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All Journal Jurnal Informatika dan Teknik Elektro Terapan Sinkron : Jurnal dan Penelitian Teknik Informatika JUTIK : Jurnal Teknologi Informasi dan Komputer ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Nasional Komputasi dan Teknologi Informasi JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tunas Journal of Computer Networks, Architecture and High Performance Computing Yayasan Cita Cendikiawan Al Khwarizmi Djtechno: Jurnal Teknologi Informasi Majalah Ilmiah UPI YPTK Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Jurnal Ilmu Komputer dan Sistem Komputer Terapan (JIKSTRA) Teknik: Jurnal Ilmu Teknik dan Informatika J-Intech (Journal of Information and Technology) Jurnal Minfo Polgan (JMP) Prosiding Snastikom Jurnal Teknik Informatika INFORMATIKA Jurnal Pengabdian Masyarakat Gemilang (JPMG) PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Informatika Progres Prioritas : Jurnal Pengabdian Kepada Masyarakat Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Biner : Jurnal Ilmu Komputer, Teknik dan Multimedia Deli Sains Informatika JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Ilmiah Sistem Informasi Jurnal Pengabdian Nasional (JPN) Indonesia Jurnal Intelek Dan Cendikiawan Nusantara Jurnal Intelek Insan Cendikia Jurnal Manajemen Informatika, Sistem Informasi dan Teknologi Komputer (JUMISTIK) Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Ilmu Komputer dan Informatika Journal of Education Transportation and Business Jurnal Bisnis Digital dan Enterpreneur (BISENTER) MURAKOM
Adidtya Perdana, Adidtya
Universitas Negeri Medan
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Optimasi Random Forest Menggunakan Genetic Algorithm untuk Klasifikasi Kualitas Udara Berdasarkan Data ISPU(Indeks Standar Pencemaran Udara) Albert Ramadhan Manik; Alfin Syahri; Adidtya Perdana
Jurnal Intelek Dan Cendikiawan Nusantara Vol. 2 No. 6 (2025): Desember 2025 - Januari 2026
Publisher : PT. Intelek Cendikiawan Nusantara

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

Abstract

Kualitas udara merupakan faktor penting yang berdampak langsung terhadap kesehatan manusia dan lingkungan. Peningkatan aktivitas industri, transportasi, dan urbanisasi menyebabkan konsentrasi polutan udara semakin tinggi, sehingga diperlukan metode klasifikasi kualitas udara yang akurat sebagai dasar pengambilan keputusan. Penelitian ini bertujuan untuk meningkatkan performa algoritma Random Forest dalam mengklasifikasikan kualitas udara berdasarkan Indeks Standar Pencemaran Udara (ISPU) melalui optimasi hyperparameter menggunakan Genetic Algorithm (GA). Dataset yang digunakan berupa data kualitas udara harian wilayah Tangerang Selatan periode 2020–2022, dengan enam parameter polutan utama yaitu PM2.5, PM10, SO₂, CO, O₃, dan NO₂, serta tiga kategori kualitas udara: Good, Moderate, dan Unhealthy. Tahapan penelitian meliputi preprocessing data, pembagian data secara stratified, pelatihan model Random Forest baseline, optimasi hyperparameter menggunakan GA, serta evaluasi performa model. Hasil penelitian menunjukkan bahwa optimasi menggunakan Genetic Algorithm mampu meningkatkan akurasi pengujian dari 81,73% menjadi 82,23% serta mengurangi indikasi overfitting pada model. Analisis feature importance menunjukkan bahwa CO dan PM2.5 merupakan parameter paling berpengaruh dalam klasifikasi kualitas udara. Hasil ini membuktikan bahwa Genetic Algorithm efektif digunakan untuk mengoptimasi Random Forest dan meningkatkan akurasi klasifikasi kualitas udara berbasis ISPU.
Optimasi Kurva Daya Turbin Angin Menggunakan Model Logistic Berbasis Particle Swarm Optimization (PSO) Henrydunan, John Bush; Purba, Jogi; Amanah, Fadilla; Perdana, Adidtya
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1252

