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Ema Utami

Universitas Amikom Yogyakarta

Author-ID : 259410

Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Veterinary Other

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CATTLE BODY WEIGHT PREDICTION USING REGRESSION MACHINE LEARNING Anjar Setiawan; Utami, Ema; Ariatmanto, Dhani
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1521

Increasing efficiency and productivity in the cattle farming industry can have a significant economic impact. Cow health and productivity problems directly impact the quality of the meat and milk produced. In the cattle farming industry, it can help predict cow weight oriented to beef and milk quality. The importance of predicting cow weight for farmers is to monitor animal development. Meanwhile, for traders, knowing the animal's weight makes it easier to calculate the price of the animal meat they buy. This research aims to predict cow weight by increasing the results of smaller MAE values. The methods used are linear Regressor (LR), Random Forest Regressor (RFR), Support Vector Regressor (SVR), K-Neighbors Regressor (KNR), Multi-layer Perceptron Regressor (MLPR), Gradient Boosting Regressor (GBR), Light Gradient boosting (LGB), and extreme gradient boosting regressor (XGBR). Producing cattle weight predictions using the SVR method produces the best values, namely mean absolute error (MAE) of 0.09 kg, mean absolute perception error (MAPE) of 0.02%, root mean square error (RMSE) of 0.08 kg, and R-square of 0.97 compared to with other algorithm methods and the results of statistical correlation analysis showed several significant relationships between morphometric variables and live weight.

STACKING ENSEMBLE LEARNING AND INSTANCE HARDNESS THRESHOLD FOR BANK TERM DEPOSIT ACCEPTANCE CLASSIFICATION ON IMBALANCED DATASET Bangun Watono; Ema Utami; Dhani Ariatmanto
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024 - SENIKO
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2022

Bank term deposits are a popular banking product with relatively high interest rates. Predicting potential customers is crucial for banks to maximize revenue from this product. Therefore, bank term deposits acceptance classification is an important challenge in the banking industry to optimize marketing strategies. Previous studies have been conducted using machine learning classification techniques with the imbalanced Bank Marketing Dataset from the UCI Repository. However, the accuracy results obtained still need to be improved. Using the same dataset, this study proposes an Instance Hardness Threshold (IHT) undersampling technique to handle imbalanced datasets and Stacking Ensemble Learning (SEL) for classification. In this SEL, Decision Tree, Random Forest, and XGBoost are selected as base classifiers and Logistic Regression as meta classifier. The model trained on SEL with the dataset undersampled using IHT shows a high accuracy rate of 98.80% and an AUC-ROC of 0.9821. This performance is significantly better than the model trained with the dataset without undersampling, which achieved an accuracy of 90.30% and an AUC-ROC of 0.6898. The findings of this research demonstrate that implementing of the suggested IHT undersampling technique combined with SEL has been evaluated to effectively enhance the performance of term deposit classification on the dataset.

Tuberculosis Diagnosis From X-Ray Images Using Deep Learning And Contrast Enhancement Techniques Risma, Vita Melati; Utami, Ema
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4315

Tuberculosis (TB) is an infectious disease that poses a global health threat. Early diagnosis through chest X-ray (CXR) imaging is effective in reducing transmission and improving patient recovery rates. However, the limited number of radiologists in high TB burden areas hampers rapid and accurate detection. This study aims to improve TB diagnosis accuracy using deep learning models. Convolutional Neural Networks (CNN) are applied to analyze CXR images to support automated detection in regions with limited radiology personnel. The method involves image processing using Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image quality. A public dataset consisting of 2,188 images was used, with preprocessing steps including resizing, normalization, and augmentation. The DenseNet201 model was employed as the main architecture, trained for 10 epochs with various batch sizes to evaluate its performance. Results show that the combination of CLAHE and DenseNet201 achieved the highest accuracy of 94.84%. Image quality enhancement with CLAHE proved to improve accuracy compared to models without preprocessing. This research contributes to enhancing the efficiency of automated early TB detection, reducing reliance on radiologists, and accelerating clinical decision-making.

