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Journal : Building of Informatics, Technology and Science

Penerapan Algoritma K-Means dan K-Medoid untuk Pengelompokkan Data Pasien Covid-19 Gurning, Umairah Rizkya; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 3 No 1 (2021): June 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (374.908 KB) | DOI: 10.47065/bits.v3i1.1003

Abstract

At the end of 2019 in Wuhan, China, a new virus was discovered, that is Corona Virus Disease 2019. This virus causes serious health problems and has been declared as pandemic since March 11, 2020. It has caused death and claimed thousands of lives. This virus spreads very quickly and in various ways such as direct contact with patients, travelers, owners of congenital diseases and many other transmissions. To suppress the spread of this virus, the government has carried out various ways such as social distancing and screening or impromptu swabs in crowded centers. Due to the many types of transmission from this virus, this research was conducted to classify Covid-19 cases in Dumai City. It is hoped that the results of this study can be used as an illustration of the grouping of Covid-19 patient data by applying K-Means and K-Medoid as a grouping algorithm based on the type of transmission, age, gender, health services and district. Based on this research, the K-Means algorithm is more optimal than K-Medoid in classifying Covid-19 patient data, especially in Dumai City. It is proven that the best DBI K-Means value is 0.139 with k = 4
Pengembangan Sistem Informasi Konversi Kelapa di Kabupaten Indragiri Hilir-Riau Adhiva, Jeni; Mustakim, Mustakim; Suryani, Penti
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (470.078 KB) | DOI: 10.47065/bits.v3i3.1039

Abstract

Coconut plants are very much found throughout Indonesia, especially located in Riau province, making coconut plantations an economic base, the largest coconut plantation area to be precise in Indragiri Hilir Regency which has a coconut plantation area of 501,576 hectares. The contribution of coconut plantations in Indragiri Hilir makes Indonesia as a whole as the center of the largest coconut plantations in the world. The majority of Indragiri Hilir people work as farmers. The lack of knowledge and understanding of farmers regarding the management and utilization of coconut production is one of the reasons for the lack of coconut processing business in Indragiri Hilir Regency. It is necessary to have information related to the processing of coconut derivative products. This of course can be an illustration to farmers about the benefits that farmers get when selling coconuts that have been processed into products. Based on the results of ICT Literacy processing, 83% was obtained, this shows that post-harvest information is very important about the processing and utilization of coconut plants. Therefore we need an information system that can provide information about the use or management of coconut plants and the conversion of coconut derivatives in Indragiri Hilir district.
Dimensional Data Unsupervised Learning Using an Analytic Hierarchy Process in Determining Attributes in the Classification Algorithm Putri, Shinta Ayunda; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 4 No 1 (2022): June 2022
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (390.553 KB) | DOI: 10.47065/bits.v4i1.1752

Abstract

Systematic keyword is needed in improving the quality of higher education, one of which is the needed to increase the competence of graduates every year. In increasing student graduation, it is necessary to classify student graduation to find out whether the student is said to be on time (TW) or possibility on time (KTW) using the BPNN and PNN methods. The data used is the Alumni data of the 2013-2020 Information System study program with 7 criteria use, namely GPA, Total Credits, Number of Repetitive Courses, Taking TA Curse in Semester 7, Procrastination, Self-Confidence, and Discipline. The data obtained is then carried out in the process of sharing training data and testing data using K-Means Clustering with the aim; of getting the best accuracy results. Furthermore, the classification stage using BPNN and PNN resulted in an accuracy of 98% and 95% with learning rate of 0.125 and a spread value of 0.1
Analisis Sentimen Terhadap Publisher Rights Dalam Mengunggah Konten Digital Menggunakan Ensemble Learning Putri, Anisa; Mustakim, Mustakim; Novita, Rice; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5179

