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All Journal Semantik TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal Pilar Nusa Mandiri JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan ILKOM Jurnal Ilmiah Jurnal Tekno Insentif JURTEKSI Jurnal Teknologi Terpadu Building of Informatics, Technology and Science Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Algoritma Electronic Journal of Education, Social Economics and Technology Jurnal Informatika: Jurnal Pengembangan IT Journal of Informatics and Interactive Technology (JIITE) INJECT Interdisciplinary Journal of Communication Journal of Digital Market and Digital Currency
Fandy Setyo Utomo
Universitas AMIKOM Purwokerto
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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Sentiment Analysis of pegipegi.com Review on Google Play Store with Naïve Bayes Balit, Muhamad Naufal Burhanuddin; Utomo, Fandy Setyo
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3913

Abstract

In the current era, a shift in consumer behavior is evident in the use of online platforms for booking tickets, involving various services such as flights, hotels, trains, buses, and entertainment. PegiPegi.com, as a rapidly growing online travel agent in Indonesia, demonstrates success by understanding the value of technology and maintaining strong partnerships. This phenomenon also impacts sentiment analysis, where users of this platform often provide reviews. This research aims to apply the Naïve Bayes classification method in sentiment analysis of PegiPegi.com reviews, focusing on understanding customer satisfaction and service improvement. By combining these approaches, the study contributes to a deeper understanding of user responses to OTA services and presents the evaluation results of the Multinomial Naive Bayes classification model with an accuracy rate of 89.5%. The high precision in the Negative class indicates the model's ability to identify negative reviews. However, there are challenges in classifying the Neutral class, suggesting potential for further improvement. Nevertheless, the F1-score of 0.522 reflects a good balance between overall precision and recall.
K-MEANS ALGORITHM TO DETERMINE MARKETING STRATEGY AT CODEVERSE COMPUTER ACCESSORIES STORE Burhanuddin Balit, Muhamad Naufal; Utomo, Fandy Setyo
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 2 (2024): Maret 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i2.3064

Abstract

Abstract: Artificial Intelligence (AI) is currently gaining popularity across various industries, including healthcare, finance, and others. In this study, AI technology is employed to devise an optimal marketing strategy for Code Verse Computer Accessories Store using the K-Means algorithm. As part of machine learning, the K-Means algorithm, categorized under unsupervised learning, is implemented to cluster sales data for computer accessory products over the last three months of 2023. The results of the K-Means analysis identify two main clusters. Cluster one (Cluster 1) comprises products such as Mouse, Keyboard, Monitor, Headset, and Speaker, indicating consistent purchasing patterns and high consumer interest. Recommendations are made to increase stock for Cluster 1. Meanwhile, Cluster two (Cluster 2) consists of Mic products with lower interest, and it is not advisable to increase stock. The implementation of K-Means provides insights into purchasing patterns, enabling Code Verse to develop more effective marketing and inventory management strategies. Keywords: K-Means algorithm; artificial intelligence; clustering  Abstract: Kecerdasan Buatan (AI) kini meraih popularitas dalam berbagai industri, termasuk sektor kesehatan, keuangan, dan lainnya. Pada penelitian ini, teknologi AI digunakan untuk merancang strategi pemasaran optimal bagi Toko Aksesoris Komputer CodeVerse dengan menggunakan Algoritma K-Means. Sebagai bagian dari machine learning, Algoritma K-Means, yang termasuk dalam kategori unsupervised learning, diimplementasikan untuk mengelompokkan data penjualan produk selama tiga bulan terakhir tahun 2023. Hasil dari analisis K-Means mengidentifikasi dua cluster utama. Cluster pertama (Cluster 1) terdiri dari produk Mouse, Keyboard, Monitor, Headset, dan Speaker, menunjukkan pola pembelian yang konsisten dan tingginya minat konsumen. Rekomendasi untuk menambah stok diberikan. Sementara itu, Cluster kedua (Cluster 2) terdiri dari produk Mic dengan minat lebih rendah, dan tidak disarankan untuk menambah stok. Implementasi K-Means memberikan wawasan tentang pola pembelian, memungkinkan CodeVerse mengembangkan strategi pemasaran dan manajemen persediaan yang lebih efektif.            Keywords: Algoritma k-means; kecerdasan buatan; clustering
PROGRAM PENDAMPINGAN PENULISAN ILMIAH UNTUK MENINGKATKAN JUMLAH PUBLIKASI ILMIAH MAHASISWA Sarmini, Sarmini; Saputro, Rujianto Eko; Utomo, Fandy Setyo; Hidayatulloh, Hanif; Indriyani, Ria; Ramadhan, Rio Fadly
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 7, No 4 (2023): December
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v7i4.17570

