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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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A Context-Aware Ensemble Learning Framework for Regression on Heterogeneous Tabular Data Purwadi Purwadi; Muhammad Bagus Bintang Timur; Arief Wibowo
Dinamik Vol 31 No 2 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i2.10544

Abstract

Regression on heterogeneous tabular data remains a challenging problem in machine learning due to mixed numerical and categorical features, non-linear relationships, and context-dependent feature relevance. In many real-world datasets, feature contributions vary across spatial, temporal, and categorical contexts, reducing the effectiveness of conventional regression and ensemble methods that treat all features uniformly. This paper proposes a context-aware ensemble learning framework for regression on heterogeneous tabular data, where contextual information is explicitly modeled through structured feature grouping. Contextual attributes are organized into predefined context groups and integrated into the learning pipeline to capture context-dependent feature interactions. The framework evaluates multiple ensemble models, including Random Forest, XGBoost, and LightGBM, under consistent preprocessing and evaluation settings. Model performance is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) with k-fold cross-validation, while interpretability is enhanced through Explainable Artificial Intelligence (XAI) techniques using feature importance analysis and SHAP values. Experimental results demonstrate that explicit context modeling consistently improves regression performance across all evaluated ensemble methods compared to baseline approaches. The proposed framework contributes a systematic and generalizable approach to context-aware regression and ensemble interpretability, supported by experimental results showing consistent reductions in MAE and RMSE.
PENERAPAN ALGORITMA K-MEANS CLUSTERING UNTUK ANALISIS KEPUASAN PESERTA PROGRAM PELATIHAN KARIER MAHASISWA MENGGUNAKAN ALTAIR AI STUDIO 2026 (RAPIDMINER) Wahyu Setiawan; Arief Wibowo
EDUCATIONAL : Jurnal Inovasi Pendidikan & Pengajaran Vol. 6 No. 3 (2026)
Publisher : Pusat Pengembangan Pendidikan dan Penelitian Indonesia (P4I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51878/educational.v6i3.13079

Abstract

Career training programs for university students represent one form of soft skill development service and workforce-readiness preparation provided by universities and related institutions. Evaluating participant satisfaction with program implementation is an important component in assessing success and formulating strategies for continuous quality improvement. However, conventional satisfaction analysis is often unable to objectively reveal participant patterns and segmentation. This study aims to examine the application of the K-Means Clustering algorithm for analyzing and clustering the satisfaction levels of 70 participants in a career training program based on three evaluation dimensions: training materials, instructors, and event implementation. Data processing was carried out using Altair AI Studio 2026 (formerly known as RapidMiner Studio) with a 1–5 Likert rating scale and Min-Max normalization. Cluster quality was evaluated using the Davies Bouldin Index (DBI). The results show that the optimal number of clusters is K=3, with a DBI value of 0.079. The three groups formed consist of: a high-satisfaction cluster (68 participants; 97.1%), a moderate-satisfaction cluster (1 participant; 1.4%), and a low-satisfaction cluster (1 participant; 1.4%). This distribution indicates an overall very high level of satisfaction, while also identifying two participants with partial dissatisfaction patterns that require special attention. These segmentation results can serve as a basis for data-driven decision-making for organizers in improving the quality of future career training programs. ABSTRAK Program pelatihan karier bagi mahasiswa merupakan salah satu bentuk layanan pengembangan soft skill dan kesiapan memasuki dunia kerja yang diselenggarakan oleh perguruan tinggi maupun lembaga terkait. Evaluasi kepuasan peserta terhadap pelaksanaan program menjadi komponen penting dalam menilai keberhasilan dan merumuskan strategi peningkatan kualitas secara berkelanjutan. Namun, analisis kepuasan secara konvensional sering kali tidak mampu mengungkap pola dan segmentasi peserta secara objektif. Penelitian ini bertujuan untuk mengetahui penerapan algoritma K-Means Clustering untuk menganalisis dan mengelompokkan tingkat kepuasan 70 peserta program pelatihan karier berdasarkan tiga aspek evaluasi: evaluasi materi, evaluasi narasumber, dan evaluasi pelaksanaan acara. Pengolahan data dilakukan menggunakan Altair AI Studio 2026 (sebelumnya dikenal sebagai RapidMiner Studio) dengan skala penilaian Likert 1–5 dan normalisasi Min-Max. Evaluasi kualitas cluster menggunakan Davies-Bouldin Index (DBI). Hasil penelitian menunjukkan jumlah cluster optimal adalah K=3 dengan nilai DBI sebesar 0,079. Tiga kelompok yang terbentuk terdiri atas: cluster kepuasan tinggi (68 peserta; 97,1%), cluster kepuasan sedang (1 peserta; 1,4%), dan cluster kepuasan rendah (1 peserta; 1,4%). Distribusi ini mengindikasikan tingkat kepuasan yang sangat tinggi secara keseluruhan, sekaligus mengidentifikasi dua peserta dengan pola ketidakpuasan yang bersifat parsial dan memerlukan perhatian khusus. Hasil segmentasi ini dapat menjadi dasar pengambilan keputusan berbasis data bagi penyelenggara dalam meningkatkan kualitas program pelatihan karier selanjutnya.
Improving Students' Artificial Intelligence Literacy through Hybrid Training in Supporting the Competency of the Society 5.0 Era Supiyandi Supiyandi; Chairul Rizal; Irman Efendi; Muhammad Noor Hasan Siregar; Arief Wibowo
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.1038

