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All Journal International Journal of Electrical and Computer Engineering Sainteks Jurnal Ilmu Komputer dan Informasi Jurnal Teknik Elektro Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek Semesta Teknika Suhuf Jurnal Ilmiah KOMPUTASI Knowledge Engineering and Data Science Wikrama Parahita : Jurnal Pengabdian Masyarakat Jurnal Pilar Nusa Mandiri CogITo Smart Journal Indonesian Journal of Information System Dinamisia: Jurnal Pengabdian Kepada Masyarakat JMM (Jurnal Masyarakat Mandiri) Justek : Jurnal Sains Dan Teknologi CARADDE: Jurnal Pengabdian Kepada Masyarakat JURTEKSI JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) Sang Pencerah: Jurnal Ilmiah Universitas Muhammadiyah Buton Infotekmesin Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi JIKA (Jurnal Informatika) Community Empowerment JPM: JURNAL PENGABDIAN MASYARAKAT Bima Abdi: Jurnal Pengabdian Masyarakat Journal of Telecommunication, Electronics and Control Engineering (JTECE) Insearch: Information System Research Journal KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jutech: Jurnal Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science J-Icon : Jurnal Komputer dan Informatika Science and Technology: Jurnal Pengabdian Masyarakat Journal of Informatics and Information Security Prosiding SeNTIK STI&K Sasambo: Jurnal Abdimas (Journal of Community Service) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi Journal of Computer Science Advancements
Widi Hastomo
Institut Teknologi Dan Bisnis Ahmad Dahlan
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Transportation Other

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Improved Banking Customer Retention Prediction Based on Advanced Machine Learning Models Linda Wahyu Widianti; Adhitio Satyo Bayangkari Karno; Hastomo, Widi; Aryo Nur Utomo; Dodi Arif; Indra Sari Kusuma Wardhana; Deon Strydom
Indonesian Journal of Information Systems Vol. 7 No. 2 (2025): February 2025
Publisher : Program Studi Sistem Informasi Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/ijis.v7i2.10364

Abstract

The quick growth of the banking sector is reflected in the rise in the number of banks. In addition to the intense competition among banks for new customers, efforts to keep existing ones are essential to minimizing potential losses for the company. To ascertain whether customers will leave the bank or remain customers, this study will employ churn forecasts. A 1,750,036-customer demographic dataset, which includes data on bank customers who have left or are still customers, is used in the training process to compare five machine learning technology models in order to investigate the improvement of binary classification prediction accuracy. These models are Decision Tree, Random Forest, Gradient Boost, Cat Boost, and Light Gradient Boosting Machine (LGBM). According to the study's results, LGBM performs better than the other four models since it has the highest recall and accuracy and the fewest False Negatives. The LGBM model's corresponding accuracy, precision, recall, f1 score, and AUC are 0.8789, 0.8978, 0.8553, 0.8758, and 0.9694. This demonstrates that, in comparison to traditional methods, machine learning optimization can produce notable advantages in churn risk classification. This study offers compelling proof that sophisticated machine learning modeling can revolutionize banking industry client retention management.
Exloratory Data Analysis Untuk Data Belanja Pelanggan dan Pendapatan Bisnis Widi Hastomo; Adhitio Satyo Bayangkari Karno; Sudjiran; Dodi Arif; Eka Sally Moreta
Infotekmesin Vol 13 No 2 (2022): Infotekmesin: Juli, 2022
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/infotekmesin.v13i2.1547

Abstract

A more quantifiable perspective is assuming the role of mechanistic management in an effort to enhance business based on its capacity to transform data into knowledge and insight. The industry has not completely supported its business strategy also with driven data. Using a transaction dataset taken from one of the Kaggle.com challenges, this experiment attempts to determine consumer spending patterns and Retail Fashion business revenues (H&M Personalized Fashion Recommendations). The results of the experiment are the number of transactions based on customer age, the most sales product and one-time purchased item, and the type of product that generates the highest and smallest income. The approach employed is EDA using the Python language. In order for businesses to generate analytical findings that provide future perspectives and to help identify the gap by delivering analytical results in the form of suggestions that can be perpetuated, the findings of this experiment are intended to support the capabilities of simulation. The challenge in this experiment is the abundance of datasets, which necessitates a suitable operating environment.
Menggunakan Xception, Transfer Learning, dan Permutasi untuk Meningkatkan Klasifikasi Ketidaksempurnaan Permukaan Baja: Using Xception, Transfer Learning, and Permutation to Improve the Classification of Steel Surface Imperfections Setiawati, Popong; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi; Setiawan, Iwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.1258

