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All Journal TEKNIK INFORMATIKA Dinamik SEGMEN Jurnal Manajemen dan Bisnis GEMA TEKNOLOGI Techno.Com: Jurnal Teknologi Informasi Jurnal Simetris Syntax Jurnal Informatika Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Mahasiswa FEB Prosiding SNATIF Jurnal Ketahanan Nasional Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Berkala Epidemiologi Seminar Nasional Informatika (SEMNASIF) CESS (Journal of Computer Engineering, System and Science) E-Dimas: Jurnal Pengabdian kepada Masyarakat JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal Ilmiah FIFO Jurnal Pilar Nusa Mandiri InComTech: Jurnal Telekomunikasi dan Komputer Prosiding Seminar Nasional Teknoka JRST (Jurnal Riset Sains dan Teknologi) JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Sisfokom (Sistem Informasi dan Komputer) JURNAL EDUCATION AND DEVELOPMENT Jiko (Jurnal Informatika dan komputer) JSiI (Jurnal Sistem Informasi) Explore IT : Jurnal Keilmuan dan Aplikasi Teknik Informatika JURIKOM (Jurnal Riset Komputer) Jurnal Telematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Ilmu Komputer dan Bisnis Syntax Idea Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Mnemonic Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi International Journal of Advances in Data and Information Systems Journal of Computer Science and Engineering (JCSE) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Teknik Informatika (JUTIF) Jurnal Pewarta Indonesia JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia Media Publikasi Promosi Kesehatan Indonesia (MPPKI) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Journal Of Human And Education (JAHE) Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Economic Reviews Journal Jurnal Algoritma Jurnal Ticom: Technology of Information and Communication Berita Kedokteran Masyarakat Jurnal Hasil Pengabdian Masyarakat (JURIBMAS) Journal of Systems Engineering and Information Technology J-Icon : Jurnal Komputer dan Informatika Jurnal Teknik Indonesia Research Horizon IIJSE Jurnal Relawan dan Pengabdian Masyarakat REDI Jurnal Pengabdian Masyarakat Nasional Health Dynamics Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) Prosiding SeNTIK STI&K Journal of Medical and Health Science Jurnal Ilmu Kesehatan Immanuel Jurnal Ekonomi, Manajemen, Akuntansi dan Keuangan
Arief Wibowo
Universitas Budi Luhur
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Accuracy Assessment of Monthly Rainfall Predictions using Seasonal ARIMA and Long Short-Term Memory (LSTM) Akbar, Ahmad Aldizar; Darmawan, Yahya; Wibowo, Arief; Rahmat, Hayatul Khairul
Journal of Computer Science and Engineering (JCSE) Vol 5, No 2: August (2024)
Publisher : ICSE (Institute of Computer Sciences and Engineering)

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

Abstract

Hydro meteorological disasters are common in Indonesia. Rainfall predictions can help mitigate the impact of these disasters. This research aims to compare the accuracy of monthly rainfall prediction models using Seasonal Autoregressive Integrated Moving Average (SARIMA) and Long Short-Term Memory (LSTM) methods. The input data consists of monthly rainfall records from four locations: Sampali, Kualanamu, Belawan, and Tuntungan, located around Medan, North Sumatra. The dataset spans from 2000 to 2020, with training data from 2000 to 2018 and test data from 2019 to 2020. The accuracy assessment reveals that Belawan has the largest RMSE values for both models, measuring 27.68 mm for LSTM and 28.36 mm for SARIMA. Belawan records the highest MAE values, with LSTM and SARIMA yielding 5.65 mm and 5.79 mm, respectively. SARIMA models effectively capture general trends and seasonality in linear time series data with clear patterns but struggle with extreme changes or sharp fluctuations due to their reliance on linear relationships. In contrast, LSTMs are effective at modeling complex, non-linear relationships, making them suitable for capturing general trends, seasonal patterns, and more complicated variations in the data. Understanding the characteristics of the data is crucial before applying SARIMA or LSTM models.
Analisis Sentimen Popularitas Capres dan Pilpres pada Media Sosial Twitter: Perbandingan Algoritma SVM, KNN, dan Naïve Bayes Rojakul, Rojakul; Sumardianto, Sumardianto; Wibowo, Arief
Techno.Com Vol. 23 No. 2 (2024): Mei 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i2.10135