Abstract

Digital content encompasses various forms of information, ranging from informative text to interactive videos. YouTube, as one of the most popular social media platforms, is widely used in Indonesia. However, the proposed Publisher Rights Bill or the Draft Presidential Regulation on the Responsibility of Digital Platforms for Quality Journalism has sparked debate. In the context of YouTube, this regulation has the potential to threaten content creators. Negative reactions from various parties highlight concerns about the impact of this regulation. Therefore, this study aims to analyze sentiment towards Publisher Rights in the uploading of digital content using an ensemble learning approach. The analysis found that 60% of the sentiment was negative, reflecting concerns about copyright, royalties, or ethical issues. A total of 32% of the sentiment was neutral, indicating uncertainty or a lack of information, and only 8% of the sentiment was positive, supporting the policy of protecting publisher rights and recognizing their value and contributions. This study employed ensemble techniques based on Bagging (Random Forest) and Boosting (Adaboost), where the accuracy of Random Forest was higher at 83% compared to Adaboost's accuracy of 68%.
Perbandingan Algoritma Linear Regression, Support Vector Regression, dan Artificial Neural Network untuk Prediksi Data Obat Putri, Suci Maharani; Novita, Rice; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5184

Abstract

Regression is a crucial focus in various fields aiming to forecast future values to aid decision-making and strategic planning. Different regression algorithms have their advantages and disadvantages, and their performance can vary depending on the data characteristics. Therefore, further analysis is needed to identify the appropriate algorithm that provides the best solution for the problem at hand. This study compares three popular regression algorithms: Linear Regression (LR), Support Vector Regression (SVR), and Artificial Neural Network (ANN) to predict drug data at a pharmacy in Riau province. Currently, the pharmacy lacks an accurate method for estimating monthly drug needs, relying instead on rough estimates. This often results in either shortages or overstock, leading to losses, especially if the drugs expire. Three types of drugs, namely Amoxicillin, Antacids, and Paracetamol were selected to test the proposed algorithms. The analysis and comparison show that the SVR algorithm outperforms the others on all three drug types when focusing on the RMSE metric. However, when the focus is on the MAPE metric, the ANN algorithm proves to be superior. Although LR does not excel in any metric, all three algorithms (LR, SVR, and ANN) have MAPE values below 10%, indicating highly accurate predictions. This accuracy is evidenced by the prediction results of all proposed models, which effectively follow the patterns and trends in the actual data
Penerapan Algoritma K-Medoids dan FP-Growth dengan Model RFM untuk Kombinasi Produk Pertiwi, Tata Ayunita; Afdal, M.; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5268

Abstract

Competition in the business world has increased, resulting in companies having to optimize sales and retain their customers. Customers are an important company asset that must be well looked after. The aim of customer segmentation is to understand customer purchasing behavior so that companies can implement appropriate marketing strategies. Aurel Mini Mart is a retail business that does not yet consider the recency, frequency and monetary value of customer shopping. So far, promotions have been carried out only based on estimates, without taking into account accurate data and information. This research combines the RFM model with data mining techniques to segment customers. Based on the 5 clusters formed from the clustering process, gold customers are in cluster 1 which has high loyalty with low recency value, high frequency and high monetary value. This shows that customers in this segment often make purchases for quite large amounts of money. Meanwhile, customers in clusters 2, 3, 4, and 5 are dormant customers who rarely make transactions and the amount of money spent is also small. After the customer segmentation process is complete, the next step is to use the FP-Growth Algorithm to associate the products purchased by customers. This aims to obtain a better product combination, so that the sales strategy can be more effective and the company can make a profit.
Sentimen Analisis Social CRM Pada Media Sosial Instagram Menggunakan Machine Learning Untuk Mengukur Retensi Pelanggan F. Safiesza, Qhairani Frilla; Afdal, M; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5269

Abstract

To create and maintain a superior competitive advantage in a knowledge-based economy, businesses must be able to utilize data and manage customer relationships through the implementation of Customer Relationship Management (CRM), particularly Social CRM. Social CRM is a renewal of business strategy that is created to engage customers in a collaborative conversation and create mutually beneficial value in a trusted and transparent business environment. Seeing this development as one of the successful culinary companies in the Souvenir sector in Pekanbaru, the company must be able to process all the information obtained. Currently, the company has never analyzed comments on social media, especially the Instagram account. These comments are useful for evaluation material and can be a parameter of customer satisfaction and to see the potential for customer retention. To assess positive and negative comments on the Instagram account, sentiment analysis can be carried out using machine learning, namely 3 classification algorithms, namely Naive Bayes Classifier (NBC), Support Vector Machine (SVM) and Random Forest (RF). The sentiment results show that the SVM and NBC algorithms obtain the best accuracy of 74.26% compared to RF, and the results of the social CRM analysis show that customers are more satisfied with the company in terms of products, services, and actions taken by the company, so that the company is considered capable of retaining its customers.
Analisis Sentimen Masyarakat Terhadap Pinjaman Online di Twitter Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor Afandi, Rival; Afdal, M; Novita, Rice; Mustakim, Mustakim
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5300

