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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science Infotekmesin International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Ilmiah Intech : Information Technology Journal of UMUS Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknik Informatika (JUTIF) IJISTECH JAMU : Jurnal Abdi Masyarakat UMUS Moneter : Jurnal Keuangan dan Perbankan Journal of Computing Theories and Applications Jurnal Informatika: Jurnal Pengembangan IT Advance Sustainable Science, Engineering and Technology (ASSET)
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Transportation Other

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Fermentasi Limbah Kotoran Sapi menjadi Pupuk Organik, Solusi Peningkatan Sirkular Ekonomi bagi Peternak Sapi Kusmiyati Kusmiyati; Sigit Muryanto; Mahmud; Farrikh Alzami; Risky Yuniar Rahmadieni
CARADDE: Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 2 (2025): Desember
Publisher : Ilin Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31960/caradde.v8i2.3097

Abstract

Penyalahgunaan antibiotik di kalangan generasi muda menjadi masalah kesehatan global yang signifikan, yang dapat berujung pada resistensi antibiotik. Pengetahuan yang tepat mengenai penggunaan antibiotik yang bijak sangat penting untuk mencegah fenomena ini. Tujuan: Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman siswa SMA Pelita 2 Jakarta mengenai penggunaan antibiotik yang tepat dan bahaya resistensi antibiotik. Metode: Kegiatan dilaksanakan dalam satu sesi berdurasi 4 jam, melibatkan 45 siswa,  melalui penyuluhan edukatif yang mencakup penyampaian materi interaktif, demonstrasi laboratorium, dan permainan edukatif. Evaluasi dilakukan  menggunakan pre-test dan post-test, serta formulir penilaian berbasis skala Likert untuk menilai kualitas pelaksanaan kegiatan. Analisis perubahan skor pengetahuan dilakukan menggunakan Uji Wilcoxon Signed-Rank. Hasil: Hasil pre-test dan post-test menunjukkan peningkatan pengetahuan yang signifikan, dengan persentase siswa yang memiliki pengetahuan tinggi meningkat dari 69,77% menjadi 93,00%. Penilaian  skala Likert juga menunjukkan skor tinggi dengan skor rata-rata lebih dari 4 dari 5 pada kategori kejelasan materi, relevansi topik, dan penguasaan materi oleh pemateri . Uji statistik non-parametrik menunjukkan perbedaan skor yang signifikan antara pre-test dan post-test. Kesimpulan: Berdasarkan hasil tersebut, kegiatan edukasi ini terbukti efektif dalam meningkatkan pemahaman siswa mengenai penggunaan antibiotik yang tepat serta risiko  resistensi, sekaligus meningkatkan kesadaran mereka akan pentingnya penggunaan antibiotik yang bijak. Edukasi ini juga berpotensi untuk diadaptasi pada sekolah lain sebagai upaya berkelanjutan dalam pencegahan resistensi antibiotik di tingkat masyarakat
PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Dewi Pergiwati; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Farrikh Alzami; Rama Aria Megantara
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

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Abstract

Ikan merupakan bahan pangan lauk-pauk utama yang dikonsumsi manusia untuk menunjang protein hewani dan zat-zat lain yang diperlukan tubuh. Ikan merupakan lauk-pauk pilihan utama yang memiliki harga relative murah dan mudah didapat. Namun pada nyatanya konsumsi ikan di Indonesia sangat rendah dibandingkan dengan negara-negara yang memiliki potensi sumberdaya perikanan yang jauh lebih rendah seperti negara Jepang, Korea Selatan, serta negara-negara di Asia lainnya. Di sisi lain, salah satu kekayaan Indonesia yang sangat berlimpah pada sector perairan adalah biota ikan. Dengan kondisi demikian, upaya peningkatan konsumsi ikan akan memberikan multiflier effect dalam lingkungan masyarakat. Selain meningkatkan tingkat kesehatan serta kecerdasan, juga semakin menggairahkan sektor perikanan untuk dapat mendorong peningkatan penyerapan tenaga kerja, meningkatkan pendapatan serta kesejahteraan pada masyarakat khususnya profesi nelayan, pembudidaya ikan, pengolah hasil ikan serta pihak terkait lainnya. Maka, perlu ditingkatkan kemampuan pengenalan ikan secara otomatis dengan bantuan computer untuk mengenali jenis-jenis ikan yang sangat beragam guna mempermudah proses pengelolaan dan distribusi ikan. Oleh karena itu dalam penelitian ini, peneliti ini mengusulkan untuk melakukan analisis dampak pre-processing dari kombinasi algoritma CLAHE dan DCP yang diterapkan dalam klasifikasi ikan dengan Random Forest.
Pemanfaatan Artificial Intelligence untuk Meningkatkan Efisiensi Layanan Birokrasi pada Organisasi Perangkat Daerah Pemerintah Provinsi Jawa Tengah: Utilization of Artificial Intelligence to Improve the Efficiency of Bureaucratic Services in Regional Government Organizations of Central Java Province Farrikh Alzami; Muhammad Naufal; Dewi Agustini Santoso; Dewi Pergiwati; Heni Indrayani; Karis Widyatmoko; Rama Aria Megantara
JAMU : Jurnal Abdi Masyarakat UMUS Vol. 6 No. 02 (2026): Februari
Publisher : LPPM Universitas Muhadi Setiabudi

