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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi POSITIF Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi Jurnal Pendidikan Informatika dan Sains Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Computatio : Journal of Computer Science and Information Systems RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Khatulistiwa Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Pilar Nusa Mandiri JTERA (Jurnal Teknologi Rekayasa) Jurnal Sains dan Informatika INOVTEK Polbeng - Seri Informatika Matrix : Jurnal Manajemen Teknologi dan Informatika JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal Jurnal Informatika Universitas Pamulang Jurnal Teknoinfo Jurnal Sisfokom (Sistem Informasi dan Komputer) KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Indonesian Journal of Applied Informatics KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Teknologi Terapan Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika JISICOM (Journal of Information System, Infomatics and Computing) EVOLUSI : Jurnal Sains dan Manajemen Building of Informatics, Technology and Science JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) JISKa (Jurnal Informatika Sunan Kalijaga) Jurnal Teknologi Informasi dan Multimedia Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) JUSTINDO (Jurnal Sistem dan Teknologi Informasi Indonesia) JISA (Jurnal Informatika dan Sains) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Teknik Informatika (JUTIF) Jurnal Digit : Digital of Information Technology Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Science in Information Technology Letters Journal of Soft Computing Exploration Jurnal Indonesia : Manajemen Informatika dan Komunikasi Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer International Journal Software Engineering and Computer Science (IJSECS) Jurnal Sains dan Teknologi Jurnal Sains dan Teknologi International Journal Science and Technology (IJST) Malcom: Indonesian Journal of Machine Learning and Computer Science Journal of Scientific Research, Education, and Technology Jikom: Jurnal Informatika dan Komputer Journal of Data Science Theory and Application NERO (Networking Engineering Research Operation) SmartComp Jurnal Ilmu Komputer dan Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi Emitor: Jurnal Teknik Elektro IJISCS (International Journal of Information System and Computer Science)
Donny Avianto
Universitas Teknologi Yogyakarta
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Klasifikasi Kelayakan Bantuan Pendidikan Menggunakan Metode Decision Tree Ida Kumala Sari; Arief Hermawan; Donny Avianto
INSOLOGI: Jurnal Sains dan Teknologi Vol. 5 No. 3 (2026): Juni 2026
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/insologi.v5i3.8104

Abstract

This study aims to implement the Decision Tree method to classify scholarship eligibility based on student data. The dataset used consisted of 4,424 student records with 35 numerical attributes covering academic, administrative, and socioeconomic aspects. The preprocessing stage included data quality checking, feature selection, and dataset splitting into training and testing data with a ratio of 70:30. From the initial 35 attributes, 16 main attributes were selected as the most relevant features to the target variable. The classification model was developed using the Decision Tree algorithm and evaluated using a confusion matrix, accuracy, precision, recall, and F1-score. The results showed that the model achieved an accuracy of 75.08% and a weighted average F1-score of 75.16%. Attributes such as Curricular units 2nd sem (approved), Curricular units 2nd sem (grade), and Tuition fees up to date were identified as the most influential factors in the classification process. Although the dataset had a class imbalance condition, this study maintained the original data distribution without applying oversampling techniques such as SMOTE in order to preserve the actual conditions of the scholarship selection process. In addition to providing fairly good classification performance, the Decision Tree method was also able to produce a transparent, interpretable, and easy-to-understand model through the resulting decision tree structure.
KLASIFIKASI ALGORITMA K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE UNTUK PREDIKSI STATUS KELULUSAN MAHASISWA S1: COMPARATION OF K-NEAREST NEIGHBOR, NAIVE BAYES, DECISION TREE TO PREDICT UNDERGRADUATE STUDENTS TO GRADUATE ON TIME Enggar Novianto; Arief Hermawan; Donny Avianto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3434

Abstract

Students are a crucial factor that must be considered in seriously evaluating study programs. The indicator of the success of the study program is the length of time it takes to complete the study. The study period is the time when students complete their studies. In addition, student study time reflects the level of student learning performance. In a broader perspective, the average student study time affects the quality of study programs and therefore student study time is used as one of the criteria in determining the assessment by the National Accreditation Board for Higher Education (BAN PT). The purpose of this study was to understand how well the K-Nearest Neighbor, Naive Bayes, Decision Tree performed to predict undergraduate students of the Law Study Program, Faculty of Law, Sebelas Maret University, graduating on time using the RapidMiner application. From the results of the testing and prediction process with the RapidMiner application using the three methods that have been carried out. The K-Nerest Neighbor (KNN) method obtained an accuracy of 96.67%, in the prediction test using the Naïve Bayes method it obtained an accuracy of 77.33%, while the Decision Tree method obtained an accuracy of 94.00%. So that the K-NN method is the best method in comparative classification in predicting student graduation on time with a predicted accuracy value of 96.67%.
Prediksi Promosi Pegawai dengan Stacking Ensemble dengan SMOTE-ENN dan SHAP Andri Yudha Pratama; Arief Hermawan; Donny Avianto
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9879

Abstract

The paradigm of human resource management in the digital era demands an objective and data-driven employee promotion process. However, the extreme class imbalance (ratio 10.74:1) has the potential to introduce bias against minority groups that deserve promotion. This study proposes a stacking ensemble classification framework consisting of Random Forest, XGBoost, and LightGBM as base learners and Logistic Regression as a meta-learner, with the integration of SMOTE-ENN and two-level SHAP interpretability. This study shows that the application of SMOTE-ENN before cross-validation can result in a biassed performance estimate of up to +110% on the F1-Score; thus, the use of imblearn.Pipeline is proposed, which restricts resampling only to the training fold. Based on the evaluation using 10-fold stratified cross-validation free from data leakage, the stacking ensemble model achieved an accuracy of 0.9022, precision of 0.4342, recall of 0.4889, F1-score of 0.4598, and AUC-ROC of 0.8053. Although it did not achieve the highest F1 score, this model attained the best recall value among competitive models, making it relevant for contexts sensitive to false negative errors. SHAP analysis identifies avg_training_score, age, and performance_index as the main determinants of promotion decisions. The proposed framework provides a methodological contribution to model evaluation on imbalanced data while offering a more transparent and accountable decision support system to support the implementation of meritocracy in both government and corporate organisations.
Educational data mining for informatics grade prediction using a hybrid K-means framework Roselilie Simbulan; Arief Hermawan; Donny Avianto
Jurnal Pendidikan Informatika dan Sains Vol. 15 No. 1 (2026): Jurnal Pendidikan Informatika dan Sains
Publisher : Universitas PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/saintek.v15i1.10728

Abstract

This study proposes a two-phase hybrid Educational Data Mining (EDM) framework that integrates K-Means clustering with supervised classification to predict students' final grade categories in Informatics within the Kurikulum Merdeka competency-based assessment system. The dataset consists of 281 Grade X students, all of whom achieved scores above the Minimum Achievement Criterion (KKTP = 75), with prediction focused on three grade categories: A (≥86), B (80–85), and C (75–79). Four summative assessment scores (S1, S7, S8, and S9) were used as input features. In the first phase, K-Means generated three clusters (Silhouette Score = 0.3198), and the resulting cluster labels were added as an additional feature. In the second phase, Random Forest and Logistic Regression were optimized using Grid Search with 5-Fold Stratified Cross-Validation, while SMOTE was employed to address class imbalance. The results show that Logistic Regression outperformed Random Forest, achieving a test accuracy of 59.65% and a Macro F1-Score of 0.5899, whereas Random Forest achieved 49.12% accuracy and a Macro F1-Score of 0.4799 and exhibited signs of overfitting. Feature importance analysis identified S7, S8, and S9 as the most influential predictors, while the cluster-derived feature contributed more strongly to Random Forest than to Logistic Regression. These findings suggest that well-regularized linear models may generalize better than ensemble methods on small datasets with narrow score distributions. The proposed framework is best positioned as a screening-support tool for early formative intervention in competency-based educational settings.
Implementasi Sistem Klasifikasi Batik Menggunakan MobileNet dengan Integrasi Chatbot Retrieval Augmented Generation Rizki Purnomo Pratama; Donny Avianto
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.863

Abstract

As an Indonesian cultural heritage recognized by UNESCO, batik features various motifs laden with philosophical values, yet public knowledge about batik patterns and their significance remains limited. This study presents a mobile-based batik classification system integrating MobileNetV2 architecture with a Retrieval-Augmented Generation (RAG) chatbot to provide interactive learning experiences, enabling users to identify batik patterns through image recognition while obtaining detailed information via conversational AI.This study adopts MobileNetV2 considering its efficiency on mobile devices. This model achieves an optimal balance between accuracy and computational performance. Model was trained on a balanced dataset of 5,000 images covering five pattern classes (Parang, Truntum, Kawung, Mega mendung, and Merak), achieving training accuracy of 98.97% and testing accuracy of 96.8%. The RAG-based chatbot, orchestrated using LangChain and Qdrant, enhances user interaction by retrieving relevant information from a curated knowledge base, ensuring contextual factual responses about batik's history, philosophy, and cultural significance. React Native was adopted as the development framework to ensure cross-platform operability. This implementation contributes to cultural heritage preservation by making batik knowledge more accessible through modern technology, combining computer vision and natural language processing in a unified platform.
Analisis Pengaruh Preprocessing Data dan Hyperparameter Tuning pada Backpropagation Neural Network dalam Klasifikasi Stroke Asrul Gunawan; Arief Hermawan; Donny Avianto
Bulletin of Computer Science Research Vol. 6 No. 2 (2026): February 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i2.956