Improving Infant Cry Recognition Using MFCC And CNN-Based Audio Augmentation Setyoningrum, Nuk Ghurroh; Utami, Ema; Kusrini, Kusrini; Wibowo, Ferry Wahyu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4373

Recognizing infant cries is essential for understanding a baby's needs; however, previous research has struggled with imbalanced datasets and limited feature extraction techniques. Conventional methods utilizing CNN without data augmentation often failed to accurately classify minority classes such as belly pain, burping, and discomfort, resulting in biased models that predominantly recognized majority classes. This study proposes an MFCC-based data augmentation pipeline, incorporating time stretching, pitch scaling, noise addition, polarity inversion, and random gain adjustments to increase dataset diversity and enhance model generalization. By applying this approach, the dataset size was expanded from 457 to 8,683 samples, and a CNN model with three convolutional layers, ReLU activation, and max pooling was trained for cry pattern classification. The results indicate a substantial accuracy improvement from 78% to 98%, with F1-scores for minority classes rising from 0.00 to above 0.90, confirming that augmentation effectively addresses dataset imbalance. This research advances computer science and artificial intelligence, particularly in audio signal processing and deep learning for healthcare applications, by demonstrating the role of data augmentation in improving cry classification performance. Future directions include integrating multimodal data (visual and physiological signals), exploring advanced deep learning architectures, and developing real-time applications for smart baby monitoring systems to further enhance infant cry recognition technology.

Improving Model Capability for Sentiment Trend Analysis in Hotel Visitor Reviews with Bi-LSTM Multistage Approach Yanuargi, Bayu; Utami, Ema; Kusrini, Kusrini; Parikesit, Arli Aditya
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5185

This study focuses to improve the sentiment analysis of hotel reviews using Multistage mechanism of two-stage approach based on the Bidirectional Long Short-Term Memory (Bi-LSTM) architecture with 53,000 data from 28 hotels in Yogyakarta that captured from google maps review for hotel in Yogyakarta. Hotel customer reviews often contain mixed sentiment expressions, making it crucial to filter out only sentences with a single dominant sentiment to avoid ambiguity. In the first stage, the model detects sentiment at the token level and counts the number of sentiment expressions in each sentence. Only sentences with a single polarity are passed to the final classification stage. In the second stage, the overall sentiment is classified as positive, negative, or neutral using pooled contextual representations. Experimental results from 30 iterations demonstrate consistently high performance, with precision, recall, and F1-scores above 0.95, and overall accuracy exceeding 96%. The confusion matrix analysis shows strong model performance, although some challenges remain in distinguishing between positive and neutral sentiment. Additionally, sentiment trend analysis of hotel reviews from properties such as Lafayette Boutique Hotel and The Westlake Resort Jogja reveals dynamic shifts in guest perception over time. This multistage mechanism approach proves effectiveness of improving sentiment classification accuracy by avoid the bias on sentiment and also in providing valuable temporal insights for monitoring customer satisfaction.

Performance Comparison of Child Stunting Prediction Support Vector Machine vs Random Forest with Grid Search Optimization Elim, Marthinus Ikun; Utami, Ema
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5285

Stunting is a serious global health problem, particularly in developing countries. Its prevalence is high in Indonesia, reaching approximately 24.4% among children under five in 2021. This condition, defined as failure to thrive due to chronic malnutrition, repeated infections, and a lack of psychosocial stimulation, has long-term impacts on an individual's cognitive development and productive capacity. This study aims to conduct a comparative analysis of the Support Vector Machine and Random Forest algorithms in predicting stunting in children, with a focus on evaluating the impact of hyperparameter optimization using Grid Search on model performance. This study used the public stunting dataset from Kaggle and included data preprocessing steps such as handling missing values, duplication, encoding, and scaling. The data was then divided into 80% for training, 10% for testing, and 10% for validation. Comprehensive evaluation metrics such as precision, recall, F1-score, and ROC-AUC were also used to assess model performance. Grid Search optimization was applied to both models to find the best hyperparameter combination. Experimental results showed that Grid Search optimization significantly improved the accuracy of the SVM model from 94.29% to 98.37%. Meanwhile, the Random Forest model demonstrated very high performance, achieving 99.59% accuracy both before and after Grid Search optimization. These findings underscore the significant potential of machine learning models in supporting stunting prevention efforts for public health intervention policies. This research contributes to the development of machine learning-based decision support systems for public health, particularly in early detection and intervention strategies for stunting.