Abstract

The very rapid development of technology has had a big impact on humans. The influence of technological developments that we can feel is in the financial sector. One thing that is quite popular lately is online loans. Pinjol or online loan is a fast and easy online money lending service via an application or website, with fast approval and disbursement, but often has high interest and short tenors. On Twitter, review comments and information used are stored in text form. One of the processes for retrieving text mining information in the text category is Sentiment Analysis to see whether a sentiment or opinion tends to be Positive, Negative or Neutral in the reviews of Pinjol application user comments. In the data collection results there were 600 initial data, namely 122 Positive reviews, 432 Negative reviews and 43 Neutral reviews. Then the sentiment classification process using the Naive Bayes and K-NN algorithms produces accuracy, precision and recall of 68%; 83% and recall 74% on the Naive Bayes algorithm, while the results of accuracy, precision and recall on K-NN are 72%; 74% and recall 96% with experiments using 80% training data and 20% test data
Implementasi Algoritma Random Forest Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online Digoogle Play Store Wibisono, Yudistira Arya; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5368

Abstract

Online lending programs are examples of financial service platforms offered directly by commercial fintech players. However, there are rampant cases of fraud and unethical actions by some online lenders such as threatening and harassing billing methods due to late payments. This research aims to classify sentiment from user reviews of online loan applications on the Google Play Store into positive, negative, or neutral categories. This research conducts sentiment analysis of user reviews of online loan applications such as AdaKami, AdaModal, Cairin, FinPlus and UangMe using a text mining approach. This approach can perform sentiment classification on user reviews quickly. Data was collected using the scrapping technique on the Google Play Store and obtained a total of 200 data on each online loan application. The modeling used in this research is the division of training data and test data as much as 80:20. The highest accuracy results using the Random Forest algorithm are Cairin and UangMe applications with 85% accuracy. While the application that gets the lowest accuracy result is the AdaModal application with 75% accuracy. A visualization analysis using word clouds was also conducted to understand the context of user reviews of the pinjol apps. The results show that users almost always discuss loan limits in every sentiment across the five apps.
Perbandingan Performa Algoritma NBC, C4.5, dan KNN dalam Analisis Sentimen Masyarakat terhadap Krisis Petani Muda pada Media Sosial Facebook Nurkholis, Nurkholis; Permana, Inggih; Salisah, Febi Nur; Mustakim, Mustakim; Afdal, M
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6082

Abstract

In Indonesia, young farmers face various challenges and crises that hinder the growth and sustainability of the agricultural sector. They face obstacles such as lack of access to capital, limited technology, climate change, and low selling prices for their crops. In addition, they also often face problems in obtaining accurate and relevant information in an effort to facilitate better decision-making in agricultural businesses, so that the interest of young people today to become farmers is decreasing. The study aims to Compare the Performance of NBC, C4.5, and KNN Algorithms in the Analysis of Public Sentiment towards the Young Farmer Crisis on Facebook Social Media. The application of the K-Fold Cross Validation method is (K = 10). Sentiment analysis is carried out with 3 labels (positive, negative, and neutral). The data used in making the classification model (data from preprocessing the stemming column) using (Google Colab) amounted to 4,878 data with Positive sentiment of 43.13% (2,104), Neutral 39.59% (1,931), Negative 17.28% (843) from the initial data without nested comments, which is 4,981 and the total number of Facebook data is 2,900 likes, 6,700 comments, and 3.3 million viewers. The accuracy of the NBC algorithm is 57.32%, the C4.5 algorithm is 98.42%, and the KNN algorithm (K = 19) is 97.33%. It can be concluded that the results of the comparison of the performance of the three algorithms using (Rapidminer10.3), the C4.5 algorithm gets a higher accuracy of 98.42% and is superior because it produces a decision tree.
Co-Authors . Ishomuddin . Ria Wulandari A'yunin, Elia Nur A.A. Ketut Agung Cahyawan W Abd. Rasyid Syamsuri Abdi Negara Abdillah, Fardan Abdul Fattah Abdul Fattah Abdul Hadid abdullah, akhyar abdullah, akhyar abdullah, akhyar abdullah, akhyar Abidah, Dinah Abror, Naufal Achmad Abubakar Adan, Ingrid Ayu Lestari Ade Rahmat, Ade Adelina Hasyim Adhiva, Jeni Adnan Adnan Adrian Adrian Adrianton A. Aedah, Nur Afandi, Rival Agustien Lilawati Agustina Ahirudin, Idul Ahmad Muzaki Ahmad, Hariadi Aini, Delvi Nur Akmalia Putri, Fina Aksa, Moh. Al Ayubi, Moch T A Alajai, Rafly Ali Akbar Ali Rosidi Ali, Akbar Aliffiani, Siti Almin, Muh. Robil Amin, Novi Nurul Andi Nikhlani Andriansyah Andriansyah Andriawan, Ahmad Rizky Anha, Hamna Anhar, Mujahid Anisa Putri anne Hafina, anne Ansar, Zulfitrah Anugrah Puspita Ayu Muhammad Anwar Syahputra, Syofyan ANWAR, DEDY Aprilla, Tyas Aprillia, Tyas Ardjanhar, Asni ARIASIH, ARUM Ariasih, Rr Arum Arif Marsal Ariyanti, Dyah Ariyanti, Fajar Arumsari, Imas Asbar Asbar, Asbar Asnawi , Meinarni Asni Asni Asnita Virlayani, Asnita Asrianto, Rudy Asrianto Asrori, Tamam Asrul Asrul Assiddiq, Muhammad Azwar Astri, Rahmi Aulia Astri, Zul Astuti, Farida Herna Aswar Aswar Atiqa, Nur Audina, Ayu AYU, GLADIS Azlan, Azlan Azzahra, Aura Baharuddin, Andi Farid Baiq Sarlita Kartiani, Baiq Sarlita Kartiani Balkis, Ratu Batubara, Ana Uzla Baya, Nur Berlian Berlian Cahyanti, Novi Putri Cholidi Cholidi Dani Hari Tunggal Prasetiyo Danial, Adang Darmadi, Herry Darmawan Darmawan Dewa Bagus Sanjaya Dewi Mustika