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All Journal 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 STRING (Satuan Tulisan Riset dan Inovasi Teknologi) CCIT (Creative Communication and Innovative Technology) Journal Journal of Information System, Applied, Management, Accounting and Research METIK JURNAL 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 Djtechno: Jurnal Teknologi Informasi JURNAL KOMUNIKASI DAN BISNIS Ascarya: Journal of Islamic Science, Culture and Social Studies Jurnal PkM (Pengabdian kepada Masyarakat) Humantech : Jurnal Ilmiah Multidisiplin Indonesia 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 Jurnal Ticom: Technology of Information and Communication The Indonesian Journal of Computer Science Seminar Nasional Riset dan Teknologi (SEMNAS RISTEK) EDUCATIONAL: Jurnal Inovasi Pendidikan dan Pengajaran 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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Deteksi Emosi Teks X Berbahasa Indonesia Menggunakan Bi-LSTM dengan Seleksi Fitur Chi-Square Wangsajaya, Yosia Heartha Dhalasta; Setyowati, Erlin; Wibowo, Arief
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2658

Abstract

Emotions are an important indicator in understanding public responses on social media, particularly X, which is the main medium for public expression. This study aims to develop a classification model for the emotions of Indonesian-speaking X users using a Bidirectional Long Short-Term Memory (Bi-LSTM) approach combined with data mining-based feature selection techniques. A dataset of approximately 6,000 tweets was collected through X scraping based on keywords and hashtags representing six main emotions: anger, sadness, fear, happiness, love, and surprise, from January 2024 to March 2025. The obtained data was processed through text normalization, stop word removal, and tokenization stages. Features were extracted using TF-IDF and selected using the Chi-Square method to improve classification performance. Tweets were labeled with emotions manually and semi-automatically. The Bi-LSTM model was trained and tested using accuracy, precision, recall, and F1-score metrics. Initial test results showed an accuracy of 86.3%, with the best performance on the emotions “happy” and “angry.” This study shows that the integration of deep learning and data mining can improve the accuracy of automatic emotion detection in Indonesian text. The main contribution of this study is the integration of Chi-Square feature selection with Bi-LSTM for Indonesian text, which has not been widely explored before.
Kearifan Lokal sebagai Fondasi Ketahanan Masyarakat Pesisir Menghadapi Ancaman Tsunami di Pesisir Kabupaten Bantul, Daerah Istimewa Yogyakarta Rahman, Fathin Aulia; Wibowo, Arief; Achadi, Abdul Haris
Jurnal Ketahanan Nasional Vol 31, No 3 (2025)
Publisher : Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jkn.112955

Abstract

Penelitian ini bertujuan untuk menganalisis peran kearifan lokal sebagai fondasi ketahanan masyarakat pesisir Kabupaten Bantul dalam menghadapi ancaman tsunami. Latar belakang penelitian ini berasal dari tingginya ancaman bencana Tsunami di wilayah pesisir selatan Yogyakarta yang menuntut strategi mitigasi berbasis nilai lokal dan sosial budaya. Metode penelitian menggunakan pendekatan kuantitatif dengan membagikan kuesioner kepada 179 responden masyarakat pesisir serta didukung oleh analisis deskriptif dan interpretatif terhadap variabel pengetahuan lokal, keyakinan masyarakat, praktik adat, pola komunikasi, dan kepekaan lingkungan. Hasil penelitian menunjukkan bahwa variabel Keyakinan Masyarakat menjadi aspek paling dominan dengan nilai rerata indeks 4,13, khususnya indikator dorongan budaya/agama untuk gotong royong saat bencana (4,31) yang memperlihatkan kekuatan solidaritas sosial. Sementara itu, indikator kemampuan mengamati tanda-tanda alam (2,61) merupakan aspek terendah yang menandakan menurunnya pengetahuan ekologis tradisional. Temuan ini mengindikasikan bahwa nilai budaya dan spiritualitas masih menjadi kekuatan utama dalam membangun ketahanan sosial, namun perlu diimbangi dengan revitalisasi pengetahuan lokal dan penguatan sistem peringatan dini. Kesimpulannya, sinergi antara kearifan lokal dan teknologi modern menjadi kunci penting dalam membangun resiliensi masyarakat pesisir terhadap ancaman tsunami. 
PERENCANAAN STRATEGIS SISTEM INFORMASI KLINIK KECANTIKAN MENGGUNAKAN PENDEKATAN WARD AND PEPPARD (Studi Kasus PT Visi Putri Pramudhita Visi Beauty Clinic) Monica, Silvi; Wibowo, Arief
Journal of Information System, Applied, Management, Accounting and Research Vol 10 No 1 (2026): JISAMAR (February 2026)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisamar.v10i1.2221