Abstract

Data imbalance and scale differences between features are often the main factors that reduce the performance of neural network-based classification models. This study aims to analyze the effect of data preprocessing and hyperparameter tuning on the performance of Backpropagation Neural Network (BPNN) in stroke classification. This study used a stroke dataset from the Kaggle platform consisting of 5,110 patient data with 10 clinical features. The evaluation was conducted using five schemes and consisted of several data balancing techniques. These techniques include no balancing, SMOTE, and ADASYN. In addition, the evaluation also involved data normalization including no normalization, MinMaxScaler, and Z-Score. The BPNN model used has an architecture of 19 input neurons, 29 neurons in the hidden layer, and 1 output neuron. Hyperparameter tuning was performed by finding the best learning rate and number of epochs. The evaluation results showed that the model in scheme one has limitations. This limitation is most visible in identifying stroke classes. The application of SMOTE and MinMaxScaler in scheme two proved that the results were better and its performance increased significantly. On the other hand, the combination of ADASYN and Z-Score in scheme three showed more stable performance and was able to detect stroke cases more accurately. The hyperparameter tuning process in schemes four and five also proved to improve performance. The best results were obtained in scheme five, with an accuracy of 96.47%, a precision of 97.34%, a recall of 95.62%, and an F1-score of 96.47%. These findings indicate that the combination of adaptive balancing techniques, distribution-based normalization, and optimal parameter tuning is very effective in improving the accuracy and stability of BPNN for stroke classification.
Speech-Based Virtual Assistant for Mental Health Support Through Natural Interaction Dimas Rizqi Kurniawan; Donny Avianto
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 6 (2025): December 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i6.6585

Abstract

Mental health is a significant global concern. Indonesia has reported high rates of depression and anxiety, compounded by limited emotional outlets. Although AI virtual assistants are prevalent in e-commerce and education, their application in mental health remains underexplored. Existing solutions are predominantly text-based and transactional, which restricts empathetic and natural interactions. This study develops a voice-based assistant by integrating Automatic Speech Recognition (ASR), a generative AI for empathetic responses, and a Text-to-Speech (TTS) module fine-tuned on an Indonesian dataset to adapt accent and prosody. The system underwent both technical evaluation and human testing to assess its feasibility and user experience. The results showed that the TTS model converged effectively with low loss. Human evaluation indicated 'good' interaction (MS = 3.91, SD = 0.02), 'good' AI responses (MS = 3.83, SD = 0.26), and 'fair' TTS naturalness (MOS = 3.27, SD = 0.05). Most participants found the assistant's responses meaningful, pleasant, and helpful in managing low to moderate anxiety. These results suggest that a voice-based assistant has the potential to support mental health in Indonesia. Future work should enhance speech naturalness and utilize a larger participant pool for evaluation.
Pendekatan Hybrid: Naïve Bayes dan Decision Tree untuk Prediksi Kerusakan Mesin pada Industri Manufaktur PT X Iin Rohmatika Aulia; Arief Hermawan; Donny Avianto
IJAI (Indonesian Journal of Applied Informatics) Vol 9, No 2 (2025)
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/ijai.v9i2.97905

Abstract

Abstrak : Perkembangan teknologi sistem informasi banyak dirasakan di setiap sector ekonomi. PT X merupakan perusahaan manufaktur di bidang percetakan, dimana produktivitas dipengaruhi dari efisiensi mesin. Optimasi produktivitas mesin dapat dilakukan dengan prediktif maintenance. Penelitian ini bertujuan untuk mengembangkan teknik data mining dalam prediktif kerusakan mesin produksi. Fokus utama penelitian adalah untuk mengklasifikasi kerusakan mesin berdasarkan data historis pada PT X. Model klasifikasi yang akan dikembangkan menggunakan algoritma model Naïve Bayes dan Decision Tree. Dalam klasifikasi ada 2 label keputusan yaitu tingkat resiko (tinggi, sedang rendah) dan kegiatan preventif (Ya,Tidak) Evaluasi dilakukan dengan menilai akurasi dan efektivitas setiap model. Hasil uji klasifikasi preventif dengan model Naïve Bayes memiliki nilai akurasi 97,90 %, sedangkan dengan model Decision Tree memiliki nilai akurasi 77%. Hasil uji klasifikasi tingkat resiko dengan model Naïve Bayes nilai akurasi 98% sedangkan dengan model Decision Tree nilai akurasinya 100%. hasil uji menunjukan untuk label preventif dengan 2 kelas lebih baik menggunakan model Naïve Bayes sedangkan label tingkat resiko dengan 3 kelas lebih baik menggunakan model Decision Tree. Hasil uji ini dapat dijadikan acuan Perusahaan X khususnya divisi maintenance dalam melakukan penjadwalan prediktif maintenance. Metode ini juga dapat diterapkan pada Perusahaan lain jika memiliki data historis kerusakan mesin, memiliki mesin dengan jenis operasional yang relevan, dan memiliki tujuan dan klasifikasi yang sesuai.===================================================Abstract :The advancement of information system technology has significantly impacted all economic sectors. PT X, a manufacturing company in the printing industry, experiences productivity fluctuations that are strongly influenced by machine efficiency. Optimizing machine productivity can be achieved through predictive maintenance. This study aims to develop data mining techniques for predicting machine failures in production. The primary focus is to classify machine failures based on historical data from PT X. The classification models employed are the Naïve Bayes algorithm and the Decision Tree algorithm. Two classification labels are used: risk level (high, medium, low) and preventive action (Yes, No). Evaluation was conducted by measuring the accuracy and effectiveness of each model. The classification results for the preventive action label showed that the Naïve Bayes model achieved an accuracy of 97.90%, while the Decision Tree model reached 77%. For the risk level label, the Naïve Bayes model achieved 98% accuracy, and the Decision Tree model achieved 100%. The findings indicate that the Naïve Bayes model is more suitable for binary classifications such as preventive actions, while the Decision Tree model performs better in multi-class classifications such as risk levels. These results can serve as a reference for PT X’s maintenance division in scheduling predictive maintenance. Moreover, the method can be applied to other companies, provided they have historical machine failure data, machines with similar operational characteristics, and compatible classification objectives
Segmentasi Nasabah Kartu Kredit Berdasarkan Pola Transaksi untuk Penentuan Profil Nasabah Irfan Budiyanto; Arief Hermawan; Donny Avianto
JURNAL INFORMATIKA DAN KOMPUTER Vol 9, No 3 (2025): Oktober 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat - Universitas Teknologi Digital Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26798/jiko.v9i3.1669

Abstract

Segmentasi nasabah kartu kredit penting untuk optimasi strategi pemasaran dan personalisasi layanan. Penelitian ini mengusulkan sistem segmentasi nasabah berdasarkan pola transaksi, yaitu frekuensi dan nilai transaksi, menggunakan algoritma K-Means Clustering. Dataset dari Kaggle, yang telah melalui tahap preprocessing, digunakan untuk mengidentifikasi cluster optimal. Metode Elbow dan Silhouette digunakan untuk menentukan jumlah cluster, dan keduanya mengindikasikan jumlah cluster optimal sebanyak 3, dengan titik siku pada grafik inersia di k=3 dan skor Silhouette tertinggi juga di k=3.  Hasilnya, terdapat tiga cluster nasabah: nasabah aktif tarik tunai (ditandai dengan tingginya penggunaan cash advance), nasabah pasif (dengan frekuensi dan nilai transaksi rendah), dan nasabah aktif transaksi pembelian (dengan aktivitas pembelian tinggi dan penggunaan cash advance rendah). K-Means terbukti efektif dalam membagi nasabah menjadi tiga cluster berbeda ini.  Segmentasi ini memungkinkan strategi pemasaran yang lebih tertarget, seperti penawaran produk finansial yang relevan untuk setiap cluster, dan pada akhirnya dapat meningkatkan kepuasan nasabah serta profitabilitas.
Perbandingan Model Deep Learning LSTM, GRU, dan Bi-LSTM untuk Prediksi Hujan Harian Australia Menggunakan Teknik SMOTE Risnanto, Ari; Nugroho, Bayu Tri; Hermawan, Arief; Avianto, Donny
JURNAL FASILKOM Vol. 16 No. 2 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i2.11691

Abstract

Climate change causing increasingly erratic rainfall patterns, triggering an increase in hydrometeorological disasters such as floods, droughts, and declining agricultural productivity. Therefore, accurate rainfall prediction is crucial for mitigation and decision-making. However, previous research often focuses solely on accuracy metrics without evaluating the model's computational burden, and often ignores the problem of class imbalance in weather datasets. This study evaluates the performance and computational efficiency of LSTM, GRU, and Bi-LSTM deep learning models for daily rainfall prediction using the historical Australian meteorological dataset weatherAus. The novelty of this study lies in the comprehensive mapping between predictive quality and resource efficiency after dataset balancing. The preprocessing stage includes handling missing values, categorical data transformation, data leakage prevention, data sharing, and the application of SMOTE oversampling. The results of the area under the curve (AUC-ROC) evaluation show that the GRU model is superior with a value of 0.85, surpassing LSTM and Bi-LSTM, respectively, at 0.84. In the rain class recall metric, GRU again leads (0.70), compared to LSTM (0.67), and Bi-LSTM (0.57). Computational evaluation, GRU is significantly more efficient with the fastest training time (1,306.26 seconds), followed by LSTM (2,259.12 seconds), and Bi-LSTM (13,348.43 seconds). Peak RAM usage relatively comparable, GRU (2,053.77 MB), LSTM (1,971.47 MB), and the highest Bi-LSTM (2,242.60 MB). These findings conclude that GRU is recommended as the most optimal model that balances accuracy and efficiency, LSTM as an alternative, while Bi-LSTM is considered less effective. Future research recommended to explore hybrid architectures or ensemble learning to capture more complex spatiotemporal patterns.
Co-Authors Adicahya, Bina Sukma Adityo Permana Wibowo Alwani, Adie G. Amalia Rizki Wulandari Andri Yudha Pratama Apriansyah, Ferryma Arba Ardiansyah, Diky Aribowo Aribowo Arief Hermawan Arieska Restu Harpian Dwika Ashari, Nadia Asrul Gunawan Aziz Perdana Baiq Nurul Azmi Bayu Tri Nugroho, Bayu Tri Bimantoro, Nazar Iqbal Bowo Hirwono Budiyanto, Irfan Cahaya Muzaddidah Dewi, Amelia Citra Dian Wijayanti Dimas Dwi Kurniawan Dimas Rizqi Kurniawan Dwi Ratnawati, Dwi Edi Priyanto Enggar Novianto Enggar Novianto Erfin Nur Rohma Khakim Fadhila, Arifa Farras Fadilah, Faiz Fahri Putra Herlambang Fakharudin, Panji Rangga Adzan Fajar Faqih, Allan Bil Febiansyah Annaufal Ahnaf Fauzi Ferdinandus Edwin Penalun Gumilang, Muhammad Satrio Gunawan, Asrul Hanif, Rifqi Fadhlurrahman Hardiyantari, Oktavia Ida Kumala Sari Iin Rohmatika Aulia Ilmy Eka Handayani Imantoko Imantoko Indra Maulana Iqbal, Muhammad Izza Irfan Budiyanto Jagad Raya Ramadhan Khalifatur Rauf Kusumastuti, Asriana Dyah Laode Izat Trianto Haradin Lidya Nurmala Eva Maulana, Adha Muh Arifandi Muhammad Irsyad Indra Fata Muhammad Kusban Muhammad Rizki Muhammad Rizki Nasmah Nur Amiroh Novaldy, Olwin Kirab Nur Widiastuti Nurazila, Siti Octavianus, Yonathan Perdana, Aziz Purba, Yurjaa Ghoniyyan Putra, Kristianto Pratama Dessan Rahma Nur Azizah Reski Noviana Rian Oktafiani Rian Oktafiani Rianto Rianto Risnanto, Ari Rizarta, Rusma Eko Fiddy Rizki Purnomo Pratama Rizky Samudra Falasyfa Roselilie Simbulan Roy Fasti Rubangi Rubangi Rudi, Rudiono Rusma Eko Fiddy Rizarta Saputra, Candra Heru Satriya Adhitama Setiawan, Muhhamad Ajun Siti Rokhanah Soraya Fatmawati Sri Wulandari SRI WULANDARI Sutarman Sutarman Syafrudin, Teguh Syahab, Alfin Syarifuddin Teguh Syafrudin Tri Untoro, Iwan Hartadi Tri Widodo Vivianti Wahid, Ach. Nur Aqil Wayan Praka