Abstract

Untuk memaham bagamana tokoh publk dpersepskan dan drespon oleh masyarakat d era meda sosial, analsis sentimen sangat berguna. Ini terutama berlaku karena popularitas tokoh publik meningkat di era meida sosial. Tujuan dari penelitian ini adalah untuk mengatasi masalah tersebut dan memberikan pemahaman yang bermanfaat tentang bagiamana masyarakat bertindak terhadap pemlhan presiden dan capres yang saat ini sangat diperdebatkan di medai sosial, serta bagiamana hal tu berdampak pada opn publk secara keseluruhan, khususnya d Twtter. Stud n bertujuan untuk mengkategorkan tweet emosonal ke dalam kategor postf atau negatf dengan menggunakan algortma pembagan terstruktur sepert Support Vector Machnes (SVM), Nave Bayes (NB), dan K-Nearest Neghbor. Hasl pengujan menunjukkan bahwa algortma NB memlk tngkat akuras 94,62% dan press 100%, mengalahkan SVM dan K-NN dalam menyelesakan kasus kepercayaan.
Implementasi Eksplorasi Data Analisis dan Visualisasi Data Terpadu Kesejahteraan Sosial (DTKS) DKI Jakarta Rangkuti, Muhammad Yusuf Rizqon; Adita, Ita; Wibowo, Arief
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.11113

Abstract

Data Terpadu Kesejahteraan Sosial (DTKS) bertujuan untuk memahami data mengenai individu dan keluarga miskin atau kurang beruntung sehingga pemerintah dapat menetapkan kebijakan sosial yang tepat. Namun data di DTKS seringkali tidak akurat dan banyak mengandung duplikat. Untuk mengatasi permasalahan tersebut, penelitian ini menggunakan teknik analisis data eksploratif (EDA) dan visualisasi data dengan bantuan Google Colaboratory. Penelitian ini menggunakan data DKI Jakarta yang berjumlah 1,04 juta orang. Kami menganalisis berbagai faktor seperti usia, menerima atau tidaknya tunjangan kesejahteraan, latar belakang pendidikan, dan status pekerjaan. Hasilnya menunjukkan Jakarta Timur memiliki jumlah penduduk miskin dan pengangguran terbanyak. Rendahnya tingkat pendidikan merupakan salah satu penyebab  utama kemiskinan, dimana mayoritas penduduk hanya mengenyam pendidikan sekolah dasar atau sederajat. Selain itu, Jakarta Timur juga menjadi wilayah dengan jumlah penerima Kartu Indonesia Pintar (KIP) terbanyak. Kesimpulan penelitian ini adalah bahwa program dan kampanye kesadaran tentang pentingnya pendidikan dan keterampilan kejuruan perlu diperkuat untuk mengurangi kemiskinan dan pengangguran.   Kata kunci: Exploratory Data Analysis, EDA, DTKS, Visualisasi
Comparison of Naive Bayes Method with Support Vector Machine in Helpdesk Ticket Classification Wibowo, Arief; Hariyanto, Hariyanto
Journal of Applied Informatics and Computing Vol. 7 No. 2 (2023): December 2023
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v7i2.6376

Abstract

The technical support department or helpdesk department is a unit that requires a quick response in handling its tasks. The company's helpdesk team can consist of several individuals who know specific or specialized issues. Typically, technical problems are handled with an application that can track issues based on tickets. Ticket queue systems are used to facilitate control over the actions of the service or repair provided by the team. Helpdesk applications assist in addressing issues reported by users and then help upper-level management distribute tasks and monitor the helpdesk team's performance, including providing solutions to users' various problems. This research aims to predict the placement of fields that serve assistance based on the corpus users provide in the natural language. Prediction modelling is done using the Naïve Bayes and Support Vector Machine algorithms. The modelling results show that the accuracy rate of helpdesk service prediction with the Naïve Bayes algorithm reaches 82.06%, while the accuracy rate of prediction with the Support Vector Machine algorithm reaches 85.30%.
Taxpayer Awareness Classification Using Decision Tree and Naive Bayes Methods Maskur A, Moch Riyadi; Wibowo, Arief
Journal of Applied Informatics and Computing Vol. 8 No. 1 (2024): July 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i1.6654