Dewi, Bela Purnama Dewi, Luh Yuliani Dewi, Luthfia Dwynne, Zaira Cindya Dwynne ELIHAMI, ELIHAMI Elsye Souvriyanti, Elsye Eriany, Eriany F. Safiesza, Qhairani Frilla Fadhilah, Faiz Fadila Fadila, Fadila Fadloli, Ahmad Siraj Fadly, Imam Faisal, Fahikra Fandi Ahmad Fardan Abdillah M Farhan, Hamim Farida Farida Fathurrahman Fathurrahman Fauziyyah, Aisyah Nur Febi Nur Salisah, Febi Nur FEBRIANTI, THRESYA Fhadli Noer, Fhadli Fidela, Mia Fiqhuddin, Moh Agung Firda Firda Fitri, Li Idi'il Fitriah, Ma’idatul Fitriah Fitryana, Fitryana Ginting, Maulidna Giopani , Luna Aliska Giopani Gultom, Golfrid Gurning, Umairah Rizkya Hadi, M. Samsul Hafan Sutawardana, Jon Hakzah Hamka Ilyas Hamna, Hamna HANDARI, TRI Handayani, Dwi Iryaning Handayani Hanifah, Nida harni harni, harni Hartati Hartati Hartati, Aluh Harwan Harwan Haryono Haryono Hasan Hasan Hasan, Nurhatimah Hasbullah Hasbullah Hasia Marto Hasrudin, La Ode Hasrullah Hasrullah, Hasrullah Hayatudin, Hayatudin Herdiannisa, Zahra Adinda Herwina, Wiwin Herwina, Wiwin Heryanto Susilo Hidayat, Dinul Husainy, Husainy Husna, Nur Alfa I Nengah Suastika I Wayan Kertih I Wayan Lasmawan I Wayan Suastra Ichwanul Mustakim Ida Bagus Putrayasa igam, Mingriani Ikhsani, Yulia Inggih Permana Insiano, Dewi Angraini Ipah Saripah, Ipah Iqrawati, Iqrawati Irfan, Ahmad Zainul Irfan, M Zainul Irmayani Irmayani Irwan Irwan Irwandi, Erno IRWANSYAH Isa, Muhammad Ikhlasul Amalsyah Iskandar Iskandar Isma, Adi Ismail Ismail Ismail Ismaya Ismaya Israyani, Israyani Istianah Surury Iswardani, Kurnia Ita Sarmita Samad, Ita Sarmita Iwan Suyatna Jabri, Umiyati Jamal Bake, Jamal Jamal Rauf Husain Jamsir Jamsir, Jamsir Jasman Jasman Jaswandi, Lalu Jaya, Nur Musfirah Jeki , Jeki Jeki, Jeki jenianti, rina Jeniska, Jeniska Jumiati Jumiati Juraeba, Juraeba justawan, justawan K, Dwi Putri KA Bukhori Kadriani, Kadriani Kambolong, Makmur Karo-Karo, Justaman Arifin Kartika Nugraheni Kartini, Dwi Putri Kartini, Ragita Ayu Kasim, Nursidah Kasmaida kasmaida Kasmar, Kasmar Katimin, Katimin KETUT SUKARMA . Khojin Supriadi, Khojin Khozin, Khozin Kisman Salija, Kisman Kurdi Kurdi Kurnia Dermawan, Aji Kurnia, Dian Kurniawansyah, Fito Cahya Kusumaningati, Walliyana Kusumastuti, Nurry Ayuningtyas Ladamay, Ode Mohamad Man Arfa legito, Legito Lin, Ming Lisa Andriani Listiani Listiani Liwaul, Liwaul Liwaul, Liwaul Loka, Septi Kenia Pita loko, joko Lukman, Mutiara Sakinah M Afdal M Dyah, Mutmainnah Muinuddin M. Afdal M. Afdal, M. Afdal M. Faqih M.Pd Prof. Dr. I Nyoman Sudiana . Maemunah Maemunah malasari, elfi Marhayudi, Putut Marlisman, Desty Pratiwi Martinus Agus S Mas Ahmad Baihaqi Mas'ud, Anis Anshari Mas'ud, Hidayati Masnur Masnur Mastuti, Indah Maulidi, M. Irfan Maulidzam, Khofan Mediaty Meliana Meliana Melkisidik, Melkisidik MH, Nurdin Mirnandaulia, Meutia Misbahuddin Misbahuddin Mizna Sabilla Moh Nurul Qodir Moh. Erkamim Mohamad, Abdul Basir Bin Muh Yusuf Muh. Jabir Muhamad Ajwar Muhammad Basri Muhammad Farhan Muhammad Muhibbi Muhammad Natsir Muhammad Riza Muhammad Taufiq Muhammad, Gazali Mujiburrahman Mujiburrahman Mujiono Mujiono Munir Munir, Munir