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All Journal Jurnal Ilmu Komputer Teknika Jurnal Informatika Sinergi Telematika Jurnal Informatika dan Teknik Elektro Terapan Jurnas Nasional Teknologi dan Sistem Informasi InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Format : Jurnal Imiah Teknik Informatika Sistemasi: Jurnal Sistem Informasi Informatika Mulawarman: Jurnal Ilmiah Ilmu Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT Information Management For Educators And Professionals (IMBI) Informatika Pertanian Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Komtika (Komputasi dan Informatika) JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Inotera Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) Jurnal Sains Riset Jurnal Sosial Humaniora Sigli JTIK (Jurnal Teknik Informatika Kaputama) JOURNAL OF INFORMATICS AND COMPUTER SCIENCE G-Tech : Jurnal Teknologi Terapan Jurnal Sistem Komputer dan Informatika (JSON) Medika Teknika : Jurnal Teknik Elektromedik Indonesia Infotek : Jurnal Informatika dan Teknologi Idealis : Indonesia Journal Information System International Journal of Engineering, Science and Information Technology JUTECH : Journal Education and Technology Conten : Computer and Network Technology Brilliance: Research of Artificial Intelligence Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Energi Elektrik Jurnal Bangun Abdimas IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Journal Of Artificial Intelligence And Software Engineering Jurnal Malikussaleh Mengabdi Jurnal Komtika (Komputasi dan Informatika) Jurnal Informatika Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN) Jurnal Pengabdian Masyarakat Teknologi Informasi (PERANTI)
Nurdin Nurdin
Universitas Malikussaleh
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Environmental Image Analysis for Smoke-Free Area Compliance Mapping Using YOLOv11 and Geographic Information Systems Lisa Mulia Al Ikhlas; Dahlan Abdullah; Nurdin Nurdin
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.13043

Abstract

Smoke-Free Areas are implemented to protect public health; however, monitoring and evaluating compliance remain challenging due to the lack of automated and spatially integrated monitoring systems. This study aims to develop a mapping and classification system for Smoke-Free Area (KTR) compliance using the YOLOv11 object detection algorithm and Geographic Information System (GIS)-based spatial analysis on environmental images. Data collection was conducted in Banda Sakti District, Lhokseumawe, at nine observation locations consisting of places of worship, public open spaces, and workplaces. The dataset consisted of three object classes, namely smoking activity, cigarette, and ashtray, combined with spatial variables such as latitude and longitude, where latitude represents the north–south geographic position and longitude represents the east–west geographic position of each observation point. These spatial variables were integrated with the YOLOv11 detection results to enable the mapping and visualization of KTR violations within the GIS environment. The YOLOv11 model was evaluated using precision, recall, mAP50, and mAP50-95 metrics. Experimental results showed that the model achieved a precision value of 0.772, recall of 0.741, mAP50 of 0.770, and mAP50-95 of 0.602, indicating moderate object detection performance under various environmental conditions. Spatial analysis results revealed that out of 145 observation points, 112 points were categorized as major violations, 25 points as minor violations, and only 8 points as compliant areas. Therefore, the integration of YOLOv11 and GIS provides a digital-based approach for supporting Smoke-Free Area compliance monitoring and spatial analysis.
Perbandingan Algoritma K-Means dan Hierarchical Clustering dalam Pengelompokan Prestasi Akademik Siswa Hari Sampurno; Muhammad Nasrullahil Wafi; Nurdin Nurdin
FORMAT Vol 15 No 1 (2026)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/format.2026.v15.i1.008