Development of Double-Tail Generative Adversarial Network with Adaptive Style Transfer for Anime Background Production with Makoto Shinkai's Stylization Purwanto, Agus; Kusrini, Kusrini; Utami, Ema; Agustriawan, David
Scientific Journal of Informatics Vol. 12 No. 1: February 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i1.20850

Purpose: Traditionally, 2D anime production involves the expertise of experienced animators and is labor-intensive and time-consuming. Generative adversarial networks (GANs) have been developed to create high-quality anime over the years. However, the developed GANs still have caveats, such as the presence of artifacts, high-frequency noise, color and semantic structure mismatches, blurring, and texture issues. Additionally, research on AI-generated anime images with a particular style is still lacking. Thus, this study aimed to develop double-tail generative adversarial network (DTGAN) with adaptive style transfer to generate quality anime background images aligning with Makoto Shinkai's anime style. Methods: A dataset of real world and anime images was collected and preprocessed. The training was run, and an inference process was done to generate background images with the anime style of Makoto Shinkai using DTGAN with adaptive style transfer. Evaluations of the images produced were performed using visual comparison and quantitative analysis using Fréchet Inception Distance (FID) and peak signal-to-noise ratio (PSNR). Result: Compared to other methods, the images generated by DTGAN with adaptive style transfer had the lowest FID and highest PSNR values of.38.7 and 19.4 dB, respectively. Visual comparison of the images against other methods and real anime image of Makoto Shinkai demonstrated that images from DTGAN had the best quality that matched Makoto's style, as observed from color, background preservation, photorealistic style, and light contrast. Novelty: These findings suggest that DTGAN with adaptive style transfer using adaptive instance normalization (AdaIN) and linearly adaptive denormalization (LADE) outperforms other methods, highlighting its practical use for 2D anime production.

Perbandingan Algoritma DBSCAN dan K-Means Clustering untuk Pengelompokan Data Gangguan PT. PLN UID Kalselteng Wijaya, Tri Amri; Utami, Ema; Al Fatta, Hanif
Innovative: Journal Of Social Science Research Vol. 4 No. 1 (2024): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v4i1.8920

Perusahaan Listrik Negara (PLN) merupakan salah satu perusahaan Badan Usaha Milik Negara, Dalam pendistribusian listrik sering terjadi gangguan yang menyebabkan pasoka listrik sering terdampak diantranya terjadi pemadaman, Bentuk pencegahan gangguan dilakukan dengan cara pengelompokan titik-titik gangguan yang sering terjadi, Untuk mengatasi permasalahan Terdapat berbagai metode yang dapat digunakan dalam pengelompokan data, diantaranya : DBSCAN dan K-Means. Tujuan dari penelitian ini untuk melakukan pengelompokan data gangguan menggunakan metode DBSCAN dan K-Means. Percobaan dilakukan menggunakan dataset sebanyak 2000 data yang terdiri dari 2 atribut: lattitude dan longtitude. Berdasarkan penelitian ini, Setelah melalui beberapa percobaan diperoleh hasil bahwa K-Means lebih unggul daripada DBSCAN dalam mengelompokkan data gangguan. Algoritma K-Means memiliki nilai SI terbaik sebesar 0,581 yang terletak pada percobaan dengan nilai k = 6.

Comparison of Feature Extraction Methods for Conducting Sentiment Classification in Ternate Malay Language using Machine Learning Approaches Surya, Satria Dwi; Utami, Ema; Yaqin, Ainul
PROtek : Jurnal Ilmiah Teknik Elektro Vol 11, No 3 (2024): Protek: Jurnal Ilmiah Teknik Elektro
Publisher : Program Studi Teknik Elektro Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/protk.v11i3.7262

Local people in Ternate, North Maluku, often use local languages to communicate on social media. This poses a challenge for newcomers to understand the implied meaning and emotions of the messages conveyed through social media. This research aims to develop a natural language processing (NLP)-based emotion classification method that can be applied to Ternate Malay text datasets. The application of NLP is expected to improve the accuracy of emotion detection and classification in the text. The research was conducted by applying and comparing the performance of several classification models trained using Ternate Malay text datasets. The models used include SVM (Support Vector Machine), K-Nearest Neighbors (KNN) Random Forest, Decision Tree and Logistic Regression. Each model is applied using BoW (Bag-of-Words) and Word2Vec vectorization representations. The evaluation results show that the BoW+SVM model provides the highest performance with 77% accuracy, followed by BoW+Random Forest (75%) and BoW+Logistic Regression (73%). Thus it can be concluded that NLP can be applied to the Ternate Malay language dataset to classify emotions based on text.