Abstract

Accurate wind turbine power curve modeling plays a crucial role in performance evaluation, energy yield estimation, and data-driven control strategies. However, actual power curves often exhibit non-linear behavior influenced by atmospheric variability, measurement noise, and SCADA anomalies, making conventional modeling approaches less effective. This study proposes an optimized logistic power curve model whose parameters are tuned using Particle Swarm Optimization (PSO) to improve predictive accuracy. The analysis uses the Wind Turbine SCADA Dataset from Kaggle, which undergoes extensive preprocessing including physical rule filtering, outlier detection with the Interquartile Range (IQR) method, anomaly removal, and smoothing of the power signal. A three-parameter logistic model is selected due to its ability to capture the typical S-shaped relationship between wind speed and power output. PSO is applied to identify optimal model parameters by minimizing the Mean Squared Error (MSE), utilizing 40 particles over 200 iterations. The optimized model achieves strong predictive performance with RMSE of 404.09, MAE of 179.96, and R² of 0.904 on the test set, indicating that more than 90% of the variability in actual power can be explained by wind speed. Residual analysis reveals heteroscedastic patterns and slight overestimation in mid-range wind speeds, yet overall model consistency remains high. Comparative evaluation against Linear Regression, Random Forest, and logistic modeling using curve_fit shows that the Logistic–PSO approach provides the most accurate and stable predictions. These findings demonstrate that combining logistic modeling with PSO offers an effective and robust method for data-driven wind turbine power curve optimization.
Optimasi Parameter Model LightGBM Menggunakan Algoritma Grey Wolf Optimizer untuk Prediksi Penyakit Ginjal Kronis Muhammad Alfin; Alvin Hafiz; Muhammad Budi Akbar; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1263

Abstract

Chronic kidney disease is an increasingly prevalent health issue that requires more precise clinical data-based early detection methods to enable timely and appropriate treatment. This study focuses on developing a predictive model for chronic kidney disease using the Light Gradient Boosting Machine (LightGBM) algorithm and enhancing its performance through hyperparameter optimization with the Grey Wolf Optimizer (GWO). The dataset used originates from public sources and undergoes several preprocessing steps, including missing value imputation, categorical feature encoding, outlier handling, initial feature selection, and stratified data splitting to maintain model quality. Three modeling approaches were evaluated: LightGBM with default parameters, LightGBM enhanced using Random Search, and LightGBM optimized with GWO. The experimental results indicate that the baseline model already performs well, Random Search improves accuracy and F1-score, and GWO achieves the highest AUC-ROC value despite requiring longer computation time. Significance testing through cross-validation shows that the performance differences among the three models are not statistically significant, suggesting that the observed improvements are not strong enough to determine a definitively superior optimization method. The feature importance analysis highlights that clinical indicators such as creatinine levels, glomerular filtration rate, blood pressure, and urine protein contribute most prominently to the prediction. Overall, the study demonstrates that LightGBM is a reliable model for early detection of chronic kidney disease, and hyperparameter optimization still offers added value that can support the development of AI-based clinical decision-support systems
Feature Selection pada Dataset NSL-KDD Menggunakan Algoritma Genetic Algorithm untuk Deteksi Serangan Jaringan Freyro Dobry Sianipar; Ruth Amelia Vega S Meliala; Yoseph Christian Sitanggang; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1275

Abstract

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.  
Penerapan Metaheuristik Genetic Algorithm pada Optimasi Hyperparameter Decision Tree untuk Klasifikasi Penyakit Jantung Arifin, Muhammad Hidayatul; Harahap, Salsa Nabila; Nababan, Sirus Daniel H; Perdana, Adidtya
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 9, No 1 (2026): Februari 2026
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v9i1.10205

Abstract

Abstrak - Penyakit jantung merupakan salah satu penyebab kematian utama secara global, sehingga deteksi dini yang akurat menjadi krusial dalam upaya penyelamatan pasien. Algoritma Decision Tree sering digunakan sebagai alat bantu diagnosis karena kemampuannya menghasilkan aturan keputusan yang transparan. Namun, algoritma ini memiliki kelemahan mendasar berupa kecenderungan overfitting dan ketidakstabilan akurasi saat menangani data medis yang kompleks. Penelitian ini mengusulkan integrasi Genetic Algorithm (GA) sebagai metode metaheuristik untuk mengoptimalkan konfigurasi hyperparameter pada Decision Tree guna meningkatkan performa klasifikasi. Eksperimen dilakukan menggunakan dataset Heart Disease yang telah melalui tahap pra-pemrosesan ketat, termasuk eliminasi data duplikat dan transformasi fitur kategorikal. Proses optimasi dirancang dengan fungsi evaluasi khusus yang memprioritaskan sensitivitas (Recall) untuk meminimalisir risiko kesalahan deteksi pasien sakit (False Negative). Hasil pengujian menunjukkan bahwa model hasil optimasi GA berhasil meningkatkan Recall secara signifikan sebesar 12,12% (dari 75,76% menjadi 87,88%) dan Akurasi sebesar 4,92% dibandingkan model baseline. Selain itu, struktur model menjadi jauh lebih efisien dengan reduksi kedalaman pohon dari 10 tingkat menjadi 3 tingkat. Temuan ini membuktikan bahwa metode yang diusulkan mampu menghasilkan sistem diagnosis yang tidak hanya lebih akurat dan sensitif, tetapi juga lebih sederhana dan mudah diinterpretasikan oleh tenaga medis.Kata kunci : Decision Tree; Genetic Algorithm; Optimasi Hyperparameter; Prediksi Penyakit Jantung; Abstract - Heart disease is one of the leading causes of death globally, making accurate early detection crucial for patient survival. The Decision Tree algorithm is often used as a diagnostic tool due to its ability to generate transparent decision rules. However, this algorithm has fundamental weaknesses such as overfitting tendencies and accuracy instability when handling complex medical data. This study proposes the integration of Genetic Algorithm (GA) as a metaheuristic method to optimize hyperparameter configurations in Decision Trees to improve classification performance. Experiments were conducted using the Heart Disease dataset that had undergone rigorous pre-processing stages, including duplicate data elimination and categorical feature transformation. The optimization process was designed with a special evaluation function that prioritizes sensitivity (Recall) to minimize the risk of false patient detection (False Negative). The test results showed that the GA-optimized model significantly increased Recall by 12.12% (from 75.76% to 87.88%) and Accuracy by 4.92% compared to the baseline model. In addition, the model structure became significantly more efficient with a reduction in tree depth from 10 levels to 3 levels. These findings prove that the proposed method is capable of producing a diagnostic system that is not only more accurate and sensitive, but also simpler and easier to interpret by medical personnel.Keywords: Decision Tree; Genetic Algorithm; Hyperparameter Optimization; Heart Disease Prediction;
PENGEMBANGAN APLIKASI BERBASIS ARTIFICIAL INTELLIGENCE DALAM REKOMENDASI JALUR PENDIDIKAN BERDASARKAN MINAT DAN KEMAMPUAN SISWA M. Rizki Andrian Fitra; Neysa Talitha Jehian; Delvita Aulia Artika; Bunga Dwi Febrianti; Adidtya Perdana
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3959

Abstract

Many high school and vocational students in Indonesia experience confusion when choosing a college major due to a lack of understanding of their own potential and limited access to relevant information. This study aims to develop an Artificial Intelligence (AI)-based major recommendation system that is personal, adaptive, and transparent. The system is designed using a Hybrid Recommendation System approach, combining Content-Based Filtering, Rule-Based System, and a Weighted Scoring Algorithm, with weights based on hobbies, academic grades, favorite subjects, personality, and career aspirations. The technologies used include Laravel (backend), Vue.js (frontend), and Python API for the AI component. Trial results with 15 students showed that over 60% of respondents found the system very helpful, while over 30% found it moderately helpful and felt the recommendations aligned with their interests and goals, indicating the system’s effectiveness in supporting educational decision-making. The system is also flexible for further development in terms of both datasets and algorithms. Future enhancements include the integration of personality tests such as MBTI, implementation of feedback-based machine learning, and cross-school testing for broader validation. This system is expected to become a data-driven educational solution that supports digital transformation in the education sector.
PERANCANGAN APLIKASI ABSENSI KELAS PRESENTENSE.COMSCI BERBASIS GLIDE SEBAGAI PLATFORM NO-CODE Evelyn Keisha Silalahi; Aurela Khoiri Nasution; Josua Anugrah Deo Tampubolon; Rut Kezia Imburi; Adidtya Perdana
Jurnal Teknologi Informasi dan Komputer Vol. 12 No. 1 (2026): JUTIK : Jurnal Teknologi Informasi dan Komputer, Edisi April 2026
Publisher : LPPM Universitas Dhyana Pura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36002/jutik.v12i1.3977