Abstract

This community service program aimed to improve university students’ Artificial Intelligence (AI) literacy through hybrid training that supports the competencies required in the Society 5.0 era. The rapid advancement of digital technology has increased the need for students to understand, utilize, and critically evaluate AI technologies in academic and professional contexts. The program was implemented using a hybrid learning approach that combined face-to-face and online learning activities through educational counseling, workshops, interactive discussions, and practical simulations of AI applications. The participants were university students who received training in basic AI concepts, ethical use of AI, digital literacy, and the implementation of AI technologies to support academic activities and twenty-first-century competencies. The instruments used in this activity included training modules, digital presentation media, observation sheets, and pre-test and post-test evaluations to assess participants’ understanding before and after the training sessions. The findings indicated that the hybrid training successfully improved students’ understanding of Artificial Intelligence, enhanced their ability to use AI technologies in academic activities, and increased their awareness of ethical and responsible AI use. Furthermore, the hybrid learning model provided flexible, interactive learning experiences that promoted active participation and strengthened students’ adaptability to the digital transformation in the Society 5.0 era. The program also demonstrated that AI literacy plays a significant role in supporting students’ readiness for technology-driven educational and professional environments. Therefore, hybrid AI literacy training can serve as an effective and relevant model for developing digital competencies in higher education and supporting the transformation of education in the Society 5.0 era.
Comparison of Individual Algorithms (Decision Tree, Naïve Bayes, and Support Vector Machine) and Ensemble Voting in Predicting Students’ On-Time Graduation Based on Course Grades Sevtian Ferdian; Miechael Miechael; Arief Wibowo
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 8 No. 1 (2026): Maret
Publisher : Universitas Wahid Hasyim

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

Abstract

Education plays an important role in improving the quality of human resources and supporting a country’s progress toward becoming a developed nation. Higher education institutions serve as one of the providers of formal education, where the quality of these institutions is measured through accreditation. One of the key indicators influencing accreditation is the outcomes and achievements of the Tri Dharma of higher education, which include the timeliness of student graduation. This study aims to compare models for predicting on-time student graduation using three machine learning algorithms, namely Decision Tree, Naïve Bayes, and Support Vector Machine (SVM), as well as their combination through the Ensemble Voting method. The prediction is based on historical grade data from courses taken during semesters one to four. The research methodology adopts the Cross-Industry Standard Process for Data Mining (CRISP-DM), which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The dataset used in this study consists of 2,471 records with 11 attributes. Data preprocessing was conducted through data cleaning and class balancing using under sampling techniques. The results indicate that the Ensemble Voting model using the Soft Voting method achieves the best performance, with an accuracy of 91.80%, precision of 91.87%, and recall of 91.80%, outperforming the individual models of Decision Tree, Naïve Bayes, and SVM. The implementation of this model can be utilized to predict students’ on-time graduation based on course grade inputs. Therefore, this research can serve as a supporting tool for early detection of potential delays in student graduation.
Purchasing Behavior Based Consumer Segmentation on TikTok Shop in Indonesia Using K-Means Wulan Novita Sari; Arief Wibowo
SEGMEN: Jurnal Manajemen dan Bisnis Vol 22, No 1 (2026): SEGMEN Jurnal Manajemen dan Bisnis
Publisher : FE Program Studi Manajemen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/sjmb.v22i1.9251

Abstract

This study aims to analyze consumer segmentation on TikTok Shop in Indonesia based on purchasing behavior. The rapid growth of social commerce, particularly through TikTok Shop, has changed consumer shopping patterns, creating challenges for businesses in understanding diverse consumer characteristics to design effective marketing strategies.This study uses a quantitative approach with primary data collected through questionnaires distributed to TikTok Shop users in Indonesia. The variables used include Age (X1), Purchase Frequency (X2), and Type of Product Purchased (X3). The data was analyzed using the K-Means clustering method to classify consumers into homogeneous segments. The results of the study show that TikTok Shop consumers can be grouped into several different segments with different purchasing patterns, spending levels, and product preferences. These findings have practical implications for developing targeted and personalized marketing strategies
Artificial Intelligence in Green and Sustainable Investment: a Bibliometric and Systematic Literature Review Kamalia, Antika Zahrotul; Wibowo, Arief; Mahdiana, Deni
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5287

Abstract

Green and sustainable investment has gained increasing global attention due to the urgency of the climate crisis, social demands, and the adoption of Environmental, Social, and Governance (ESG) principles. However, research on the application of artificial intelligence (AI) in this domain remains fragmented and lacks a comprehensive mapping. This study aims to map the trends, research directions, and key findings related to AI in green and sustainable investment using a bibliometric and systematic literature review (SLR) approach. Data were retrieved from the Scopus database and screened with the PRISMA framework, resulting in 24 articles analyzed through VOSviewer and thematic synthesis. The results indicate significant developments in energy efficiency, green buildings, machine learning, and sustainability, alongside an expanding pattern of international collaboration. Nonetheless, limitations remain, including insufficient cross-sectoral integration, limited empirical studies in developing countries, and the lack of AI models that holistically incorporate risk, ESG, and SDGs indicators. The main contribution of this study lies in providing a structured literature mapping that can serve as a foundation for developing more integrative AI frameworks and expanding research contexts to optimize sustainable green investment. These findings are expected to be valuable for researchers and practitioners in advancing innovation and strengthening the AI-driven sustainable finance ecosystem.
Optimizing Bag of Words and Word2Vec with Vocabulary Pruning and TF-IDF Weighted Embeddings for Accurate Chatbot Responses in Indonesian Treasury Services Aprianto, Eko; Mahdiana, Deni; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5370

Abstract

The high volume of support tickets submitted to the HAI DJPb Service Desk has caused delays and inconsistent response quality in payroll-related inquiries across Indonesian treasury work units (Satker). To improve the accuracy and efficiency of public service responses, this research proposes an optimized text-vectorization framework for chatbot development using a hybrid combination of Bag of Words (BoW), Word2Vec, vocabulary pruning, and TF-IDF weighted embeddings. The dataset consists of 2024 ticket logs, curated FAQs, and questionnaire data related to the Satker Web Payroll Application. The method includes preprocessing (snippet removal, normalization, tokenization, stopword removal, stemming), vocabulary pruning based on empirical frequency thresholds (<5 and >80) while preserving domain-specific technical terms, and semantic weighting through TF-IDF. Four vectorization models—BoW, BoW with pruning, Word2Vec, and Word2Vec + TF-IDF—were evaluated using cosine similarity, response time, and accuracy. Results show that BoW achieved the highest accuracy of 88.32%, while Word2Vec produced the most stable response time with an average of 47.32 ms and a cosine similarity of 0.99. The findings demonstrate that frequency-based representations remain highly effective for structured administrative datasets, while weighted embeddings improve semantic relevance. This study contributes to the field of Informatics by providing an efficient hybrid vectorization framework tailored for Indonesian administrative language, enabling more accurate and scalable chatbot solutions for e-government services.
Random Forest and Artificial Neural Network Data Mining for Environmental and Public Health Risk Modeling in Flood-Prone Urban Areas of Indonesia Mahdiana, Deni; Ebine, Masato; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5387

Abstract

Floods in urban Indonesia pose severe environmental and public health challenges, exacerbating water contamination, vector proliferation, and disease outbreaks. Rapid urbanization, inadequate drainage systems, and climate change have intensified these impacts, emphasizing the need for integrated predictive frameworks. This study aims to develop a Data Mining (DM)-based modeling approach that combines environmental and health indicators to predict flood-related disease risks. Random Forest (RF) and Artificial Neural Network (ANN) algorithms were applied to multi-domain datasets from 30 flood-prone urban sub-districts between 2018 and 2023, encompassing rainfall, drainage density, land use, and water quality variables, integrated with disease incidence data such as diarrhea, dengue, and leptospirosis. The ANN model achieved superior predictive performance (93% accuracy, AUC 0.93) compared to RF (90% accuracy, AUC 0.90), identifying rainfall intensity, drainage density, and coliform contamination as the most influential predictors. These results demonstrate the capability of AI-driven DM techniques to capture complex interdependencies between environmental and health systems. The developed framework contributes to the field of informatics by providing a scalable, data-driven early warning tool for flood-related health risks, supporting evidence-based decision-making in disaster risk management and enhancing public health resilience in rapidly urbanizing regions.
Pengaruh Kualitas Sistem dan Kualitas Pelayanan Terhadap Loyalitas Pengguna dengan Kepuasan Pengguna Sebagai Variabel Intervening Aplikasi Digital Korlantas pada Kantor Polisi Sektor Ciledug Kota Tangerang Rizky Tarmudzi; Arief Wibowo
Economic Reviews Journal Vol. 5 No. 1 (2026): Economic Reviews Journal
Publisher : Masyarakat Ekonomi Syariah Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/mrj.v5i1.973

Abstract

The digital transformation of public services requires government institutions, including the Indonesian National Police (Polri), to provide efficient and responsive technology-based services. The Digital Korlantas application is one of Polri’s initiatives aimed at facilitating online traffic-related services. However, low user satisfaction and loyalty—particularly in local sectors such as the Ciledug Police Sector, Tangerang City—indicate challenges in its implementation. This study aims to analyze the influence of system quality and service quality on user loyalty, with user satisfaction as a mediating variable. A quantitative approach was used with Partial Least Square Structural Equation Modeling (PLS-SEM) as the analytical technique. The sample consisted of 100 respondents who were users of the Digital Korlantas application within the jurisdiction of the Ciledug Police Sector. The results show that both system quality and service quality have a positive and significant impact on user satisfaction. Furthermore, user satisfaction significantly mediates the relationship between system and service quality and user loyalty. These findings highlight the critical role of improving both technical and service aspects of the application to enhance user satisfaction and loyalty in the context of digital public services.
Penerapan Data Mining Menggunakan Teknik Classification Untuk Melihat Potensi Kepatuhan Wajib Pajak Badan Anuqman Fitriadi; Qamarullah Popalia; Arief Wibowo
JURIKOM (Jurnal Riset Komputer) Vol. 13 No. 1 (2026): Februari 2026
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/jurikom.v13i1.9354

Abstract

The application of data mining using classification techniques has significant potential to assist tax authorities in identifying and mapping the compliance levels of corporate taxpayers. This study aims to develop a corporate taxpayer compliance classification model using the Naive Bayes algorithm based on the ratio of Annual Tax Return (SPT) filing and the ratio of tax payments. The data used consist of aggregated data from Tax Service Offices (Kantor Pelayanan Pajak/KPP) for the 2022–2024 period obtained from the Directorate General of Taxes. The research stages follow the Knowledge Discovery in Databases (KDD) methodology, which includes data selection, preprocessing, transformation, modeling, and evaluation. The experimental results indicate that the Naive Bayes model is able to classify compliance levels with an accuracy of 100%, precision of 1.00, recall of 1.00, and an F1-score of 1.00. These findings suggest that the SPT filing ratio is the dominant factor in determining corporate taxpayer compliance. The proposed model can be utilized as a decision support system to assist tax authorities in determining supervision and guidance priorities for corporate taxpayers
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