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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Teknologi dan Manajemen Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding SNATIF Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan PROtek : Jurnal Ilmiah Teknik Elektro Sistem : Jurnal Ilmu-Ilmu Teknik JEEMECS (Journal of Electrical Engineering, Mechatronic and Computer Science) Conference SENATIK STT Adisutjipto Yogyakarta Management and Economics Journal (MEC-J) JTAM (Jurnal Teori dan Aplikasi Matematika) JURNAL INSTEK (Informatika Sains dan Teknologi) CYCLOTRON Jurnal Abadimas Adi Buana Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Elektro dan Komputer TRIAC JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JEECAE (Journal of Electrical, Electronics, Control, and Automotive Engineering) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Jurnal Sistem Informasi dan Informatika (SIMIKA) JATI (Jurnal Mahasiswa Teknik Informatika) CIVITAS (JURNAL PEMBELAJARAN DAN ILMU CIVIC) International Journal of Advances in Data and Information Systems Journal of Computer Networks, Architecture and High Performance Computing Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Teknik Informatika (JUTIF) International Journal of Robotics and Control Systems Jurnal Teknologi dan Manajemen ALINIER: Journal of Artificial Intelligence & Applications SinarFe7 Jurnal Penelitian Journal of Information Systems and Technology Research Jurnal Informatika Teknologi dan Sains (Jinteks) JAPI: Jurnal Akses Pengabdian Indonesia Jurnal Ilmiah Edutic : Pendidikan dan Informatika Internet of Things and Artificial Intelligence Journal JEECS (Journal of Electrical Engineering and Computer Sciences) TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Information Technology International Journal (ITIJ) Al-Zayn: Jurnal Ilmu Sosial & Hukum Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal ilmiah teknologi informasi Asia Jurnal PETISI (Pendidikan Teknologi Informasi) Jurnal Aplikasi Sains Data
Dwi Arman Prasetya
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Pengujian Fungsional Website Crusher Report Berbasis Machine Learning Menggunakan Metode Robustness Testing Adhigiadany, Chelsea Ayu; Hindrayani, Kartika Maulida; Prasetya, Dwi Arman
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2014

Abstract

Website dan Machine Learning menjadi kebutuhan penting perusahaan dalam rangka meningkatkan efektivitas kinerja. Salah satu implementasi integrasi website dengan Machine Learning adalah website Crusher Report milik PT XYZ. Website yang dirancang dengan memanfaatkan LARS, PostgreSQL, dan Flask ini sudah diuji secara ketangkasan model dalam memprediksi. Penelitian ini bertujuan untuk menguji keandalan website Crusher Report sebagai user interface milik PT XYZ menggunakan pendekatan Black Box Testing dengan metode Robustness Testing. Skenario pengujian yang digunakan yaitu dengan memberikan input diluar ketentuan website. Hasil pengujian menunjukkan bahwa website mampu menangani seluruh input tidak valid dengan baik melalui notifikasi kesalahan dan pengaturan nilai input otomatis, menghasilkan tingkat keberhasilan pengujian sebesar 100%. Temuan ini menunjukkan bahwa website Crusher Report efektif dalam mendeteksi dan mengelola kesalahan input, serta layak digunakan sebagai platform pendukung operasional crusher PT XYZ.
Prediksi Harga Saham Menggunakan Model Mixture Autoregressive (MAR) (Studi Kasus : Saham Perusahaan Rokok) Ristiyani, Sintiya; Mohammad Idhom; Dwi Arman Prasetya
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.2721