Abstract

ABSTRAKKurangnya pengetahuan dan keterampilan mahasiswa Fakultas Ilmu Komputer untuk menulis artikel ilmiah menyebabkan rendahnya jumlah luaran jurnal ilmiah yang dihasilkan oleh mahasiswa sebagai luaran program Merdeka Belajar Kampus Merdeka (MBKM) di semester gasal tahun akademik 2022/2023. Berdasarkan permasalahan tersebut, kami mengusulkan pelaksanaan kegiatan webinar pendampingan penulisan jurnal ilmiah bagi mahasiswa Fakultas Ilmu Komputer yang bertujuan untuk meningkatkan kemampuan menulis artikel ilmiah dan meningkatkan jumlah publikasi ilmiah mahasiswa sebagai luaran program Merdeka Belajar Kampus Merdeka. Ada tiga tahapan dalam pelaksanaan kegiatan yaitu tahap persiapan, implementasi dan evaluasi. Kegiatan pendampingan dilakukan secara daring dengan menghadirkan narasumber dari Universiti Teknikal Malaysia (UTeM) dan diikuti oleh 117 mahasiswa yang mengikuti program MBKM di semester genap 2022/2023. Dampak dari kegiatan webinar yang telah dilaksanakan yakni meningkatnya jumlah publikasi artikel ilmiah mahasiswa Fakultas Ilmu Komputer sebagai luaran program MBKM di semester genap tahun akademik 2022/2023. Berdasarkan data yang diperoleh dari Fakultas Ilmu Komputer Universitas AMIKOM Purwokerto terdapat sejumlah 192 tulisan artikel ilmiah mahasiswa yang telah dikirimkan ke beberapa jurnal nasional. Kata kunci: assistance; merdeka belajar kampus merdeka; scientific writing; collaboration; research ABSTRACTComputer Science Faculty students' lack of knowledge and skills to write scientific articles has resulted in the low number of scientific journals produced by students as outputs of the Merdeka Belajar Kampus Merdeka (MBKM) Program in the odd semester of the 2022/2023 academic year. Based on these problems, we propose implementing a webinar to assist scientific journal writing for students of the Faculty of Computer Science, which aims to improve the ability to write scientific articles and increase the number of students' scientific publications as an output of the Merdeka Belajar Kampus Merdeka program. There are three stages in implementing activities: preparation, implementation, and evaluation. Mentoring activities were carried out online by presenting resource persons from Universiti Teknikal Malaysia (UTeM) and were attended by 117 students taking part in the MBKM program in the even semester 2022/2023. The impact of the webinar activities that have been carried out is the increase in the number of publications of scientific articles by Faculty of Computer Science students as an output of the MBKM program in the even semester of the 2022/2023 academic year. Based on data from the Faculty of Computer Science, Universitas AMIKOM Purwokerto, 192 student scientific articles have been submitted to several national journals. Keywords: accompaniment; study center; games; collaboration; research
Factors Influencing User Adoption of Mobile Payment System: An Integrated Model of Perceived Usefulness, Ease of Use, Financial Literacy, and Trust Utomo, Fandy Setyo; Suryana, Nanna; Azmi, Mohd Sanusi
Journal of Digital Market and Digital Currency Vol. 2 No. 2 (2025): Regular Issue June 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jdmdc.v2i2.31

Abstract

In the digital age, mobile payment systems have revolutionized financial transactions by offering convenience, efficiency, and security. This study aims to explore the factors influencing the adoption of the mobile payment system in Indonesia, focusing on perceived usefulness (PU), perceived ease of use (PEU), financial literacy (FL), and perceived trust (PT). Data was collected from 400 respondents using an online survey and analyzed using SmartPLS 3 software. The results indicate that PU and PEU significantly impact users' intention to use (BI) the mobile payment system, with path coefficients of 0.928 (t-value = 28.570) and 0.955 (t-value = 154.251) respectively. PEU also positively influences PU (β = 0.955, p < 0.001). FL was found to affect PT significantly (β = 0.222, p = 0.006), which in turn influences BI (β = 0.068, p = 0.059), although the direct effect of PT on BI was marginally non-significant. The R^2 values for BI, PT, and PU were 0.977, 0.814, and 0.912 respectively, indicating a high explanatory power of the model. This study extends the Technology Acceptance Model (TAM) by integrating FL and PT, providing a comprehensive understanding of the factors driving mobile payment adoption. The findings offer valuable insights for developers, service providers, and policymakers to enhance user experience, build trust, and improve FL, ultimately promoting higher adoption rates of mobile payment systems. Future research should consider a more diverse population and explore additional factors such as social influence and facilitating conditions to validate and extend these findings further.
Optimasi Algoritma Pemilihan Soal pada POMDP Berbasis Advantage Actor-Critic untuk Model Ujian Adaptif Anggriani, Epri; Setyo Utomo, Fandy
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.22-2.3004

