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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 MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer 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
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology 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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PENCAPAIAN KLASIFIKASI TERBAIK BERBASIS PERBAIKAN CITRA CLAHE DAN DARK CHANNEL PRIOR PADA SPESIES IKAN Pergiwati, Dewi; Anggi Pramunendar, Ricardus; Puji Prabowo, Dwi; Alzami, Farrikh; Megantara, Rama Aria
Jurnal Teknik Informatika UMUS Vol 7 No 2 (2025): November
Publisher : Universitas Muhadi Setiabudi

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

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.
Decision Tree Classification for Reducing Alert Fatigue in Patient Monitoring Systems Herfiani, Kheisya Talitha; Nurhindarto, Aris; Alzami, Farrikh; Budi, Setyo; Megantara, Rama Aria; Soeleman, M Arief; Handoko, L Budi; Rofiani, Rofiani
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8414

Abstract

The development of information technology in healthcare opens new opportunities to improve continuous patient monitoring. A major challenge is alert fatigue, where medical personnel are overwhelmed by excessive notifications, reducing concentration, work efficiency, and potentially compromising patient safety. This study presents a proof-of-concept application of the Decision Tree algorithm to analyze alert triggering factors in patient monitoring systems. The dataset is a synthetic health monitoring dataset from Kaggle, containing 10,000 entries with vital parameters including blood pressure, heart rate, oxygen saturation, and glucose levels, designed with deterministic logical relationships between threshold indicators and alert outcomes. The imbalanced dataset (73.67% alert triggered, 26.33% no alert) was intentionally not processed using imbalanced learning techniques to demonstrate Decision Tree's capability in processing structured health data and producing interpretable classifications. The research methodology included data preprocessing, exploratory data analysis, data splitting (90% training, 10% testing), GridSearchCV optimization, and performance evaluation. Results showed perfect metrics (100% accuracy, precision, recall, F1-score), reflecting the deterministic nature of the synthetic dataset rather than real-world clinical complexity. Feature importance analysis identified blood pressure as the most dominant variable, followed by heart rate and glucose levels. This study demonstrates Decision Tree's interpretability and feature importance analysis capabilities in health data contexts, establishing a methodological framework that requires validation on real clinical Electronic Health Record (EHR) data for practical application in reducing alert fatigue and supporting informed clinical decisions.
Multi-Disease Retinal Classification Using EfficientNet-B3 and Targeted Albumentations: A Benchmark on Kaggle Retinal Fundus Images Dataset Saputra, Kurniawan Aji; Alzami, Farrikh; Kurniawan, Defri; Naufal, Muhammad; Muslih, Muslih; Megantara, Rama Aria; Pramunendar, Ricardus Anggi
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15530

Abstract

Retinal diseases remain one of the leading causes of blindness worldwide. This study develops a deep learning pipeline for multiclass retinal disease classification using EfficientNet-B3 combined with Albumentations to improve generalization. We target four classes: cataract, diabetic retinopathy, glaucoma, and normal. We use the Kaggle Retinal Disease dataset (4,217 fundus images) divided into 70% training, 10% validation, and 20% testing. Images are resized to 224×224 and augmented with horizontal flip, random brightness contrast, CLAHE, shiftscale rotate, crop, gamma correction, and elastic transformation. The EfficientNet-B3 backbone is refined after head training with warm-up and learning rate regularization (batch normalization, dropout). After 50 epochs, the best validation performance reaches 0.9526, and on the hold-out test set, the model achieves 95.38% overall accuracy. The F1 scores per class were 1.0000 (diabetic retinopathy), 0.9685 (cataract), 0.9255 (normal), and 0.9184 (glaucoma). Confusion analysis showed that most errors involved glaucoma being misclassified as normal, likely due to optic disc similarities. These results demonstrate that EfficientNet-B3 with targeted augmentation provides accurate and reliable multi-disease screening of fundus images, with the potential to support faster and more consistent triage in clinical workflows. Future research should expand clinical validation and explore attention mechanisms or multimodal input to reduce glaucoma-normal ambiguity.
Explainable Machine Learning-Based Decision Tree Model for Early Detection of Hypertension Risk Sofiani, Hilda Ayu; Maulana, Isa Iant; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Rizqa, Ifan; Santoso, Dewi Agustini; Nugraini, Siti Hadiati
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 1 (2026): Article Research January 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i1.15585

Abstract

Hypertension is one of the leading causes of cardiovascular disease and is often referred to as a “silent killer” because it typically remains asymptomatic until serious complications, such as stroke or kidney failure, occur. Early detection of hypertension risk is therefore essential to enable timely intervention and prevention. This study aims to develop an explainable machine learning–based Decision Tree model for early detection of hypertension risk using clinical and lifestyle data. The balanced dataset includes variables such as age, body mass index (BMI), blood pressure, family history, smoking habits, stress levels, and sleep duration. The dataset used in this study was obtained from the “Hypertension Risk Prediction Dataset” available on the Kaggle platform, consisting of 1,985 patient records and 11 main features covering variables such as age, body mass index (BMI), systolic and diastolic blood pressure, family history, smoking habits, stress level, physical activity, and sleep duration. The dataset is balanced between the hypertension and normal categories, enhancing the reliability of the classification results. The model was constructed using a Decision Tree Classifier implemented in Scikit-learn and validated through cross-validation to minimize overfitting. Model performance was assessed using accuracy, precision, recall, F1-score, and AUC-ROC metrics. The results indicate that the model achieved an accuracy of 96% and an AUC of 0.9645, demonstrating excellent classification performance. The motivation behind this research lies in the growing need for interpretable artificial intelligence models in healthcare, where transparency and explainability are critical for clinical trust and ethical decision-making. Unlike black-box models, the Decision Tree approach allows clinicians to trace each prediction path, understand contributing variables, and apply insights in real-world medical settings. The primary advantage of this model lies in its transparency, as each prediction can be interpreted through explicit decision rules. Overall, this explainable and high-performing model shows strong potential as a clinical decision support tool for early hypertension screening and prevention programs.
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
Comprehensive Benchmark of Yolov11n, SSD MobileNet, CenterFace, Yunet, FastMtCnn, HaarCascade, and LBP for Face Detection in Video Based Driver Drowsiness Go, Agnestia Agustine Djoenaidi; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Winarno, Sri; Pramunendar, Ricardus Anggi; Megantara, Rama Aria; Maulana, Isa Iant; Arif, Mohammad
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8678

Abstract

Face detection is a critical foundation of video-based drowsiness monitoring systems because all downstream tasks such as eye-closure estimation, yawning detection, and head movement analysis depend entirely on correctly identifying the face region. Many previous studies rely on detector-generated outputs as ground truth, which can introduce bias and inflate model performance . To avoid this limitation, I manually constructed a ground truth dataset using 1,229 frames extracted from 129 yawning and microsleep videos in the NITYMED dataset. Ten representative frames were sampled from each video using a face-guided extraction script, and all frames were manually annotated in Roboflow following the COCO format to ensure accurate bounding box labeling under varying lighting, head poses, and facial deformation. Using this manually annotated dataset, I conducted a comprehensive benchmark of seven face-detection algorithms: YOLOv11n, SSD MobileNet, CenterFace, YuNet, FastMtCnn, HaarCascade, and LBP. The evaluation focused on localization quality using Intersection over Union (IoU ≥ 0.5) and Dice Similarity, allowing each algorithm’s predicted bounding box to be directly compared against human defined ground truth. The results show that HaarCascade achieved the highest IoU and Dice scores, particularly in frontal and well-lit frames. FastMtCnn also produced strong alignment with a high number of correctly matched frames. CenterFace and SSD MobileNet demonstrated smooth bounding box fitting with competitive Dice scores, while YOLOv11n and YuNet delivered moderate but stable performance across most samples. LBP showed the weakest results, mainly due to its sensitivity to lighting variations and soft-texture regions. Overall, this benchmark provides an unbiased and comprehensive comparison of modern and classical face-detection algorithms for video-based driver-drowsiness applications.
Dampak Penggunaan Data Augmentasi Terhadap Akurasi MobileNetV2 Dalam Deteksi Mikrosleep Berbasis Rasio Aspek Mata Maulana, Isa Iant; Riadi, Muhammad Fatah Abiyyu; Alzami, Farrikh; Naufal, Muhammad; Azies, Harun Al; Pramunendar, Ricardus Anggi; Basuki, Ruri Suko
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8719

Abstract

Detecting microsleep is important in preventing accidents caused by decreased alertness, especially in activities that require high concentration such as driving. This study aims to develop an image-based microsleep detection model using the MediaPipe FaceMesh. The EAR value is only used for the tagging process that forms the basis for dataset creation. The main problem investigated is how to produce a classification model that can accurately distinguish between normal eye conditions and microsleep conditions using image data taken from eye area snippets. To address this issue, this study applies a series of stages, starting from dataset formation, initial processing in the form of image size adjustment, normalization, and quality improvement through data augmentation, to model training using the MobileNetV2 architecture with transfer learning and fine-tuning techniques. The results of the experiment show that the use of data augmentation strategies has a significant effect on improving model performance, with the best configuration producing a test accuracy of 87.54 percent, with other high performance metrics, namely Precision of 88.64 percent, Recall (Sensitivity) of 87.14 percent, and F1-Score of 87.34 percent. These findings prove that an eye area image-based approach combined with a convolutional neural network model is capable of providing promising performance in detecting microsleep conditions. These findings prove that an approach based on eye area images combined with a convolutional neural network model can deliver promising performance in detecting microsleep. This research is expected to form the basis for the development of a more effective microsleep detection system that can be implemented in real world environments.
Analisis Perbandingan Algoritma Naive Bayes Classifier dan Support Vector Machine untuk Klasifikasi Berita Hoax pada Berita Online Indonesia Ramadhan Rakhmat Sani; Yunita Ayu Pratiwi; Sri Winarno; Erika Devi Udayanti; Farrikh Alzami
Jurnal Masyarakat Informatika Vol 13, No 2 (2022): JURNAL MASYARAKAT INFORMATIKA
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.13.2.47983

Abstract

Masyarakat mampu mengkonsumsi tiap informasi yang tersebar di internet dengan cepat dan terkadang informasi yang beredar tidak selalu memberikan kebenaran yang sesuai dengan kenyataannya (hoax). Demi mendapatkan keuntungan dan mencapai tujuan pribadi, hoax seringkali sengaja dibuat dan dibagikan. Informasi yang didapatkan dari hoax tentunya dapat mempengaruhi masyarakat karena menimbulkan keraguan dan kebingungan terhadap informasi yang diterima Oleh karena itu, penelitian ini membahas tentang bagaimana mengklasifikasikan berita hoax berbahasa Indonesia mengenai isu kesehatan menggunakan TF-IDF serta algoritma Naïve Bayes Classifier dan Support Vector Machine dengan 4 model yang berbeda sehingga mampu memprediksi sebuah berita hoax atau valid. Pada penelitian ini dataset yang dikumpulkan sebanyak 287 diantaranya 200 valid dan 87 hoax. Hasil evaluasi model penelitian ini dengan menggunakan 4 model berbeda pada masing-masing algoritma, diperoleh nilai classification report terbesar untuk algoritma NBC pada model Complement Naïve Bayes dengan hasil precision 95.4%, recall 95.4%, f1-score 95.4% dan accuracy 93.1%. Sedangkan nilai classification report terbesar untuk algoritma SVM pada kernel Sigmoid dengan hasil precision 95.6%, recall 100%, f1-score 97.7% dan accuracy 96.5%. Sehingga dapat disimpulkan bahwa hasil performa rata-rata dari algoritma SVM memiliki kinerja yang lebih baik jika dibandingkan dengan algoritma NBC dalam melakukan klasifikasi berita hoax mengenai isu kesehatan.
Addressing Extreme Class Imbalance in Multilingual Complaint Classification Using XLM-RoBERTa Ariyanto, Muhammad; Alzami, Farrikh; Sani, Ramadhan Rakhmat; Gamayanto, Indra; Naufal, Muhammad; Winarno, Sri; Iswahyudi
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11606