Abstract

Land and Building Tax (PBB) has a big influence on a region's PAD. Therefore, regions always strive to increase PBB income as much as possible. Many factors influence the increase in PBB, one of which is public awareness of taxes. Lack of public awareness of taxes causes PBB income to also decrease, and has implications for regional PAD. And conversely, if public awareness of taxes is high, PBB and PAD revenues will also increase. Therefore, a system is needed to measure public awareness of taxes in the region. If public awareness of taxes can be measured, then the relevant agencies can evaluate and map taxpayers in which sub-districts have high or below average awareness. There are several factors that influence taxpayer awareness, including ownership status, tax sector, assessment category, and the number of receivable payments over the past 5 years. By knowing the awareness of taxpayers, the relevant agencies can review the targets for achieving PBB revenue and issue warning letters to taxpayers whose awareness of PBB is lacking. This research uses decision tree and Naive Bayes methods to classify 666,580 datasets obtained from the Cianjur Regency Regional Revenue Management Agency. The stages are carried out by data collection, data preprocessing, training data labeling, classification process, and evaluation. The result of this research is a system that can predict whether taxpayers are aware or not in a sub-district and sub-district or rural area using decision trees and Naive Bayes. The accuracy obtained from the decision tree method was 93.73%, while the accuracy obtained from the Naive Bayes method was 85.61%.
SVM OPTIMIZATION WITH INFORMATION GAIN FEATURE SELECTION TO INCREASE THE ACCURACY OF SENTIMENT ANALYSIS OF INCREASING THE COST OF THE HAJJ Hidayat, Manarul; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2217

Abstract

Everyone's freedom to express their opinions is now poured into a platform known as social media. This platform allows people in the digital world to communicate with each other using the internet. YouTube is one of the most popular social media platforms worldwide. In 2023, the Government, in this case the Ministry of Religious Affairs of the Republic of Indonesia and Commission VIII of the House of Representatives have approved the Hajj Travel Cost 1444 H/2023 AD with a range of Rp90,050,637.26 per regular pilgrim. In contrast to the government of the Kingdom of Saudi Arabia, which implemented a policy of reducing the cost of the Hajj package by 30% from 2022. This has caused pros and cons to the hajj cost increase. Public opinion on social media is the focus of this research to conduct sentiment analysis. Sentiment analysis has been developed through various methods, but there are still many challenges to produce accurate sentiment analysis. The challenges include accuracy, binary classification, data sparsity, and polarity shift. One of the challenges in improving accuracy is the focus of this research. In this study, the Support Vector Machine method is applied and Information Gain feature selection is added. The accuracy results obtained in this study are the Support Vector Machine method (87%) and Support Vector Machine combine with information gain feature selection (89%). It can be concluded, the support vector machine method combined with information gain feature selection proves an increase in accuracy by 2%.
Improving Diabetes Prediction Accuracy in Indonesia: A Comparative Analysis of SVM, Logistic Regression, and Naive Bayes with SMOTE and ADASYN Rahmawati, Selly; Wibowo, Arief; Masruriyah, Anis Fitri Nur
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 8 No 5 (2024): October 2024
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v8i5.5980

Abstract

This study aims to enhance the accuracy of diabetes prediction models in Indonesia by comparing the performance of Support Vector Machines (SVM), Logistic Regression, and Naïve Bayes algorithms, both with and without synthetic oversampling techniques such as SMOTE and ADASYN. The research addresses the issue of imbalanced datasets in medical diagnostics, specifically in predicting diabetes among Indonesian patients, where such imbalance often leads to biased predictions. A comprehensive dataset comprising 657 patient records from a Regional General Hospital in Indonesia was used, with 70% of the data allocated for training and 30% for testing. The results indicate that the SVM model combined with SMOTE achieved the highest accuracy of 95.8% and an AUC of 99.1, underscoring the effectiveness of these techniques in improving prediction performance. The findings of this study highlight the importance of selecting appropriate oversampling methods and algorithms to optimize diabetes prediction accuracy in the Indonesian context, providing valuable insights for future healthcare strategies.
Clustering Analysis of Cadet Profiles Using Age, Recency, Frequency and Monetary Methods Using K-Means and K-Medoids Algorithms Nursyi, Muhamad; Sumarna, Presma Dana Scendi; Wibowo, Arief
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14170

Abstract

Banten Maritime Polytechnic is a new academic school established in 2019 so that the formulation of data management is still being sought to be suitable and optimal, there are many obstacles if the data is not managed properly, starting from the recruitment of prospective cadets in taking sailor competency training such as not optimal socialization. According to data from the 2021 Transportation Human Resource Development Agency, it explains that there are still few enthusiasts, especially at the Banten Maritime Polytechnic. The purpose of this study is to analyze the profile of cadets in taking sailor competency training using the age, recency, frequency and monetary methods in categorizing data and clustering with the k-means and k-medoids algorithms so that the data can be used for cadet services and related parties in the Banten Maritime Polytechnic database. This analysis can also be used for mapping in recruiting prospective cadets in taking sailor competency training so that they can see opportunities and optimize target markets. This research was conducted in 2023 based on the latest data on the 2022-2023 academic year cadet profile at the Banten Maritime Polytechnic. The results of this analysis data can be used for cadets who have not graduated and have graduated in finding work partners and channeling cadets to the shipping industry. So it is very important to manage and cluster cadet profile data in taking this sailor competency training. The use of the K-means and K-medoids algorithms helps in compiling data groupings that have large data. It works by looking at the number of small groups or groups whose numbers are represented by the variable K. To be able to group the existing data, the K-means algorithm runs iteratively from each existing data point to the K group that has been created. The results of the study are cadet profile grouping data that can be managed again for strategies and management formulations at the Banten Maritime Polytechnic, especially in increasing the recruitment of prospective cadets in taking sailor competency training.
Analysis of CSR Program Against Regional Inequality in Bogor Regency Using K-Means and Random Forest Algorithms Rizkiyanto, Muhamad Ardiansyah; Sabirin, Sahril; Wibowo, Arief
CCIT (Creative Communication and Innovative Technology) Journal Vol 18 No 1 (2025): CCIT JOURNAL
Publisher : Universitas Raharja

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/ccit.v18i1.3518

Abstract

Bogor Regency is vast and has significant economic and social potential. Collaboration between businesses and local governments is essential to achieve regional development goals. Corporate Social Responsibility (CSR) plays a role in sustainable economic, enhancing the quality of life for the community. CSR can be implemented independently by companies or supported by the CSR Support Group (TF-TJSL). The Bogor Regency is divided into three development areas, the Western, Central and Eastern, with regional inequality reflected in a Williamson index of 0.731. CSR has the potential to reduce these inequalities through positive contributions. This study analyzes CSR programs on regional inequality in Bogor Regency using data mining technology with K-Means and Random Forest algorithms. The K-Means algorithm shows the optimal result with the best silhouette score at K=2 with a score of 0.76268, reflecting a clear separation between clusters representing regional inequality. The Random Forest algorithm shows excellent classification ability with an accuracy of 0.985 and other evaluations of precision, recall, and f1-score are almost perfect, which indicates its effectiveness in classifying data into three clusters according to development areas. The regression model evaluation results are also good, with a very low MSE (0.003961), indicating minimal prediction error.
Refining Diabetes Diagnosis Models: The Impact of SMOTE on SVM, Logistic Regression, and Naïve Bayes Wibowo, Arief; Masruriyah, Anis Fitri Nur; Rahmawati, Selly
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 7 No 1 (2025): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v7i1.596

Abstract

Accurate diabetes classification is a significant challenge in medical diagnostics, especially in imbalanced datasets. This study addresses this issue by introducing A New Modified Weighted SMOTE (ANMWS), integrated with Priority of Attribute by Expert Judgement (PAEJ) framework, to enhance the performance of machine learning models for imbalanced data. PAEJ categorizes attributes into three levels—high, medium and low priority—based on expert knowledge, while ANMWS applies weighted oversampling using these priority levels to generate synthetic data more representative of real-world cases. The proposed method was evaluated using three algorithms: Support Vector Machine (SVM), Logistic Regression, and Naïve Bayes. Results indicate that applying ANMWS algorithm with PAEJ framework significantly improved predictive performance, with AUC values increasing to 0.995 for SVM, 0.993 for Logistic Regression, and 0.990 for Naïve Bayes, compared to 0.980, 0.978, and 0.975, respectively, using standard SMOTE. Additionally, precision and recall for SVM improved by 5% and 7%, respectively. These findings demonstrate the critical role of ANMWS algorithm and PAEJ framework in addressing class imbalance, providing a reliable method for early diabetes diagnosis and informed clinical decision-making.
Co-Authors - Arientawati - Sumardianto Abdul Rachman Achadi, Abdul Haris Adita, Ita Afifah Khaerani Afifatussalamah, Rizka Ahmad Sururi Ahmad Sururi Akbar, Ahmad Aldizar Al Fatach, M Khabib Anggraini, Julaiha Probo Anita Anita Diana Antika Zahrotul Kamalia Anugrah Sandy Yudhasti Anuqman Fitriadi Apriati Suryani Ardhianto, Angga Ardianah, Eva Ari Wibowo Arief Umarjati Asep Permana Atik Ariesta Bayu Sadewo Bayu Satria Pratama Binarto, Antonius Jonet Bintang, Bagus Boerhan Hidayat, Boerhan Chairul Rizal Chintya Paramitha Danar Wido Seno Danniswara, Ahmad Deni Mahdiana Diah Indriani Didik Hariyadi Raharjo Didin Muhidin Dwi Kristanto Dwi Yulianti Dyah Retno Utari Dyah Retno Utari, Dyah Retno Ebine, Masato Efendi, Irman Eko Aji Putra Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Febrilliani, Jihan Sastri Fenny Irawati Ferdian, Sevtian Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitri Rachmilah Fadmi Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ghapur, Abdul Gurdani Yogisutanti Hadidtyo Wisnu Wardani Hananto, Agustia Handoko, Andy Rio Hanindita, Meta Herdiana Hari Basuki Notobroto Haris Achadi, Abdul HARIYANTO HARIYANTO Harun Nasrullah Hassan, Shiza Hayatul Khairul Rahmat Henry Henry Herriyawan, Herriyawan Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil Ida Ariyani Hasanah Indah Rizky Mahartika Indra Indra Inge Virdyna Irfan Hadi Irfan Nurdiansyah Istiqoomatun Nisaa Jasmine, Meuthia Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karyaningsih, Dentik KRESNO YULIANTO Kresno Yulianto KUNTORO Kuntoro Kuntoro Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus Manurung, Ridho Parmonangan Maria Adiningsih Marlina, Hesti Martens, Brigitta Griselda Maskur A, Moch Riyadi Megananda Hervita Permata Sari Megawati, Rina Miechael, Miechael Miftahul Arifin Miftahul Arifin Mochammad Rizky Royani Moh Makruf Monica, Silvi Muhamad Fadel Muhammad Bagus Bintang Timur, Muhammad Bagus Bintang Muhammad Febrian Rachmadhan Amri Muhammad Noor Hasan Siregar Muhammad Risky Mulyati Mulyati Nazihah, Fasya Nendi, Nendi Ningrum, Yogi Ajeng Nugroho, Angelika Pratiwi Widya Nur Aisiyah Widjaja, Nur Aisiyah Nur Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Popalia, Qamarullah Poppy Ruliana Pradiptha, Anindya Putri Prastiyo, Krisna Probo Anggraini, Julaiha Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Rachmah Indawati Rahman, Fathin Aulia Rahmawati, Nur Anisah Rakhman, Abdulah Rakhmat Rakhmat Rakhmat Rakhmat RAMAYU, I Made Satrya Rangkuti, Muhammad Yusuf Rizqon Ratna Ayu Sekarwati Ratna Ayu Sekarwati Relawanto, Bowo Ria Puspitasari Riama Simanjuntak Ridho Dwi Maulida Rika Nurhayati Riki Ramdani Saputra Rina Megawati Ririh Yudhastuti Risaychi, Diva Ajeng Brillian Ristiana, Ina Riza, Yeni Rizkiyanto, Muhamad Ardiansyah Roedi Irawan Rojakul, Rojakul Rosita Dewi, Erni Ruliana, Poppy Rusdah Ruwirohi, Jan Everhard Ryo Tanaka Sabirin, Sahril Sadewo, Bayu Santoso, Febrina Mustika Saptari Wijaya Mulia Sari Anggar Kusuma Melati Sari, Fransiska Vina Sari, Wulan Novita Sasongko, Raden Satiri Satiri, Satiri Selamet Riyadi Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setya Haksama Setyowati, Erlin Shofinurdin Shofinurdin Siddik Chaniago, Fajar Sigit Ari Saputro Sigit Budi Nugroho Siregar, Sutan Syahdinullah SITI NURUL HIDAYATI Sitti Aliyah Azzahra Soenarnatalina Melaniani Sudewo, Andika Hasbigumdi Sugiyarta, Ahmad Sujiharno Sujiharno Sumarna, Presma Dana Scendi Suntoro, Dimas Fahmi Supiyandi Supiyandi Syahirah, Afifah Tarmudzi, Rizky Tarwan Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wasis Budiarto Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan