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All Journal Jurnal Media Infotama Techno.Com: Jurnal Teknologi Informasi JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Informatika dan Teknik Elektro Terapan Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization Indonesian Journal of Artificial Intelligence and Data Mining JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat To Maega: Jurnal Pengabdian Masyarakat Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi bit-Tech Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurnal Abdi Insani Journal of Advanced in Information and Industrial Technology (JAIIT) Nusantara Science and Technology Proceedings Kalam Cendekia: Jurnal Ilmiah Kependidikan Journal of Information System and Technology (JOINT) TIERS Information Technology Journal International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Iqtisaduna COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Dan Tekonologi Komputer (JITEK) Jurnal Hasi Penelitian Dan Pengkajian Ilmiah Eksakta - JPPIE Jurnal Informatika Teknologi dan Sains (Jinteks) Concept: Journal of Social Humanities and Education JIKTI : Jurnal Ilmiah Komputer Terapan dan Informasi Mujtama': Jurnal Pengabdian Masyarakat Journal of Artificial Intelligence and Digital Business TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi ILTEK : Jurnal Teknologi Jurnal Elektronika dan Teknik Informatika Terapan Horizon: Indonesian Journal of Multidisciplinary Modem : Jurnal Informatika dan Sains Teknologi Joong-Ki Jurnal Informatika Dan Tekonologi Komputer Jurnal Publikasi Sistem Informasi dan Manajemen Bisnis Mujtama’ Jurnal Pengabdian Masyarakat Teknik: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Muhammad Muharrom Al Haromainy
Universitas Pembangunan Nasional Veteran Jawa Timur
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The Impact of Small Group Interaction Techniques on Student Achievement in Reading Comprehension Ryan Purnomo; Ganal Arief Rahmawan; Tri Septianto; Muhammad Muharrom Al Haromainy; Istian Kriya Almanfakulti; Jeziano Rizkita Boyas
Kalam Cendekia: Jurnal Ilmiah Kependidikan Vol 12, No 1 (2024): Kalam Cendekia: Jurnal Ilmiah Kependidikan
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jkc.v12i1.84526

Abstract

Penelitian ini bertujuan untuk mengeksplorasi efektivitas pengajaran membaca dalam kurikulum bahasa Inggris di Universitas Nahdlatul Ulama Sidoarjo dengan menggunakan dua metode pengajaran yang berbeda: interaksi kelompok kecil dan metode konvensional. Pendekatan penelitian ini bersifat kuantitatif dengan fokus pada perbandingan kemampuan pemahaman membaca antara kelompok eksperimen dan kelompok kontrol. Studi ini menyelidiki apakah siswa yang diajarkan melalui interaksi kelompok kecil menunjukkan kemampuan membaca yang lebih unggul dibandingkan dengan mereka yang diajarkan menggunakan metode konvensional. Penelitian ini menggunakan pendekatan kuantitatif, mengungkapkan perbedaan yang signifikan dalam kemampuan pemahaman membaca secara keseluruhan. Nilai uji-t yang dihitung untuk pemahaman membaca umum adalah 7,85, melebihi nilai kritis p<.05 dengan uji satu sisi sebesar 1.671 (d.f.= 60). Berdasarkan analisis ini, dapat disimpulkan bahwa siswa di kelompok eksperimen menunjukkan keahlian yang lebih besar dibandingkan dengan mereka di kelompok kontrol, menunjukkan bahwa teknik interaksi kelompok kecil lebih efektif daripada metode konvensional di Universitas Nahdlatul Ulama Sidoarjo.
Optimizing Gaussian Mixture Model Using Principal Component Analysis for Welfare Clustering Rafif Ilafi Wahyu Gunawan; Muhammad Muharrom Al Haromainy; Achmad Junaidi
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.v8i3.3310

Abstract

Welfare inequality among regions remains a fundamental challenge in achieving balanced development across East Java Province. The complexity of social, economic, and development indicators often obscures the true patterns of regional welfare. To address this issue, this study proposes a more efficient analytical approach by integrating Principal Component Analysis (PCA) and the Gaussian Mixture Model (GMM) to cluster regions based on welfare levels. The dataset, obtained from the Central Bureau of Statistics (BPS) of East Java for the 2010–2024 period, includes diverse social and economic indicators. PCA was employed to reduce dimensionality and eliminate variable correlations, preserving the essential information within the data. The resulting principal components were then analyzed using GMM to uncover welfare clustering patterns. Based on the evaluation using the Bayesian Information Criterion (BIC) and silhouette score, the optimal configuration was achieved with two clusters, a tolerance of 1e-2, a maximum iteration of 200, and a silhouette score of 0.3403. The first cluster represented regions with higher welfare conditions, while the second indicated relatively lower welfare. These findings demonstrate that the PCA–GMM integration not only improves clustering accuracy but also enhances interpretability of welfare distribution across regions. Future studies may combine PCA with non-linear dimensionality reduction techniques such as Uniform Manifold Approximation and Projection (UMAP) to preserve local structures within complex datasets. Such integration is expected to reveal subtler and more dynamic welfare patterns, offering deeper insights into regional development disparities.
Comparative Analysis of LSTM and GRU Algorithms for Inflation Rate Forecasting Moh. Angga Ardiyansyah; Muhammad Muharrom Al Haromainy; Achmad Junaidi
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.v8i3.3370

Abstract

Inflation is a critical economic indicator that directly affects price stability, purchasing power, and the formulation of fiscal and monetary policies. In East Java, inflation has demonstrated considerable year-to-year volatility, creating significant challenges for policymakers in maintaining regional economic stability. This situation highlights the need for forecasting models that are both accurate and capable of adapting to complex economic data patterns. This study presents a comparative analysis of two deep learning algorithms Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) for forecasting year-on-year (YoY) inflation in East Java using data from January 2005 to December 2024. The dataset was processed using Min–Max normalization and a 12-month sliding window to capture long-term dependencies and seasonal variations. Model performance was evaluated using RMSE, MAE, and MAPE. The findings demonstrate that no single model performs best across all metrics. The LSTM4 model with a [128,128] architecture achieved the lowest MAE and MAPE values, indicating superior average predictive accuracy and stronger capability in learning complex long-term inflation patterns. In contrast, the GRU1 [64,64] model produced the lowest RMSE and the shortest training time, highlighting its efficiency in minimizing extreme prediction errors and reducing computational cost. These results offer valuable insights for policymakers in East Java: LSTM is more suitable for applications requiring high prediction accuracy, whereas GRU is preferable for real-time or resource-efficient forecasting systems, especially in fast-changing economic environments.
Analisis Perbandingan Deteksi Penyakit Daun Jagung Menggunakan YOLO dan CNN Mohammad Habim Hazidan Rifqi; Muhammad Muharrom Al Haromainy; Afina Lina Nurlaili
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.v8i3.3392

Abstract

This study compares the performance of two deep learning methods, You Only Look Once version 8 (YOLOv8) and the Convolutional Neural Network (CNN) EfficientNetB0, in detecting and classifying maize leaf diseases. The background of this research stems from the importance of early plant disease identification to support food security, as well as the limitations of manual inspection methods, which are slow, subjective, and inefficient. The study combines primary and secondary data, totaling 2,000 images that underwent undersampling, augmentation, resizing, and bounding box annotation for YOLO training needs. Both models were trained on the same dataset with an 80% training and 20% testing split. YOLOv8n was trained using a transfer learning approach for 30 epochs, while the CNN was trained using EfficientNetB0 with similar training parameters. The results show that YOLOv8 achieved high detection performance with an mAP@0.5 of 0.985 and the highest class accuracy in the Healthy category (0.99). Meanwhile, the CNN demonstrated more stable classification performance, achieving the highest accuracy in the Grey Leaf Spot class (0.99) and a validation accuracy of 0.96. The comparison indicates that YOLO excels in object detection and disease localization in field images, whereas the CNN is more consistent in classifying segmented leaf images. These findings provide practical implications for real world deployment: YOLOv8 is suitable for real time detection in field conditions, including potential integration into mobile based early warning systems for farmers, while EfficientNetB0 is more appropriate for offline or laboratory based classification of static leaf samples.
Design of Thesis Topic Recommendation System Using TF-IDF and Cosine Similarity Muhammad Baihaqi Arrisalah; Muhammad Muharrom Al Haromainy; Achmad Junaidi
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.v8i3.3579

