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All Journal International Journal of Electrical and Computer Engineering ComEngApp : Computer Engineering and Applications Journal JURNAL SISTEM INFORMASI BISNIS Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro CommIT (Communication & Information Technology) Jurnal Ilmiah Kursor Jurnal Informatika Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) ELINVO (Electronics, Informatics, and Vocational Education) Annual Research Seminar INFORMAL: Informatics Journal Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Register: Jurnal Ilmiah Teknologi Sistem Informasi Proceeding of the Electrical Engineering Computer Science and Informatics Edu Komputika Journal Format : Jurnal Imiah Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab SISFOTENIKA Journal of Information Technology and Computer Science (JOINTECS) JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi JIKO (Jurnal Informatika dan Komputer) AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control CogITo Smart Journal IT JOURNAL RESEARCH AND DEVELOPMENT Insect (Informatics and Security) : Jurnal Teknik Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL REKAYASA TEKNOLOGI INFORMASI Abdimas Dewantara PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI JURNAL INSTEK (Informatika Sains dan Teknologi) ILKOM Jurnal Ilmiah Compiler Jiko (Jurnal Informatika dan komputer) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JSiI (Jurnal Sistem Informasi) CYBERNETICS Digital Zone: Jurnal Teknologi Informasi dan Komunikasi IJID (International Journal on Informatics for Development) Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Teknologi Terpadu EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik NUKHBATUL 'ULUM : Jurnal Bidang Kajian Islam Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi JISKa (Jurnal Informatika Sunan Kalijaga) Buletin Ilmiah Sarjana Teknik Elektro Indonesian Journal of Business Intelligence (IJUBI) bit-Tech Mobile and Forensics Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC) Jurnal Pengabdian Masyarakat Bumi Raflesia Cyber Security dan Forensik Digital (CSFD) Jurnal Abdi Insani Journal of Computer System and Informatics (JoSYC) International Journal of Advances in Data and Information Systems Journal of Innovation Information Technology and Application (JINITA) Journal of Education Informatic Technology and Science Jurnal Bumigora Information Technology (BITe) Jurnal Teknologi Informatika dan Komputer SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal REKSA: Rekayasa Keuangan, Syariah dan Audit Jurnal Teknik Informatika (JUTIF) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Computer Science and Information Technology (CoSciTech) Phasti: Jurnal Teknik Informatika Politeknik Hasnur Jurnal Pengabdian Masyarakat Indonesia Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi EDUTECH : Jurnal Inovasi Pendidikan Berbantuan Teknologi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Pengabdian Kepada Masyarakat Patikala Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Puan Indonesia Jurnal Informatika Teknologi dan Sains (Jinteks) Techno Wahana Lambda: Jurnal Ilmiah Pendidikan MIPA dan Aplikasinya Engineering Science Letter Journal of Novel Engineering Science and Technology Jurnal Informatika: Jurnal Pengembangan IT Jurnal Software Engineering and Computational Intelligence Mohuyula : Jurnal Pengabdian Kepada Masyarakat Scientific Journal of Informatics semanTIK Jurnal Informatika Medis (J-INFORMED) Darma Abdi Karya: Jurnal Pengabdian Kepada Masyarakat Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika JOCHAC Jurnal Repositor Proceeding of Informatics Collaborations and Dessimenation Meeting (Infocoding)
Imam Riadi
Department Of Information Systems - Universitas Ahmad Dahlan
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PERANCANGAN KLASIFIKASI PASIEN STROKE DENGAN METODE K-NEAREST NEIGHBOR Rahmat Ardila Dwi Yulianto; Imam Riadi; Rusydi Umar
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3454

Abstract

Stroke is a disease characterized by impaired brain function caused by a lack of oxygen supply and blood flow to the brain, affecting several brain functions that make sufferers experience difficulty in carrying out activities. the classification of stroke patients found is still in the form of medical records that have not been integrated so it takes longer time to detect. The K-NN algorithm is part of a machine learning algorithm that can be used to classify one of the cases, namely the classification of stroke patients. K-NN is used as a class determining algorithm to enter new data that is input according to the format. Based on the results obtained, this study leads to system design using the Unified Model Language (UML) and system user interface design.
ANALISIS KINERJA MACHINE LEARNING UNTUK DETEKSI KONTEN PENIPUAN BERBAHASA INDONESIA DI TWITTER Imroatul Khuluqi Izzah; Imam Riadi; Abdul Fadlil
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7124

Abstract

The development of information technology has changed the way people interact in the digital space, including through the Twitter platform, which is widely used to share information and opinions. However, this convenience has also led to the emergence of fraudulent content such as fake investments, fictitious sweepstakes, and fictitious donation requests. This study aims to analyze and compare the performance of five machine learning algorithms, namely Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB), in detecting fraudulent Indonesian language content on Twitter. The dataset consists of 5.221 Indonesian language tweets that have been manually labeled into two classes, fraud and non-fraud. All tweets were processed through text preprocessing stages, including data cleaning, case folding, normalization, tokenization, filtering, and stemming, before being represented as numerical vectors using Word2Vec. Classification was performed using 10-fold cross-validation with evaluation metrics of accuracy, precision, recall, and F1-score. The results show that Random Forest achieved the best performance with accuracy of 85.6%, followed by SVM (84.0%), Logistic Regression (83.6%), Decision Tree (81.2%), and Naïve Bayes (78.4%). The main contribution of this study is to provide a systematic empirical comparative analysis of classification algorithms for detecting Indonesian fraudulent content on Twitter, which remains relatively underexplored. These findings show that the combination of Word2Vec and Random Forest can effectively capture the semantic context of short texts and can serve as a reference for developing automatic detection systems for fraudulent content on social media.
PENGEMBANGAN MODEL DEEP LEARNING UNTUK DETEKSI SUARA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK NORITA SINAGA; Imam Riadi; Herman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.8061

Abstract

Sound detection and keyword recognition in audio signals have become rapidly growing research areas due to their wide range of applications, from intelligent audio surveillance to human-computer interaction systems. This study aims to develop a deep learning model based on Convolutional Neural Networks (CNN) to automatically detect and classify specific words in speech recordings. The focus of this research is the detection of the keywords "dog" and "children" contained in speech data. The research methodology includes data preprocessing through noise reduction and normalization, as well as data augmentation techniques such as pitch shifting to improve the robustness of the model. Audio features are extracted using the Short-Time Fourier Transform (STFT) to generate visual representations in the form of spectrograms, which serve as the primary input to the CNN architecture. Experimental results show that the developed model successfully classified the target keywords with an accuracy of 90,00%. The model proved effective in recognizing both spectral and temporal patterns of spoken keywords and has the potential to be implemented in real-time sound detection systems.
PELATIHAN PEMANFAATAN GENERATIVE ARTIFICIAL INTELLIGENCE UNTUK LITERASI DIGITAL MAHASISWA STIDKI AL-AZIZ BATAM Pastima Simanjuntak; Rika Harman; Yohanni Syahra; Herman Yuliansyah; Imam Riadi
PUAN INDONESIA Vol. 8 No. 1 (2026): Jurnal PUAN Indonesia Vol. 8 No. 1 Juli 2026
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v8i1.516

Abstract

The development of Generative Artificial Intelligence (AI) presents significant opportunities to support digital literacy and creativity in higher education. However, utilization of this technology among students still faces challenges, including limited understanding, technical skills, and ethical awareness. This community service activity was conducted on January 31, 2026, at STIDKI Al-Aziz Batam, involving 18 student participants from two study programs. The activity employed an educative-participatory approach comprising material presentations, interactive discussions, and hands-on practice sessions. Post-training evaluation was conducted using a Likert-scale questionnaire (scale 1-5) covering three dimensions: conceptual understanding, perceived benefits, and participant satisfaction. Results showed a mean score of 4.21 out of 5 (84.2%) for conceptual understanding of Generative AI, a mean perceived benefit score of 4.35 (87.0%), and an overall satisfaction score of 4.40 (88.0%). Furthermore, 88.9% of participants (16 of 18) met the minimum understanding threshold (score >= 4). These findings demonstrate that structured, practice based training significantly enhances students digital literacy competence and ethical awareness of AI use. The activity provides a tangible contribution to strengthening students digital capacities and is relevant for sustainable development.
Analisis Komparatif Random Forest dan Support Vector Machine untuk Klasifikasi Tingkat Keparahan Serangan Siber Reyhanssan Islamey; Sri Winiarti; Imam Riadi
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 14 Issue 1 April 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v14i1.36558

Abstract

The escalating volume and sophistication of cyberattacks on network infrastructures processing massive daily traffic have overwhelmed security teams in prioritizing incident responses rapidly and accurately, a phenomenon known as alert fatigue. This study aims to analyze and compare the performance of the Support Vector Machine (SVM) and Random Forest (RF) algorithms for classifying cyberattack severity levels (Low, Medium, and High). The study uses the public Cyber Security Attacks dataset, consisting of 40,000 network traffic records reduced to 20,000 clean entries through preprocessing and feature engineering. The methodology includes data cleaning, selecting 10 significant features using SelectKBest, standardizing numerical features, and evaluating models across three data split scenarios (70:30, 80:20, and 90:10) using a stratified splitting approach. Experimental results show that SVM consistently outperforms RF across all scenarios, with the best performance in the 80:20 split, achieving 98.92% accuracy and a weighted average F1-Score of 0.99 using hyperparameter configurations of C = 100 and gamma = 0.01. The superiority of SVM lies in its ability to model non-linear relationships and complex feature interactions in data with overlapping class boundaries. In contrast, RF exhibits an over-prediction bias toward the minority class (’Low’) due to the class_weight=’balanced’ mechanism and limitations of axis-based separation. These findings confirm that SVM with a Radial Basis Function (RBF) kernel is more suitable for cyberattack severity classification, particularly in automated incident detection systems requiring balanced precision and recall as well as reliable decision-making.
Audit Kredit Digital Berbasis Explainable Artificial Intelligence (XAI): Tinjauan Pustaka Sistematis Muhammad Arief Sutisna; Imam Riadi; Abdul Fadlil
Jurnal Teknologi Terpadu Vol 12 No 1 (2026): Juli, 2026
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v12i1.2614

Abstract

Digital transformation in the financial sector encourages the application of Artificial Intelligence (AI) in the credit audit process. While AI is capable of improving the speed and accuracy of risk assessments, modern models such as deep learning are black box, raising issues of transparency and accountability—two things that are critical in credit audits that must comply with regulations and build stakeholder trust. This study uses the Systematic Literature Review (SLR) approach to synthesize the scientific literature that discusses the application of Explainable Artificial Intelligence (XAI) in digital credit audits. The SLR process includes: Query formulation (e.g. "explainable AI", "credit audit", "interpretability model"), Screening of studies based on relevance, methodological quality, and year range of publication, Extraction of key data (XAI method, dataset type, prediction model, tools used, and key findings), and Comparative synthesis analysis. Based on the Systematic Literature Review, it was found that the main XAI methods are SHAP and LIME, the most commonly used prediction models are Random Forest and XGBoost and several repeated weaknesses were found, including limitations in the representativeness of the dataset, the risk of overfitting, and the trade-off between the level of accuracy and the level of interpretability. Thus, the proper integration of  XAI is expected to increase transparency, fairness, and trust in AI-based digital credit audits, while paving the way for more ethical audit practices and in line with regulatory demands.
Hybrid LSTM Forecasting Framework with Mutual Information and PSO–GWO Optimization for Short-Term SARS-CoV-2 Prediction in Indonesia Nastiti, Faulinda Ely; Musa, Shahrulniza; Riadi, Imam
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5485

Abstract

SARS-CoV-2 remains an endemic challenge in Indonesia, requiring reliable short-term forecasting tools that support informatics, digital epidemiology, and data-driven public health systems. Standard LSTM models, while widely used for epidemic forecasting, face notable limitations such as sensitivity to poor weight initialization, and reduced ability to capture interactions within heterogeneous high-dimensional data—resulting in inconsistent performance. This research introduces ADELMI (Adaptive Deep Learning Metaheuristic Intelligence), a unified hybrid forecasting framework specifically designed not only to enhance forecasting accuracy but also to overcome core weaknesses of traditional LSTM architectures when applied to complex epidemic datasets. ADELMI integrates Mutual Information and Pearson Correlation for dual feature selection with a hybrid Particle Swarm–Grey Wolf Optimization (PSO–GWO) approach for optimizing LSTM parameters. The dataset includes 657 daily observations and 82 epidemiological, vaccination, and meteorological variables sourced from the Ministry of Health and BMKG (2020–2021). Feature selection reduced the dataset to 20 relevant predictors for recovery and death and one dominant predictor for positive cases. The optimized 50-unit LSTM with early stopping achieved highly accurate 7-day forecasts, producing MAPE scores of 0.01% (positive cases), 1.44% (recoveries), and 3.00% (deaths) across 5-fold cross-validation. These results significantly outperform ARIMA, SIR, and baseline LSTM models. By unifying dual feature selection with hybrid PSO–GWO optimization, ADELMI improves LSTM stability, weight initialization, and multivariate interaction modeling, delivering more reliable forecasts across heterogeneous datasets. This advancement strengthens informatics through DL-metaheuristic multivariate epidemic modeling and enables proactive, adaptive surveillance against evolving threats such as influenza hybrids.
ANALISIS ESTIMASI PENYAKIT TANAMAN TOMAT MENGGUNAKAN PENDEKATAN MACHINE LEARNING TINJAUAN PUSTAKA SISTEMATIS Faiz Rafdhi; Imam Riadi; Anton Yudhana
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 4 (2025): EDISI 26
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i4.6779

Abstract

Deteksi dini penyakit pada tanaman buah sangat penting untuk menjaga produktivitas dan mutu hortikultura. Keterlambatan mengenali gejala dapat menimbulkan kerugian signifikan, baik dari sisi panen maupun ekonomi petani. Kemajuan machine learning (ML) dan deep learning (DL) menawarkan solusi inovatif melalui diagnosis otomatis berbasis citra daun. Penelitian ini meninjau literatur secara sistematis menggunakan kerangka PRISMA untuk mengkaji dataset, performa model, keterbatasan, tren algoritma, serta arah penelitian selanjutnya. Dari 176 artikel, 50 lolos seleksi, dengan 35 fokus pada penyakit tanaman buah. Hasil kajian menunjukkan bahwa Convolutional Neural Network (CNN) dan variasinya masih mendominasi lebih dari 75% studi. Akurasi model sangat tinggi pada dataset laboratorium (95–99%), menurun pada data lapangan (in-the-wild) seperti PlantDoc (90–96%). PlantVillage tetap menjadi dataset utama, meski uji generalisasi menuntut data lapangan yang lebih beragam. Tantangan meliputi domain shift, class imbalance, keterbatasan label tingkat severitas, serta kendala implementasi di perangkat edge. Kontribusi ilmiah kajian ini berupa rekomendasi riset masa depan diarahkan pada pengembangan dataset lapangan standar, integrasi hybrid CNN–GCN, domain adaptation, data sintetik, segmentasi untuk estimasi severitas, serta Edge AI yang real-time dan dapat dijelaskan (explainable AI). Kajian ini menekankan pentingnya inovasi algoritmik, dataset realistis, dan integrasi IoT/edge untuk sistem diagnosis yang akurat, adaptif,  dan berkelanjutan.
Anomaly Detection in Cloud Device-Based Information Technology Infrastructure Using Isolation Forest Algorithm Andi Zulherry .; Imam Riadi; Rusydi Umar
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 2 (2026): Issues January 2026
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Cloud device-based information technology infrastructure generates large volumes of operational data that are dynamic and heterogeneous, increasing the complexity of monitoring and anomaly detection processes. Conventional rule-based approaches and supervised learning methods are often less effective due to limited labeled data and their inability to detect newly emerging anomaly patterns. Therefore, this study aims to apply and evaluate the Isolation Forest algorithm as an anomaly detection method for cloud device-based information technology infrastructure. The research data consist of system and network performance metrics, including CPU usage, memory utilization, disk activity, and network traffic collected from a cloud environment. The research stages include data preprocessing, normalization, and feature selection to improve data quality and model performance. The Isolation Forest algorithm is implemented using an unsupervised learning approach, where anomalies are identified based on the algorithm’s ability to isolate data points that exhibit characteristics deviating from the majority of normal data. Model performance is evaluated using a confusion matrix with accuracy, precision, recall, and F1-score metrics, while parameter optimization is conducted using the grid search method to obtain the best configuration. The results indicate that the Isolation Forest algorithm is able to detect anomalies effectively, achieving high accuracy and a good balance between precision and recall. The model with optimal parameters demonstrates improved performance by reducing detection errors compared to the baseline configuration. Thus, the Isolation Forest algorithm can serve as a reliable and scalable solution to support monitoring activities and enhance the reliability of cloud infrastructure.
Workshop Literasi Digital dan Keamanan Informasi Bagi Guru dan Siswa SMA Negeri 1 Sedayu Imam Riadi; Fithriatus Shalihah; Putri Taqwa Prasetyaningrum; Bambang Robiin
Mohuyula : Jurnal Pengabdian Kepada Masyarakat Vol 4, No 2 (2025): Desember
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/mohuyula.4.2.42-50.2025

Abstract

Perkembangan teknologi digital yang pesat menuntut peningkatan literasi digital dan kesadaran akan keamanan informasi di kalangan generasi muda, khususnya di tingkat SMA. Tujuan dari kegiatan pengabdian ini adalah untuk memberikan pelatihan mengenai literasi digital dan keamanan informasi kepada guru dan siswa SMA Negeri 1 Sedayu. Metode yang digunakan dalam pelatihan ini adalah pendekatan workshop interaktif yang meliputi ceramah, diskusi, dan simulasi praktis. Materi yang disampaikan mencakup pengenalan terhadap literasi digital, ancaman siber seperti phishing dan malware, serta cara melindungi data pribadi di dunia maya. Hasil dari kegiatan ini menunjukkan peningkatan signifikan dalam pemahaman peserta mengenai cara melindungi informasi pribadi dan mengenali ancaman siber. Berdasarkan evaluasi menggunakan pre-test dan post-test, peserta mengalami peningkatan pengetahuan mengenai literasi digital dan keamanan informasi, dengan 100% peserta mampu mengidentifikasi ancaman siber setelah pelatihan. Pelatihan ini juga berhasil meningkatkan kesadaran peserta tentang bahaya kejahatan siber, seperti cyberbullying, serta langkah-langkah pencegahan yang dapat dilakukan. Kesimpulannya, pelatihan ini berhasil mencapai tujuannya dalam meningkatkan pemahaman dan keterampilan peserta dalam menghadapi tantangan dunia digital. Diharapkan pelatihan ini dapat menjadi model untuk kegiatan serupa di sekolah lain, guna menciptakan lingkungan digital yang lebih aman dan bijak di kalangan generasi muda.
Co-Authors Abdul Fadlil Abdul Fadlil Abdullah Hanif Abdullah Hanif Abe, Tuska Achmad Nugrahantoro Achmad Syauqi Ade Elvina Adiniah Gustika Pratiwi Agung Wahyudi Agus Wijayanto Ahmad Azhar Kadim Ahmad Azhari Ahmad Luthfi Ahmad, Muhammad Sabri Aini, Fadhilah Dhinur Ainunna’imah Akbar, Zulfikri Al Amany, Sarah Ulfah Alawi, Hanna Syahida Alwas Muis Andi Zulherry Andi Zulherry . Andrianto, Fiki Anggara, Rio Annisa, Putri Anshori, Ikhwan Anton Yudahana Anton Yudhana Anton Yudhana ANWAR, FAHMI anwar, nuril Apriliani, Evinda Aprilliansyah, Deco Arif Rahman Arif Rahman Arif Rahman Arif Wirawan Muhammad Arif Wirawan Muhammad Arif Wirawan Muhammad Ariqah Adliana Siregar Arizona Firdonsyah Asno Azzawagama Firdaus Asruddin Astika AyuningTyas, Astika Aulia, Aulia Aulyah Zakilah Ifani Bambang Robiin Bashor Fauzan Muthohirin Basir, Azhar Basit Adhi Prabowo Bernadisman, Dora Budi Barata Kusuma Utami Budin, Shiha Busthomi, Iqbal D.E Purwadi Putra, Izzan Julda Davito Rasendriya Rizqullah Putra Davito Rasendriya Rizqullah Putra Deco Aprilliansyah Dedy Sumarhadi Devaldi Caliesta Octadiani Dewi Astria Faroek Dewi Estri Jayanti Dewi Estri Jayanti H Dian Novianti Dikky Praseptian M Djou, M Rosyidi Dwi Aryanto Eddy Irawan Aristianto Ediansa, Oka Eko Brillianto Eko Handoyo Eko Handoyo Elfatiha, Muhammad Ihya Aulia Elvina, Ade Ervin Setyobudi Fadhilah Dhinur Aini Fadhilah Dhinur Aini Fadlil , Abdul Fahmi Anwar Fahmi Auliya Tsani Faiz , Muhammad Nur Faiz Isnan Abdurrachman Fakhri, La Jupriadi Fanani, Galih Farid Suryanto Fatmawaty, Virdiana Sriviana FAULINDA ELY NASTITI Fauzan Natsir Fauzan, Fauzan Firdonsyah, Arizona Firmansyah Firmansyah Firmansyah Firmansyah Fithriatus Shalihah Fitri, Fitriyani Tella Fitriyani Tella Furizal Furizal, Furizal Galih Fanani Galih Pramuja Inngam Fanani Guntur Maulana Zamroni Guntur Maulana Zamroni, Guntur Maulana Gusti Chandra Kurniawan Habie, Khairul Fathan Hafizh, Muhammad Nasir Hanif, Abdullah Harman, Rika Haruno Sajati Haryanto, Eri Helmiyah, Siti Herman Herman Herman Herman Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah Herman Yuliansyah, Herman Hero Wintolo Hidayati, Anisa Nur Himawan I Azmi Iis Wahyuningsih Ikhsan Zuhriyanto Ikhwan Anshori Imroatul Khuluqi Izzah Indah Purnama Sari Iqbal Busthomi Irhas Ainur Rafiq Irhash Ainur Rafiq Iwan Tri Riyadi Yanto, Iwan Tri Riyadi Jamalludin Jamalludin Jamalludin, Jamalludin Jayawarsa, A.A. Ketut Joko Handoyo Joko Triyanto Kariyamin, Kariyamin Kartoirono, Suprihatin Kurniawan, Endang Kurniawan, Gusti Chandra Kusuma, Ridho Surya Laura Sari Luh Putu Ratna Sundari M. Rosyidi Djou M.A. Khairul Qalbi Mahsun Mahsun Maulana, Irvan Mega Fatimah Rosana Merita Arini Mhd. Basri Miladiah Miladiah Miladiah, Miladiah Mohammad Faiq Badruz Zaman Muchlas Muchlas Muflih, Ghufron Zaida Muh. Hajar Akbar Muhajir Yunus Muhamad Abduh, Muhamad Muhamad Caesar Febriansyah Putra, Muhamad Caesar Febriansyah Muhammad Abdul Aziz Muhammad Abdul Aziz Muhammad Arief Sutisna Muhammad Fahmi Mubarok Nahdli Muhammad Faqih Dzulqarnain Muhammad Faqih Dzulqarnain, Muhammad Faqih Muhammad Fauzan Gustafi Muhammad Ihya Aulia Elfatiha Muhammad Irwan Syahib Muhammad Kunta Biddinika Muhammad Muhammad Muhammad Nur Faiz Muhammad Yanuar Efendi Muhammad Zulfadhilah Muhammad ‘Arif Bin Mohamad Munawaroh Munawaroh Murinto Murinto Murinto Murni Murti, Raden Hario Wahyu Musa, Shahrulniza Mushab Al Barra Mustafa Mustafa Mustafa Mustafa NANNY, NANNY Nasrulloh, Imam Mahfudl Nasution, Dewi Sahara Nia Ekawati Nia Ekawati Niki Ratama NORITA SINAGA Nur Hamida Siregar Nur Miswar Nur Widiyasono, Nur Nuril Anwar Nuril Anwar, Nuril Nurmi Hidayasari Panggah Widiandana Prabowo, Basit Adhi Pradana Ananda Raharja Prakoso, Danar Cahyo Prambudi, Rizal Prambudi Prasetyaningrum, Putri Taqwa Purwaningrum, Santi Purwanto Purwanto Purwono Purwono, Purwono Puspa Ira Dewi Candra Wulan Putri Annisa Putro, Aldibangun Pidekso Raden Hario Wahyu Murti Raden Mohamad Herdian Bhakti Rafiq, Irhash Ainur Rahmat Ardila Dwi Yulianto Ramadhani, Erika Ramansyah Ramansyah Rauli, Muhamad Ermansyah Rauli, Muhamad Ermansyah Reyhanssan Islamey Ridho Ikhram Ridho Ikhram Ridho Surya Kusuma Rika Harman Rio Widodo Riski Yudhi Prasongko Rivai, Zulki Yanto Rizal Prambudi Rochmadi, Tri Roni Anggara Putra Rosalia Setia Nursanti Rudy Ansari Rudy Ansari Ruslan, Takdir Rusydi Umar Rusydi Umar Rusydi Umar Ruuhwan Safiq Rosad Sahiruddin Sahiruddin Salim, Mansyur Santi Purwaningrum Sari, Laura Shiha Budin Simanjuntak, Pastima Sismadi, Wawan Sri Mulyaningsih Sri Winiarti Sri Winiarti Sri Winiati St Rahmatullah Sudaryanto Sudaryanto Sudinugraha, Tri Sugandi, Andi Suhartono, Bambang Sukma Aji Sunardi Sunardi - Sunardi Sunardi sunardi sunardi Sunardi, Sunardi Suprihatin Suprihatin Suprihatin Suprihatin Suprihatin Suprihatin Supriyanto Syaefudin, Rizal Syahida Alawi, Hanna Syahrani Lonang Syarifudin, Arma Taufiq Ismail Taufiq Ismail Tawar Tawar Tole Sutikno Tri Ferga Prasetyo Tri Lestari Tri Lestari Tri Rochmadi Triyanto, Joko Umar, Rusdy Veithzal Rivai Zainal Verry Noval Kristanto W, Yunanri Wahyusari, Retno Wardiwiyono, Sartini Wasito Sukarno Wawan Sismadi Weni Hawariyuni, Weni Wicaksono Yuli Sulistyo Wicaksono Yuli Sulistyo Widiandana, Panggah WIDODO, RIO Winiati, Sri Wintolo, Hero Wisnu Pranoto Yana Mulyana Yana Mulyana Yana Safitri, Yana Yohanni Syahra Yudi Kurniawan Yudi Kurniawan Yudi prayudi Yulian Wahyu Permadi Yuliansyah, Herman Yuliansyah, Herman Zein, Wahid Alfaridsi Achmad