Abstract

Evaluation in learning through assessment plays an important role as a measure of success and assesses student competency achievement. In this context, CAT as an IRT-based adaptive assessment solution has been widely used, but has technical limitations such as heuristic question selection, dependence on question banks, and being undimensional. In addition, to solve decision-making problems in the context of adaptive testing, a general approach that can be used is policy-based reinforcement learning, such as policy gradient, particularly the REINFORCE algorithm. However, this algorithm has limitations such as high gradient variance and lacks a state-value function evaluation mechanism, making it unable to provide direct feedback on the quality of the actions taken. The purpose of this study is to optimize adaptive decision making in the POMDP framework using the Advantage Actor-Critic (A2C) algorithm, one of the Reinforcement Learning approaches. The actor generates a question selection policy based on the belief state of the NCDM model, while the critic evaluates the quality of actions to maximize cumulative rewards. The results show that in an adaptive environment, A2C performs better than the baseline, with an accuracy of 0.952 and an average reward of 18.56 in 20-question episodes, and an accuracy of 0.934 and a reward of 22.58 in 25-question episodes. In contrast, the baseline only achieved an average accuracy of around 0.789 and 0.760 in the 20 and 25 question episodes, and a reward of 14.19 and 16.80 in the 20 and 25 question episodes. The results of the study show that optimization with A2C can improve the personalization of exam question selection. This study contributes to the development of a more effective adaptive exam model, while also opening up opportunities for further research.
IMPROVING HANDWRITTEN DIGIT RECOGNITION USING CYCLEGAN-AUGMENTED DATA WITH CNN–BILSTM HYBRID MODEL Muhtyas Yugi; Utomo, Fandy Setyo; Barkah, Azhari Shouni
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 2 (2025): JITK Issue November 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i2.6982

Abstract

Handwritten digit recognition presents persistent challenges in computer vision due to the high variability in human handwriting styles, which necessitates robust generalization in classification models. This study proposes an advanced data augmentation strategy using Cycle-Consistent Generative Adversarial Networks (CycleGAN) to improve recognition accuracy on the MNIST dataset. Two architectures are evaluated: a standard Convolutional Neural Network (CNN) and a hybrid model combining CNN for spatial feature extraction and Bidirectional Long Short-Term Memory (BiLSTM) for sequential pattern modeling. The CycleGAN-based augmentation generates realistic synthetic images that enrich the training data distribution. Experimental results demonstrate that both models benefit from the augmentation, with the CNN-BiLSTM model achieving the highest accuracy of 99.22%, outperforming the CNN model’s 99.01%. The study’s novelty lies in the integration of CycleGAN-generated data with a CNN–BiLSTM architecture, which has been rarely explored in previous works. These findings contribute to the development of more generalized and accurate deep learning models for handwritten digit classification and similar pattern recognition tasks.
Digitalisasi Pengelolaan PAM Desa: Optimalisasi Pencatatan Meteran Air dengan Barcode Scanning di BUMDES Candi Mulya Fandy Setyo Utomo; Chyntia Raras Ajeng Widiawati; Anies Indah Hariyanti; Yuli Purwati
JURPIKAT (Jurnal Pengabdian Kepada Masyarakat) Vol. 7 No. 1 (2026)
Publisher : Politeknik Piksi Ganesha Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37339/jurpikat.v7i1.2721

Abstract

This community service program aims to enhance the efficiency and transparency of clean water service management in Candinegara Village by implementing a barcode scanning-based water meter recording system. Problems faced by the Candi Mulya Village-Owned Enterprise (BUMDES) include manual recording errors, late billing, and a lack of transparency in water usage data. To address these issues, a Progressive Web App (PWA) for customers and a monitoring dashboard for administrators were developed, enabling real-time data monitoring. Implementation methods included outreach, application usage training, and the implementation of a barcode scanning system for meter recording. Evaluation results through a questionnaire distributed to 28 respondents showed a Perceived Usefulness (PU) score of 3.75, Perceived Ease of Use (PEOU) of 3.44, Attitude Toward Using (ATU) of 3.85, and Behavioral Intention to Use (BIU) of 3.63, reflecting positive acceptance of the application. In conclusion, the implementation of a barcode scanning-based digital system has successfully increased the accuracy, efficiency, and transparency of Village Water Supply (PAM) management and received a positive response from the community and BUMDES managers.
Enhancing the Robustness of Adaptive Class Activation Mapping (AD-CAM) Against Noisy Facial Expression Data Using Preprocessing and Adaptive Normalization Sugianto, Dwi; Hariguna, Taqwa; Utomo, Fandy Setyo
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1005