Abstract

Selecting a thesis topic is a critical stage in a student’s academic journey and frequently poses substantial cognitive and procedural challenges. This study reports the design and implementation of the Computer Science Thesis Recommendation System (SRSIK Hub), a web-based decision-support platform aimed at improving the efficiency and accuracy of thesis topic selection. The primary novelty of this research lies in the systematic integration of Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity within a large-scale academic corpus to model fine-grained semantic relevance between student interests and prior thesis documents, enabling more precise and transparent recommendations than conventional keyword-based searches. The system adopts a content-based filtering approach and processes approximately 4,000 thesis records collected from multiple university repositories. Textual data are preprocessed and transformed using TF-IDF vectorization, while Cosine Similarity is employed to rank candidate topics according to relevance. System effectiveness was evaluated using the WebUse Framework involving 75 student respondents. The evaluation yielded an overall score of 4.44 out of 5, indicating high usability, strong information quality, and reliable system functionality. This performance score demonstrates that the proposed recommendation model is not only technically sound but also practically applicable in real academic settings, where it can significantly reduce topic selection time and uncertainty for students. The results confirm that SRSIK Hub effectively supports students in identifying research topics aligned with their academic interests and competencies. Beyond local deployment, the system is transferable to other institutions for scalable thesis recommendation support.
KLASIFIKASI PENYAKIT KULIT BERBASIS SUPPORT VECTOR MACHINE DENGAN EKSTRAKSI FITUR ABCD RULE Al Danny Rian Wibisono; Eka Prakarsa Mandyartha; Muhammad Muharrom Al Haromainy
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 1 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i1.6039

Abstract

Penyakit kulit merupakan masalah kesehatan yang signifikan, gejala dari penyakit ini berupa gatal, nyeri, mati rasa, dan kemerahan. Penyakit ini dapat disebabkan oleh beberapa faktor seperti virus, jamur, dan mikroorganisme. Menurut data Dinas Kesehatan Surabaya tahun 2019, prevalensi penyakit kulit dan jaringan subkutan mencapai 4,53%, menjadikannya penyakit terbanyak keenam yang dialami masyarakat. Oleh sebab itu, pada penelitian ini diusulkan sebuah penelitian mengenai klasifikasi penyakit kulit menggunakan Support Vector Machine melalui analisis fitur ABCD Rule. Pada penelitian ini akan dilakukan labeling pada 5 kelas penyakit kulit yang akan digunakan sebagai data latih dan data uji melalui 7 tahapan utama yakni Pengumpulan Dataset Citra Penyakit Kulit, Pre-processing Inpaint Talea, Pre-processing Gaussian Blur dan Normalisasi Mask, Segmentasi Thresholding Otsu Bitwise, Restorasi Kontur, Ekstraksi Fitur ABCD Rule, dan klasifikasi menggunakan Support Vector Machine (SVM). Sebanyak 4 skenario pengujian dilakukan untuk menemukan model terbaik, dimana skenario pengujian melibatkan pengaturan pembagian data yang berbeda, kernel berbeda, dan parameter yang berbeda pada model Support Vector Machine (SVM). Melalui skenario tersebut didapatkan hasil terbaik, yaitu Akurasi sebesar 86,42%, Spesifisitas sebesar 96,60%, dan Sensitivitas sebesar 86,42%. Hal ini menunjukkan bahwa metode yang diusulkan memiliki kinerja yang cukup baik dalam mengklasifikasikan jenis penyakit kulit. Penelitian ini tidak hanya berpotensi dalam meningkatkan diagnosis penyakit kulit secara efisien, tetapi juga mendorong pengembangan sistem deteksi berbasis teknologi untuk mendukung layanan kesehatan kulit yang lebih terjangkau dan andal.
Analisis Pengaruh Strategi Augmentasi Data Terhadap Performa Model Hybrid Cnn–Swin Transformer dalam Klasifikasi Citra Mikroskopis Malaria Ramadhani, Muhammad Nabil; Haromainy, M. Muharrom Al; Wahanani, Henni Endah
HORIZON: Indonesian Journal of Multidisciplinary Vol. 4 No. 4 (2026): HORIZON: Indonesian Journal of Multidisciplinary
Publisher : Lembaga Intelektual Muda (LIM) Maluku

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54373/hijm.v4i4.7178

Abstract

Malaria diagnosis through microscopic images still faces challenges due to variations in the shape, size, and quality of blood cell images that can reduce the generalization ability of deep learning models. One approach to address this problem is data augmentation, but the effectiveness of various augmentation strategies on a hybrid Convolutional Neural Network (CNN) and Swin Transformer model has not been systematically compared. This study aims to compare the effect of five data augmentation strategies on the performance of the Hybrid CNN–Swin Transformer model in classifying malaria microscopic images. The NIH Malaria dataset consisting of 27,558 blood cell images was divided using the stratified split method with a ratio of 80:10:10. The model was trained using five augmentation strategies, namely Baseline (A0), Basic (A1), Geometric (A2), Advanced (A3), and RandAugment (A4), then evaluated using accuracy, precision, recall, F1-score, ROC-AUC, and Train-Val Gap. All strategies produced accuracy above 96% and ROC-AUC above 99%. Strategy A3 provided the highest classification performance with an accuracy of 97.79%, an F1-score of 97.78%, and an ROC-AUC of 99.52%, while strategy A2 showed the most stable generalization ability based on the Train-Val Gap value. The results of this study provide empirical evidence that the choice of augmentation strategy affects the performance and generalization of the model, and serve as a reference in the development of a Hybrid CNN–Swin Transformer-based malaria classification system.
Workshop Desain Kreatif: Pengenalan dan Pelatihan Aplikasi Desain Praktis Canva di Pondok Pesantren Nurul Huda Puger Muhammad Muharrom Al Haromainy; M. Sa’aduddin Abdillah Yusuf; Arraya Akhsa Putra Priyadizah; Muhammad Rifaldi Syaril Mashafy; Dhimas Wahyu Prayogi
Mujtama': Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2025): Mujtama’ Jurnal Pengabdian Masyarakat
Publisher : Universitas Muhammadiyah Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32528/mujtama.v5i2.4168

Abstract

Pesatnya perkembangan teknologi digital telah menciptakan kesenjangan keterampilan digital, terutama di komunitas pendidikan berbasis pesantren di wilayah pedesaan. Untuk menjawab tantangan tersebut, tim Kuliah Kerja Nyata Tematik Inovasi Pesantren (KKN-T IP) UPN "Veteran" Jawa Timur menyelenggarakan kegiatan pengabdian masyarakat berupa workshop desain grafis menggunakan aplikasi Canva di Pondok Pesantren Nurul Huda Al Azizah, Puger. Kegiatan ini menerapkan metode pelatihan partisipatif dengan pendekatan praktik langsung melalui kompetisi desain poster. Sebanyak 15 peserta mengikuti workshop, yang terdiri dari sesi materi, ice breaking, praktik desain, serta evaluasi. Hasil evaluasi menunjukkan tingkat kepuasan peserta sangat tinggi dengan skor rata-rata 9,47 dari skala 10, serta respons kualitatif positif terhadap kemudahan penggunaan Canva dan metode pelatihan yang interaktif. Kegiatan ini terbukti efektif dalam meningkatkan keterampilan desain dasar dan literasi digital peserta. Ke depan, pelatihan lanjutan dengan materi yang lebih kompleks serta integrasi ke dalam program pesantren dapat menjadi langkah strategis untuk pengembangan berkelanjutan keterampilan digital santri.
Prediksi Inflasi Bulanan Menggunakan LightGBM dan Optimasi Hyperparameter Berbasis Optuna Dian Maharani; Anggraini Puspita Sari; Muhammad Muharrom Al Haromainy
Techno.Com Vol. 25 No. 3 (2026): August 2026
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v25i3.16313

Abstract

Inflasi merupakan indikator makroekonomi penting yang mencerminkan perubahan tingkat harga barang dan jasa serta berpengaruh terhadap stabilitas ekonomi dan pengambilan kebijakan. Prediksi inflasi menjadi tantangan karena data inflasi memiliki karakteristik deret waktu yang dinamis, dipengaruhi oleh berbagai faktor ekonomi, serta mengandung hubungan nonlinier. Tujuan penelitian ini adalah menghasilkan model prediksi inflasi bulanan Indonesia dengan menerapkan metode LightGBM serta melakukan optimasi hyperparameter menggunakan Optuna guna meningkatkan kinerja model. Dataset penelitian mencakup data inflasi dan berbagai indikator makroekonomi Indonesia pada tahun 2014–2024 yang dikumpulkan dari Bank Indonesia dan Badan Pusat Statistik (BPS). Tahapan penelitian meliputi preprocessing, feature engineering, pembagian data, pemodelan, optimasi hyperparameter, dan evaluasi menggunakan MAE, RMSE, dan R². Berdasarkan pengujian yang dilakukan, diperoleh hasil bahwa optimasi hyperparameter mampu meningkatkan performa model secara signifikan dengan menghasilkan MAE sebesar 0,0434, RMSE sebesar 0,0561, dan R² sebesar 0,9259. Hasil tersebut menunjukkan bahwa LightGBM yang dioptimasi menggunakan Optuna mampu memprediksi inflasi bulanan dengan tingkat akurasi yang tinggi dan efektif dalam menangkap pola nonlinier pada data inflasi.   Kata kunci - Inflasi, LightGBM, Optuna, Deret waktu, Prediksi          
IMPLEMENTASI METODE TF-IDF DAN ALGORITMA NAIVE BAYES DALAM APLIKASI DIABETIC BERBASIS ANDROID I Wayan Alston Argodi; Eva Yulia Puspaningrum; Muhammad Muharrom Al Haromainy
Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol. 3 No. 2 (2023): Juli : Jurnal Teknik Mesin, Elektro dan Ilmu Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/teknik.v3i2.2009