Abstract

The development of information technology requires every organization, including beauty clinics, to have an information system that is well-planned, integrated, and aligned with business strategy. This study aims to develop a strategic information system plan for Visi Beauty Clinic using the Ward and Peppard model as the main approach. The model is used to analyze the internal and external business environment, the internal and external IS/IT environment, as well as to formulate IS/IT management strategies. Various analytical techniques are applied, including Value Chain, SWOT, PEST, Five Forces, and the McFarlan Strategic Grid to comprehensively map application needs. The results show that Visi Beauty Clinic faces intense industry competition, increasing customer bargaining power, and demands for technology-based services. On the other hand, the clinic has opportunities to enhance its competitive advantage through service innovation, data integration, and strengthened information systems. Based on these findings, this study proposes an application portfolio that includes an electronic medical record system, health service system, executive information system, supply chain management, financial system, pharmacy application, human resource system, and an integrated distributed database. The resulting IS/IT strategic blueprint provides direction for application development based on strategic, operational, and potential priorities. Overall, the formulated IS/IT strategic plan supports business process efficiency, improves service quality, and strengthens the competitive position of Visi Beauty Clinic in the beauty industry. The implementation of these recommendations is expected to create added value and support the achievement of the company’s long-term vision.
Analisis Segmentasi Pelanggan dengan Algoritma K-Means pada Data Penjualan Nazihah, Fasya; Danniswara, Ahmad; Wibowo, Arief
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2489

Abstract

Competition in the world of sales is becoming increasingly fierce, so store owners need the right strategy to understand customer behavior patterns and increase sales. One of the most widely used data analysis methods is K-Means Clustering, which can be used to find patterns and trends in sales data. This study was conducted with the aim of determining customer segmentation based on sales transaction data in order to obtain customer groups with similar characteristics. The method applied in this study was the K-Means algorithm on a sales dataset with a total of 1,289 customer data. Cluster quality was evaluated using the Davies-Bouldin index (DBI), with a DBI result of 0.077, indicating excellent cluster quality. The analysis resulted in three customer clusters, namely: the first cluster (C1) consisting of loyal buyers with 562 customers, the second cluster (C2) consisting of occasional buyers with 279 customers, and the third cluster (C3) consisting of buyers with an average purchase of 448 customers. The implication of these research results is that management can develop more appropriate marketing strategies, such as providing a personal approach to loyal customers and designing specific strategies to attract occasional buyers to become more loyal. Thus, these research results can serve as a basis for more effective marketing decision-making.
ANALISIS PENGELOMPOKKAN DISTRIBUSI FASILITAS PENDIDIKAN DI INDONESIA DENGAN METODE KLASTERISASI Nurfadhiilah, Annisa; Martens, Brigitta Griselda; Wibowo, Arief
Jurnal Education and Development Vol 14 No 1 (2026): Vol 14 No 1 Januari 2026
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v14i1.7598

