilisia Sifaunnufus Ms, Fi Imanur Sigit Adinugroho Sinana, Admi Rut Sintiya, Karena Siswahyudi, Puad Siti Mutdilah Sofyanda, Erika Yussi Sri Wulan Utami Vitandy Sueddi Sihotang Sugihdharma, Joseph Ananda Sulandri, Sulandri Sutawijaya, Bayu Syahidi, Aulia Akhrian Syahputra, Indra K. Taufik Hidayat Timothy Julian Tirana Noor Fatyanosa, Tirana Noor Titus Christian Ubaydillah, Achmad Afif Utaminingrum, Fitri Vasha Farisi Sarwan Halim Very Sugiarto, Very Vivy Junita Wafi, Muhammad Wahyu Ardiansyah, Mohammad Wahyu Satriyo Wibowo Wahyudi, Hafif Bustani Wayan F. Mahmudy Wayan Firdaus Mahmudy Welly Purnomo Whita Parasati Wicaksono, Satrio A. Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Wiratama Ahsani Taqwim Wirdhayanti Paulina Yoga Tika Pratama Yudi Muliawan Yuita Arum Sari Zayn, Afta Ramadhan Zulfikarrahman, Muhammad

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