Abstract

Diabetes is a serious disease that occurs when the pancreas does not produce enough insulin as a hormone that regulates blood sugar in the body. This disease also has an impact on health. This research builds an Android-based application called Diabetic to help classify and provide information related to diabetes and analyze the performance of the Term Frequency Inverse Document Frequency method and the Naive Bayes algorithm. The Term Frequency Inverse Document Frequency method is a technique for calculating the presence of words in a collection of documents by creating document vectors. The Naive Bayes algorithm is an algorithm that uses probability to solve a classification case. This algorithm has an efficient and fast calculation. Based on this research, it is known that the Naive Bayes Algorithm produces an accuracy of 66% by taking a computation time of 39 seconds with a memory consumption of 80 to 351 mb.
Co-Authors Abdillah, Ikhwan Abdul Rezha Efrat Najaf Achmad Andrian Maulana Achmad Junaidi Achmad Rozy Priambodo Agung Mustika Rizki, Agung Mustika Agus Wibowo Agus Zainal Arifin Ahmad Saikhu Akbar, Fawwaz Ali Al Danny Rian Wibisono Al Fatih, Abdullah Alya Izzah Zalfa Rihadah Ramadhani Nirwana Putri Ananda Ayu Puspitaningrum Andreas Nugroho Sihananto Angga Lisdiyanto Anggraini Puspita Sari Anggraini Puspita Sari Anita Puspitasari Annisa Dwi Puspitarini Anugerah, Rico Putra Arraya Akhsa Putra Priyadizah ASHARI, FAISAL Avi Sunani Aviolla Terza Damaliana Azira, Volem Alvaro Azira Basuki Rahmat Masdi Siduppa Bima Arya Kurniawan Budi Nugroho Chairil, Augustin Mustika Chastine Fatichah Christianty, Theressa Marry Clara Diva Paramitha Darmawan, Marcellinus Aditya Vitro Dhimas Wahyu Prayogi Dian Maharani Dinda Friska Oktaviana Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eva Yulia Puspaningrum Fania Imelda Safitri Faris Syaifulloh Farkhan Fauzi, Zaky Ahmad Ferdi Firdaus Ega Pratama Ferry Trilaksana Putra Fetty Tri Anggraeny Firza Prima Aditiawan Fitrani, Laqma Dica Ganal Arief Rahmawan Gusti Eka Yuliastuti Hajjar, Debrina Octrisya Hardiansyah, In Naka Malik Henni Endah Wahanani Hidra Amnur I Wayan Alston Argodi I Wayan Alston Argodi Istian Kriya Almanfakulti Jeziano Rizkita Boyas Kartini Kartini Kevin Iansyah Kusuma Wardani, Amalia Dwi Lailatul Musyaffaah Lina Nurlaili, Afina Lintang Putri Permatasari Lusi Kurnia Lusian Nandang Arjamulia M. Sa’aduddin Abdillah Yusuf Mandyartha, Eka Prakarsa Maulana Herza, Fakhri Maulana, Hendra Maulana, Vieri Arief Moh. Angga Ardiyansyah Mohammad Habim Hazidan Rifqi Mohammad Setyo Wardono Muhamad Fihris Aldama Muhammad Albert Nur Agathon Muhammad Baihaqi Arrisalah Muhammad Daffa Arifin Muhammad Helmi Satria Fedianto Muhammad Izdihar Alwin Muhammad Rifaldi Syaril Mashafy Muzdalifah, Nayani Alya Aquila Nia Dwi Puspitasari Nugroho, Budi Nur Nafisatul Fitriyah Nurlaili, Afina Lina Nurlaili, Afina Lina Permatasari, Reisa Pratama Wirya Atmaja Prinafsika Putra, Chrystia Aji Putra, Gredy Christian Hendrawan Raden Kokoh Haryo Putro Rafie Ishaq Maulana Rafif Ilafi Wahyu Gunawan Ramadhani, Muhammad Nabil Retno Mumpuni Reza, Reno Alfa Riza Satria Putra Rizka Fadhillah, Irnanda Ryan Purnomo Samodera, Bayu Sari, Rizky Buana Satrio, Deva Dwi Setyawan, Dimas Ari Shalehuddin Albawani, Raden Siregar, Talitha Aurora Nadenggan Sujayanti, Forentina Kerti Pratiwi Suprapti Taufiqqurrahman, Husain Tompo Panjaitan Tri Septianto Trimono, Trimono Triyana, Dimas Volem Alvaro Azira Azira Wahyu Eko Pujianto Wahyu Fahrul Ridho Wahyu Syaifullah JS Waluya, Onny Kartika Waskito, Achmad Derajat Winarti ., Winarti Yisti Vita Via