Abstract

Kualitas permukaan baja yang diproduksi sangat penting untuk meningkatkan daya saing dalam industri baja. Tingginya tingkat cacat pada permukaan baja merupakan masalah serius yang berdampak pada kualitas keluaran. Pengendalian yang masih dilakukan secara manual dan visual saat ini hanya dapat dilakukan oleh orang-orang dengan bakat dan keahlian tertentu. Pengamatan dengan metode konvensional ini memerlukan waktu yang lama, lamban, dan presisi yang rendah. Saat ini, perkembangan teknik pembelajaran mendalam memungkinkan deteksi cacat permukaan baja secara otomatis dengan tingkat akurasi yang tinggi. Arsitektur Xception digunakan dalam pekerjaan ini untuk menerapkan strategi pembelajaran mendalam. Teknik permutasi dan augmentasi digunakan untuk mengatasi ketidakseimbangan data. Model yang dikembangkan dapat membedakan empat jenis cacat pada permukaan baja. Koleksi 7.095 foto permukaan baja digunakan dalam prosedur pelatihan. Jika dibandingkan dengan tidak menggunakan transfer learning, hasil pengukuran kinerja proses pelatihan dengan menggunakan transfer learning (Imagenet) menunjukkan hasil yang lebih baik. Pelatihan pembelajaran transfer menghasilkan skor akurasi masing-masing sebesar 94,9% dan 97,7% untuk data pelatihan dan validasi. Sedangkan hasil penilaian nilai kerugian untuk data latih dan validasi masing-masing sebesar 19,4% dan 14,4%.
OPTIMASI CONVOLUTION NEURAL NETWORK UNTUK DETEKSI COVID-19 Hastomo, Widi; Karno, Adhitio Satyo Bayangkari; Bakti, Indra
RADIAL : Jurnal Peradaban Sains, Rekayasa dan Teknologi Vol. 10 No. 2 (2022): RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi
Publisher : Universitas Bina Taruna Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37971/radial.v10i2.299

Abstract

Abstrak: Optimasi Convolution Neural Network Untuk Deteksi Covid-19. Kondisi pandemi seperti sekarang ini diperlukan sebuah algoritma pembelajaran mesin untuk mendeteksi covid-19 secara otomatis berdasarkan pada gambar rontgen dada guna memudahkan dalam mambantu pengambil keputusan. Penelitian ini ingin membandingkan arsitektur CNN AlexNet dan MobileNetV2 untuk mendeteksi (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. Data himpunan rontgen dada yang digunakan sejumlah 4000 yang berasal dari kaggle.com, 0.8 data dibagi untuk pelatihan sedangkan 0.2 nya digunakan untuk pengujian. Optimizer yang digunakan yaitu keras SGD momentum, dengan nilai learning rate 0.005 dan momentum 0.9, serta epoch 50. Ukuran gambar untuk input yaitu 224x224 serta ukuran batch 32. Hasil optimasi dari kedua algoritma tersebut yaitu, MobileNetV2 lebih baik untuk mendeteksi covid-19 dengan nilai akurasi presisi mencapai 99%. Penelitian selanjutnya dapat membandingkan algoritma CNN yang lainnya serta data himpunan yang lebih banyak. Kata kunci: CNN; AlexNet; MobileNetV2; Covid-19 Abstract: Convolution Neural Network Optimization for Covid-19 Detection. In the current pandemic conditions, a machine learning algorithm is needed to detect COVID-19 automatically based on chest X-ray images to make it easier to assist decision makers. Aim study be disposed for compare the architecture of CNN AlexNet and MobileNetV2 to detect (a) covid-19, (b) lung opacity, (c) normal, (d) viral pneumonia. The data set of chest X-rays used are 4000 from kaggle.com, 0.8 of the data is shared for training while 0.2 is used for testing. The optimizer used is hard SGD momentum, with a value of leaning rate 0.005 and momentum 0.9, and epoch 50. The image size for the input is 224x224 and the batch size is 32. The optimization results from the two algorithms are, MobileNetV2 is better for detecting covid-19 with an accuracy value The precision reaches 99%. Future research can compare other CNN algorithms and larger data sets. Keywords: CNN; AlexNet; MobileNetV2; Covid-19
Development of Adaptive Lecture Scheduling System using Genetic Algorithm Case Study: Ahmad Dahlan Institute of Technology and Business Ardana, Nandika Bayu; Hastomo, Widi; Arman, Shevti Arbekti
Journal of Computer Science Advancements Vol. 2 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jsca.v2i4.1310

Abstract

Optimal course scheduling is a crucial aspect in supporting the efficiency of the teaching and learning process in higher education. In many institutions, lecture scheduling is still done manually or with static methods that are not adaptive to changing needs and limited resources. This research aims to develop an adaptive lecture scheduling system using genetic algorithms, with a case study at ITB Ahmad Dahlan. Genetic algorithms were chosen because of their ability to solve complex optimization problems with high efficiency, such as managing dynamic variables such as lecturer availability, rooms, and lecture time preferences. In this research, data related to courses, lecturers, time, classroom availability, and curriculum requirements are integrated into the designed system to generate an optimal course schedule. The development process involved several key stages, including requirements analysis, system design, algorithm implementation, and performance evaluation. Genetic algorithm implementation is done by simulating various scheduling scenarios to find the most optimal solution. The results show that the developed system is able to produce a more efficient and clash-free course schedule compared to traditional scheduling methods. In addition, the system also allows higher flexibility in adjusting the schedule to changes that may occur, such as the addition or reduction of classes. Thus, this research makes a significant contribution in improving the quality of educational services at ITB Ahmad Dahlan as well as offering solutions that can be adopted by other educational institutions facing similar challenges.
Optimalisasi Pemanfaatan Absensi Digital untuk Modernisasi Layanan Administrasi Pemerintahan Desa Nuraisyah, Nuraisyah; Hastomo, Widi
JPM: Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2026): January 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jpm.v6i3.2922

Abstract

This community service activity was conducted in Suka Damai Village to improve the quality of village administrative services through participatory mentoring in the implementation of a digital attendance system. The main problems faced by the partner included the use of manual attendance records, low administrative efficiency, a high risk of recording errors, and limited digital literacy among village officials. This program aimed to enhance the capacity and independence of village officials in managing attendance administration in an orderly, accurate, and transparent manner. The service implementation employed a participatory mentoring approach that actively involved village officials through needs analysis, system usage training, intensive mentoring, as well as monitoring and evaluation stages. The results indicate positive changes among the partners, reflected in increased understanding of information technology, reduced reliance on manual attendance records, and improved independence of village officials in managing the digital attendance system sustainably. Furthermore, the attendance administration process became more structured, accessible, and supportive of transparent village governance. This community service program provides a practical contribution to empowering village officials and has the potential to be replicated in other villages facing similar administrative challenges.
Optimalisasi Pengembangan UMKM dan Edukasi Lingkungan sebagai Wujud Pengabdian Mahasiswa KKN di Kelurahan Panunggangan Barat Purwianti, Zahra Clarita; Al-Ghifari, Muhammad Ridho; Amellya, Renny Dwi; Sabillah, Isti’ Anatus; Permata, Jelita; Merlina, Merlina; Belva, Nasywah Sabina; Putri, Syalma Awalya; Arliando, Tommy; Azizah, Zahratul; Putra, Yoga Rarasto; Hastomo, Widi
Bima Abdi: Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Bima Abdi: Jurnal Pengabdian Masyarakat
Publisher : Yayasan Pendidikan Bima Berilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53299/bajpm.v6i1.3050

Abstract

Program pengabdian masyarakat ini dilaksanakan untuk menjawab permasalahan minimnya penerapan digitalisasi pembayaran dan media promosi UMKM, serta kurangnya kesadaran masyarakat terhadap kebersihan lingkungan di Kelurahan Panunggangan Barat. Sebagian pelaku UMKM belum memanfaatkan pembayaran digital QRIS, dan belum memiliki media promosi seperti banner. Sementara di sisi lingkungan masih terbatasnya tempat sampah dan minimnya partisipasi masyarakat dalam menjaga kebersihan. Kegiatan ini bertujuan untuk mengoptimalkan pengembangan UMKM melalui pembuatan banner dan pendampingan penggunaan QRIS, serta meningkatkan kepedulian lingkungan melalui pembuatan tempat sampah daur ulang, edukasi pengelolaan sampah, dan kegiatan kerja bakti. Metode pelaksanaan menggunakan pendekatan partisipatif melalui observasi, sosialisasi, pelatihan, pendampingan, dan kolaborasi dengan warga setempat. Hasil kegiatan menunjukkan adanya peningkatan pemahaman bagi pelaku UMKM terhadap pentingnya media promosi dan transaksi digital, serta meningkatnya partisipasi masyarakat dalam menjaga kebersihan lingkungan. Program ini memberikan dampak positif dari sisi ekonomi, sosial, dan lingkungan, serta menjadi langkah awal dalam mendukung kemandirian dan keberlanjutan masyarakat.
EFFICIENTNET MODEL FOR BONE AGE PREDICTION Hastomo, Widi; Sestri, Elliya; Ningsih, Silvia
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 12 No. 1 (2025): Desember 2025
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.v12i1.4355

Abstract

Abstract: Accurate bone age estimation is essential for monitoring pediatric growth, diagnosing endocrine disorders, and supporting clinical decision-making. Although deep learning has improved prediction accuracy, limited studies have systematically examined how increasing model depth affects performance and reliability. This study evaluates the effectiveness of progressively deeper convolutional neural networks, specifically EfficientNet variants B0 to B5, for bone age estimation from hand radiographs. Experiments were conducted using 12,611 hand X-ray images from the RSNA Pediatric Bone Age Challenge dataset on Kaggle. To ensure fair comparison, all models were trained using a unified and consistent training pipeline. Model performance was evaluated using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), and Pearson correlation coefficient. The results show a consistent improvement in prediction accuracy as model depth increases. Among the evaluated models, EfficientNet-B5 achieved the best performance, with an MAE of 21.5 months, MAPE of 6.23%, CCC of 0.9148, and Pearson’s r of 0.9203. These findings confirm that model scaling plays a critical role in enhancing prediction robustness and clinical reliability. Future work should emphasize external validation across diverse populations and incorporate interpretability techniques, such as Grad-CAM, to improve clinical transparency and trust. Keywords: bone age prediction; deep learning; model evaluation; clinical validation Abstrak: Estimasi usia tulang yang akurat sangat penting untuk memantau pertumbuhan anak, mendiagnosis gangguan endokrin, dan mendukung pengambilan keputusan klinis. Meskipun pembelajaran mendalam telah meningkatkan akurasi prediksi, studi yang secara sistematis meneliti bagaimana peningkatan kedalaman model memengaruhi kinerja dan keandalan masih terbatas. Studi ini mengevaluasi efektivitas jaringan saraf konvolusional yang semakin dalam, khususnya varian EfficientNet B0 hingga B5, untuk estimasi usia tulang dari radiografi tangan. Eksperimen dilakukan menggunakan 12.611 gambar sinar-X tangan dari dataset RSNA Pediatric Bone Age Challenge di Kaggle. Untuk memastikan perbandingan yang adil, semua model dilatih menggunakan alur pelatihan yang terpadu dan konsisten. Kinerja model dievaluasi menggunakan Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Concordance Correlation Coefficient (CCC), dan koefisien korelasi Pearson. Hasil menunjukkan peningkatan yang konsisten dalam akurasi prediksi seiring dengan peningkatan kedalaman model. Di antara model yang dievaluasi, EfficientNet-B5 mencapai kinerja terbaik, dengan MAE sebesar 21,5 bulan, MAPE sebesar 6,23%, CCC sebesar 0,9148, dan Pearson’s r sebesar 0,9203. Temuan ini menegaskan bahwa penskalaan model memainkan peran penting dalam meningkatkan optimasi prediksi dan keandalan klinis. Penelitian selanjutnya dapat menekankan validasi eksternal di berbagai populasi dan menggabungkan teknik interpretasi, seperti Grad-CAM, untuk meningkatkan transparansi dan kepercayaan klinis. Kata kunci: prediksi usia tulang; deep learning; evaluasi model; validasi klinis
Benchmarking Five Machine Learning Models for Accurate Steel Plate Defect Detection Sestri, Ellya; Karno, Adhitio Satyo Bayangkari; Hastomo, Widi
CogITo Smart Journal Vol. 11 No. 2 (2025): Cogito Smart Journal
Publisher : Fakultas Ilmu Komputer, Universitas Klabat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31154/cogito.v11i2.753.382-401

Abstract

Early detection of defects in steel plates is essential to ensure structural integrity and product quality in the metal manufacturing industry. This study compares the performance of five machine learning algorithms Support Vector Classifier (SVC), Nu-Support Vector Classifier (NuSVC), Decision Tree (DT), Random Forest (RF), and CatBoost (CB) to classify seven categories of steel plate defects using 26 technical features from a publicly available dataset on Kaggle. The preprocessing pipeline included outlier detection (IQR method), class imbalance correction using SMOTE, and feature normalization via StandardScaler. The models were evaluated using classification metrics such as Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Log Loss. Results revealed that the CatBoost algorithm achieved the most balanced and consistent performance, with an AUC of 0.93, accuracy of 68.3%, and the lowest Log Loss value (0.786). In contrast, the Decision Tree showed severe overfitting with perfect training performance but poor generalization (Log Loss = 15.72). This study highlights the promise of CatBoost as an interpretable and efficient solution for automated defect detection in steel manufacturing, while also offering transparent reproducibility pathways for further research.
EDUKASI KEUANGAN MASYARAKAT DAN KESADARAN LINGKUNGAN MELALUI SENI VISUAL DI KELURAHAN CIBODAS Fakhri, Muhamad Naufal; Faikoh, Siti; Natasya, Fatin; Prasetyo, Aditya Dwi; Melyawati, Melyawati; Rahman, Muhammad Khosyi; Nurmala, Risma; Widiyawati, Wita; Rochman, Yuanda; Azie, Yusril; Putra, Yoga Rarasto; Hastomo, Widi
JMM (Jurnal Masyarakat Mandiri) Vol 9, No 5 (2025): Oktober
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v9i5.34004

Abstract

Abstrak: Permasalahan utama adalah rendahnya pemahaman anak terkait konsep menabung serta kurangnya kepedulian masyarakat terhadap kebersihan dan penghijauan lingkungan PKM/Pengabdian ini bertujuan untuk mengedukasi masyarakat tentang keuangan melalui seni visual. Kegiatan dilaksanakan melalui metode partisipatif berupa sosialisasi, mural edukatif, senam komunitas, penghijauan, dan pendampingan kegiatan PAUD serta Posyandu. Mitra kegiatan mencakup kader Posyandu yaitu 5 orang, guru PAUD berjumlah 3 orang, dan tokoh masyarakat. Evaluasi dilakukan melalui observasi, wawancara, dan dokumentasi. Hasilnya menunjukkan peningkatan softskill mahasiswa dengan presentase 85% dalam hal komunikasi dan kolabori, serta peningkatan pemahaman anak terhadap konsep menabung dan kebutuhan-keinginan dengan presentasi 60%. Kegiatan mural dan mading visual juga meningkatkan kesadaran warga terhadap kebersihan dan keberlanjutan lingkungan. Program ini menunjukkan dampak sosial yang signifikan dan berkelanjutan.Abstract: The primary issue is children’s limited understanding of the concept of saving, along with the community’s insufficient concern for cleanliness and environmental greening. This Community Service Program (PKM) aims to educate the community about finance through visual arts. The activities were carried out using a participatory approach, including socialization, educational murals, community exercise, reforestation, and assistance in early childhood education (PAUD) and Posyandu activities. The program partners included five Posyandu cadres, three PAUD teachers, and community leaders. Evaluation was conducted through observation, interviews, and documentation. The results showed an 85% increase in students’ soft skills in terms of communication and collaboration, as well as a 50% improvement in children’s understanding of the concept of saving and distinguishing between needs and wants. Mural and visual board activities also raised community awareness of cleanliness and environmental sustainability. This program demonstrates a significant and sustainable social impact.
Co-Authors Adhitio Satyo Agita Tunjungsari Ahmad Eko Saputro Ahmad Eko Saputro Ahmad Eko Saputro Aji Digdoyo Aji Digdoyo Al-Ghifari, Muhammad Ridho Ambardi Ambardi Ambardi Ambardi Ambardi, Ambardi Amellya, Renny Dwi Aminudin Ardana, Nandika Bayu Arif, Dody Arliando, Tommy Aryo Nur Utomo Asy-Syifa, Zahwa Zia Azie, Yusril Azis, Nur Bakti, Indra Basri, Lody Saladin Bayangkari Karno, Adhitio Satyo Belva, Nasywah Sabina Chufran, Indra Bakti Daruningsih, Kukuh Deon Strydom Deswandi, Arief Diana Yusuf Digdoyo, Aji Dodi Arif Dodi Arif Dody Arif Eka Sally Moreta Eka Sally Moreta Eko Ahmad Eko Ahmad Eko Hadiyanto Elliya Sestri Eva Karla, Eva Fahrul Razi Fahrul Razi Faikoh, Siti Fakhri, Muhamad Naufal Faqihudin Faqihudin Fiedha Nasution Fiqhri, Zul Fitriyani Fitriyani Handayani, Sri Setya Harini Agusta Holmes Rolandy Kapuy Hudaa, Syihaabul Ignatius Joko Dewanto, Ignatius Joko Indra Bakti Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Indra Sari Kusuma Wardhana Ire Puspa Wardhani Iwan Setiawan Kalbuana, Nawang Kamilia, Nada Kardian, Aqwam Rosadi Kasoni, Dian Kusuma Wardhana, Indra Sari Linda Wahyu Widianti LM Rasdi Rere LM Rasdi Rere Lussiana ETP Lyscha Novitasari Maeda, Serly Masriyanda, Masriyanda Meika Syahbana Rusli Melyawati Melyawati, Melyawati Muhammad Mardani, Muhammad Nada Kamilia Nada Kamilia Nada Kamilia Nani Kurniawati Natasya, Fatin Nia Yuningsih Nia Yuningsih Nisfiani, Ervina Nur Aini Nuraisyah Nuraisyah Nurhidayati, Aulia Nurmala, Risma Permata, Jelita Prasetyo, Aditya Dwi Purwianti, Zahra Clarita Putra, Yoga Rarasto Putri , Basmallah Ramadhani Aisyah Putri, Dhea Ananda Putri, Syalma Awalya Rahman, Ibadu Rahman, Muhammad Khosyi Rasyiddin, Ahmad Rere, L.M Rasdi Reza Fitriansyah Reza Fitriansyah Rochman, Yuanda Rudy Yulianto Rudy Yulianto Sabillah, Isti’ Anatus Saputro, Ahmad Eko Sestri, Elliya Sestri, Ellya Setiawati, Popong Shevti Arbekti Arman Silvia Ningsih Siswahyudianto Soegijanto Soegijanto Soleha, Maratus Stevianus Stevianus Sudarto Usuli Sudarwanto, Pantja sudjiran Sudjiran Sukardi, Sukardi Sundoro, Aries Surawan, Tri Sutarno Sutarno Sutarno Sutarno Sutarno Syamsu, Muhajir Syihaabul Hudaa Tri Surawan Tri Surawan Vany Terisia Wardhana , Indra Sari Kusuma Widiyawati, Wita Yayat Sujatna Yayat Sujatna Yayat Sujatna, Yayat Yoga Rarasto Putra Yoga Rarasto Putra Yoga Rarastro Putra Yulianti Muthmainnah, Yulianti Yuningsih, Nia Yusuf Yusuf YUSUF, DIANA Zahratul Azizah