Abstract

In real-world computer vision applications, visual data is often corrupted by noise, reducing both the accuracy and interpretability of deep learning models. This study proposes an enhanced AD-CAM framework that integrates noise-aware preprocessing and adaptive normalization to improve robustness in both prediction and visual explanation. Experiments were conducted on the FER2013 facial expression dataset augmented with Gaussian, salt-and-pepper, and speckle noise. Using ResNet-50 as the backbone, the proposed method demonstrated significant gains across multiple evaluation metrics, including Robust Accuracy (RA), Drop Coherence (DC), Area Under Robustness Curve (AURC), and Signal-to-Noise Ratio (SNR). Compared to the baseline, the model achieved over 10% accuracy improvement and up to 0.16 DC reduction under noise. Qualitative visualizations showed that the improved model consistently highlighted semantically relevant facial regions, maintaining interpretability even under severe input degradation. These results support the adoption of noise-aware interpretability frameworks for more reliable and trustworthy deployment in real-world vision systems.
Penerapan Algoritma Kalman Filter Dan Yolo Mengukur Efektifitas Aruco Marker Bagi Tunanetra YULIANTO, KOKO EDY; SAPUTRO, RUJIANTO EKO; UTOMO, FANDY SETYO
Jurnal Tekno Insentif Vol 19 No 2 (2025): Jurnal Tekno Insentif
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah IV

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36787/jti.v19i2.1948

Abstract

Abstrak Mobilitas dan aksesibilitas merupakan tantangan utama bagi individu dengan disabilitas visual. Penelitian ini mengevaluasi efektivitas sistem navigasi berbasis multi-sensor untuk tunanetra dengan menggabungkan Algoritma Kalman Filter dan YOLO dalam mendeteksi Aruco Marker. Fokus penelitian adalah meningkatkan akurasi dan efisiensi navigasi melalui integrasi teknik deteksi deep learning dan prediksi matematis secara real-time. Hasil eksperimen menunjukkan bahwa Kalman Filter memiliki waktu deteksi lebih cepat, dengan rata-rata 0,1090 detik untuk objek Botol Plastik dan 0,1069 detik untuk objek Kombinasi. YOLO mencatat waktu sedikit lebih cepat namun dengan komputasi lebih berat. Kalman Filter mencatat efisiensi waktu 12,5%–13,3% lebih baik pada objek tertentu dan akurasi sebesar 94,50% (Botol Plastik) serta 96,00% (objek Kombinasi), lebih tinggi dibandingkan YOLO. Kombinasi kedua algoritma ini memberikan solusi navigasi yang akurat dan efisien untuk tunanetra, serta berpotensi dikembangkan lebih lanjut sebagai sistem navigasi real-time yang andal. Kata kunci: Kalman Filter, YOLO, Aruco Marker, Navigasi Tunanetra, Efektivitas Deteksi Abstract Mobility and accessibility remain major challenges for individuals with visual impairments. This study evaluates the effectiveness of a multi-sensor navigation system for the visually impaired by integrating the Kalman Filter algorithm and YOLO for Aruco Marker detection. The research focuses on improving the accuracy and efficiency of navigation by combining deep learning-based detection with real-time mathematical prediction. Experimental results show that the Kalman Filter achieves faster detection times, averaging 0.1090 seconds for Plastic Bottle objects and 0.1069 seconds for Combination objects. While YOLO recorded slightly faster raw detection times, Kalman Filter demonstrated 12.5%–13.3% better computational efficiency for certain objects. In terms of accuracy, Kalman Filter achieved 94.50% for Plastic Bottle objects and 96.00% for Combination objects, outperforming YOLO’s 92.00% and 93.50%, respectively. The integration of both algorithms offers a promising and optimal solution for the development of reliable real-time navigation systems for the visually impaired. Keywords: Kalman Filter, YOLO, Aruco Marker, Blind Navigation, Detection Effectiveness
Understanding Multidimensional Patient Feedback as Healthcare Communication: An Interdisciplinary Computational Analysis Bahari, Aris Ridky Setiya; Berlilana; Utomo, Fandy Setyo
INJECT (Interdisciplinary Journal of Communication) Vol. 10 No. 2 (2025)
Publisher : FAKULTAS DAKWAH UIN SALATIGA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18326/inject.v10i2.6046

Abstract

Patient feedback represents an important form of healthcare communication through which patients articulate experiences, evaluations, and expectations toward healthcare services. In practice, this communication is often conveyed through unstructured, subjective, and multidimensional narratives, in which a single message may simultaneously address multiple service aspects. Such characteristics complicate the systematic interpretation of patient communication, particularly when sentimental expressions are unevenly distributed and dominated by positive evaluations. This study aims to examine patient feedback as a communicative practice in healthcare by analyzing multidimensional sentiment expressions from an interdisciplinary communication perspective. Computational methods are not positioned as the primary contribution of this study, but are employed as analytical tools to support the interpretation of large-scale patient communication data. An aspect-based sentiment analysis framework with a multilabel classification scheme is used to capture how sentiments are communicated toward predefined service aspects. The dataset consists of 1,131 anonymized patient feedback texts collected from JIH Purwokerto Hospital. To reduce interpretive bias caused by imbalanced sentiment distributions that may obscure less explicit communication expressions, label-based data balancing strategies are applied. Indonesian language modeling is used to accommodate the informal and context-dependent characteristics of patient narratives. The findings indicate that this approach enables a more structured reading of patient communication across service aspects, particularly in identifying explicit positive and negative sentiments. In contrast, neutral sentiment remains more difficult to identify due to its implicit and contextual nature, reflecting the complexity of patient communication strategies. Overall, this study demonstrates that computational analysis can function as a supportive instrument in healthcare communication research for systematically mapping multidimensional patient feedback, provided that the results are interpreted contextually rather than mechanically.
Co-Authors Adiya, Az Zahra Dwi Nur Afit Ajis Solihin Aisha Hukama Setyowati Aji Saeful Aji Septa, Adrian Ajis Solihin, Afit Amar Al Farizi Anas Nur Khafid Anggini, Melisa Anggraeni, Mutia Dwi Anggraini, Nova Anggriani, Epri Anies Indah Hariyanti Azhari Shouni Barkah Azmi, Mohd Sanusi Bagus Adhi Kusuma Bahari, Aris Ridky Setiya Baihaqi, Wiga Maulana Balit, Muhamad Naufal Burhanuddin Berlilana Berlilana Berlilana Burhanuddin Balit, Muhamad Naufal Churil Aeni, Agustina Chyntia Raras Ajeng Widiawati Chyntia Raras Ajeng Widiawati Darmono Dedi Purwanto, Dedi Didi Prasetyo Dwi Krisbiantoro, Dwi Dzaky Candy Fahrezy Fadhilah, Siti Nur Febriansyah Husni Adiatma Giat Karyono Giat Karyono Hanif Hidayatulloh Hendra Marcos, Hendra hidayatulloh, hanif Ilham, Rifqi Arifin Imam Tahyudin Indriyani, Ria Jamie Mayliana Alyza Kafilla, Princess Iqlima Kusuma, Bagus Adhi Kusuma, Velizha Sandy Lasmedi Afuan Lubna, Zuhriyatul Lukita, Dita Maulana Baihaqi, Wiga Mohd Fairuz Iskandar Othman Mohd Nazrin Muhammad Mohd Sanusi Azmi Muaziz, Imam Muhamad Naufal Burhanuddin Balit Muhtyas Yugi Murtiyoso Murtiyoso Nandang Hermanto Nanna Suryana Nikmah Trinarsih Nugroho, Khabib Adi Nur Cholis Romadhon Octavia, Annisa Suci Prayoga, Fandhi Dhuga Pungkas Subarkah Purbo, Yevi Septiray Purwidiantoro, Moch. Hari Pyawai, Hero Galuh R. Vitto Mahendra Putranto Ramadhan, Aziz Ramadhan, Rio Fadly Rifqi Arifin Ilham RR. Ella Evrita Hestiandari Rujianto Eko Saputro Sagita, Selvi Samsul Arifin Sarmini - Sarmini Sarmini Sarmini Sekhudin Sekhudin Setiabudi, Rizki Setiawan, Ito Shafira, Lulu Shendy Filanzi Slamet Widodo Sofa, Nur Sri Hartini Subarkah, Pungkas Sugianto, Dwi Suryana, Nanna Taqwa Hariguna Titi Safitri Maharani Trinarsih, Nikmah Turino, Turino Utomo, Dadang Wahyu Wahid, Arif Mu'amar Wibisono, Arif Cahyo Wiga Maulana Baihaqi Yuli Purwat Yuli Purwati Yuli Purwati Yulianto, Koko Edy