Systematic Literature Review of the Development of Financial Information Systems in Churches Wicaksono, Sherif Aji; Wijaksana, Candra Putra; Ngaeni, Nurus Sarifatul; Utami, Ema
International Journal of Computer and Information System (IJCIS) Vol 4, No 4 (2023): IJCIS : Vol 4 - Issue 4 - 2023
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v4i4.143

This study was conducted to investigate the factors that influence the development of financial information systems in churches. The study used a systematic literature review with 10 studies that met the inclusion criteria. The findings of this study suggest that user involvement, technology selection, and change management are all important factors that influence the successful development of financial information systems in churches. Churches that are considering developing a financial information system should pay attention to these factors to increase their chances of success.Keywords : Financial information systems.

Co-Authors , Anggit Dwi Hartanto A.A. Ketut Agung Cahyawan W AA Sudharmawan, AA Abdul Malik Zuhdi Abdullah Ardi Abdullah, Riska K Abdulrahmat E Ahmad Abyan Fauzi Widihasani Achmad Yusron Arif Ade Pujianto Adi Surya Adiatma, Biva Candra Lutfi Adipradana, Candra Afif, Muhammad Sholih Afifah Nur Aini Afis Julianto Aflahah Apriliyani Afu Ichsan Pradana Agun Nurul Widiyanto Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetio Agung Budi Prasetyo Agung Dwi Cahyanto Agung Nurhidayatullah, Rizqy Agung Susanto Agus Fathurahman Agus Fatkhurohman AGUS PURWANTO Agustin, Tinuk Agustina Srirahayu Agustina, Nova Ahmad Fauzi Ahmad Febri Diansyah Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Fikri Iskandar Ahmad Hajar Ahsan, Muhammad Rafiqudin Ahsan, Muhammad Rafiqudin Ain, Quratul Ainul Yaqin Ainul Yaqin Ainul Yaqin Aji Said Wahyudi Hidayat Akbar Akbar Akhmad Dahlan Al Fathir As, Rahmat Saudi Aldy A Kulakat Alfansani, Abdul Rauf Alfin Mahadi Alimuddin Yasin Alin, Octhavia Almi Yulistia Alwanda Alqowiy, Mohd Qorib Alsyaibani, Omar Muhammad Altoumi Alva Hendi Muhammad Alva Hendi Muhammad Alva Hendi Muhammad Alvhinia Meinda Amitaba Alvian Trias Kurniawan Alvian Trias Kurniawan Alvina Felicia Watratan Amir Fatah Sofyan Amir, Fail Amrullah, Ahmad Afief Amrullah, Ahmad Afief Amrullah, Yusuf Amri Andang Wijanarko Andhika Wisnu Widyatama Andhika Wisnu Widyatama Andi Sunyoto Andrie Prajanueri Kristianto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto Anggit Dwi Hartanto, Anggit Dwi Anggit Hartanto Anggriandi, Dendi Anip Moniva Anisa Rahmanti Anisya Nursyah Gusman Anjar Setiawan Annisa Rahayu P Anwar Sadad Ardi, Abdullah Arfian Hendro Priyono Arham Rahim Ari Rudiyan Arief Setyanto Arief, M.Rudyanto Arif Nur Rohman Arif Rahman Arif Santoso Arif Sutikno Arif, Achmad Yusron Aris Setiyadi aristin chusnul khotimah Arli Aditya Parikesit Armadiyah Amborowaty Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Armadyah Amborowati Arvi Pramudyantoro Arya Luthfi Mahadika Asrawi, Hannan Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asro Nasiri Asrul Abdullah Astica, Yustikamasy Atin Hasanah Aziza Devita Indraswari Bambang Sumantri, R Bagus Bangun Watono Banu Dwi Putranto Basri, Nur Faizal Bayu Setiaji Béjar, Rodrigo Martínez Bety Wulan Sari BHANU SRI NUGRAHA Bima Widianto Bisono, Hadi Hikmadyo Biva Candra Lutfi Adiatma Bonifacius Vicky Indriyono Bonifacius Vicky Indriyono, Bonifacius Vicky Brahmantha, Gede Putra Aditya Budi, Agung Prasetio Buyut Khoirul Umri Cahya Pangestu, Galang Candra Adipradana Candra Aditya Pinuyut Carolina, Vinnesa Patricia Catur Iswahyudi Catur Iswahyudi Catur Riyono Heri Wibowo Cecep Yedi Permana Chan Uswatun Khasanah Chavid Syukri Fatoni Christina Andriyani Constantin Menteng D. Diffran Nur Cahyo Dalillah Razan S Danar Putra Pamungkas, Danar Putra Dandi Sunardi Dany Fajar Kristanto Saputro Wibowo David Agustriawan Dede Sandi Dedy Ikhsan Dedy Sugiarto Deny Nugroho Triwibowo Dewi Yustika Lakoro Dhana Aulia Ayu Kurniawan Dhanar Intan Surya Saputra DHANI ARIATMANTO Dhani Ariatmanto Dhani Ratna Sari Dhani Ratna Sari, Dhani Ratna Dibyo Sudarsono Dimaz Arno Prasetio Dina Juni Marianti Dloifur Rohman Al Ghifari Donni Prabowo Donny Yulianto Dwi Ahmad Dzulhijjah Dwi Hartanto, Anggit Dwi Hartono, Anggit Dwi Rahayu Dwi Yuli Prasetyo Dzulhijjah, Dwi Ahmad Edhy Sutanta (Jurusan Teknik Informatika IST AKPRIND Yogyakarta) Edi, Mohammad Eko Boedijanto, Eko Eko Darmanto Eko Pramono Eko Pramono Eko Pramono Eko Pramono Eko Purwanto Elim, Marthinus Ikun Elvis Pawan Elvis Pawan Emha T. Luthfi Emha T. Luthfi, Emha T. Emha Taufik Lutfi Emha Taufiq Lutfi Emha Taufiq Lutfi, Emha Taufiq Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emha Taufiq Luthfi Emilya Ully Artha Emilya Ully Artha Enie Yuliani Enni Lindrawati Ermawan, Bagas Restya Erwin Syahrudin Esha Alma'arif Fachruddin Edi Nugroho Saputro Fadhillah, Akmal Rafi Fahmi Ilmawan Fahry, Fahry Fail Amir Faisal Fadhila Fajar Ardanu Fajar Rohman Hariri Fajar Surya Putro Farid Fitriadi Fariz Zakaria Fathoni Dwiatmoko Fatoni, Chavid Syukri Fendi Sumanto Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Ferry Wahyu Wibowo Fersellia, Fersellia Fidya Farasalsabila Firdaus, M. Haikal Firdiyan Syah Firdiyan Syah Firstyani Imannisa Rahma Firstyani Imannisa Rahma Firza Septian Fitrah Eka Susilawati Fitriana, Frizka Fitriani Fitriani Fitrony, Fachri Ayudi Gabriel Bintang Timur Gardyas Bidari Adninda Ghifari, Dloifur Rohman Al Gusti F Rahman Gusti Fathur Rakhman Habib, Muhammad Hafidh Rezha Maulana Hafidz Sanjaya Hafidz Sanjaya, Hafidz Hafiz Ridha Pramudita Hafiz Ridha Pramudita, Hafiz Ridha Hajar, Muhammad Rizky Halim Bayuaji Sumarna Hamdani, Nahrowi Hamdikatama, Bimantyoso Hanafi Hanafi Hanafi Hanafi Hanafi Hani Setiani Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta Hanif Al Fatta HANIF AL FATTA Hanif Al-Fatta Hardita, Veny Cahya Hartanto, Anggit Dwi Hartatik Hary Susanto Hasna Nirfya Rahmandhani Hastari Utama Hedy Leoni Helmawati, Nita Henderi . Hendi Muhammad, Alva HENDRA SETIAWAN Hendrawan, Ivan Rifky Hendrik Setiawan, Hendrik Herda Dicky Ramandita Herlandro Tribiakto Hidayat, Jati Arif Hikmianto, Riki Hirmayanti Hirmayanti, Hirmayanti Hudha, Yans Safarid I Dewa Bagas Suryajaya, I Dewa Bagas I Made Adi Purwantara I Wayan Rangga Pinastawa Idris Idris Idris Idris Ikrahmi Ikrahmi Imam Ainuddin P Ina Sholihah Widiati, Ina Sholihah Indarto Indarto Irawan, Ridwan Dwi Irawan, Rio Irma Yanti Irsyad Khalid Ilyas Irwan Siswanto Iskandar, Ahmad Fikri Isra Andika Bakhri Ivan Rifky Hendrawan Ivan Rifky Hendrawan Ivan Rifky Hendrawan Jangkung Tri Nugroho Januario Freitas Araujo Bernardo Jihadul Akbar Juni Marianti, Dina Kartikasari Kusuma Agustiningsih Kasim, Rafli Junaidi Khifni Beyk Ahmad Khoirunnita, Aulia Khusnawi Khusnawi Krisnawati Krisnawati Kriswantoro, Andi Kurniawan, Mei Kurniawan, Muhammad Bayu Kurniawan, Muhammad Bayu Kusnawi Kusnawi KUSRINI Kusrini Kusrini Kusrini Kusrini, Kusrini Kuswantoro, RB. Hendri Langgeng Hadi Prasetijo Lewu, Retzi Lindrawati, Enni Lisa Dinda Yunita M Imam Budi Laksamana M. Imam Budi Laksamana M. Imam Budi Laksamana M. Nuraminudin M. Rudyanto Arief M. RUDYANTO ARIEF M. Rudyanto Arief M. Rudyanto Arief M. Rudyanto Arief M. Suyanto M. Suyanto, M. M. Syafri Lamato M. Ulil Albab M. Zainal Arifin M. Ziaurrahman Ma'ruf Aziz Muzani Mahdi Ridho Mahmud Zunus Amirudin Marianti, Dina Juni Maringka, Raissa Martina Endah Pratiwi Maulana Brama Shandy Megantara, Nugraha Asthra Mei P Kurniawan Mei P Kurniawan Mei P.Kurniawan MEI PARWANTO KURNIAWAN Miftah Alfian Firdausy Mochammad Yusa Mochammad Yusa Mochammad Yusa Mochammad Yusa, Mochammad Moh Muhtarom Mohammad Edi Monalisa Fatmawati Sarifah Moniva, Anip Mudawil Qulub Muh Adha Muh Adha Muh Wal Ikram Muh Wal Ikram Muhajir, Fadhil Muhamad Arldi Megantara Muhamad Fatahillah Z Muhamad Paliya Sadana Muhamad Ridwan Muhammad Akbar Maulana Muhammad Altoumi Alsyaibani Muhammad Anwar Fauzi Muhammad Arfina Afwani Muhammad Fadli Muhammad Fadly Muhammad Fajrian Noor Muhammad Firdaus Abdi Muhammad Ilyas Prakanada Muhammad Lathifuddin Arif Muhammad Noor Arridho Muhammad Noor Arridho Muhammad Paliya Sadana Muhammad Resa Arif Yudianto Muhammad Ricky Perdana Putra Muhammad Rosikhu Muhammad Rusdi Rahman Muhammad Surahmanto Muhammad Suyanto, Muhammad Muhammad Syaiful Anam Muhammad Syukri Mustafa Muhammad Syukri Mustafa, Muhammad Syukri Mukhadimah Mukhlishah, Aiman Mursyid Ardiansyah Mutiara Dwi Anggraini NABILA OPER NAHROWI HAMDANI Nahrun Hartono Nahrun Hartono, Nahrun Nalda Kresimo Negoro Napianto, Riduwan Nasiri, Asro Ngaeni, Nurus Sarifatul Ngajiyanto, Ngajiyanto Ni Nyoman Utami Januhari, Ni Nyoman Nita Helmawati Nova Agustina Nova Noor Kamala Sari Nugroho Setio Wibowo Nugroho, Jangkung Tri Nugroho, Muhammad Agung Nuk Ghurroh Setyoningrum Nuk Ghurroh Setyoningrum Nur Hamid Sutanto Nur Hamid Sutanto Nur?aini, Nur?aini Nura Nugraha, Icha Nurcahyo, Azriel Christian Nurfaizah Nurfaizah Nurfajri Asfa Nurhasan Nugroho Nuri Cahyono Nurmasani, Atik Nurul Ilma Hasana Kunio Nurul Pratiwi, Annisa Okfan Rizal Ferdiansyah Oktariani, Deta Olivia Maria Inacio Tavares Omar Muhamammad Altoumi Alsyaibani Omar Muhammad Altoumi Alsyaibani Pangera, Abas Ali Patmawati Hasan Pebri Antara Prabowo Budi Utomo Pramudyantoro, Arvi Pranata, Caraka Aji Prasetio, Agung Budi Prasetyo, Ade Prasetyo, Yoga Adi Pratama, Rendy Bagus Pratama, Zudha Prayoga, Dimas Prayoga, Mahendra Bayu pujiharto, eka wahyu Pulungan, Linda Nurul Taqwa Purnawan Purnawan Purwidiantoro, Moch. Hari Purwoko, Agus Putra, Muhammad Ricky Perdana Putu Putrayasa Qolbun Salim As Shidiqi Qolbun Salim As Shidiqi Raditya Maulana Anuraga Rahardyan Bisma Setya Putra Rahmad Ardhani Rahmat Rahmat Rahmat Taufik R.L Bau Rahmatullah, Sidik Rakhma Shafrida Kurnia Ramadoni, Ramadoni Ramdhani, Arie Rahmat Rantung, Tessa Vatma Rasyida, Zulfa Raynaldi Fatih Amanullah Resty Wulanningrum Reyhan Dwi Putra Reyhan Dwi Putra Rhomita Sari Ria Andriani Ricki Firmansyah Rifki Fahmi Rifqi Anugrah Rifqi Mizan Aulawi Riska Kurniyanto Abdullah Risma, Vita Melati Rismayani Rismayani Riyanto Riyanto Rizki Firdaus Mulya Rizky Arya Kurniawan Rizky Handayani Rizky Handayani Rizqa Luviana Musyarofah Rizy, M. Alfa Rodney Maringka Ronaldus Morgan James Roshandri, Wien Fitrian Roshandri, Wien Fitrian S, Muhammad Sabri Sabar, Alfrida Safor Madianto Saiful Bahri Salibana, Chlyfen Richard Samsul Bahri Samuel Adhi Bagaskoro Sapta Hary Surya Wibowo Saputra, Artha Gilang Saputra, Artha Gilang Sarah Bunda Desi Bawan Sarah Bunda Desy Bawan Sari, Rita Novita Sari, Yunita Sartika Sarkawi - Sartje Mala Rangkoly Sasoko, Wasis Haryo Selamet Riadi Selvi Marcellia Selvy Megira Setiawan Budiman Setiawan, Bambang Abdi Setiawan, Hendi Setya Putra, Rahardyan Bisma Sidiq Wahyu Surya Wijaya Sigit Sugiyanto Sigit Suryono Siregar, Akhmad Arif Faisal Siswo Utomo, Mardi Slameto, Andika Agus Sodikin, Muh Ikbal Sofyan Pariyasto Sofyawati, Siti Sri Hartati Sri Hartati Sri Wahyuni Sri Yanto Qodarbaskoro Subastian Wibowo Sudarmawan Sudarmawan Sudarmawan, Sudarmawan Sudirman, San Sukoco Sukoco Sukoco Sukoco Sukrisno Amikom Suliswaningsih Suliswaningsih Suparyati Suparyati Supriadi, Oki Akbar Surya Ade Saputera Surya, Satria Dwi Suryono, Sigit Suryono, Wachid Daga Sutanto, Nur Hamid Sutrisno Sutrisno Suwanto Raharjo Suwanto Suwanto Suyadi - Suyatmi Suyatmi Swastikawati, Claudia Syah, Firdiyan Syah, Firdiyan Syahrudin, Erwin Syarham, Syarham Tamaulina Br Sembiring Tamrizal A. M. Tamsir, Kurniawati Tantoni, Ahmad Tantoni, Ahmad Teguh Ansyor Lorosae Tigus Juni Betri Tika Novita Sari Tikasni, Elisa Tinuk Agustin Tommy Dwi Putra TONNY HIDAYAT Toto Indriyatmoko Toto Rusianto Tri Amri Wijaya Tri Yusnanto Triana Triana Triwerdaya, Aji Tuhpatussania, Siti Tutut Maitanti Ulinuha, Hinova Rezha Veny Cahya Hardita Verra Budhi Lestari Vian Ardiyansyah Saputro Wahyu Ciptaningrum Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyu Hidayat Wahyudi Hidayat, Aji Said wahyuni, wenti ayu Wicaksono, Sherif Aji Widijanuarto, Satyo Widjiyati, Nur Wijaksana, Candra Putra Wijaya, Tri Amri Yans Safarid Hudha Yanuargi, Bayu Yaqin, Aiinul Yefta Tolla Yetman Erwadi Yohanes Aryo Bismo Raharjo Yosef Murya Kusuma Ardhana Yulianto Mustaqim Yulita Fatma Andriani Yumarlin MZ Yusa, Mochammad Zakaria, Fariz Zitnaa Dhiaaul Kusnaa Washilatul Arba'ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zitnaa Dhiaaul Kusnaa Washilatul Arba’ah Zulfa Rasyida Zulpan Hadi

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