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Abstract

Transformasi digital birokrasi menuntut pemerintah daerah untuk meningkatkan efisiensi dan kualitas layanan publik. Artificial intelligence (AI) merupakan salah satu teknologi yang memiliki potensi besar dalam mendukung otomasi administrasi, pengolahan data, serta peningkatan responsivitas layanan pemerintahan. Namun, tingkat pemahaman dan kesiapan aparatur sipil negara (ASN) dalam memanfaatkan AI masih belum merata, terutama terkait aspek etika dan pelindungan data. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan kapasitas ASN Organisasi Perangkat Daerah (OPD) Pemerintah Provinsi Jawa Tengah dalam memanfaatkan AI secara tepat, aman, dan bertanggung jawab guna mendukung efisiensi layanan birokrasi. Metode pelaksanaan kegiatan berupa workshop tatap muka yang meliputi penyampaian materi konseptual, studi kasus pemanfaatan AI di sektor publik, diskusi interaktif, serta praktik penggunaan AI dalam konteks administrasi pemerintahan. Hasil kegiatan menunjukkan peningkatan pemahaman peserta terhadap konsep AI, kemampuan mengidentifikasi potensi penerapan AI dalam tugas birokrasi, serta meningkatnya kesadaran terhadap aspek etika dan keamanan data. Kegiatan ini menunjukkan bahwa pendampingan akademik melalui workshop praktis mampu memberikan kontribusi nyata dalam mendukung transformasi digital birokrasi di tingkat pemerintah daerah.
Comparing Decision Tree and Optimized LightGBM for Attrition Prediction Dhea Maharani; Farrikh Alzami; MY. Teguh Sulistyono; Aris Nurhindarto; Dewi Agustini Santoso; Muslih Muslih; Henry Bastian
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12678

Abstract

Employee turnover poses a considerable challenge for organizations, impacting productivity and raising recruitment expenses. This research seeks to evaluate the effectiveness of Decision Tree and Light Gradient Boosting Machine (LightGBM) models in forecasting employee attrition. The study utilizes a quantitative experimental design, leveraging a secondary dataset sourced from Mendeley. Before model development, data preprocessing was performed, and model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Each algorithm was assessed under three different configurations baseline, regularization, and hyperparameter tuning through GridSearchCV. The experimental findings indicate that the Decision Tree model is prone to overfitting and has limited capabilities in detecting attrition classes, even though optimization raises the ROC-AUC score to 0.80. In comparison, LightGBM demonstrates more reliable and consistent performance. The Tuned LightGBM model achieved the highest performance on the test dataset, with an Accuracy of 0.81, a Precision of 0.82, a Recall of 0.71, F1-Score of 0.76, and an ROC-AUC of 0.85. An analysis of feature importance reveals that job satisfaction, work-life balance, emotional commitment, work experience, and allowances are the key factors influencing attrition prediction. These results indicate that LightGBM not only performs exceptionally well, but it is also able to offer insights into the critical factors that are important for data-driven retention strategies.
An Integrated Topic–Sentiment Analysis of User Reviews in Vidio Application Using BERTopic and IndoRoBERTa Nalendra Whisnu Pinilih; Ika Novita Dewi; Farrikh Alzami
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12952

Abstract

User reviews on digital platforms provide valuable insights into user experience; however, the large volume and unstructured nature of such data make systematic analysis challenging. In the case of the Vidio application, user feedback frequently reflects concerns related to advertisements, subscription systems, and technical performance. Nevertheless, existing researches often apply sentiment analysis and topic modeling separately, limiting the ability to understand how specific discussion themes are associated with user sentiment. To address this limitation, this research proposes an integrated topic–sentiment analysis approach for analyzing user reviews of the Vidio application collected from the Google Play Store. After filtering and quality control, 8,854 reviews were retained for further analysis using BERTopic for topic modeling and IndoRoBERTa for sentiment classification. The topic modeling process was optimized through parameter tuning, resulting in an improvement of the coherence score from 0.4076 to 0.6878, indicating better semantic consistency among the identified topics. Meanwhile, the sentiment classification model achieved an accuracy of 72%, although its performance was affected by class imbalance, particularly in identifying neutral sentiment. The analysis identified seven primary topics, where advertising-related issues emerged as the dominant topic and were strongly associated with negative sentiment, followed by concerns regarding subscription mechanisms and login accessibility. In contrast, content-related topics, particularly sports broadcasts, were consistently associated with positive sentiment. Furthermore, statistical evaluation confirmed a significant relationship between topic categories and sentiment distribution. Overall, the findings demonstrate that integrating topic modeling and sentiment analysis provides a more comprehensive understanding of user opinions and can support improvements in application quality and user experience.
Improving YOLO12 Performance Using Efficient Channel Attention For Ship Object Detection Richard Christoper Subianto; Muhammad Naufal; Farrikh Alzami
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13067

Abstract

Ship object detection in aerial imagery remains a critical challenge due to complex marine backgrounds, varying object scales, and occlusion, which often lead to unstable model performance. This research proposes integrating the Efficient Channel Attention (ECA) module into the YOLO12-L architecture to enhance feature selectivity and prediction robustness. The model was trained for 500 epochs on the Ship Detection from Aerial Images dataset, comprising 621 images and 1,951 annotated ship instances, and performance was evaluated across five distinct random seeds to ensure statistical reliability. Quantitative results demonstrate that the proposed YOLO12-L + ECA model achieved a median Average Precision (mAP@50) of 71.32% and a Precision of 92.5%, outperforming the baseline YOLO12-L model. To evaluate statistical validity, a Paired Bootstrap Median Test with 100 resamples confirmed a statistically significant improvement in median performance (Δ = +1.01%, p = 0.02). Furthermore, the standard deviation of mAP@50 decreased from 1.1% in the baseline to 0.3% in the ECA model, representing a 72.7% reduction in performance variance. Computational efficiency analysis revealed that the ECA module introduced negligible overhead, adding merely 5 parameters (totaling 26,389,880) and keeping FLOPs constant at 89.4, while maintaining a high inference speed of 10.7 FPS (a marginal 2.5% reduction). These findings confirm that ECA effectively suppresses background noise, stabilizes detection outputs, and provides statistically significant improvements without compromising architectural efficiency. The proposed architecture offers a lightweight and reliable solution for automated maritime monitoring systems, particularly in challenging visual environments.
Performance Analysis of YOLO26 in Pothole Detection on an Indonesian Road Dataset Mohammad Alwi Nanda Saputra; Farrikh Alzami; Christy Atika Sari
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13585

Abstract

Road damage is one of the infrastructure problems that can compromise safety, comfort, and the smooth flow of traffic. The road inspection process, which is still carried out manually, requires a relatively large amount of time, labor, and cost, making a more efficient method necessary. Advances in computer vision and deep learning technologies enable the automatic detection of road damage through an object detection approach. This study aims to analyze the performance of the YOLO26l model as a baseline model in detecting four categories of road damage such as potholes, alligator cracking, lateral cracking, and longitudinal cracking using the Road Damage Indonesia Dataset. The dataset was divided into 70% training data, 15% validation data, and 15% testing data. The training process was conducted using the pre-trained weights from yolo26l.pt via the Ultralytics framework without any architectural modifications or the application of image enhancement methods. Performance evaluation was conducted using the Precision, Recall, mAP@0.50 (mAP@0.50), and mAP@0.50:0.95 (mAP@0.50:0.95) metrics. The results of the study show that the YOLO26l model achieved a Precision of 0.7028, a Recall of 0.6492, a mAP@0.50 of 0.6890, and a mAP@0.50:0.95 of 0.3391. Analysis using a confusion matrix, precision–recall curve, and visualization of the detection results showed that the model was able to identify all four categories of road damage well, although there were still some objects that went undetected under poor lighting conditions, due to small object sizes, or complex road surface textures. Based on these results, it can be concluded that YOLO26l performs well as a baseline model for road damage detection on the Indonesian road dataset
LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping Ricardus Anggi Pramunendar; Ashraf Alomoush; Dwi Puji Prabowo; Rama Aria Megantara; Farrikh Alzami; Nurul Anisa Sri Winarsih; Dewi Pergiwati; Guruh Fajar Shidik
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3433

Abstract

Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism to automate hyperparameter tuning for an Inception-based CNN. The original FOX algorithm applies fixed movement weights throughout optimization, causing search to stagnate early. LDW-FOX gradually reduces exploration intensity across iterations, pushing search toward exploitation as it converges. Five hyperparameters, namely learning rate, dropout rate, hidden layer size, activation function, and optimizer, were tuned on a balanced 3,000 image UAV dataset spanning three vegetation density classes. Manual tuning peaked at 61.00 percent test accuracy but varied considerably across epoch settings. LDW-FOX reached a peak test accuracy of 82.48 percent and a mean of 58.47 percent, outperforming the original FOX, whose mean was 55.30 percent. LDW-FOX showed a more consistent training-test gap than other swarm-based methods, with LDW variants beating unmodified counterparts under equal budgets. High variance across configurations indicates broader generalization needs testing.
Enhancing Brain Tumor Classification on Mildly Imbalanced Datasets Using Categorical Focal Cross-Entropy Muhammad Naufal; Harun Al Azies; Farrikh Alzami; Novita Kurnia Ningrum; Rivaldo Mersis Brilianto; Pratidina Kusuma Dewi
Jurnal Masyarakat Informatika Vol 17, No 2 (2026): November 2026 (Ongoing Issue)
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.17.2.82619

Abstract

Brain tumor is a dangerous disease that requires accurate diagnosis, but medical datasets often suffer from class imbalance, resulting in bias in deep learning-based classification models. This study proposes using Categorical Focal Cross Entropy (CFCE) in a Convolutional Neural Network (CNN) to address this issue by comparing it with Categorical Cross-Entropy (CCE). CFCE is designed to emphasize minority class samples and hard-to-classify examples, thereby reducing the dominance of the majority class. Experiments were conducted on a brain tumor dataset with class imbalance, where the CNN model with CFCE achieved 84.57% accuracy, 83.57% precision, 85.27% recall, and 83.97% F1-score, outperforming the model with CCE (81.30% accuracy, 81.77% precision, 82.83% recall, and 81.15% F1-score). These results show that Focal Loss effectively improves the classification performance on imbalanced data, with a better ability to detect brain tumors, especially in the minority class. This study contributes to developing a more robust and reliable deep learning-based diagnosis system for medical applications.
Co-Authors Abdul Syukur Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Arifin, Zaenal Aris Marjuni Aris Nurhindarto Aris Nurhindarto ARIYANTO, MUHAMMAD Ashari, Ayu Ashraf Alomoush Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Azzami, Salman Yuris Adila Brilianto, Rivaldo Mersis Budi, Setyo Candra Irawan Candra Irawan Caturkusuma, Resha Meiranadi Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Choirinnisa, Dina Christy Atika Sari Dewi Agustini Santoso Dewi Agustini Santoso Dewi Agustini Santoso Dewi Pergiwati Dhea Maharani Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Enrico Irawan Erika Devi Udayanti Esa Wahyu Andriansyah Fahmi Amiq Farah Syadza Mufidah Fikri Diva Sambasri Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Firman Wahyudi, Firman Fitri Susanti Ghina Anggun Go, Agnestia Agustine Djoenaidi Guruh Fajar Shidik Hadi, Heru Pramono Hartono, Andhika Rhaifahrizal Harun Al Azies Harun Al Azies Hasan Aminda Syafrudin Heni Indrayani Henry Bastian Herfiani, Kheisya Talitha Ifan Rizqa Ika Novita Dewi Ika Novita Dewi Indra Gamayanto Indra Gamayanto Indrayani, Heni Iswahyudi ISWAHYUDI ISWAHYUDI Jumanto Karin, Tan Regina Khariroh, Shofiyatul Khoirunnisa, Emila Krisnawati, Dyah Ika Kukuh Biyantama Kukuh Biyantama Kurniawan Aji Saputra Kurniawan, Defri Kusmiyati Kusmiyati Kusmiyati Kusmiyati Kusmiyati*, Kusmiyati Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Mahmud Mahmud Marjuni, Aris Maulana, Isa Iant Megantara, Rama Aria Mila Sartika Mila Sartika, Mila Mira Nabila Mira Nabila Moch Arief Soeleman Moh Hadi Subowo Moh Yusuf, Moh Moh. Yusuf Mohammad Alwi Nanda Saputra Mohammad Arif Muhammad Naufal Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih Muslih Muslih MY. Teguh Sulistyono MY. Teguh Sulistyono Nabila, Mira Nalendra Whisnu Pinilih Novita Kurnia Ningrum Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Nurwijayanti Pergiwati, Dewi Pratidina Kusuma Dewi Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Puspitarini, Ika Dewi R. Daniel Hartanto Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Riadi, Muhammad Fatah Abiyyu Ricardus Anggi Pramunendar Richard Christoper Subianto Rifqi Mulya Kiswanto Rini Anggraeni Risky Yuniar Rahmadieni Ritzkal, Ritzkal Rivaldo Mersis Brilianto Rofiani, Rofiani Rohman, M. Hilma Minanur Ruri Suko Basuki Sambasri, Fikri Diva Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Sasono Wibowo Sejati, Priska Trisna Sendi Novianto Sendi Novianto Sigit Muryanto Sigit Muryanto, Sigit Sinaga, Daurat Soeleman, Arief Soeleman, M Arief Sofiani, Hilda Ayu Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyono, Teguh Sulistyowati, Tinuk Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Widyatmoko Karis Winarsih, Nurul Anisa Sri Yuniar Rahmadieni, Risky Yunita Ayu Pratiwi Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati