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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Imam Cholissodin
Fakultas Ilmu Komputer, Universitas Brawijaya
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Klasifikasi Tingkat Laju Data Covid-19 Untuk Mitigasi Penyebaran Menggunakan Metode Modified K-Nearest Neighbor (MKNN) Imam Cholissodin; Felicia Marvela Evanita; Jeffrey Junior Tedjasulaksana; Kukuh Wicaksono Wahyuditomo
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 8, No 3: Juni 2021
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021834400

Abstract

COVID-19 atau Coronavirus Disease 2019 merupakan sebuah penyakit yang disebabkan oleh virus yang dapat menular melalui saluran pernapasan pada hewan atau manusia dan menyebabkan ribuan orang meninggal hampir di seluruh dunia, sehingga dinyatakan sebagai sebuah pandemi di banyak negara, termasuk di Indonesia. Kasus COVID-19 pertama kali ditemukan di Indonesia pada tanggal 2 Maret 2020, dalam menangani pandemi COVID-19 pemerintah menerapkan social distancing dengan menjaga jarak antara satu sama lain sejauh lebih dari 1 meter dan menerapkan protokol kesehatan yang telah diatur saat melakukan aktivitas di luar rumah sesuai anjuran World Health Organization (WHO). Rendahnya kesadaran masyarakat Indonesia dalam menerapkan social distancing dan protokol kesehatan menyebabkan bertambahnya kasus positif COVID-19 di Indonesia secara signifikan sehingga banyak korban yang meninggal, oleh karena itu pada penelitian ini kami membuat sistem klasifikasi tingkat laju data COVID-19 untuk mitigasi penyebaran di seluruh provinsi di Indonesia dengan menggunakan metode Modified K-Nearest Neighbor (MKNN) dengan hasil keluaran berupa kelas laju penyebaran yaitu laju penyebaran rendah yang artinya mitigasi penybarannya tinggi, kemudian kelas laju penyebaran sedang yang artinya mitigasi penyebarannya sedang, dan laju penyebaran tinggi yang berarti mitigasi penyebaran rendah dan dijelaskan lebih lanjut pada bagian metodologi penelitian. Hasil keluaran dari sistem bertujuan untuk meningkatkan kesadaran masyarakat Indonesia dalam mencegah COVID-19 dengan melihat kelas laju penyebaran pada masing-masing provinsi di Indonesia. Alasan penggunaan metode Modified K-Nearest Neighbor pada penelitian ini adalah karena metode Modified K-Nearest Neighbor merupakan salah satu metode klasifikasi yang cukup baik, dimana pada metode ini dilakukan pemvalidasian dan pembobotan yang bobot nya ditentukan dengan menghitung fraksi dari tetangga berlabel yang sama dengan total jumlah tetangga. Parameter yang digunakan dalam proses klasifikasi adalah jumlah kasus positif, jumlah orang yang sembuh, dan jumlah orang yang meninggal akibat COVID-19. Data yang digunakan pada penelitian ini berasal dari situs resmi kementerian kesehatan republik Indonesia yang dapat diakses pada link https://infeksiemerging.kemkes.go.id/ dengan jumlah data latih sebanyak 374 data pada tanggal 12 Mei 2020 sampai 22 Mei 2020  dan data uji sebanyak 136 data pada tanggal 23 Mei 2020 sampai tanggal 26 Mei 2020 , hasil akurasi yang dihasilkan adalah 97,79% dengan nilai K = 3. AbstractCOVID-19 or Coronavirus 2019 is a disease caused by a virus that can be transmitted through the respiratory tract to animals or humans and causes more people to die around the world, making it a pandemic in many countries, including Indonesia. COVID-19 cases were first discovered in Indonesia on March 2, 2020. Under the COVID-19 pandemic agreement, the government imposed a social grouping with a grouping of more than 1 meter apart from one another and the transfer of related health protection when carrying out activities outside the home as directed by the World Health Organization(WHO). Considering the Indonesian people in implementing social preservation and protecting health policies increase the positive acquisition of COVID-19 in Indonesia significantly related to the number of victims who died, therefore in this study, we created a COVID-19 data level assessment system for transfer mitigation in all provinces in Indonesia by using the Modified K-Nearest Neighbor (MKNN) method with the output in the form of a spread rate class, namely a low spread rate which means that the spread mitigation is high, then the medium spread rate class which means the spread mitigation is moderate, and the spread rate is high which means low spread mitigation which is further explained in the section on the research methodology. The purpose of the system output is to increase the awareness of the Indonesian people in preventing COVID-19. The parameters used in the classification process are the number of positives, the number of people recovered, and the number of people died by COVID-19 by looking at the class distribution rate in each province in Indonesia. The reason for using the Modified K-Nearest Neighbor method in this research is because the Modified K-Nearest Neighbor method is a fairly good classification method, where this method is validated and weighted whose weight is determined by calculating the fraction of neighbors labeled the same as the total of  neighbors number. The data used in this study was released from the official website of the Ministry of Health of the Republic of Indonesia which can be accessed at the link https://infection.infemerging.kemkes.go.id/ with a total of 374 training data from May 12, 2020 to May 22, 2020 and test data As many as 136 data from 23 May 2020 to 26 May 2020, the resulting accuracy was 97.79% with a K = 3.
Pengembangan Deteksi Citra Mobil untuk Mengetahui Jumlah Tempat Parkir Menggunakan CUDA dan Modified YOLO Sisco Jupiyandi; Fadhil Rizqullah Saniputra; Yoga Pratama; Muhammad Robby Dharmawan; Imam Cholissodin
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6, No 4: Agustus 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3353.96 KB) | DOI: 10.25126/jtiik.2019641275

Abstract

Besarnya lahan pada parkir dan jumlah kendaraan roda empat dalam hal ini adalah mobil, dapat menjadi kendala bagi pengendara lain dalam mengetahui posisi parkir mana yang masih dapat digunakan. Sistem pengembangan perparkiran yang ada masih kurang maksimal dalam memanfaatkan lahan dan efisiensi waktunya. Berdasarkan banyaknya kendaraan mobil yang semakin bertambah, maka kebutuhan akan lahan parkir juga semakin dibutuhkan. Banyak sekali sistem yang belum dapat menangani berbagai permasalahan yang ada. Sistem ini dapat mengetahui jumlah slot pada lahan parkir dengan akurat sehingga memudahkan pengelola. Selain itu sistem ini juga dikembangkan agar waktu pencarian lahan parkir oleh pengguna parkir bisa sangat cepat. Sistem ini menggunakan penerapan pemrograman GPU yang dikombinasi dengan Modified Yolo (M-Yolo). GPU pada M-Yolo dibutuhkan untuk mengolah citra sekaligus mengolah data untuk mendeteksi citra mobil dan jumlah mobil secara paralel. Hasil uji coba menunjukkan bahwa dengan menggunakan GPU dibandingkan dengan CPU dapat mempercepat waktu komputasi rata-rata sebesar 0,179 detik dengan rata-rata akurasi sebesar 100%.AbstractThe width of parking lot and the number of cars in the parking lot can be an obstacle for motorists to know the parking area in which part is still empty. Parking systems that exist at this time are still not maximal in the utilization of parking lots and time efficiency. Based on the number of vehicles that are growing, then the need for parking space is also more needed. Many of the existing parking systems have not been able to handle the various problems. This system can know the number of slots on the parking lot, making it easier for operators to know the empty parking lot. In addition, this system will also be designed so that parking time search by parking users doesn’t take a long time. This system uses implementation of GPU programming mixed with Modified Yolo (M-Yolo). GPU on M-Yolo is needed to process images while processing data to detect car and the number of cars using parallel computing. The test results show that using the GPU compared to the CPU can speed up the average computing time by 0.179 seconds and it obtained an average accuracy of 100%.
Optimasi Penjadwalan Perkuliahan Dengan Menggunakan Hybrid Discrete Particle Swarm Optimization (Studi Kasus: PTIIK Universitas Brawijaya) Muhammad Syafiq; Imam Cholissodin; Himawat Aryadita
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 4 (2017): April 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1044.837 KB)

Abstract

Generally, timetabling is done by using conventional tables or spreadsheets. As a result, its affect the quality of timetable and it could drain time and energy if data is considered in thousands. Based on these problems, it requires an intelligent system that not only automates its process but also optimizes the result. PSO is proved effective to solve multidimensional and multiparameter optimization problems compared with other methods. DPSO is used in this study because of combinatorics problems. Various strategies are also used in this method such as clustering particle representation, transposition method for particle movement, time-variant approach, guided random strategies, particle's position repair strategies, and multithreading. The strategy is expected to improve timetabling result. With the various strategies that have been used, this study will use “Hybrid Discrete Particle Swarm Optimization” approach. The test results showed the combination of parameters that resulting the best fitness is: bloc_min=0.6, bloc_max=1, bglob_min=0.6, bglob_max=1, brand_min=0, brand_max=0.002, particle total 2 and iteration total 50,000. The resulting fitness is 248,515.76 with the total execution time is 1 hour 46 minutes 14 seconds and 600 milliseconds.
Deteksi Kesalahan Ejaan dan Penentuan Rekomendasi Koreksi Kata yang Tepat Pada Dokumen Jurnal JTIIK Menggunakan Dictionary Lookup dan Damerau-Levenshtein Distance Tusty Nadia Maghfira; Imam Cholissodin; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 6 (2017): Juni 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (786.853 KB)

Abstract

Writing is a way to deliver and share information among people. It is now even easier to do because of the help of technology such as computers, smartphones and internet. For example, writing and publication of research journal is made to share and enhance knowledge. Generally, the publication of research journal is accommodated by educational institutions both national and international such as JTIIK (Jurnal Teknologi Informasi & Ilmu Komputer) Faculty of Computer Science UB. Before journal is published, a journal should pass editing process by editor to check if there is some mistake and deficiency such as spelling error. However, in their work, editor also accidentally making mistake that will lead to many error spelling that still exist even though editing process has been done. Some misspelled word can change the meaning of knowledge that the author want to deliver and cause misunderstanding of information among the readers. Based on these problems, researcher propose error spelling detection and correction system using Dictionary Lookup and Damerau-Levenshtein Distance. Dictionary Lookup method is considered effective in determining a word including validity or invalidity of the word based on availability or unavailability of the word in Lexical Resource. In addition, Damerau-Levenshtein Distance can provide better correction than Levenshtein Distance. The best precision and recall result for correction simultaneously are 0.78 and 1 from second test scenario.
Prediksi Penerimaan Zakat menggunakan Metode Support Vector Regression (SVR) dengan Flower Pollination Algorithm (FPA) Tusiarti Handayani; Imam Cholissodin; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 7 (2017): Juli 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1313.87 KB)

Abstract

Payment and distribution of zakat in Indonesia managed by Badan Amil Zakat, which of one is Lembaga Amil Zakat Infaq and Shadaqah Muhammadiyah (LAZISMU) Malang. Fluctuations in the level of zakat fund received by LAZISMU Malang affect in the amount of zakat fund that can be distributed to communities in the all of Malang region. Zakat reception forecasting is so needed to determined the amount of zakat received, so that solution anticipation when the fund is less than the distribution target can be done as early as possible. Prediction are made on this research is using Support Vector Regression (SVR) with Flower Pollination Algorithm (FPA) method. SVR used to make prediction of zakat received based on historical data zakat received, and then FPA used to make optimization value from parameter to be used on SVR method. Data used as many as 64 historical data from Juli 2011 up to Oktober 2016 data received of zakat for the one of Lembaga Amil Zakat Nasional at Malang region that is LAZISMU Malang. The results of tests performed on the prediction zakat using SVR with FPA on zakat revenue data from 2011 to 2016 resulted value of 0.2497 in the fitness and 3.0048 in the MAPE which means average difference between of actual data and predict result is Rp144.741.
Optimasi Penjadwalan Damping Mahasiswa Difabel Menggunakan Algoritma Genetika (Studi Kasus PSLD Universitas Brawijaya) Mukh. Mart Hans Luber; Imam Cholissodin; Candra Dewi
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (944.678 KB)

Abstract

Mentoring scheduling for Disabled student is the preparation schedule implementation to a companion who served in the division of working time. On the good scheduling process then will maximize service to disabled students. This scheduling problem is difficult because the number of accompanying relative limited compared to the number of disabled students. The schedule created by the workload evenly to each escort. In this study applied the concepts of problem solving scheduling by using genetic algorithms. Application of genetic algorithm to find the optimal solution. In the settlement of this problem use an integer representation with the length of chromosome 45 genes that each section of the gene showed code mentoring. The method used is the crossover one-cut method of point mutation process, using the method of reciprocal exchange and mutation on the selection process using the method of elitism selection. From the results of testing that has been done optimal parameters obtained using a 100 generation with fitness value 0.966. The final results obtained in the form of a mentoring schedule for 5 days.
Optimasi Multiple Travelling Salesman Problem Pada Pendistribusian Air Minum Menggunakan Algoritme Particle Swarm Optimization (Studi Kasus: UD. Tosa Malang) Rinindya Nurtiara Puteri; Agus Wahyu Widodo; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (776.05 KB)

Abstract

If the distribution application was not run optimally ,it can obstruct the distribution of drinking water process.The tardiness of drinking water transmission become an obstacle in the process and it is also effected by many factors, such as sales ignorance about the shortest path to where the customers are.So this system can lead and make the process easier to determine the shorthest path. In that Distribution obstacle we called it Multiple Travelling Salesman Problem because implicate more than one factor .One of the main purpose from this research is to determine the shortest path for every saleses.This thesis uses Particle Swarm Optimization Algorithm. There were some thesis talked about Multiple Travelling Salesman Problem but to PSO method is scarce. Particle Swarm Optimization is one of the method that solved M-TSP which is that method will gives some effective solutions.Based on the background,researcher choose to use The optimization of Multiple Travelling Salesman Problem application,and in drinking water distribution use Particle Swarm Optimization Algorithm. The result that had been proved show the route sequence that used is better from optimum parameter made 30 iterations and with 90 particle.
Optimasi Multiple Travelling Salesman Problem Pada Pendistribusian Air Minum Menggunakan Algoritme Genetika (Studi Kasus: UD. Tosa Malang) Sayyidah Karimah; Agus Wahyu Widodo; Imam Cholissodin
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 9 (2017): September 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1074.867 KB)

Abstract

A good distribution is one of the company's strategy to increase the productivity of the company. Distribution strategy is indispensable in bottled drinking water, because bottled water business has increased every year. Distributor of bottled water has a variety of types and brands of goods with different packaging forms. The number of shipping destinations poses many problems in the distribution process, because it takes more time to arrive at different address and distances. This research has a goal to create a system that can help the process of distribution of goods with number of sales more than one, the problem is called Multiple Traveling Salesman Problem (M-TSP). One method to solve M-TSP problem is to use genetic algorithm, so it can determine the route with the shortest distance that will be visited by every sales. The genetic algorithm process uses permutation representations with chromosome length according to many customer orders and the number of orders on each sales, each gene is a number representing the customer number and the number of orders that each salesperson should visit. The test results show that the route sequence generated from the application of the genetic algorithm is better than that applied to the distributor with a total distance of 89.3 km and the fitness difference is 10.656578. The optimal parameters were obtained by generating population size 180, 400 generation and crossover rate 0.6 and mutation rate 0.4.
Seleksi Fitur Dengan Particle Swarm Optimization Untuk Pengenalan Pola Wajah Menggunakan Naive Bayes (Studi Kasus Pada Mahasiswa Universitas Brawijaya Fakultas Ilmu Komputer Gedung A) Satria Habiburrahman Fathul Hakim; Imam Cholissodin; Agus Wahyu Widodo
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1061.426 KB)

Abstract

The Presence system of students in the Faculty of Computer Science, Brawijaya University is still using the manual system that is very prone to be misused by the students as entrusted that presence to his friend. Therefore we need a system that has been digitized and also fast in finding solution problem. Optimization method is a method of searching for faster solutions. For this time the researchers is using the Particle Swarm Optimization (PSO) method, that method was inspired by the social behavior of bird movements in their daily lives. While the method of classification is a method that is closely related to the probability hypothesis. So there are 2 methods and have different functions in facial recognition at the student presences where PSO here is as a feature selection and Naive Bayes here as a classification engine as well as a function to get fitness. In the test results obtained that iteration with the best total fitness value is on the number of particles 38 with the highest total fitness is 13,38, then on testing the effect of the number of iterations obtained the conclusion that the largest total fitness is at iteration 190 is 36,799, in other words the greater of iteration the fitness is also better and the last test is on testing for the weight of inertia is 1,2 with the highest total fitness result is 1,588.
Optimasi Penjadwalan Mata Pelajaran Menggunakan Algoritme Genetika (Studi Kasus: SMK Negeri 2 Kediri) Muhammad Fuad Efendi; Imam Cholissodin; Edy Santoso
Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Vol 1 No 10 (2017): Oktober 2017
Publisher : Fakultas Ilmu Komputer (FILKOM), Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (829.338 KB)

Abstract

The process of drafting schedules manually felt less efficient because it takes a long time. The problem of drafting the schedule will be complex if the number of components is more large amount of data from each component. The expected schedule is not just a schedule that does not clash, but a schedule that can adapt to some constraints that must be met within the schedule. Genetic Algorithms are algorithms that are iterative, self-adjusting and probabilistic algorithms in search for global optimization. The process of chromosome initialization generated from teacher assignment data by integer representation of each gene containing randomly generated assignment codes. Each chromosome with the highest fitness value is a representation of the subject schedule solution. From the testing process that has been done, has obtained the parameters of Genetic Algorithm is the best population number is 90, the value of the combination of Cr and Mr is 0.5 and 0.5, and the number of generations as much as 40000. The process of finding solutions using these parameters obtained the value of fitness that is 0,8451.
Co-Authors Achmad Arwan Adam Syarif Hidayatullah Adhipramana Raihan Yuthadi Adhitya Wira Castrena Adinugroho, Sigit Ageng Wibowo Agus Wahyu Widodo Aldino Caturrahmanto Alfen Hasiholan Alif Fachrony Ana Holifatun Nisa Anandita Azharunisa Sasmito Andika Eka Putra Andriko Hedi Prasetyo Anggi Novita Sari Anim Rofi'ah Annisa Alifia Annisaa Amalia Safitri Aqmal Maulana Tisno Nuryawan Ardiansyah Setiajati Arief Andy Soebroto Arina Indana Fahma Arsti Syadzwina Fauziah Atika Anggraeni Aulia Dinia Aulia Herdhyanti Aulia Jasmin Safira Azmi Makarima Yattaqillah Bahruddin El Hayat Bana Falakhi Bayu Andika Paripih Bayu Rahayudi Benita Salsabila Bisma Anassuka Bondan Sapta Prakoso Brendy Oscar Munthe Brigitta Ayu Kusuma Wardhany Budi Darma Setiawan Budi Santoso Candra Dewi Cindy Cynthia Nurkholis Citra Nadya Dwi Irianti Daisy Kurniawaty Danastri Ramya Mehaninda Daneswara Jauhari Daniel Agara Siregar Dellia Airyn Diah Priharsari Dian Eka Ratnawati Dieni Anindyasarathi Dinda Adilfi Wirahmi Diva Kurnianingtyas Dyah Ayu Wahyuning Dewi Edy Santoso Ega Ajie Kurnianto Elisa Julie Irianti Siahaan Ellita Nuryandhani Ananti Elmira Faustina Achmal Ema Agasta Ema Rosalina Eriq Muh. Adams Jonemaro Ersya Nadia Candra Fadhil Rizqullah Saniputra Fahri Ariseno Faizatul Amalia Faturrahman Muhammad Suryana Fayza Sakina Maghfira Darmawan Febriyani Riyanda Felicia Marvela Evanita Fendra Gunawan Ficry Agam Fathurrachman Fikhi Nugroho Fildzah Amalia Firda Priatmayanti Fitra Abdurrachman Bachtiar Franklid Gunawan Galih Ariwanda George Alexander Suwito Ghulam Mahmudi Al Azis Gregorius Dhanasatya Pudyakinarya Guruh Adi Purnomo Gusti Reza Maulana Heny Dwi Jayanti Heru Nurwarsito Himawat Aryadita Holiyanda Husada Husin Muhamad I Gusti Ayu Putri Diani Ibnu Rasyid Wijayanto Ichwanda Hamdhani Ika Oktaviandita Indriati Indriati Irma Lailatul Khoiriyah Ishak Panangian Sinaga Istiana Rachmi Izzatul Azizah Jeffrey Junior Tedjasulaksana Khairinnisa Rifna Khairiyyah Nur Aisyah Komang Anggada Sugiarta Kresentia Verena Septiana Toy Kukuh Wicaksono Wahyuditomo Kukuh Wicaksono Wahyuditomo Laila Restu Setiya Wati Lailil Muflikhah Leni Istikomah Liwenki Jus'ma Olivia M. Ali Fauzi M. Khusnul Azhari Mahendro Agni Giri Pawoko Marji Marji Maulana Ahmad Maliki Maulana Putra Pambudi Mauldy Putra Pratama Mentari Adiza Putri Nasution Michael David Moch Bima Prakoso Moh. Ibnu Assayyis Mohammad Aditya Noviansyah Mohammad Angga Prasetya Askin Mohammad Toriq Muhammad Aghni Nur Lazuardy Muhammad Dio Reyhans Muhammad Fahmi Hidayatullah Muhammad Fuad Efendi Muhammad Halim Natsir Muhammad Hasbi Wa Kafa Muhammad Hidayat Muhammad Maulana Solihin Hidayatullah Muhammad Nadzir Muhammad Rizal Ma'rufi Muhammad Robby Dharmawan Muhammad Rois Al Haqq Muhammad Shafaat Muhammad Syafiq Muhammad Tanzil Furqon Muhammad Taufan Mukh. Mart Hans Luber Nabila Lubna Irbakanisa Nabilla Putri Sakinah Nadia Natasa Tresia Sitorus Nadia Siburian Nadiah Nur Fadillah Ramadhani Nining Nahdiah Satriani Noerhayati Djumaah Manis Novanto Yudistira Novirra Dwi Asri Nur Afifah Sugianto Nur Firra Hasjidla Nurul Hidayat Nurul Inayah Obed Manuel Silalahi Panji Husni Padhila Priscillia Vinda Gunawan Putra Pandu Adikara Putri Ratna Sari Radita Noer Pratiwi Randy Cahya Wihandika Ratih Kartika Dewi Rayhan Tsani Putra Renata Rizki Rafi` Athallah Restu Fitriawanti Reyvaldo Aditya Pradana Reza Aprilliana Fauzi Rien Difitria Rinindya Nurtiara Puteri Rio Cahyo Anggono Riski Ida Agustiyan Rizal Aditya Nugroho Rizal Setya Perdana Rizaldy Aditya Nugraha Rizky Ramadhan Rosintan Fatwa Rowan Rowan Sabrina Nurfadilla Salsabila Multazam Sandya Ratna Maruti Sari Narulita Hantari Satria Habiburrahman Fathul Hakim Sayyidah Karimah Shafira Eka Aulia Putri Shelly Puspa Ardina Shibron Arby Azizy Shinta Anggun Larasati Sisco Jupiyandi Siti Mutdilah Sofi Hidyah Anggraini Stefanus Bayu Waskito Supraptoa Supraptoa Sutrisno Sutrisno Tara Dewanti Sukma Tibyani Tibyani Timothy Bastian Sianturi Tobing Setyawan Tony Faqih Prayogi Tusiarti Handayani Tusty Nadia Maghfira Uke Rahma Hidayah Uswatun Hasanah Utaminingrum, Fitri Vergy Ayu Kusumadewi Veronica Kristina Br Simamora Vinesia Yolanda Vivilia Putri Agustin Vivin Vidia Nurdiansyah Wahyu Bimantara Wanda Athira Luqyana Wicky Prabowo Juliastoro Windy Adira Istiqhfarani Yessica Inggir Febiola Yoga Pratama Yoseansi Mantharora Siahaan Yudha Ananda Kresna Yudo Juni Hardiko Yuita Arum Sari Yunico Ardian Pradana Yusuf Afandi Zanna Annisa Nur Azizah Fareza