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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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Optimizing Clustering Analysis to Identify High-Potential Markets for Indonesian Tuber Exports Prasetya, Dwi Arman; Sari, Anggraini Puspita; Idhom, Mohammad; Lisanthoni, Angela
Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol. 7 No. 1 (2025): February
Publisher : Jurusan Teknik Elektromedik, Politeknik Kesehatan Kemenkes Surabaya, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/skzqbd57

Abstract

Agriculture is a key contributor to Indonesia's economic growth, with tubers representing the second most important food crop. Despite their significance, the export value of Indonesia’s tuber crops has not yet reached its full potential given the decline in the value of tuber exports since 2021. One of the contributing factors is the restricted range of export market options. This study aims to analyze export trade patterns to identify the most high-potential markets for Indonesian tuber commodities.  Clustering analysis is used as a key method to identify market locations by grouping countries based on similar trade characteristics. Clustering was conducted using the Gaussian Mixture Model (GMM), which enhanced by Particle Swarm Optimization (PSO) and evaluated by silhouette score and DBI. The dataset is collected from Indonesia’s Central Bureau of Statistics from 2019 to 2023, focusing on 5 kinds of tuber exports with total of 455 entries and 8 columns. Using the AIC/BIC method, the optimal number of clusters obtained is 2 which are low market opportunities (cluster 0) and high market oppurtunities (cluster 1). Results showed that the GMM model without optimization has silhouette score of 0.7602 and DBI of 0.8398, while the GMM+PSO model achieved an improved silhouette score of 0.8884 and DBI of 0.5584. Both score are categorized as strong structure but, GMM+PSO has higher silhouette score and lower DBI score, demonstrating the effectiveness of PSO in enhancing the clustering model’s performance. The key potential markets for Indonesian tuber exports are primarily concentrated in Asia, including countries such as China, Malaysia, Thailand, Vietnam, Hong Kong, and United States.
COMPARISON OF DECISION TREE AND RANDOM FOREST METHODS IN THE CLASSIFICATION OF DIABETES MELLITUS Maulidiyyah, Nova Auliyatul; Trimono, Trimono; Damaliana, Aviolla Terza; Prasetya, Dwi Arman
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8316

Abstract

Diabetes mellitus is a deadly disease caused by the failure of the pancreas to produce enough insulin. Indonesia ranks fifth in the world with the number of people with diabetes in 2021 at around 19.47 million, and this number continues to increase. One of the main challenges in diabetes management is to make the right classification between type 1 and type 2 diabetes, as misdiagnosis can result in inappropriate treatment and worsen the patient's condition. This study uses a machine learning approach to compare Decision Tree and Random Forest methods in classifying type 1 and type 2 diabetes mellitus. The goal is to identify the most effective model in predicting the type of diabetes based on medical record data. The comparison was done using k-fold cross validation and confusion matrix. The results showed that Random Forest provided an average accuracy of 94%, while Decision Tree reached 93% during cross validation testing. Although both models were able to perform well in classification, Random Forest showed a more stable performance and a slight edge in accuracy over Decision Tree. Evaluation with the confusion matrix showed that the Decision Tree model achieved 93% accuracy compared to Random Forest's 91%. In addition, the Decision Tree model also had a lower number of prediction errors, 7, compared to 9 for Random Forest. The most influential variables in classification also differed between the two models, showing the unique advantages and characteristics of each approach.
Application of Convolutional Neural Network (CNN) for Web-Based Translation of Indonesian Text into Sign Language Prameswari, Diajeng; Larasati; Muhammad Naswan Izzudin Akmal; Prismahardi Aji Riyantoko; Dwi Arman Prasetya
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): 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.v1i2.12

Abstract

Communication for the deaf and hard of hearing is often hindered by the limited number of sign language interpreters. This research aims to develop a web-based text-to-text sign language translation system using Convolutional Neural Networks (CNN) to bridge this communication gap. The system is built with the ASL Alphabet dataset containing 87,000 images from 29 classes (A-Z, SPACE, DELETE, NOTHING). The CNN model was designed with three convolutional layers and trained for 15 epochs using 80% of the data, while 20% of the data was used for testing. The user interface was developed using Streamlit for ease of use. Training results showed a training accuracy of 98.96% and a validation accuracy of 98.61% at the 15th epoch. Model evaluation yielded an overall accuracy of 98%, with high precision, recall, and F1-score values for most classes. This research demonstrates the significant potential of CNN in developing automatic sign language translators, which is expected to improve information accessibility and inclusivity for the deaf community.
Statistical Analysis of Infant Malnutrition Cases in North Sumatra Before and After COVID-19 Using the Wilcoxon Test Sitanggang, Desi Daomara; Putri, Serlinda Mareta; Agustin, Sesillia; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): 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.v1i2.16

Abstract

Child malnutrition remains a very important public health issue in Indonesia. Malnutrition is a condition of deficiency in energy and essential nutrients that can lead to impaired physical growth, mental development, and an increased risk of mortality in children. The prevalence of malnutrition among toddlers in Indonesia is still quite high and shows disparities between regions, especially in provinces with high poverty rates. One province of concern is North Sumatra, which, according to data from the Ministry of Health, has had a significant incidence of malnutrition in the last five years. This condition was exacerbated by the emergence of the COVID-19 pandemic at the end of 2019, which has had a major impact on various sectors of life, including family health and economy. The pandemic caused significant disruptions to primary healthcare systems, including a decrease in posyandu activities, immunizations, and monitoring of children's nutritional status. The decline in household income during the pandemic made it difficult for families to meet their balanced nutritional food needs. A UNICEF study showed an increased risk of acute malnutrition in children during the pandemic, especially in previously vulnerable areas. To measure the impact of the COVID-19 pandemic on the incidence of child malnutrition, a statistical approach that can compare data before and after the pandemic is needed. This study aims to analyze the difference in the incidence of child malnutrition before and after the COVID-19 pandemic in North Sumatra Province using the Wilcoxon test method. Using the Wilcoxon Signed-rank Test statistical method, a comparative analysis was performed between the medians of the data from 2018 and 2023. The results of the study showed that there was a difference between the medians of the two data sets.
Application of K-Means Clustering for Regency/City Clustering in East Java Based on 2024 Human Development Index Indicators Emilia, Kholidatus; Rahayu, Ayu Sri; Yuliani, Devina Putri; Prasetya, Dwi Arman; Riyantoko, Prismahardi Aji
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): 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.v1i2.21

Abstract

This study applies the K-Means clustering algorithm to group 38 regencies and cities in East Java Province based on five Human Development Index (HDI) indicators for the year 2024. These indicators include Life Expectancy (UHH), Expected Years of Schooling (HLS), Mean Years of Schooling (RLS), and Real Expenditure Per Capita (PPK). The aim of this research is to uncover hidden patterns and disparities in regional development, which can be used as a basis for more targeted and data-driven policy interventions.The optimal number of clusters was determined using three evaluation metrics: the Elbow Method, Silhouette Score, and Davies-Bouldin Index. These evaluations collectively identified three distinct clusters. Cluster 0 represents regions with high levels of development across all indicators. Cluster 1 consists of regions with moderate development levels and potential for improvement, while Cluster 2 contains regions with significantly lower values, particularly in education and income metrics.In addition to clustering, a correlation analysis was conducted to examine the relationship between HDI and its supporting indicators. The results show that Mean Years of Schooling (RLS) and Real Expenditure Per Capita (PPK) have the strongest positive correlation with HDI across all clusters. This highlights the key role of education and economic well-being in improving human development. The findings emphasize the importance of clustering analysis in shaping equitable and region-specific development strategies.
Feature Importance-Guided Ensemble Classification for Predicting Recurrence in Differentiated Thyroid Cancer Muhammad Ghinan Navsih; Wahyu Putra Pratama; Hikmata Tartila; Dwi Arman Prasetya; Tresna Maulana Fahrudin
Jurnal Aplikasi Sains Data Vol. 1 No. 2 (2025): 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.v1i2.22

Abstract

Accurate prediction of cancer recurrence is critical for improving patient monitoring and personalized treatment planning. In this study, we propose a machine learning framework to predict recurrence in patients with differentiated thyroid cancer using statistically selected clinical features. Feature relevance was assessed using ANOVA for ordinal/numerical variables and the Chi-square test for one-hot encoded categorical variables, allowing us to identify the most informative predictors. We then trained three distinct classifiers—Random Forest, Logistic Regression, and XGBoost—and combined them using a hard voting ensemble strategy. The proposed ensemble achieved an accuracy of 98.7% on the test set, with particularly strong precision and recall scores for the recurrent class, indicating its potential clinical utility. Interestingly, all three base classifiers produced identical predictions on the test data, suggesting the dataset’s strong internal structure and the effectiveness of our feature selection process. This work highlights the value of integrating statistical feature selection with ensemble modeling for robust and interpretable prediction in clinical oncology applications.
PEMETAAN KARAKTERISTIK WILAYAH MISKIN DI JAWA TIMUR MENGGUNAKAN SPECTRAL CLUSTERING UNTUK PENENTUAN TARGET KEBIJAKAN Herdianti, Rahmalia Anindya; Wahyu Syaifullah Jauharis Saputra; Dwi Arman Prasetya
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 10 No 2 (2025): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v10i2.56845