Abstract

Pengelompokan prestasi akademik merupakan salah satu strategi yang dapat membantu guru dan pihak sekolah dalam melakukan intervensi pembelajaran, seperti pemberian bimbingan tambahan atau penentuan strategi pengajaran yang lebih tepat. Penelitian ini bertujuan untuk membandingkan kinerja algoritma K-Means dan Hierarchical Clustering dalam mengelompokkan prestasi akademik siswa. Dataset yang digunakan terdiri dari 1.000 data siswa dengan tiga atribut nilai, yaitu Matematika, Membaca, dan Menulis, yang diperoleh dari sumber data publik. Tahapan penelitian meliputi proses preprocessing, normalisasi data menggunakan StandardScaler, penerapan algoritma clustering, serta visualisasi hasil menggunakan scatter plot dua dimensi dan dendrogram. Evaluasi kinerja model dilakukan menggunakan Silhouette Score untuk menilai kualitas pemisahan cluster. Hasil penelitian menunjukkan bahwa algoritma K-Means memperoleh skor Silhouette sebesar 0,406, sedangkan Hierarchical Clustering memperoleh skor 0,374. Nilai tersebut mengindikasikan bahwa K-Means menghasilkan struktur pengelompokan yang lebih baik dan lebih jelas dalam membedakan tingkat prestasi siswa menjadi tiga kategori: rendah, sedang, dan tinggi. Dengan demikian, K-Means dinilai lebih sesuai untuk analisis pengelompokan prestasi akademik pada dataset ini.
APLIKASI TEKNOLOGI INTERNET OF THING PADA ROBOT PENDETEKSI KEBOCORAN GAS AMONIA (NH3) Jikti Khairina; Nurdin Nurdin; Muhammad Fikry
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.v11i1.7457

Abstract

Amonia adalah senyawa kimia dengan rumus NH3 Senyawa ini didapati berupa gas dengan bau tajam yang sangat khas, inilah yang disebut dengan bau amonia. Amonia memiliki sumbangan penting bagi keberadaan nutrisi di bumi, tetapi amonia sendiri adalah senyawa yang dapat merusak kesehatan. Jika terjadi kontak dengan gas amonia berkonsentrasi tinggi dapat menyebabkan kerusakan pada paru-paru bahkan sampai kematian. Amonia digolongkan sebagai bahan beracun jika terhirup langsung, pengangkutan amonia yang berjumlah lebih besar dari 3.500 galon (13,248 L) harus disertai dengan surat izin. Amonia umumnya bersifat basa (pKb=4.75), tetapi dapat juga bersifat sebagai asam yang amat lemah (pKa=9.25), amonia dapat terbentuk secara alami maupun sintetis. Amonia yang berada di alam merupakan hasil dekomposisi bahan organik. Di industri banyak yang menggunakan amonia sebagai salah satu bahan baku, contohnya seperti dalam penggunaan campuran bahan baku pembuatan pupuk. Gas amonia terkadang beresiko terjadi kebocoran pada pipa gas, jika terjadi kebocoran pada pipa maka dibutuhkan teknisi yang harus segera dikirimkan ke lokasi untuk mencari sumber kebocoran atau titik kebocoran pada pipa. Hal itu membuat teknisi membutuhkan tabung oksigen dan hal ini beresiko sangat tinggi dikarenakan daya tahan tabung oksigen hanya bertahan selama ±15 menit. Maka dibuatkanlah robot untuk dikirimkan ke lokasi yang berfungsi agar mengetahui informasi tentang lokasi kebocoran pipa gas dan informasi kadar gas langsung dapat diketahui melalui Android. Dalam kasus ini, rancang bangun robot menggunakan sensor MQ135. Oleh karena itu dibuatkanlah robot pendeteksi kebocoran gas dan mengetahui kadar dari gas amonia agar mempermudah teknisi dalam menemukan lokasi dan kadar gas amonia.
Implementation of a Hybrid Model Using Principal Component Analysis, K-Means, and Naïve Bayes for Tuition Fee Category Prediction Nurdin Nurdin; Jessika Jessika; Munirul Ula
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13184

Abstract

The determination of Tuition Fee Categories in higher education institutions is commonly conducted through manual verification of students’ socioeconomic documents, which may lead to subjectivity and inconsistencies in decision-making. This study proposes a hybrid machine learning approach that integrates Principal Component Analysis (PCA), K-Means Clustering, and Naïve Bayes Classifier within a semi-supervised learning framework for student socioeconomic classification based on pseudo-labels generated from clustering results. The dataset used in this study consists of 452 student records with 12 socioeconomic attributes obtained from the New Student Admission system of STAIN Teungku Dirundeng Meulaboh in 2025. Data preprocessing includes attribute selection, categorical transformation using One Hot Encoding, and feature standardization. PCA is applied to reduce dimensionality from 18 features to 12 principal components while retaining 95% of the total variance. The processed data are clustered using K-Means with the optimal number of clusters determined as 8 based on Elbow and Silhouette Score analysis. These clusters are used as pseudo-labels for training the Naïve Bayes classifier. Experimental results show that the proposed model achieves 98.89% training accuracy and 97.80% testing accuracy, with a weighted average F1-score of 0.98. The results indicate that the proposed hybrid approach is effective in capturing underlying socioeconomic patterns and provides a stable classification performance. However, the model is based on pseudo-labels rather than official tuition fee categories. Therefore, further validation using real labeled data is recommended to enhance generalizability and practical applicability.
Prediction of Remaining Productive Life of Oil Palm Plantation Soil Using Support Vector Regression with Permutation-Based Feature Importance Analysis Zara Yunizar Zainal; Nurdin Nurdin; Zharif Athaya Andarfi
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13229

Abstract

Oil palm (Elaeis guineensis Jacq.) is a strategically vital crop in Southeast Asia, yet progressive soil degradation driven by prolonged monoculture, pathogen pressure, and intensive land use poses a critical threat to long-term plantation sustainability. Existing soil assessment methods deliver static fertility classifications without quantifying the remaining productive lifespan of a given plot. This study introduces Remaining Productive Life (RPL) as a novel regression target defined as the estimated number of years before plantation soil productivity falls below a critical economic threshold. A Support Vector Regression (SVR) model with Radial Basis Function (RBF) kernel, formulated as K(xi, xj) = exp(−γ‖xi − xj‖²) with γ = 0.0303 (= 1/n_features) and regularization parameter λ = 0.5, was applied to a realistic synthetic multi-year dataset comprising 14,400 observations across 800 plantation plots spanning five soil types (Ultisol, Inceptisol, Alfisol, Peat, Oxisol) and the period 2008–2025. Thirty-three soil physicochemical, biological, management, and economic indicators constituted the input feature set. The SVR model achieved R² = 0.9141, MAE = 0.5122 years, RMSE = 1.1026 years, and MAPE = 11.883% on the independent test set, with 92.0% of predictions yielding an absolute error below one year. Permutation-based feature importance analysis identified Degradation Rate (ΔMAE = 0.2864), Plantation Age (0.2560), and Soil Productivity Index (0.2530) as the three dominant predictors, while Ganoderma Risk Index ranked fourth (0.2138), revealing the pivotal contribution of biological soil health to long-term productivity prediction. These findings establish SVR-based RPL estimation as an effective, interpretable framework for precision plantation management and proactive soil sustainability planning.
The Application of Naïve Bayes Algorithm in Detecting Hoaxes on National News Portals in Indonesia Cut Rifa Salsabil; Nurdin Nurdin; Rizki Suwanda
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13306

Abstract

The rapid advancement of information technology in Indonesia has led to a massive spread of digital disinformation, commonly known as an infodemic. The inability to filter inaccurate information manually necessitates a reliable, automated hoax detection system. This study aims to implement and evaluate the Multinomial Naïve Bayes algorithm combined with Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction to classify news articles as either factual or hoax. The research utilizes a dataset of 2,910 Indonesian news articles published in 2025, collected from verified national news portals and fact-checking websites. The text data underwent comprehensive preprocessing—including case folding, cleansing, stopword removal, and stemming—before being evaluated using 5-Fold Cross-Validation and an 80:20 data split. Experimental results demonstrate that the Naïve Bayes model achieves highly stable and competitive performance, recording an accuracy of 93.81%, a precision of 93.84%, a recall of 93.81%, an F1-Score of 93.82%, and a 5-Fold Cross-Validation F1-Score of 93.39%. Notably, the algorithm exhibited a significantly low False Negative rate, missing only 15 hoax documents out of 582 test samples. Furthermore, the trained model was successfully integrated into a real-time, web-based user interface using Streamlit. This practical implementation provides an accessible and efficient initial screening tool for the general public and journalists to assist in verifying news authenticity, thereby supporting efforts to mitigate the impact of digital hoaxes.
Facial Deepfake Detection System Using YOLOv11 and Xception Architecture Fachril Akbar; Nurdin Nurdin; Kurniawati Kurniawati
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13449

Abstract

The rapid development of artificial intelligence has led to the emergence of deepfakes, which pose serious threats to information security and public trust in digital media. This study develops a facial deepfake detection system that integrates YOLOv11 for face detection and the Xception architecture for classifying real and manipulated faces. YOLOv11 successfully localized all facial regions in the tested dataset with high confidence scores. The Xception model achieved a testing accuracy of 90.10%, with a Recall of 97.11% for the Fake class and an AUC of 0.98. Visual explanation using Grad-CAM showed that the model focused on critical areas such as the forehead, temples, and face boundaries to detect manipulation artifacts. The system was implemented as a desktop application named "Snap Detector" and passed black-box testing. However, the average processing speed of 6.26 FPS on an NVIDIA T4 GPU indicates that further optimization is needed for real-time performance.
Comparative Analysis of the SMART and ARAS Methods in a Decision Support System for Motorcycle Loan Applicant Eligibility Sri Kurnia; Dahlan Abdullah; Nurdin Nurdin; Munirul Ula; Muchlish Abdul Muthalib
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13714

Abstract

Motorcycle financing is one of the most popular consumer financing products in Indonesia. However, the credit eligibility assessment process at PT Mega Central Finance (MCF) Lhokseumawe Branch is still performed manually, making it susceptible to inconsistent evaluations and increasing the risk of non-performing loans. This study aims to implement and compare two Multi-Criteria Decision-Making (MCDM) methods, namely the Simple Multi-Attribute Rating Technique (SMART) and the Additive Ratio Assessment (ARAS), for evaluating motorcycle financing eligibility based on seven criteria, including age, monthly income, occupation, marital status, number of dependents, outstanding debt balance, and down payment (DP). The study utilized primary data from 223 loan applicants collected during the 2024 to 2025 period through interviews and document reviews using a total sampling technique. Criterion weights were determined using expert judgment from credit analysts. The results show that the SMART method classified 96 applicants as eligible and 127 applicants as not eligible, whereas the ARAS method classified 181 applicants as eligible and 42 applicants as not eligible, using a minimum eligibility threshold of 0.60. The difference in the results is primarily attributed to the distinct normalization mechanisms of the two methods. SMART is more sensitive to extreme values in highly weighted criteria, resulting in a more selective evaluation process, whereas ARAS produces a more balanced distribution of preference scores by normalizing criterion values relative to the optimal solution, leading to a more flexible assessment. The findings indicate that the two methods complement each other. SMART is recommended for organizations adopting a conservative credit approval policy, while ARAS is more suitable for organizations seeking to expand the number of eligible applicants while maintaining a balanced consideration of all evaluation criteria.
PREDIKSI RISIKO PENYAKIT JANTUNG PADA PASIEN HEMODIALISA MENGGUNAKAN OPTIMASI ALGORITMA RANDOM FOREST DAN SUPPORT VECTOR MACHINE DI RSU ARUN Khananda Raihansyah; Nurdin; Muhammad Sayuti
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.9933

Abstract

Abstrak. Penyakit jantung merupakan komplikasi yang sering terjadi dan menjadi penyebab utama morbiditas pada pasien yang menjalani terapi hemodialisa. Penelitian ini bertujuan untuk memprediksi risiko penyakit jantung pada pasien hemodialisa di RSU Arun dengan memanfaatkan algoritma Machine Learning, yaitu Random Forest dan Support Vector Machine (SVM). Total data yang digunakan adalah 160 rekam medis pasien dengan 12 parameter klinis awal. Tahapan penelitian mencakup data cleansing, feature engineering berupa penambahan fitur Drop Tensi dan Rasio Ureum/Kreatinin, Exploratory Data Analysis (EDA), serta hyperparameter tuning menggunakan GridSearchCV. Hasil penelitian menunjukkan bahwa dataset memiliki distribusi kelas target yang sangat seimbang, yaitu 80 pasien stabil dan 80 pasien berisiko komplikasi. Berdasarkan komparasi performa, kedua algoritma menghasilkan tingkat akurasi yang sama yaitu 96.88%. Namun, evaluasi berbasis kurva ROC menunjukkan bahwa model Random Forest memberikan performa terbaik sebagai model prediktif dengan skor AUC tertinggi yaitu 0.9960, dibandingkan dengan SVM (0.9921). Analisis feature importance mengungkapkan bahwa Interdialytic Weight Gain (IDWG) dan kadar Hemoglobin adalah dua prediktor paling signifikan dalam menentukan risiko komplikasi tersebut.
Analisis Komparatif Deteksi Spammer Menggunakan Kombinasi Mean Shift Clustering dan Local Mean K-Nearest Neighbor pada Lalu Lintas Jaringan Komputer OK Muhammad Majid Maulana; Hafizh Al Kautsar Aidilof; Nurdin Nurdin; Muhammad Sayuti; Rizal Tjut Adek; Zara Yunizar
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10005

Abstract

Alamat IP publik pada jaringan komputer berskala besar sering kali menghadapi risiko pemblokiran oleh layanan server global akibat aktivitas lalu lintas mencurigakan yang dipicu oleh perangkat spammer. Penelitian terdahulu telah mengusulkan sistem deteksi menggunakan kombinasi K-Medoids Clustering dan Gaussian Naïve Bayes Classifier. Namun, pendekatan statistik global tersebut masih menyisakan kelemahan berupa angka deteksi melolos (False Negative) yang signifikan akibat karakteristik ketimpangan kelas (class imbalance) yang ekstrem pada lalu lintas jaringan riil. Untuk mengatasi batasan tersebut, penelitian ini menerapkan sebuah pendekatan hybrid baru dengan mengintegrasikan algoritma Mean Shift Clustering dan Local Mean K-Nearest Neighbor (LMKNN). Data lalu lintas diekstraksi dari gateway Mikrotik RouterBoard RB850Gx2 pada jaringan Wi-Fi e-UnimalNet Universitas Malikussaleh. Algoritma Mean Shift yang berbasis densitas berhasil mengisolasi karakteristik lalu lintas secara mandiri menjadi 19 klaster spasial tanpa memerlukan inisialisasi jumlah kelompok di awal. Klaster-klaster mikro kemudian dilebur menggunakan ambang batas densitas menjadi representasi binary class (Normal dan Spammer). Selanjutnya, algoritma LMKNN diterapkan untuk mengklasifikasikan data uji dengan memanfaatkan metrik rata-rata jarak lokal guna memberikan representasi yang adil bagi kelas minoritas (spammer). Hasil eksperimen menunjukkan performa klasifikasi yang luar biasa, di mana nilai hiperparameter tetangga lokal optimal pada K=3 berhasil mencatatkan nilai Akurasi, Presisi, Recall, dan F1-Score mutlak sebesar 100%. Pendekatan ini terbukti sukses memangkas angka False Negative dari 23 data pada model konvensional sebelumnya menjadi 0 data murni, menjadikannya solusi deteksi anomali yang sangat andal dan robust untuk administrator jaringan.
Co-Authors - Miranda ., Muthmainah Adi Prasetyo Adzuha Desmi Afif Diapari Ma'aruf Lubis Afif Diapari Aflizar Aflizar Afrilia, Yesy Ahmad Junaidi Aidilof, Hafizh Al Kautsar Aji Anggara Al Khaidar Alaiya, Azna Aldi Wahdana Alqhifari, Azka Ama Zanati Amalia, Nova Amin Munthoha Ananda Faridhatul Ulva Andri Alfitra Andriyan Ginting Annisa Karima Ansharulhaq Aminsyah Arnawan Hasibuan Asrianda Asrianda Aynun, Aynun Aynun, Nur Azzanna, Maghriza bhakti wan khaledy Bustami Bustami Bustami Bustami Cesilia, Yolinda Chaeroen Niesa Chicha Rizka Gunawan Cindy Cika Pradita Cut Agusniar Cut Agusniar Cut Rifa Salsabil Dadang Priyanto Dahlan Abdullah Dahlan Abdullah Darmansyah, Arif Desky, Muhammad Aulia Dewi Astika Erni Susanti Eva Darnila Fachril Akbar Fadlisyah Fadlisyah Fadlisyah Fahrozi, Fazar