Abstract

Penelitian ini bertujuan untuk menganalisis sebaran fasilitas pendidikan di seluruh wilayah provinsi di Indonesia pada tahun 2024, mencakup jenjang mulai dari Sekolah Dasar/Madrasah Ibtidaiyah hingga tingkat Perguruan Tinggi. Sumber data berasal dari Sakernas tahun 2024 yang dirilis oleh BPS sebagai sumber data utama. Dalam penelitian ini, digunakan pendekatan statistik deskriptif untuk melihat pola distribusi fasilitas pendidikan, serta memanfaatkan metode K-Means clustering guna membagi provinsi ke dalam kategori berdasarkan tingkat ketersediaan fasilitas tersebut. Hasil evaluasi menggunakan Davies-Bouldin Index mengindikasikan bahwa pemodelan dengan dua klaster memiliki tingkat pemisahan terbaik dengan nilai indeks 0,083, sementara model lima klaster memberikan pembagian wilayah yang lebih rinci dengan skor indek sebesar 0,088. Hasil analisis menunjukkan ketimpangan signifikan dalam akses pendidikan antarprovinsi dan antarjenjang, dengan fasilitas cenderung berkurang pada jenjang pendidikan yang lebih tinggi. Provinsi di Pulau Jawa menunjukkan dominasi dalam jumlah fasilitas, sedangkan provinsi di Indonesia bagian timur masih menghadapi kekurangan. Penelitian ini memberikan dasar bagi perumusan kebijakan pemerataan pendidikan di Indonesia.
Pemodelan Tren Kasus Hiv dan Klasterisasi Wilayah menggunakan Algoritma K-Means dan Decision Tree - Studi Kasus di Kabupaten Bogor Bintang, Bagus; Triantoro, Ery; Wibowo, Arief
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10310

Abstract

Infectious diseases remain a dynamic and evolving public health threat, requiring data-driven approaches for early detection and targeted policy planning. This study aims to model spatio-temporal trends and clustering patterns of HIV transmission in Bogor Regency during the period 2020–2023 by utilizing a combination of unsupervised and supervised machine learning techniques. The dataset was obtained from the Bogor Regency Health Office and includes annual data on the number of HIV cases across 40 sub-districts. The research methodology consists of data preprocessing stages, clustering using the K-Means algorithm, and classification using a Decision Tree model. The preprocessing steps include data integration, attribute selection, temporal aggregation, handling of missing data, and normalization using Z-score. K-Means clustering is applied to identify hidden patterns in the development of HIV cases, resulting in three distinct clusters based on multi-year trends. The resulting cluster labels are then used as target classes in the supervised classification process. The Decision Tree classification model demonstrates high accuracy in predicting cluster membership, indicating a strong relationship between the temporal patterns of HIV cases and cluster identity. The integration of clustering and classification techniques provides a robust analytical framework for understanding the dynamics of HIV transmission, while also supporting the formulation of more precise, evidence-based, and region-specific public health interventions.
ANALISIS PREDIKTIF TREN WABAH DEMAM BERDARAH MENGGUNAKAN MODEL PEMBELAJARAN MESIN BERBASIS RAPIDMINER Herriyawan, Herriyawan; Timur, Muhammad Bagus Bintang; Wibowo, Arief
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10356

Abstract

Demam berdarah dengue merupakan tantangan kesehatan masyarakat yang terus berulang di wilayah tropis, termasuk Indonesia. Penelitian ini bertujuan untuk memprediksi jumlah kasus tahunan dengan memanfaatkan lima algoritma pembelajaran mesin, yaitu Regresi Linier, Decision Tree, Random Forest, Support Vector Machine (SVM), dan Neural Network. Data historis tahun 2017–2024 diolah menggunakan teknik windowing deret waktu untuk menghasilkan fitur lag yang sesuai bagi pembelajaran terawasi. Evaluasi kinerja dilakukan melalui metrik Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), serta koefisien determinasi (R²). Model Decision Tree menunjukkan performa paling unggul pada sebagian besar indikator. Prediksi untuk tahun 2025 mengindikasikan adanya peningkatan moderat jumlah kasus. Namun, rendahnya nilai R² pada seluruh model mengisyaratkan perlunya pendekatan multivariat yang lebih kompleks dengan mempertimbangkan faktor iklim, lingkungan, dan demografi. Hasil penelitian ini menegaskan pentingnya kualitas data dan pemilihan fitur yang tepat dalam peramalan epidemiologis guna mendukung perencanaan kesehatan yang lebih efektif.
The Influence of Service Features, User Interface, and Security on User Interest in Wondr Mobile Banking by BNI with Digital Trust as an Intervening Variable (Case Study of the Wondr BNI Application) Prastiyo, Krisna; Wibowo, Arief
Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE) Vol 9 No 1 (2026): Sharia Economics
Publisher : Universitas KH. Abdul Chalim Mojokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31538/iijse.v9i1.8985

Abstract

This study aims to analyse the influence of service features, interface appearance, and security on user interest in the Wondr by BNI mobile banking application, with digital trust as an intervening variable. This study uses a quantitative approach with a survey method, involving 115 respondents selected through purposive sampling. Data were collected through a Likert scale-based questionnaire and analysed using the Partial Least Squares-Structural Equation Modelling (PLS-SEM) method. The results indicate that service features and security significantly influence digital trust but do not significantly influence user interest. The interface does not significantly influence digital trust but does influence user interest. The role of digital trust in mediating the influence between service features, interface design, security, and user interest is not significantly influential. The research model shows moderate predictive relevance, with significant influence on the structural model. This study provides important insights into service features, interface design, security, and digital trust that influence user interest in mobile banking applications, particularly in the Wondr by BNI application.
Hybrid Relevance and Sentiment Classification of Indonesian Gold Tweets Using Machine Learning for Market Risk Signal Extraction Kamalia, Antika Zahrotul; Indra, Indra; Wibowo, Arief; Riwurohi, Jan Everhard; Hassan, Shiza
International Journal of Advances in Data and Information Systems Vol. 7 No. 1 (2026): April 2026 - International Journal of Advances in Data and Information Systems
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v7i1.1517

Abstract

This study proposes a hybrid relevance–sentiment classification framework to analyze public opinion on physical Antam gold from Indonesian Twitter data and to support exploratory market-risk signal extraction. Tweets were collected during February–November 2025, after preprocessing and text-normalized deduplication, 1,271 unique tweets were retained. The approach combines weak supervision (rule-/lexicon-based silver labels) with TF-IDF-based machine learning in two stages: (1) relevance classification to separate tweets genuinely discussing physical Antam gold from non-relevant contexts (e.g., ANTM stock/capital-market discussions), and (2) two-class sentiment classification (positive vs negative) applied to relevance-filtered tweets. Random Forest achieved the strongest relevance performance (Accuracy = 0.984; macro-F1 = 0.943; 5-fold CV macro-F1 = 0.928 ± 0.033). For sentiment classification, performance was moderate and close across models; the most stable model under cross-validation (Logistic Regression/Naive Bayes) was used for downstream aggregation. Sentiment outputs were aggregated into a monthly sentiment index for descriptive comparison with gold prices; the observed association was weak, indicating that the index is better interpreted as a risk-perception proxy rather than a direct price predictor.
Analisis Sentimen Opini Masyarakat Terhadap Keefektifan Pembelajaran Daring Selama Pandemi COVID-19 Menggunakan Naïve Bayes Classifier Ari Wibowo; Firman Noor Hasan; Luthfi Akbar Ramadhan; Rika Nurhayati; Arief Wibowo
Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi Volume 4 Nomor 2 Tahun 2022
Publisher : Fakultas Teknik Universitas Pancasila

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35814/asiimetrik.v4i1.3577

Abstract

Since Indonesia was affected by the Covid-19 pandemic, one of the sectors affected was Education. The government makes an online learning system policy where the system is run with an online process. Not a few of them complained about the limitations of activities issued by the government. Twitter social media is often used to express opinions about concerns about programs issued by the government. The Twitter data crawling process was carried out using the hashtag "learning from home" to get as many as 1,000 datasets, followed by the process of removing duplicates which left 524 datasets and then carrying out the implementation stage of the Naïve Bayes Classifier Algorithm. The purpose of this study was to determine the number of positive and negative sentiments from the dataset labeling classification and to determine the accuracy results of using the Naïve Bayes Classifier method as well as the results of evaluation tests on positive and negative sentiment datasets. Based on the experiment, positive sentiment was obtained as many as 480 and negative sentiment as many as 44 out of 524 datasets. The accuracy results in the evaluation test process get results of 88.5% where negative sentiments get a precision value of 12%, recall 17%, and f1-score 14%, while positive sentiments get a precesion value of 95%, recall 93%, and f1 -score 94%.
Co-Authors - Arientawati - Sumardianto Abdul Haris Achadi 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 Eko Aji Putra Eko Aprianto Endah Sarah Wanty Fajar Siddik Chaniago Farah Chikita Venna Farid Setiawan Farid Setiawan, Farid Fathin Aulia Rahman Febrilliani, Jihan Sastri Fenny Irawati Fernando, Donny Firman Noor Hasan Firmanty Mustofa, Vina Fitri Nur Masruriyah, Anis Fitriadi, Rifqi Fitriani, Netty Fransiska Vina Sari Frenda Farahdinna Fried Sinlae Ganteng Hasanudin 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 Herriyawan, Herriyawan Hidayat, Manarul Hidayat, Sarifudlin Huda, Ratu Najmil Ida Ariyani Hasanah Indah Rizky Mahartika Indra Indra Inge Virdyna Irfan Hadi Irfan Nurdiansyah Irman Efendi Istiqoomatun Nisaa Joko Sutrisno Jovansgha Avegad Jumaryadi, Yuwan Kanasfi, Kanasfi Karyaningsih, Dentik Kresno Yulianto KRESNO YULIANTO KUNTORO Kurnia Setiawan Kutanto, Haronas Larasati, Pamela Linda Lingga Desyanita Luthfi Akbar Ramadhan Mahmudah Mahmudah Mailana, Agus 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 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 Anisah Rahmawati Nur Rohman Nurcahya, Gelar Nurfadhiilah, Annisa Nurfidaus, Yasmine Nursyi, Muhamad Pattipeilohy, William Frado Pattipeilohy, William Frado Pebriaini, Prisma Andita Poppy Ruliana Pradiptha, Anindya Putri Prastiyo, Krisna Probo Anggraini, Julaiha Purwadi Purwadi Purwadi Purwadi Putra, Andi Agung Putra, Rinaldi Febryatna Duriat Qamarullah Popalia Rachmah Indawati Rahman, Fathin Aulia 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 Risaychi, Diva Ajeng Brillian Ristiana, Ina Rizkiyanto, Muhamad Ardiansyah Rizky Tarmudzi 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 Sasongko, Raden Satiri Satiri, Satiri Selamet Riyadi Selly Rahmawati Selly Rahmawati Septian Firman S Sodiq Septiani, Riska Setyowati, Erlin Sevtian Ferdian 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 Tarwan Tiaharyadini, Rizka Triantoro, Ery TRISNAWATI, WULAN Tulus Yuniasih Umam, Mohamad Hafidhul Vasthu Imaniar Ivanoti Wahyu Cesar Wahyu Desena Wahyu Setiawan Wahyudi, Widi Wahyuni, Chatarina Unggul Wangsajaya, Yosia Heartha Dhalasta Wibiyanto, Alif Dewan Daru Widiyaningrum, Diyah Kiki Widyanto, Tetrian Windhu Purnomo Wisnu Supri Harmito Wulan Novita Sari Yahya Darmawan Yudanto, Satyo Zakaria Anshori Zaqi Kurniawan