Abstract

Tingkat kemiskinan yang masih tinggi di Jawa Timur merupakan permasalahan serius yang memerlukan perhatian dan penanganan berbasis data yang komprehensif. Ketimpangan sosial ekonomi antarwilayah menyebabkan sebagian daerah tertinggal dalam pembangunan, sehingga pengelompokan wilayah miskin menjadi langkah strategis untuk membantu pemerintah dalam menentukan prioritas serta arah kebijakan pengentasan kemiskinan yang lebih efektif. Penelitian ini bertujuan untuk memetakan karakteristik wilayah miskin di Jawa Timur menggunakan algoritma Spectral Clustering dengan 11 indikator sosial ekonomi sebagai variabel analisis. Berdasarkan hasil pembobotan kontribusi variabel terhadap pembentukan klaster, indikator yang paling berpengaruh adalah garis kemiskinan (0,259418), rata-rata lama sekolah (0,259067), harapan lama sekolah (0,147613), dan indeks pembangunan manusia (0,133441). Hasil pengelompokan menunjukkan adanya dua klaster utama, yaitu wilayah dengan tingkat kemiskinan tinggi (26 kabupaten/kota) dan wilayah dengan tingkat kemiskinan rendah (12 kabupaten/kota). Evaluasi kualitas model menghasilkan nilai Davies-Bouldin Index (0,4401) dan Silhouette Score (0,6655), yang menunjukkan bahwa metode ini mampu membentuk kelompok dengan pemisahan yang cukup baik. Temuan ini diharapkan dapat menjadi dasar dalam perumusan kebijakan intervensi yang lebih terarah, berkelanjutan, dan berkeadilan sosial di Jawa Timur.
Perbandingan Kinerja Algoritma XGBoost dan CatBoost dalam Klasifikasi Risiko Penyakit Diabetes Utomo, Setyobudi; Sigit, Syauqita; Sulistyowati, Niken; Prasetya, Dwi Arman; Fahrudin, Tresna Maulana
ALINIER: Journal of Artificial Intelligence & Applications Vol. 6 No. 2 (2025): ALINIER Journal of Artificial Intelligence & Applications
Publisher : Program Studi Teknik Elektro S1 ITN Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/alinier.v6i2.14772

Abstract

Diabetes merupakan penyakit kronis dengan prevalensi global yang terus meningkat, termasuk di Indonesia. Deteksi dini sangat penting untuk mencegah komplikasi serius yang terkait dengan kondisi ini. Seiring dengan kemajuan teknologi, pendekatan machine learning semakin banyak diterapkan dalam klasifikasi penyakit dan prediksi risiko. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma gradient boosting, yaitu XGBoost dan CatBoost, dalam mengklasifikasikan risiko diabetes berdasarkan data medis pasien. Dataset yang digunakan adalah Diabetes Dataset, yang terdiri dari delapan fitur medis dan satu label target. Penelitian ini mencakup proses pra-pemrosesan data, pelatihan model dengan pembagian data latih dan uji, serta evaluasi menggunakan metrik klasifikasi seperti akurasi, presisi, recall, dan F1-score. XGBoost dipilih karena efisiensinya dalam komputasi serta adanya regularisasi bawaan yang membantu mencegah overfitting pada data numerik berskala besar. Sementara itu, CatBoost digunakan karena kemampuannya dalam menangani fitur kategorikal secara langsung melalui teknik random permutation dan ordered boosting. Hasil dari penelitian ini diharapkan dapat berkontribusi pada pengembangan sistem prediksi diabetes yang lebih akurat dan efisien serta menjadi referensi dalam penerapan algoritma boosting di bidang medis.
Implementasi XGboost dan Support Vector Regression dalam Prediksi Harga Emas dengan Bayesian Optimization Alfa, Aniysah Fauziyyah; Prasetya, Dwi Arman; Sugiarto, Sugiarto
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025126

Abstract

Harga emas menunjukkan tren peningkatan sepanjang waktu yang dipengaruhi oleh berbagai faktor spesifik. Prediksi harga emas memainkan peran penting dalam pengambilan keputusan keuangan. Namun, fluktuasi harga yang dipengaruhi oleh faktor makroekonomi menghadirkan tantangan tersendiri dalam proses peramalan, terutama bagi investor dan pelaku pasar. Penelitian ini membahas kebutuhan model prediksi harga emas yang tepat dalam lingkungan ekonomi Indonesia dengan mengintegrasikan Bayesian Optimization pada algoritma XGBoost dan Support Vector Regression (SVR) untuk meningkatkan akurasi prediksi berdasarkan data inflasi dan suku bunga. Metode yang digunakan meliputi pra-pemrosesan data, rekayasa fitur (feature engineering), pembuatan model dan Bayesian Optimization untuk tuning hyperparameter, dengan evaluasi model menggunakan metrik MAE, RMSE, dan MAPE. Hasil menunjukkan performa luar biasa dari kedua model dengan nilai R² melebihi 0,9992. Model XGBoost lebih unggul dengan MAPE sangat rendah sebesar 0,18% dibandingkan SVR sebesar 0,36%. Analisis menunjukkan bahwa Bayesian Optimization berhasil meningkatkan akurasi model dengan menemukan parameter optimal. Fitur tambahan seperti lag dan moving average terbukti efektif dalam merepresentasikan pola historis harga. Penelitian ini memberikan manfaat untuk pengambilan keputusan yang berharga bagi investor dan pembuat kebijakan dalam merumuskan strategi investasi emas di tengah ketidakpastian ekonomi.   Abstract Gold prices show an upward trend over time influenced by various specific factors. Gold price prediction plays an important role in financial decision making. However, price fluctuations influenced by macroeconomic factors present their own challenges in the forecasting process, especially for investors and market players. This study discusses the need for an appropriate gold price prediction model in the Indonesian economic environment by integrating Bayesian Optimization on the XGBoost algorithm and Support Vector Regression (SVR) to improve prediction accuracy based on inflation and interest rate data. The methods used include data pre-processing, feature engineering, model building and Bayesian Optimization for hyperparameter tuning, with model evaluation using MAE, RMSE, and MAPE metrics. The results show excellent performance of both models with R² values ​​exceeding 0.9992. The XGBoost model is superior with a very low MAPE of 0.18% compared to SVR of 0.36%. The analysis shows that Bayesian Optimization successfully improves model accuracy by finding optimal parameters. Additional features such as lag and moving averages prove effective in representing historical price patterns. This research provides valuable decision-making benefits for investors and policy makers in formulating gold investment strategies amid economic uncertainty.
ANALISIS MANAJEMEN RISIKO KEAMANAN INFORMASI SISTEM LAYANAN UPA-PKK UPN “VETERAN” JAWA TIMUR MENGGUNAKAN METODE NIST SP800-30 Suryantari, Putu Anggi; Aryananda, Rangga Laksana; Alhamda, Denisa Septalian; Sari, Anggraini Puspita; Prasetya, Dwi Arman
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 1 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i1.8779

Abstract

Keamanan informasi merupakan aspek penting dalam penerapan sistem layanan berbasis teknologi informasi di lingkungan perguruan tinggi. Sistem layanan UPA PKK UPN Veteran Jawa Timur berperan dalam mendukung kegiatan pengembangan karier dan kewirausahaan mahasiswa serta alumni sehingga memerlukan pengelolaan risiko keamanan informasi yang terstruktur. Penelitian ini bertujuan untuk menganalisis risiko keamanan informasi menggunakan metode NIST SP 800-30. Tahapan penelitian meliputi identifikasi ancaman, identifikasi kerentanan, analisis pengendalian, penilaian tingkat kemungkinan dan dampak, serta penentuan tingkat risiko. Hasil penelitian menunjukkan bahwa risiko keamanan informasi pada sistem layanan UPA PKK berada pada kategori rendah, sedang, hingga tinggi, dengan risiko dominan berada pada tingkat sedang hingga tinggi, terutama pada aspek keamanan akses sistem dan pengelolaan data. Rekomendasi pengendalian difokuskan pada penguatan mekanisme autentikasi, pembaruan sistem secara berkala, serta peningkatan prosedur keamanan. Metode NIST SP 800-30 terbukti memberikan pendekatan yang sistematis dalam pengelolaan risiko keamanan informasi pada sistem layanan UPA PKK.
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