Fajriana Fajriana Fajriana Fajriana Fajriana Fajriana, Fajriana Fasdarsyah Fasdarsyah fatimah Fatimah Fikhri, Aditya Aziz Fikran, Rifzan Fikri Fikri Gavinda, Virza Gilang Sidiq gunawan, chicha rizka Gunawan, Chichi Rizka Hafizh Al Kautsar Aidilof Hafizh Al Kautsar Aidilof Hafizh Al-Kautsar Aidilof Hamdhana, Defry Haniful Fikri Hari Sampurno Hasbul Hadi Herman Fithra Hermansyah Hermansyah I Made Ari Nrartha Ilham Manzis Ilyana, Anis Imanda, Nanda Intan Nuriani Ira Wati Irwansyahputra Irwansyahputra Isa, Muzamir Ismun Naufal Iza Rifna Jessika Jessika Jessika, Jessika Jikti Khairina Julia Ulfah Khaidar, Al Khairina, Jikti Khairul Fuadi Khairul Khairul, Khairul Khairuni Khairuni Khalis Al Muqarrabin Khananda Raihansyah Kurnia, Sri Kurniawati Kurniawati Kurniawati Lisa Mulia Al Ikhlas M Farhan Aulia Barus M Raisal Al Farisi M Rizwan M Sayuti M Suhendri M. Ali, Rahmadi Maksal Mina Marleni Marleni Maryana Maryana Maryana Maryana Maryana Maulita, Maya Maya Juwita Dewi Maysura Meisya Syahtira Meriatna Meriatna Muchlis Abdul Muthalib Muchlish Abdul Muthalib Muhammad Daud Muhammad Faisal Muhammad fauzan Muhammad Fikry Muhammad Furqan, Muhammad Muhammad Hutomi Muhammad Iqbal Muhammad Johan Setiawan Muhammad Nasir Muhammad Nasrullahil Wafi Muhammad Reza Zainal Muhammad Riansyah Muhammad Ridha Muhammad Sayuti Muhammad Sayuti Muhammad Sayuti Mukhtar Anas Mukti Qamal Muliana, Syarifah Munirul Ula Munirul Ula Munirul Ula Mutammimul Ula Muzakir Nur Nadilla Baimal Puteri Nanda Imanda NELI SUSANTI, NELI Nunsina, Nunsina Nurhabsah Nurhabsah OK Muhammad Majid Maulana Rahma Jihan Ananta Rahmad Rahmad Rahmad Rahmat Rahmat Raihan Putri Rasyada, Reza Dian Reza, Restu Rini Meiyanti Risawandi, Risawandi Riza Mirza Rizal S.Si., M.IT, Rizal Rizki Setiawan Rizki Suwanda Rizky Putra Fhonna Rizkya, Ghinni Robi Kurniawan Rusadi, Athirah Safriana Safriana Said Fadlan Anshari Salahuddin Salahuddin Salamah Salamah Salimuddin, Salimuddin Samudera, Brucel Duta Sapitri, Anggri Sari, Cut Jora Sayuti, Muhammad Siagian, Tania Annisa Siregar, Widyana Verawaty Siti Hajar Sri Kurnia Sri Kurnia Suci Fitriani, Suci Suhaili Sahibul Muna Sujacka Retno Sultan, Kana Suryana, Fitra Syandriani Harahap Taufik Taufik Taufiq Taufiq Taufiq Taufiq Taufiq Taufiq Thifal Salsabila Uci Mutiara Putri Nasution Ulfah, Julia Ulva Fitriani Utomo, Muhammad Fikri Violita Aditya Zahrah Wahdana, Aldi Wan Dinulaqli Wan, Syahputra Wawan Syahputra Wawan Wawan Yani, Muhamamd Yeni Yeni Yesy Afrilia Yesy Afrillia Yolinda Cesilia Yulisda, Desvina Zahratul Fitri Zahratul Fitri, Zahratul Zalfie Ardian Zara Yunizar Zara Yunizar Zharif Athaya Andarfi Zuraida Zuraida