Munzir, Medyantiwi Rahmawita Murtaqib Murtaqib Musayyachah, Musayyachah musdalifah, sitti Mustakim, Ichwanul Mustari, Sri Hariati Mutmainnah Mutmainnah Nadir, Nadya Adillah Najamuddin Najamuddin, Najamuddin Najamuddin, M. Najwa, Lu'Luin Ni Wayan Widya Astuti Ningsih, Etik Susilowati Novianti Novianti Nugraha, Andhika Ghalyh Nunung Cipta Dainy Nur Eni Katmas Nur Insani Nur Khasanah NUR SELO WASI, NUR SELO Nur Siti Marchamah, Dwi Nur, Ahmad Ali Nur, Indria Nurabdiansyah, Nurabdiansyah Nuralfiliani, Nuralfiliani Nurhaliza, Nana Nurjannah Nurjannah Nurkholis Nurkholis Nursyawal Nacing Pangestu, Prayugo Penti Suryani Pertiwi, Tata Ayunita Pilu, Reski Prameswari, Nadisa Ardikha Prasetya, Agil Bagus Pratiwi, Rina Sekar Purba, Darry Christine Silowaty Purwati, Ninik Endang Purwati, Ninik Endang Putri, Celine Mutiara Putri, Shinta Ayunda Putri, Suci Maharani Qomariyah, Erni Rahayu, Dwi Sri Rahima Rahima Rahmadeyan, Akhas Rahmat Siddiq, Asep Muhammad Rahmawita, Medyantiwi Ramadani, Faradila Ramadhani, Indah Ramdani, Hendi Rasyid, Muhammad Nur A. Rasyid, Muhammad Rusdi Rasyidin Rasyidin Ratmi, Ratmi Raya Sulistyowati Rayani, Dewi Rice Novita Riski, Pratiwi Rivo Nugroho Rizka, M. Arief Rizki Amalia ROFIK JALAL ROSYANAFI Romadhon, Muhammad Rosmiana, Sri Rosyanne Kushargina Rozanda, Nesdi Evrilyan RR. Ella Evrita Hestiandari Rufaidah, Rufaidah Ruris Haristiani Rusman Efendi Rustiyan, Risma S, Galuh Arieliyna Anggraini Sadi Is, Muhamad Safitri, Annisa Apriani Sahrun, Sahrun sahrun, sahrun sahrun, sahrun Sakka Samudin Salamah, Sania Salisah, Pebi Nur Sami'an, Sami'an Samin, Andriyanto Sampurno, Andhika Sari, Shintya Terisna Sartono Sartono, Sartono Sedana, I Made Shadiq, Ja'far Sidek, Muhammad Silalahi, Meriahni Siti Maimunah Siti Rohimah Siti Sarah, Siti Siti Syahidatul Helma Sofiany, Intan Rosenanda Solikhin Solikhin Solly Aryza Sri Wulandari Suarti, Ni Ketut Alit Sugianto, Dedi Suhaimi Suhaimi Suhandini T.J, Yustina Suharjo, Imam Sukman S Sulfanita, Andi Sunarto Sunarto Sunyoto, Andrik Surahman Amin, Surahman Suryodinoto, Muh. Melyo Susanto, Shobri Firman Sutarto Sutarto Sutinah Made, Sutinah Suwitra, I Ketut Suwitra, I Nyoman Syabil, Irfan Syakura, Abd Syamsiar, Syamsiar Syamsu Yusuf Syaputri, Astia Weni Syarfi Aziz Syifaul Janan T, Nurfadila Takdir Takdir, Takdir Tantra, Kadek Mas Tarigan, Enda Rasilta TATI NURHAYATI Taufiq Taufiq Taufiqurrohman , A.H. Asari Tempo, Jesika Irena Gabela Thamrin Gama, Muh Tina Susanti Titin Sriwahyuni Tjahjaningsih, Yustina Suhandini Tri Prihatiningsih Tuhfatul Janan Udiarto, Bagus K Ulfiana , Umaya Usman Made Utha, Arifin utha, arifin utha, arifin wa yanti, wa yanti Wahidah Sulkaeda Aseh Syahda Wahyudi Winarjo, Wahyudi Wathan, Lalu Elgi Hizbul Wawuru, James Van Alex Wibisono, Yudistira Arya Wicaksono6, Febri Widiarni , Adyah Widyaswari, Monica WIWIN YULIANINGSIH Yanti Komala Sari Yazika Rimbawati Yogaswara, Andre Yus Darusman, Yus Yustina Suhandini Tjahjaningsih Yuyun Wahyuni Zahro, Siti Fatimatus Zakariyah, M Fahmi Zarkasih Zarkasih Zarnelly Zarnelly Zharvina, Nihlah Zharvina Zulfikar.Z Zulkifli Rangkuti Zusyah Porja Daryanto