Abstract

Memprediksi harga saham merupakan tantangan besar dalam dunia penelitian karena tingginya risiko yang terlibat, meskipun potensi keuntungannya juga sangat besar. Hal ini mencerminkan bahwa pergerakan saham sangat dipengaruhi oleh perubahan faktor eksternal maupun internal. Oleh karena itu, memperoleh prediksi harga saham yang tepat menjadi sangat krusial guna meminimalkan kemungkinan kerugian bagi para investor. Penelitian ini dilakukan dengan tujuan untuk memprediksi harga saham dari tiga perusahaan rokok besar di Indonesia, yaitu PT. Hanjaya Mandala Sampoerna Tbk (HMSP), PT. Gudang Garam Tbk (GGRM), dan PT Wismilak Inti Makmur Tbk (WIIM). Penelitian ini menggunakan suatu model statistika bernama Mixture Autoregressive (MAR), dipilih karena kemampuannya dalam menangkap pola tidak linier serta perubahan kondisi yang sering muncul pada pergerakan harga saham. Menggunakan model MAR diperoleh model MAR dengan nilai Mean Absolute Percentage Error (MAPE) untuk masing-masing perusahaan yaitu Gudang Garam (5.46%), Sampoerna (4.52%), dan Wismilak (3.58%).
PENGGUNAAN FIREFLY ALGORITHM PADA SUPPORT VECTOR REGRESSION SEBAGAI OPTIMASI PREDIKSI HARGA CLOSE LITECOIN Millani, Alief Indy; Dwi Arman Prasetya; Eka Prakarsa Mandyartha
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 1 (2026): EDISI 27
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i1.6955

Abstract

Altcoin merupakan salah satu investasi dari Cryptocurrency yang sering diminati banyak orang dikarenakan dapat memberikan keuntungan yang besar. Namun, sering kali banyak kesalahan dalam memprediksinya dikarenakan data yang dinamis. Oleh karena penelitian ini bertujuan untuk meningkatkan akurasi prediksi harga altcoin dengan mengoptimalkan parameter Support Vector Regression (SVR) menggunakan Firefly Optimization. Metode ini diterapkan untuk menentukan nilai parameter terbaik, dan  berdasarkan nilai error terkecil. Data yang digunakan merupakan altcoin jenis Litecoin. Evaluasi performa dilakukan menggunakan metrik R², dan MAPE. Hasil penelitian menunjukkan bahwa optimasi dengan Firefly Algorithm mampu meningkatkan performa prediksi secara signifikan dibandingkan SVR tanpa optimasi. Pada Litecoin, MAPE menurun dari 9.20% menjadi 3% dengan R2 dari dari 0.83 menjadi 0.94. Meskipun waktu komputasi meningkat, kombinasi Firefly-SVR terbukti efektif dalam menghasilkan parameter optimal dan meningkatkan akurasi prediksi harga altcoin
LONGITUDINAL MODELING OF E-COMMERCE CHOICE USING LATENT GROWTH CURVE TO ASSESS INFLUENCING FACTORS AMONG LATE ADOLESCENTS Fadlila Agustina; Dwi Arman Prasetya; Aviolla Terza Damaliana
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3923

Abstract

The rapid growth of e-commerce in Indonesia has significantly influenced consumer behavior, particularly among late adolescents aged 18–21 years. This study examines the dynamic factors affecting e-commerce preferences, including price, service quality, and customer loyalty, using Latent Growth Curve Modeling (LGCM). This method was chosen for its ability to analyze variable changes longitudinally, allowing the identification of growth patterns and factors influencing shifts in consumer behavior over time. Data were collected through an online survey involving 400 respondents over three time periods. The study’s findings reveal that price is the most stable variable (intercept 0.5302, slope 0.0811), whereas service quality (intercept 0.8127, slope -0.0285) and loyalty (intercept 0.8508, slope -0.0188) show slight declines. Innovation, functioning as a covariate, significantly affects the intercept of all variables, particularly loyalty, although its impact on growth rates varies. The model demonstrates a good fit, with RMSEA (0.0730), CFI (0.9844), and TLI (0.9402), confirming its validity. Visualizations indicate that loyalty evolves more dynamically than service quality, highlighting the crucial role of innovation in customer engagement. This study emphasizes the need for e-commerce platforms to prioritize innovation and service quality improvements to foster long-term loyalty. These findings provide valuable insights into consumer behavior dynamics and offer strategic recommendations for achieving competitive advantage in the digital marketplace.
IMPLEMENTATION OF KERNEL COMBINATION GAUSSIAN PROCESS REGRESSOR IN LOYALTY PREDICTION (CASE STUDY: ONLINE MOTORCYCLE TAXI) Luqna Aziziyah; Dwi Arman Prasetya; Trimono Trimono
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i2.3945

Abstract

In the application-based transportation industry, customer loyalty is a crucial factor affecting service sustainability. This study aims to analyze and predict customer loyalty in online motorcycle taxi services in Surabaya using the Gaussian Process Regressor (GPR) with a kernel combination approach. Data were collected through a survey of 467 students from public universities in Surabaya, considering service quality, price, and innovation factors. The analysis process includes data processing, validation, cleaning, and modeling using Gaussian Process Regression techniques. The results indicate that the kernel combination in GPR effectively captures complex non-linear patterns in survey data, with low Root Mean Squared Error (RMSE) and R² values close to 1. These findings suggest that the proposed approach can provide accurate customer loyalty predictions. This study contributes to developing strategies for online motorcycle taxi service providers to enhance user experience and maintain market share. The findings highlight the importance of applying machine learning models to understand customer behavior and support data-driven business decision-making.
KLASTERISASI TINGKAT KESEJAHTERAAN MASYARAKAT MENGGUNAKAN METODE SELF ORAGNIZING MAPS DENGAN PARTICLE SWARM OPTIMIZATION Afidria, Zulfa Febi; Trimono, Trimono; Prasetya, Dwi Arman
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 11, No 1 (2026)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v11i1.7752

Abstract

Pulau Jawa juga merupakan salah satu pulau yang masih menjadi kontributor terbesar dalam pertumbuhan ekonomi Indonesia. Kontribusi Pulau Jawa diperkirakan akan menyentuh porsi hingga 58,75 persen pada tahun 2023. Namun, di balik pertumbuhan ekonominya yang pesat, Pulau Jawa masih menghadapi tantangan kesejahteraan seperti tingginya pengangguran, kemiskinan, serta rendahnya kualitas sumber daya manusia dan pendidikan. penelitian ini bertujuan untuk mengelompokkan tingkat kesejahteraan Masyarakat di Pulau Jawa menggunakan metode Self Organizing Maps dengan Particle Swarm Optimization. Metode ini dipilih karena SOM juga sangat efisien dalam mengelola data yang mengandung noise, outlier, serta nilai yang hilang karena ukuran sampelnya tidak memiliki batasan. Akan tetapi SOM juga memiliki kelemahan yaitu jumlah cluster perlu ditentukan secara spesifik dan untuk mendapatkan batas cluster peneliti harus melakukan inspeksi manual atau menggunakan algoritma cluster hierarki atau partisi. Penentuan batas cluster pada metode SOM dapat menggunakan metode Particle Swarm Optimization(PSO). Kebaruan dari penelitian ini adalah penerapan kombinasi metode SOM dan PSO dalam analisis kesejahteraan masyarakat di Pulau Jawa, yang masih jarang digunakan pada studi serupa. hasil penelitian ini menunjukkan model terbaik membentuk 3 cluster dengan nilai silhouette coefficient tertinggi sebesar 0.7293 Nilai tersebut menunjukkan bahwa struktur cluster yang terbentuk termasuk dalam kategori baik.
A Quantitative Analysis of Economic Strategy and Its Influence on Final Ranking in Magic Chess Game Using Machine Learning Fitrah, Hazza; Dafa Zain Musyafa; Nauval Theo Jovaldi; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.25

Abstract

Economic management is a fundamental strategic pillar in auto-battler games such as Magic Chess, but its quantitative impact on player performance has not been extensively studied. This research aims to empirically measure the predictive ability of economic variables on players' final rankings. We analyzed a dataset consisting of 57 match records from players at the ‘Grandmaster’ ranking level. Two modeling approaches, Multiple Linear Regression and Random Forest, were used to predict players' final rankings (values 1–8) based on three primary economic features: total gold spent, re-roll frequency, and average economic bonus. The results from the Linear Regression model showed a Mean Squared Error (MSE) of 0.5496. However, the most significant finding was the R-squared value, which was only 0.016. This extremely low R-squared value indicates that the economic variables analyzed could only explain 1.6% of the total variance in players' final rankings. The conclusion of this study is that economic metrics alone are insufficient to build a reliable model for accurately predicting final rankings. This strongly suggests that other strategic factors, such as synergy composition, item allocation, and tactical decisions on the game board, have a far more dominant influence in determining a player's success in high-level Magic Chess.
Dijkstra's Algorithm for Optimizing Humanitarian Aid Distribution Routes to Flood Victims in Cerme District, Gresik Datia Putri Nabila Br Tarigan; Desi Tristianti; Erika Fatimatul Hidayanti; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.32

Abstract

This study presents the development and analysis of a system designed to optimize the distribution routes of social aid during flood emergencies in the Cerme District, Gresik Regency. The primary objective is to ensure that logistical operations, particularly the delivery of aid to affected villages, are carried out in the most efficient and timely manner. To achieve this, Dijkstra’s Algorithm is employed due to its well-established reliability in computing the shortest path between nodes in a weighted graph. The graph used in this research is constructed based on real-world spatial data, with each node representing a village and the edges representing actual road distances obtained from mapping services. The system is implemented using an Object-Oriented Programming (OOP) paradigm in Python, which ensures modularity and scalability of the codebase. For graph modeling and shortest path computation, the NetworkX library is utilized, while the graphical user interface (GUI) is built using Tkinter to provide an interactive and user-friendly experience. The application enables users to select starting and destination points from dropdown menus, compute the shortest route dynamically, and visualize it on an interactive graph complete with route details and distances. Experimental trials were conducted by simulating various flood scenarios, and the results demonstrated that the system successfully identified optimal aid routes with minimized travel distances. These outcomes confirm the practicality and effectiveness of the proposed method. Moreover, the ability to update the graph dynamically allows the system to adapt to changes in road accessibility due to flooding. This makes the tool highly applicable in real-world disaster response scenarios. In conclusion, the developed application offers a valuable solution for both local government agencies and humanitarian volunteers, helping to improve coordination, reduce delivery time, and ensure that aid reaches flood-affected communities as efficiently as possible.
Comparative Analysis of Hierarchical Clustering and K-Medoids for Clustering Cases of Childhood Respiratory Diseases in Lamongan Regency Adelia Yuandhika; Nezalfa Sabrina; Cahya Eka Melati; Dwi Arman Prasetya; Prismahardi Aji Riyantoko
Jurnal Aplikasi Sains Data Vol. 2 No. 1 (2026): Journal of Data Science Applications.
Publisher : Program Studi Sains Data UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jasid.v2i1.37

Abstract

Abstract— Respiratory diseases affecting children remain a significant health issue in Indonesia, including in Lamongan Regency. The region faces challenges related to pediatric respiratory illnesses, particularly Childhood Tuberculosis, Pneumonia in toddlers, and Cough in toddlers, which impact children's quality of life and development. Therefore, understanding the spatial distribution and correlation patterns among these diseases is essential to support more targeted health intervention planning. This study analyzes the distribution patterns of pediatric respiratory diseases in Lamongan Regency and clusters regions based on similarities in the number of cases using an unsupervised learning approach. The method employed is Hierarchical Clustering with four distance calculation techniques: single, complete, average, and ward linkage and K-Medoids with two distance calculation techniques: euclidean and manhattan distance. The data, sourced from the Lamongan District Health Office, include four numerical variables related to respiratory diseases, aggregated by sub-districts. Data normalization was carried out using standardization, and cluster quality was evaluated using three internal metrics: Silhouette Score, Davies-Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). The analysis results indicate that the optimal number of clusters is three. Among all methods tested, the Hierarchical Clustering with ward linkage method yielded the best performance, with a Silhouette Score of 0.5447, a DBI of 0.5884, and a CHI of 20.3018. These results demonstrate that the ward linkage method is the most effective in clustering regions based on the characteristics of pediatric respiratory disease cases and can be used for mapping priority health intervention areas in Lamongan Regency.
Optimisation of Hyperparameter Tuning and Optimiser on MobileNetV2 for Batik Parang Classification Rafli, Muhammad; Prasetya, Dwi Arman; Hindrayani, Kartika Maulida
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3576

Abstract

Batik Parang is a prominent traditional motif in Indonesia, characterised by repetitive diagonal patterns and subtle visual variations across regional styles, such as Solo Parang and Yogyakarta Parang, which pose challenges for automated image classification. This study addresses this challenge by introducing an optimisation-focused framework that integrates hyperparameter tuning strategies with a lightweight convolutional neural network, extending the practical use of MobileNetV2 for fine-grained cultural motif classification. A balanced dataset of 160 batik images collected from Kaggle was employed and partitioned using an 80:20 stratified split to ensure class consistency. The model was evaluated on a limited yet representative dataset reflecting realistic small-scale cultural heritage scenarios. Two hyperparameter tuning methods, Bayesian Optimisation and Particle Swarm Optimisation, were applied to optimise learning rate, batch size, and dropout rate, while two optimisers, Adam and Adagrad, were compared to analyse their effects on convergence stability and generalisation. The training process followed a two-phase strategy consisting of transfer learning and selective fine-tuning of upper MobileNetV2 layers. Experimental results indicate that Adagrad-based configurations consistently outperform Adam-based models, which exhibited class collapse and poor generalisation. The optimal configuration, combining Adagrad with Bayesian Optimisation, achieved a validation accuracy of 91% with balanced precision, recall, and F1-score across both Parang classes. These findings demonstrate that careful optimisation enhances the reliability of lightweight CNNs and support extending the proposed framework to other cultural heritage classification tasks and resource-constrained real-time applications.
Co-Authors ', Nachrowie ., Humaidi A. A. Ngurah Gunawan Aan Nehru Awanto Achmad Junaidi Adelia Yuandhika Adhigiadany, Chelsea Ayu Aditya, Wigananda Firdaus Putra Afidria, Zulfa Febi Agustin, Sesillia Akio Kitagawa Alam, Fajar Indra Nur Alfa, Aniysah Fauziyyah Alhamda, Denisa Septalian Ali, Munawar Amrullah, Ahmad Wildan Andre Leto Andrew Arjunanda Yasin Anggraini Puspita Sari Anindha Lazuardi Aries Boedi Setiawan Arifani, Kahpi Baiquni Arifuddin, Rahman Arinda, Putri Surya Arum Puspita Ayu Aryananda, Rangga Laksana Asfiani, Ilil Musyarof Atiana Sofia Kaci Aviolla Terza Damaliana Awang, Wan Suryani Wan Azizah, Alisa Jihan Baidowi Baidowi Baidowi Baidowi Bambang Nurdewanto Barus, Indra Basitha F Hidayatulail Cahya Eka Melati Cahyani Kuswardhani, Hajjar Ayu cahyono, wahyu eko Candra Laksana Dafa Zain Musyafa Damai Arbaus, Damai Damaliana, Aviolla Terza Danang - Destiawan Danang Destiawan Datia Putri Nabila Br Tarigan Desi Tristianti Desyderius Minggu Dicky Kurniawan Diyasa, I Gede Susrama Mas Dody Pintarko Dwi Agung Ayubi E, Nachrowie Eka Prakarsa Mandyartha Ekawati, Anies Eko Wahyu Prasetyo Elta Sonalitha Sonalitha Emilia, Kholidatus Erik Roma Hurmuzi Erika Fatimatul Hidayanti Fadlila Agustina Fahrudin, Tresna Maulana Farhans, Muhammad Izzudin Febriyanti, Alvi Yuana Firdaus Firdaus Firza Prima Aditiawan Fitrah, Hazza Gatut Yulisusianto Halim, Christina Hari Fitria Windi Hendry Yudha Pratama Herdianti, Rahmalia Anindya Hesti Sholikah, Hesti Hidayatulail, Basitha F Hikmata Tartila Hiroshi Suzuki Hurmuzi, Erik Roma I Gede Susrama Mas Diyasa Ibrahim, Mohd Zamri Bin idhom, Mohammad Indra Barus Irsyadi, Muhamad Haidir Ismail, Jefri Abdurrozak Januar, Teddy Jariyah Jeki Saputra Junita Junita Kartika Maulida Hindrayani Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Kholid, Fajar Kukuh Yudhistiro, Kukuh Kurniawan, Dicky Kusuma, Dwi Febri Chandra Kusuma, Firdaus Miftakh Kuswardana, Dendy Arizki Laksana, Candra Larasati Lestari, Amanda Ayu Dewi Lisanthoni, Angela Luqna Aziziyah Maulidiyyah, Nova Auliyatul Millani, Alief Indy Mohammad Ansori Mohammad Idhom Mohammad, Bawazir Fadhil Muhaimin, Amri Muhammad Ansori Muhammad Ghinan Navsih Muhammad Muharrom Al Haromainy Muhammad Naswan Izzudin Akmal Mulyadi Mulyadi Nachrowie Nachrowie Nachrowie, Nachrowie Nambo Hidetaka Narumi Hayakawa Nauval Theo Jovaldi Nezalfa Sabrina Niken Sulistyowati Ningrum, Imelda Widya Ninik Sisharini Ninis Herawati Norma Windiyanti Novita Anggraini Nur Rachman Nur Rachman Supatmana Muda Nur Rochman Nur Rochman Nurhalizah, Cesaria Deby Permana, Iwan Setiawan Prakoso, Akbar Tri Prameswari, Diajeng Prismahardi Aji Riyantoko Puput Dani Prasetyo Adi Puput Marina Azlia Sari Putri Lestari Putri, Irma Amanda Putri, Serlinda Mareta Rabi, Abd. Rafli, Muhammad Rahayu Sri Utami Rahayu, Ayu Sri Rahman Arifuddin Rahmanda Putri, Endin Rahmawati, Adinda Aulia Respati Respati Ristiyani, Sintiya Riyantoko, Prismahardi Aji Rosariawari, Firra Rudi Wilson Sagita Rochman Salim, Hotimah Masdan Santika, Surya Sari, Andina Paramita Sigit, Syauqita Siswanto Siswanto Sitanggang, Desi Daomara Siti Nuurlaily Rukmana, Siti Nuurlaily Stanislaus Yoseph Subairi Subairi Sugiarto S Sumartono Sumartono Sumartono Suprayogi Suprayogi Suprayogi Suprayogi Surya Nanda Santika, Surya Suryantari, Putu Anggi Takahiro Kitajima Takashi Yasuno Tresna Maulana Fahrudin Trimono Trimono Trimono, Trimono Utomo, Setyobudi Wahyu Dirgantara Wahyu Putra Pratama Wahyu Syaifullah Jauharis Saputra Wahyuni, Dinar H S wangge, ferdinandus Weisrawei, Yosef Yasin, Andrew Arjunanda Yohanes U D Sipul Yosef Weisrawei Yosua Satria Bara Harmoni Yuliani, Devina Putri Yunia Dwie Nurchayanie Yusaq Tomo Ardianto