Abstract

Government complaint management systems often suffer from extreme class imbalance, where a few public service categories accumulate most reports while many others remain under-represented. This research examines whether simple class weighting can improve fairness in multilingual transformer models for automatic routing of Indonesian citizen complaints on the LaporGub Central Java e-governance platform. The dataset comprises 53,877 Indonesian-language complaints spanning 18 service categories with an imbalance ratio of about 227:1 between the largest and smallest classes. After cleaning and deduplication, we stratify the data into training, validation, and test sets. We compare three approaches: (i) a linear support vector machine (SVM) with term frequency inverse document frequency (TF-IDF) unigram and bigram and class-balanced weights, (ii) a cross-lingual RoBERTa (XLM-RoBERTa-base) model without class weighting, and (iii) an XLM-RoBERTa-base model with a class-weighted cross-entropy loss. Fairness is operationalised as equal importance for categories and quantified primarily using the macro-averaged F1-score (Macro-F1), complemented by per-class F1, weighted F1, and accuracy. The unweighted XLM-RoBERTa model outperforms the SVM baseline in Macro-F1 (0.610 vs 0.561). The class-weighted variant attains similar Macro-F1 (0.608) while redistributing performance towards minority categories. Analysis shows that class weighting is most beneficial for categories with a few hundred to several thousand samples, whereas extremely rare categories with fewer than 200 complaints remain difficult for all models and require additional data-centric interventions. These findings demonstrate that multilingual transformer architectures combined with simple class weighting can provide a more balanced backbone for automated complaint routing in Indonesian e-government, particularly for low- and medium-frequency service categories.
Evaluation of Histogram-Based Image Enhancement Methods for Facial Images in Drowsy Driver Using No-Reference Metrics Naufal, Muhammad; Al Azies, Harun; Alzami, Farrikh; Brilianto, Rivaldo Mersis
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12055

Abstract

Low-light facial images suffer significant quality degradation, leading to performance degradation in surveillance and face recognition systems, where conventional enhancement methods often produce over-enhancement or unnatural noise artifacts. This study compares three histogram equalization methods, namely HE, AHE, and CLAHE, for low-light facial image enhancement, with evaluation using no-reference quality assessment metrics, including NIQE, LOE, and Entropy, as well as visual analysis and histogram distribution. The results showed that AHE produced the lowest NIQE (4.96 ± 1.38) and the highest entropy (7.86 ± 0.11) but had significant noise artifacts, HE produced an overly even distribution with NIQE of 6.34 ± 1.41, while CLAHE showed the most balanced performance with the lowest LOE (0.07 ± 0.02) and the best visual quality when using the optimal clip limit in the range of 1.2-2.0, providing an optimal trade-off between contrast enhancement, naturalness preservation, and artifact minimization with computational efficiency below 1 ms.
Co-Authors Abda Abda Abdul Syukur Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Anggi Pramunendar, Ricardus Arifin, Zaenal Aris Marjuni Aris Nurhindarto ARIYANTO, MUHAMMAD Ashari, Ayu 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 Dewi Agustini Santoso 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 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 Hasan Aminda Syafrudin Heni Indrayani 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 Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Malim, Nurul Hashimah Ahmad Hassain 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 Arif Muhammad Naufal Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih MY. Teguh Sulistyono Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Nurwijayanti Pergiwati, Dewi Puji Prabowo, Dwi 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 Rifqi Mulya Kiswanto Rini Anggraeni Risky Yuniar Rahmadieni Ritzkal, Ritzkal Rofiani, Rofiani Rohman, M. Hilma Minanur Ruri Suko Basuki 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 Yosep Teguh Sulistyono, Marcelinus Yuniar Rahmadieni, Risky Yunita Ayu Pratiwi Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati