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All Journal Jurnal Simantec Jurnal Ilmiah Kursor TEKMAPRO Journal of Industrial Engineering and Management Scan : Jurnal Teknologi Informasi dan Komunikasi International Journal of Advances in Intelligent Informatics Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Madura Register: Jurnal Ilmiah Teknologi Sistem Informasi International Journal of Artificial Intelligence Research Teknika: Engineering and Sains Journal JIEET (Journal of Information Engineering and Educational Technology) IJEBD (International Journal Of Entrepreneurship And Business Development) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Informatik : Jurnal Ilmu Komputer Jurnal Penelitian SEINASI-KESI Journal of Information Systems and Informatics bit-Tech Journal of Robotics and Control (JRC) JATI (Jurnal Mahasiswa Teknik Informatika) e-NARODROID Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences International Journal of Data Science, Engineering, and Analytics (IJDASEA) International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Impresi Indonesia Journal of Vocational Education and Information Technology (JVEIT) Information Technology International Journal (ITIJ) Jurnal ilmiah teknologi informasi Asia Jurnal Pengabdian Masyarakat SENSASI Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
I Gede Susrama Mas Diyasa
Department Of Informatics, Faculty Of Computer Science, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation

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Maintenance Scheduling for Buildings Using Fuzzy Logic Application I Nyoman Dita Pahang Putra; I Gede Susrama Mas Diyasa; Anak Agung Diah Parami Dewi; Bambang Trigunarsyah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p05

Abstract

This research proposes an innovative approach to building maintenance scheduling using fuzzy logic. Fuzzy logic addresses uncertainty and complexity in decision-making processes concerning prioritizing and scheduling maintenance tasks. This study aims to enhance the efficiency of maintenance scheduling, reduce maintenance costs, and consider the variability in building conditions. Traditional methods, such as PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method), have limitations in accurately predicting scheduling times. At the same time, fuzzy logic offers a more precise approach to overcoming uncertainty. Implementing a maintenance scheduling model based on fuzzy logic is expected to yield a more adaptive and responsive maintenance plan in response to changes in building conditions. The results of this research are expected to contribute positively to building maintenance management by leveraging the advantages of fuzzy logic in addressing the challenges of complexity and uncertainty in building maintenance management. By applying fuzzy logic-based maintenance scheduling, it is hoped that precise and efficient building maintenance scheduling can be achieved, thereby minimizing project completion time and assisting project managers. The fuzzy logic method can be employed for construction project scheduling according to the schedule determined by the contractor. This allows the contractor to use it as a consideration for the total duration, along with detailed timing in the project proposal. For the owner, it provides insights into the potential project completion time.
Convolutional layer exertion on few-shot learning for brain tumor classification Sunarko, Victor Immanuel; Puspaningrum, Eva Yulia; Widiastuty, Riana Retno; Hadi, Surjo; Awang, Mohd Khalid; Mas Diyasa, I Gede Susrama
Jurnal Ilmiah Kursor Vol. 13 No. 2 (2025)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i2.430

Abstract

Brain tumors, though relatively rare, pose a significant threat due to their critical location within the brain, impacting essential bodily functions. Accurate and timely diagnosis is vital, but traditional diagnostic methods are time-intensive and rely heavily on large labeled datasets. This study addresses these challenges by proposing a Few-Shot Learning (FSL) framework enhanced with Convolutional Neural Networks (CNNs) to classify brain tumors using MRI images. By employing the Matching Network architecture, the model leverages limited training data through an N-way-K-shot setup. Training results demonstrated accuracy levels of 71.58% (1-shot) and 82.89% (5-shot) for 1-layer CNNs, 66.65% (1-shot) and 84.03% (5-shot) for 3-layer CNNs, and 63.43% (1-shot) and 84.94% (5-shot) for 5-layer CNNs. However, validation accuracy revealed overfitting concerns, with the highest performance at 51.56% (1-layer, 1-shot). These results underscore the potential of FSL in medical imaging while highlighting the need for advanced augmentation and feature representation techniques to improve generalization.
Performance Comparison of Gaussian Mixture Model, Hierarchical Clustering, and K-Medoids in Passenger Data Clustering Thalita Syahlani Putri; I Gede Susrama Mas Diyasa; Achmad Junaidi
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3013

Abstract

The rapid growth of urban populations and increasing reliance on public transportation in Indonesia present challenges in managing passenger demand effectively. In Surabaya, the steady rise in Suroboyo Bus passengers underscores the need for data-driven strategies to optimize fleet allocation, scheduling, and infrastructure development. Identifying passenger density patterns through clustering provides a systematic basis for decision-making. This study aims to address a local research gap by comparing three clustering algorithms Agglomerative Hierarchical Clustering (AHC), Gaussian Mixture Model (GMM), and K-Medoids on empirical passenger data. Unlike previous studies that emphasize route optimization or demand forecasting, this research highlights a comparative evaluation to determine the most effective method for handling fluctuating and outlier-prone transportation data. The dataset was obtained from the Surabaya City Transportation Office for the Purabaya–Perak route during a two-week period in 2024. Data preprocessing included attribute selection, transformation of time into numerical format, outlier detection using the Interquartile Range (IQR), and Z-Score normalization. Clustering results were assessed with the Silhouette Score and visualized using scatter plots and histograms. Findings show that K-Medoids achieved the highest Silhouette Score (0.4222), surpassing AHC (0.3657) and GMM (0.3024). K-Medoids produced more balanced clusters and stronger resilience to outliers, while AHC provided interpretable hierarchical structures, and GMM modeled complex patterns but with weaker separation. In conclusion, K-Medoids is recommended as the most suitable approach for passenger density clustering. Academically, this study contributes a comparative framework for clustering in transportation research, while practically offering insights to support data-driven public transport management in developing cities.
Comparing Structured Prompts for Denoising Noisy Certificate Text Dimas Saputra; I Gede Susrama Mas Diyasa; Eva Yulia Puspaningrum; Wan Suryani Wan Awang
IJCONSIST JOURNALS Vol 6 No 2 (2025): March
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i2.133

Abstract

This study addresses the challenge of noisy text resulting from Optical Character Recognition (OCR) on certificates, which hinders effective classification in Recognition of Prior Learning (RPL) contexts. To mitigate this issue, researchers propose the use of prompt-based denoising leveraging a Large Language Model (LLM), specifically the Gemini model, to refine the extracted text prior to classification. The methodology integrates OCR via PyTesseract, LLM-driven denoising using structured prompts (CSIR, CLEAR, and CO-STAR), and a BERT-base-uncased model for classification. Synonym replacement is also applied for data augmentation. Performance evaluation is conducted using accuracy, validation accuracy, confusion matrix, and classification reports. The results demonstrate a substantial improvement in classification performance. The baseline scenario achieved an accuracy of 82.14%, whereas the best-performing prompt structure, CO-STAR, reached 98.81%, marking an increase of over 15 percentage points. Similar trends were observed across all evaluation metrics, with CO-STAR delivering the highest precision, recall, and F1-score values. In conclusion, incorporating LLM-driven denoising through effective prompt strategies enhances the quality of OCR-extracted text and significantly boosts classification outcomes in certificate-based applications.
Improving Palm Oil Production Efficiency through Deep Learning Algorithms for Fruit Ripeness Detection in Digital Images Tsabita Rosyidah Putri; I Gede Susrama Mas Diyasa; Alfan Rizaldy Pratama
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 19, No 2: May - August 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v19i2.33569

Abstract

Oil palm is a strategic commodity in Indonesia, and its production quality is greatly influenced by the ripeness of the fruit at harvest. Manual ripeness determination is still subjective and prone to errors due to variations in worker experience and environmental conditions. Advances in computer vision and deep learning technology offer a more objective and consistent automated solution. This study aims to develop and evaluate a model for detecting the ripeness level of palm oil fruit using the YOLOv12m algorithm based on digital images. The dataset used consists of 3,375 images with three ripeness classes (unripe, semi-ripe, ripe), which are divided into training, validation, and testing data with a ratio of 70:20:10. The model was trained for a maximum of 25 epochs with an early stopping mechanism. The evaluation was conducted using precision, recall, mAP@50, and mAP@50–95 metrics. The results showed excellent performance with precision of 0.958, recall of 0.946, mAP@50 of 0.985, and mAP@50–95 of 0.882. Class-by-class analysis shows the best performance in the raw and ripe classes, while the unripe class still poses challenges due to visual similarities between transition phases. Overall, the YOLOv12m model has proven to be effective and has the potential to be applied as a more objective and efficient harvest decision support system.
Comparative Analysis of Genetic Algorithm, Flood Algorithm, and Simulated Annealing for Academic Integrity Risk Minimization in Exam Assessments Heaven Ade Aldrico; Made Hanindia Prami Swari; I Gede Susrama Mas Diyasa
Jurnal Pamator : Jurnal Ilmiah Universitas Trunojoyo Vol 19, No 2: May - August 2026
Publisher : Universitas Trunodjoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/pamator.v19i2.34340

Abstract

Examination seating assignment is a combinatorial optimization problem with direct implications for exam assessment’s academic integrity. Existing approaches commonly model constraints in terms of course enrollment adjacency or room capacity, but rarely incorporate student behavioral attributes that proxy for social familiarity and collaboration risk. This study proposes a risk-aware seating formulation in which three correlated student attributes, academic major similarity, enrollment cohort similarity, and registration timestamp proximity, are encoded as weighted pairwise penalty components within a unified fitness function. Three metaheuristic algorithms are implemented and compared: Genetic Algorithm (GA), Simulated Annealing (SA), and the Flood Algorithm (FA). Each algorithm was executed across 30 independent runs on a controlled synthetic dataset of 80 students distributed across 4 examination rooms. Performance was evaluated using descriptive statistics and the Mann-Whitney U test. FA achieved the best mean penalty (103.10) with the lowest standard deviation (1.04), followed by SA (106.03) and GA (110.73). All pairwise differences were statistically significant at α = 0.05. An ablation study further revealed that enrollment cohort similarity is the most impactful constraint parameter, with its inclusion alone sufficient to produce statistically significant algorithmic differentiation. These results demonstrate that FA is the most effective and stable algorithm for this problem formulation, and that registration timestamp proximity constitutes a novel and informative behavioral risk proxy for exam seating optimization.
Optimization EfficientNetV2 model variant using Grad-CAM for multiple MRI brain tumor classification Denisa Septalian Alhamda; Wahyu Syaifullah J; Prasetyaning Estu Pratiwi; Surjo Hadi; Wan Suryani Wan Awang; I Gede Susrama Mas Diyasa
Jurnal Ilmiah Kursor Vol. 13 No. 3 (2026)
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/kursor.v13i3.428

Abstract

Fast and accurate diagnosis plays a critical role in effectively treating brain tumors. This study optimized and evaluated the EfficientNetV2 architecture through transfer learning, fine-tuning, and data augmentation, using three variants Small, Medium, and Large to classify MRI images into four categories: glioma, meningioma, pituitary tumors, and no tumor. Grad-CAM visualization was employed to enhance interpretability, providing a clear view of the critical regions in the MRI images that influenced the model’s decisions. Grad-CAM was tested across all model variants, and the best results were observed with EfficientNetV2-Large, where the model successfully highlighted the key areas associated with brain tumors. Among the variants, EfficientNetV2-Large achieved the best performance, with 99.85% accuracy, 99.60% precision, 99.65% recall, and 99.50% F1-score. However, this model required the longest computation time of 288 seconds per step, which may not be feasible in resource- constrained environments. Overall, this study underscores the potential of EfficientNetV2 models in revolutionizing brain tumor diagnosis by balancing accuracy, efficiency, and interpretability through advanced optimization techniques.Key words: Brain tumors, MRI classification, EfficientNetV2, Grad-CAM, Deep learning.
Explainable AI-Based Learning Analytics for Adaptive Learning Personalization Using Rule-Based Reasoning and CoCoSo: Learning Analytics Berbasis Explainable AI untuk Personalisasi Pembelajaran Adaptif Menggunakan Rule-Based Reasoning dan CoCoSo Daffa Ferdinan; I Gede Susrama Mas Diyasa; Ardhon Rakhmadi
Journal of Vocational Education and Information Technology (JVEIT) Vol. 7 No. 1 (2026): Jurnal JVEIT : Vol 7 No 1 2026
Publisher : Lembaga Pengembangan dan Inovasi Universitas Dharmas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56667/jveit.v7i1.2316

Abstract

Sistem adaptive learning semakin berkembang sebagai pendekatan untuk mendukung pembelajaran yang dipersonalisasi melalui pemanfaatan data aktivitas belajar. Meskipun demikian, sebagian besar sistem yang ada masih menghasilkan rekomendasi yang kurang transparan serta belum menyediakan mekanisme yang mampu memprioritaskan intervensi pembelajaran secara sistematis. Untuk mengatasi keterbatasan tersebut, penelitian ini mengusulkan Adaptive Learning Intelligence Framework (ALIF) yang mengintegrasikan Learning Analytics, Rule-Based Reasoning, Combined Compromise Solution (CoCoSo), dan Explainable Artificial Intelligence (XAI) dalam satu kerangka pengambilan keputusan. Data penelitian diperoleh dari Learning Management System (LMS) yang mencakup nilai kuis, durasi belajar, penyelesaian materi, aktivitas pembelajaran, frekuensi remedial, dan riwayat rekomendasi siswa. Seluruh data dianalisis menggunakan Learning Analytics untuk membentuk Adaptive Learning Intelligence Index (ALII) sebagai representasi kondisi belajar siswa. Selanjutnya, rekomendasi pembelajaran dihasilkan melalui mekanisme Rule-Based Reasoning, diprioritaskan menggunakan CoCoSo, dan disertai penjelasan berbasis XAI untuk meningkatkan transparansi keputusan. Hasil penelitian menunjukkan bahwa ALIF mampu mentransformasikan data aktivitas pembelajaran menjadi rekomendasi yang lebih adaptif, objektif, dan mudah diinterpretasikan sehingga mendukung pengambilan keputusan guru secara berbasis bukti. Kebaruan penelitian terletak pada pengembangan kerangka terpadu yang menghubungkan analitik pembelajaran, inferensi berbasis aturan, pemeringkatan multikriteria, dan Explainable Artificial Intelligence dalam satu arsitektur Intelligent Educational Decision Support System yang mendukung personalisasi pembelajaran secara lebih transparan dan terstruktur.
Pendekatan Time Series Decomposition (STL) Dalam Prediksi Kecelakaan Berbasis Kepadatan Lalu Lintas Sebagai Dasar Kebijakan Di Tol Surabaya-Gempol Rakha Rizky Mahendra; Aviolla Terza Damaliana; I Gede Susrama Mas Diyasa
Jurnal Impresi Indonesia Vol. 4 No. 5 (2025): Jurnal Impresi Indonesia
Publisher : Riviera Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58344/jii.v4i5.6491

Abstract

Kecelakaan lalu lintas di jalan tol tetap menjadi masalah kritis yang mempengaruhi keselamatan publik dan stabilitasekonomi. Penelitian ini mengusulkan penggunaan dekomposisi Seasonal-Trend menggunakan LOESS (STL) untukmemprediksi risiko kecelakaan berdasarkan data volume lalu lintas di jalan tol Surabaya-Gempol. Data dari Januari 2022hingga Desember 2023, termasuk volume lalu lintas harian dan laporan kecelakaan, diuraikan menjadi komponen tren,musiman, dan residu untuk mengidentifikasi pola. Korelasi positif sedang (r = 0,4882) ditemukan antara volume lalulintas dan frekuensi kecelakaan. Analisis STL mengungkapkan puncak musiman mingguan yang konsisten di akhir pekan,terutama hari Sabtu. Model prediktif yang dikembangkan berhasil mengidentifikasi 11 hari berisiko tinggi pada Januari2024. Berdasarkan temuan tersebut, delapan rekomendasi kebijakan berbasis waktu dirumuskan, termasuk manajemenlalu lintas dinamis, pemantauan real-time, dan peningkatan pengawasan selama periode puncak. Penelitian ini menyumbangkan kerangka kerja berbasis data baru untuk manajemen keselamatan lalu lintas, menggabungkandekomposisi deret waktu dengan panduan kebijakan yang dapat ditindaklanjuti. Tidak seperti penelitian sebelumnya yanghanya berfokus pada prediksi volume, atau pada konteks jalan non-tol, penelitian ini memajukan penerapan STL untukidentifikasi risiko real-time di jalan tol Indonesia. Implikasinya menekankan integrasi sistem lalu lintas cerdas dan potensiprakiraan berbasis STL sebagai fondasi strategi keselamatan jalan nasional.
VGG-16 Transfer Learning for Accurate Classification of Three Local Durian Varieties Using Leaf Morphology Images Nuqqy Zahhar, Ahmad Haikal; Mas Diyasa, I Gede Susrama; Prami Swari, Made Hanindya
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5506

Abstract

Durian (Durio zibethinus Murr), recognized as the "king of fruits" in Southeast Asia, represents a significant genetic asset for Indonesian agriculture with high economic value. East Java leads national production, contributing 580.5 thousand tons (29.59%) of the total 19.6 million tons in 2024. However, local durian quality faces persistent challenges due to minimal maintenance practices and farmers' limited expertise in variety identification. Manual taxonomic identification based on leaf morphology requires specialized knowledge, is time-consuming, and prone to subjective errors, particularly for three popular Nganjuk varieties—local, montong, and lai—which exhibit similar leaf characteristics. Previous studies have addressed durian classification using fruit images or disease detection on leaves, but a research gap exists for variety classification specifically using leaf images with deep learning approaches. This study implements VGG-16 transfer learning architecture with ImageNet pre-trained weights to classify three durian varieties based on leaf morphology images. A dataset of 600 high-resolution images (2048×2048 pixels, 200 per class) was collected from Nganjuk orchards following standardized protocols and validated by three independent experts (two experienced farmers and one plant taxonomist), achieving substantial inter-annotator agreement (Fleiss' kappa = 0.87). Preprocessing included resizing to 224×224 pixels with bilinear interpolation, normalization to [0,1], and standardization using ImageNet statistics. Data augmentation through random rotation (±30°), horizontal flipping (48.8% probability), contrast adjustment (±50.1%), and width/height shifting (±12%) expanded the dataset fourfold to 2,400 images. Using a 90:10 train-test split (2,160:240), the VGG-16 model trained with Adam optimizer (learning rate 0.001, dropout 0.5, dense layer 256 units) achieved 97.08% accuracy after 4 epochs in 1.11 minutes. Performance metrics demonstrated high precision (0.93-1.00), recall (0.92-1.00), and F1-scores (0.95-0.99) across all classes. This research advances precision agriculture informatics by providing an automated, reliable tool for durian variety identification, supporting farmers in optimal cultivation decisions, quality control, and economic value enhancement while contributing to sustainable agricultural development and the Center for Plant Variety Protection and Agricultural Licensing (PVTPP) registration systems in Indonesia.
Co-Authors Achmad Junaidi Achmad Junaidic Adiwidyatma, Afdhal Reshanda Ahmad Naufal Mumtaz Akmal, Mohammad Faizal Alfan Rizaldy Pratama Alfiatun Masrifah Alhamda, Denisa Septalian Amanullah , Nurkholis Anak Agung Diah Parami Dewi Ardhon Rakhmadi Ardianto, Taruna Ariyono Setiawan Aryananda, Rangga Laksana Aurelia, Cenditya Ayu Aviolla Terza Damaliana Awaludin W., Moh. Haydir Awang, Mohd Khalid Azizah, Nabila Wafiqotul Bambang Trigunarsyah Bambang Trigunarsyah Budi Nugroho Cahyani Kuswardhani, Hajjar Ayu Daffa Ferdinan Denisa Septalian Alhamda Deshinta Arrova Dewi Dewi, Deshinta Arrova Dewi, Deshinta Arrowa Dimas Saputra Dwi Arman Prasetya Dwi Kusuma, Irma Erma Suryani Etniko Siagian, Pangestu Sandya Eva Yulia Puspaningrum Fara Disa Durry Fatmah Sari, Allan Ruhui Firmansyah, Taufik Nur Firya Nadhira Gideon Setya Budiwitjaksono Gideon Setya Budiwitjaksono Gunawan, Ellexia Leonie Hadi, Surjo Hafidz Amarul Ma’rufi Hajjar Ayu Cahyani Kuswardhani Halim, Christina Hamawi, Moch. Hawin Heaven Ade Aldrico Henki Bayu Seta Humairah, Sayyidah I Nyoman Dita Pahang Putra I Nyoman Dita Pahang Putra Ilham Ade Widya Sampurno Ilham Ade Widya Sampurno Intan Yuniar Purbasari Jauharis Saputra, Wahyu Syaifullah Jojok Dwiridotjahjono Kraugusteeliana Kraugusteeliana Made Hanindia Prami Swari Mandeni, Ni Made Ika Marinni Mandyartha, Eka Prakarsa Moch. Hatta Mohamad Nur Amin Mohammad Idhom Mohammad Idhom Mohammad Rafka Mahendra A Mohammad Rafka Mahendra Ariefwan Mudjahidin Muhammad Rif'an Dzulqornain Mumtaz, Ahmad Naufal Munoto Mustika, Agung Nadhira, Firya Nahusuly, Barep J. A. I. Naufal Baihaqi Moerrin Ni Made Ika Marini Mandenni Ni Made Ika Marini Mandenni Nuqqy Zahhar, Ahmad Haikal NYOMAN DITA PAHANG PUTRA, NYOMAN Prabowo, Aris Prami Swari, Made Hanindya Prasetyaning Estu Pratiwi Prasetyo, Galih Novian Prismahardi Aji Riyantoko Putri, Fitri Aulia Yuliandi Raditya, Askara Rakha Rizky Mahendra Rangga Laksana A Rangga Laksana Aryananda Refika Ayuna Sari Rheza Rizqi Ahmadi Ridho Syahdindo Rizal Harjo Utomo Sabrina Charya Floribunda Santoso, Sri Fuji Senny Meliyan Setiawan, Ariyono Setiawan, Ariyono Shodiq, Ja’far Slamet Winardi Sri Wibawani Sugeng Purwanto Sugiarto S Sugiarto Sugiarto Sugiarto, Sugiarto Sukri, Hanifudin Sulianto Bhirawa Sunarko, Victor Immanuel Surjo Hadi Suryani, Dedik Taruna Ardianto Terza Damaliana, Aviolla Thalita Syahlani Putri Trimono, Trimono Tsabita Rosyidah Putri Wafiqotul Azizah, Nabila Wahyu Caesarendra Wahyu Dwi Lestari Wahyu S.J. Saputra Wahyu Syaifullah J Wan Awang, Wan Suryani Wan Suryani Wan Awang Wan Suryani Wan Awang Wardhani, Naritha Cahya Widianto, Purwito Ridho Widiastuty, Riana Retno Wijaya, Pandu Ali Yisti Vita Via