Abstract

The development of information technology has encouraged the birth of digital solutions in various fields, including education. One of the problems often faced in the academic environment is a manual attendance system that is prone to manipulation, inefficient, and makes it difficult to record data. This research aims to design and implement a mobile-based digital attendance application called Presentense.ComSci, using Glide as a no-code platform. The research method used is Research and Development (R&D), with stages ranging from needs analysis, system design, implementation, to evaluation. This application is designed to be able to record student attendance in real-time through the institution's email verification feature, automatic time recording (timestamp), GPS location tracking, and selfie upload as proof of attendance. Attendance data is automatically saved into a Google Spreadsheet, so it can be accessed by the admin practically and safely. Tests were conducted on two classes at Medan State University with the results showing that the application was able to function properly, although there were some problems with GPS location accuracy. With this application, the attendance recording process becomes more transparent, efficient, and practical, and can be an innovative solution for attendance management in higher education.
OPTIMASI SELEKSI FITUR PREDIKSI SAHAM MENGGUNAKAN QUANTUM INSPIRED METAHEURISTIC DAN LIGHTGBM Wahyudi, Rizky; Azima Lubis, Fauzan; Aulia, Windy; Perdana, Adidtya
PROSISKO: Jurnal Pengembangan Riset dan Observasi Sistem Komputer Vol. 13 No. 1 (2026): Prosisko Vol. 13 No. 1 Maret 2026
Publisher : Pogram Studi Sistem Komputer Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/g7vb2b04

Abstract

Penelitian ini bertujuan untuk meningkatkan akurasi prediksi return harian saham perbankan Indonesia melalui penerapan metode seleksi fitur berbasis metaheuristik QIMA (Quantum-Inspired Multi-Objective Algorithm) pada model LightGBM. Permasalahan utama yang diangkat meliputi tingginya multikolinearitas antar-fitur teknikal, rendahnya validitas prediksi model baseline, serta mahalnya biaya komputasi saat menggunakan fitur penuh. Penelitian ini menggunakan 38 indikator teknikal yang kemudian direduksi menggunakan QIMA untuk memperoleh subset fitur optimal yang memaksimalkan akurasi sekaligus meminimalkan kompleksitas model. Hasil eksperimen menunjukkan bahwa model baseline mengalami overfitting dengan nilai R² negatif pada sebagian besar saham. Penerapan QIMA berhasil memperbaiki kinerja model menjadi positif pada BBCA (0,0006) dan BBNI (0,0173), serta menstabilkan performa BBRI dan BMRI. Selain itu, jumlah fitur berhasil direduksi sebesar 68,42% dengan waktu pelatihan yang lebih cepat hingga 6–7 kali lipat. Analisis fitur terpilih mengungkap bahwa volatilitas jangka pendek, return berbasis momentum, serta lag harga dan volume merupakan indikator yang paling relevan dalam memengaruhi pergerakan harga saham jangka pendek. Secara keseluruhan, studi ini membuktikan bahwa QIMA mampu meningkatkan validitas prediksi, efisiensi komputasi, dan interpretabilitas model dalam konteks pasar saham Indonesia.
Pengaruh Karakteristik Data Terhadap Performa Algoritma Sorting Nasution, Nayla Anjani; Patricia Nainggolan, Natasha; Irfandi Surbakti, Zevan; Perdana, Adidtya
Jurnal Riset Informatika dan Inovasi Vol 3 No 12 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Algoritma sorting merupakan salah satu komponen fundamental dalam ilmu komputer yang banyak digunakan dalam berbagai proses pengolahan data. Pemilihan algoritma sorting yang tepat menjadi sangat penting karena setiap algoritma memiliki karakteristik performa yang berbeda tergantung pada kondisi data yang diproses. Penelitian ini bertujuan untuk menganalisis pengaruh karakteristik data terhadap performa beberapa algoritma sorting. Algoritma yang diuji dalam penelitian ini adalah Insertion Sort, Merge Sort, dan Quick Sort. Metode penelitian yang digunakan adalah eksperimen komputasional dengan mengimplementasikan ketiga algoritma tersebut menggunakan bahasa pemrograman Python. Pengujian dilakukan pada empat jenis karakteristik data, yaitu random data, sorted data, reversed data, dan nearly sorted data, dengan variasi ukuran dataset mulai dari 1.000 hingga 10.000 elemen. Waktu eksekusi algoritma diukur menggunakan fungsi time.perf_counter() untuk memperoleh hasil pengukuran yang presisi. Hasil penelitian menunjukkan bahwa karakteristik data memiliki pengaruh yang signifikan terhadap performa algoritma sorting. Insertion Sort menunjukkan performa yang baik pada dataset kecil dan data yang hampir terurut, namun kurang efisien pada dataset berukuran besar karena kompleksitas waktunya O(n²). Sebaliknya, Merge Sort dan Quick Sort menunjukkan performa yang lebih stabil pada berbagai kondisi dataset dengan kompleksitas rata-rata O(n log n). Temuan penelitian ini diharapkan dapat membantu dalam menentukan algoritma sorting yang paling sesuai berdasarkan karakteristik data yang diproses. Kata kunci: algoritma sorting, insertion sort, merge sort, quick sort, performa algoritma.
Penyelesaian Masalah NP-Complete pada Penjadwalan Menggunakan Algoritma Welch-Powell (Graph Coloring) Silaban, Lastri Putri; Panjaitan, Bicanro Gebriyan; Kurniadi, Azis; Perdana, Adidtya
Jurnal Riset Informatika dan Inovasi Vol 3 No 12 (2026): JRIIN : Jurnal Riset Informatika dan Inovasi (INPRESS)
Publisher : shofanah Media Berkah

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Abstract

Penjadwalan mata kuliah merupakan permasalahan kompleks yang termasuk dalam kategori NP-hard karena melibatkan berbagai kendala seperti ketersediaan dosen, ruang, dan waktu. Proses penyusunan jadwal yang dilakukan secara manual sering kali menimbulkan konflik dan membutuhkan waktu yang cukup lama. Penelitian ini bertujuan untuk menerapkan algoritma Welch-Powell dengan pendekatan graph coloring dalam menyelesaikan permasalahan penjadwalan mata kuliah secara efektif dan efisien. Metode yang digunakan adalah pemodelan graf konflik, di mana setiap mata kuliah direpresentasikan sebagai simpul dan hubungan konflik sebagai sisi yang menghubungkan simpul-simpul tersebut. Proses pewarnaan graf dilakukan untuk menentukan slot waktu yang berbeda bagi mata kuliah yang saling berkonflik. Data yang digunakan terdiri dari enam mata kuliah dengan atribut dosen, kelas, dan ruang. Hasil penelitian menunjukkan bahwa algoritma Welch-Powell mampu menghasilkan penjadwalan yang tidak mengalami benturan dengan jumlah slot waktu yang efisien. Dengan demikian, metode ini dapat menjadi solusi sederhana dan efektif dalam menyusun jadwal perkuliahan secara optimal.
Co-Authors Ade Zulkarnain Adinda Soleha Adryan Rachmadsyah Ryan Afrrahman S. Effendi, Ali Agus Waruwu, Stefen Al Khowarizmi Al Rafiq, M. Faris Albert Ramadhan Manik Alby Savana HSB, Muhammad Alfin Syahri Alfin, Muhammad Alvansyah, Oka Alvin Hafiz Alya Namira Amanah, Fadilla Amelia Br Siregar, Ririn Amelia Vega S. Meliala, Ruth Anak Agung Istri Sri Wiadnyani Ananda Hafika, Rizky Andi Marwan Elhanafi Angelica Barus Anggi Silalahi Anggraini, Dea Anwar Shaleh Lbn Gaol Aqilah Defiyanti Arief Budiman Arief Budiman Arifin, Muhammad Hidayatul Arion Pardede Arnita Ashillah, Salma Asro Harahap, Fatima Audy Priscilia, Selfi Augis Dinanti Aulia, Windy Aurela Khoiri Nasution Ayu Amelia Pwrtiwi Azhara Amelia H Azima Lubis, Fauzan Br. Hutagalung, Fhadillah Budi Akbar, Muhammad Bunga Dwi Febrianti Bush Henrydunan, John Calvin Syahputra Damanik, Muhammad Rois Lukman Damayanti, Nina Afria Dedy Kiswanto Delvita Aulia Artika Dian Septiana DIdi Febrian Dinda Syafitri Dwi Syaputra Ega Pratama Ega Zuhairi Ramadhan Elistia, Callysa Erika Nia Devina Br Purba Evanthe, Hansel Evelyn Keisha Silalahi Eviyona Laurenta Br Barus Fadilah, Putri Maulidina Fadilah, Putri Mauliidna Farezi, Nazwar Fatimah Asro Harahap Fauzan Azima Lubis Felix John Pardamean Hutabarat Frengki Alfredo Matondang Freyro Dobry Sianipar Gulo, Steven Adventino Gus Rosauli Pandiangan Hafiz, Alvin Halawa, Sovantri Putra Paskah Hapzi Ali Harahap, Salsa Nabila Hasibuan, Ade Zulkarnain Hasugian, Andly Sofian Henrydunan, John Bush Hermawan Syahputra Hsb, Khairany Zuhriyyah Jinan Ichwanul Muslim Karo Karo Ilyasyah Drilanang, Muhammad Imelda, Yusmita Impana Manik, Kristin Insan Pratama Siagian, Raihan Irfandi Surbakti, Zevan Irya Shakila Syukron, Ananda Isa Hidayati Josua Anugrah Deo Tampubolon Juliana, Feby Kana Saputra S Kayla Amelia Putri Khairul Fahmi Sagala Khodotun Hadawiyah Margolang Khoiriah, Najwatul Kokod, Mario Maysan Kurniadi, Azis Lasker Pangarapan Sinaga Lestari, Yuyun Dwi Lexa, Juhraini Helfiana Lubis, Farizky Aulia M. Fahmi Arafat M. Rizki Andrian Fitra Maissy Angelica Pakpahan Manurung, Asrar Aspia mardiana Maulana Al Nouri Maulida Surbakti, Nurul Maulidina Fadila, Putri MD, Pipit Putri Hariani Mhd Zulfansyuri Siambaton Midayatul Arifin, Muhammad Mohd. Rafiif Albani Muhammad Alby Savana Hasibuan Muhammad Alfin Muhammad Budi Akbar Muhammad Fauzan Akbar Muhammad Haikal Al Majid Muhammad Hidayatul Arifin Muhammad Iqbal Fahrezzi Muhammad Khoiruddin Harahap Muhammad Kurniawan Muhammad Raffi Akbar Tanjung Muhammad Raihansyah Lubis Muhammad Rivai Muhammad Yazid Noor Muhammad Zidane Al-Kautsar Muslim Sinaga, Rizal Nababan, Sirus Daniel H Nabila Harahap, Salsa Najwatul Khoiriah Nasution, Afifah Naila Nasution, Nayla Anjani Naufal Aqiilah Asra Nazwar Farezi Nenna Irsa Syahputri Neysa Talitha Jehian Niska, Debi Yandra Noor, M Yazid Nuriana Sipahutar Nurul Ain Farhana Nurul Khairina Nurul Maulida Surbakti Panggabean, Suvriadi Panjaitan, Bicanro Gebriyan Panjaitan, Clara Kresensia Paskah Abadi Simanullang Paskal Arienda Epidonta Ginting Patricia Nainggolan, Natasha Pebiana Putri, Fahra Peter Tymoty Hutabarat Prana Walidin, Adamsyach Pratama, Ega Priscilia, Selfi Audy Purba, Jogi Putra Paskah Halawa, Sovantri Putri Handayani, Agata Raffi Akbar Tanjung, Muhammad Raihan Insan Pratama Siagian Raja Ansel Hartama Sihombing Ramadhan Manik, Albert Rangga Wahyu Pratama Rani Indah Sari Repi Meilani Putri Revidamurti Daulay Revidamurty Daulay Ridho Affandi Rifqi Putra Winanda Ririn Amelia Br Siregar Riski Immanuel Situmorang Rizko Liza Rizky Ananda Hafika Rossy Pratiwy Sihombing Rut Kezia Imburi Ruth Amelia Vega S Meliala Sahara Lani Lestari Sapta Warman Zai, Tri Saragih, Angelina S. Sarah Putri Nasutio. Sembiring, Febe Gracia Shaleh Lbn Gaol, Anwar Shaqila Rahmayani Gultom Silaban, Lastri Putri Simamora, Sevta Triwana Simanjuntak, Yesy Simarmata, Jhon Gabriel Simbolon, Agata Putri Handayani Sinaga, Christian Nicholas Sinaga, Rizal Muslim Sirlia Sahid Siti Haliza Zamili Sri Dewi Stefen Agus Waruwu Sukma, Ayman Human Suleho, Febrina Syahri, Alfin Syti Salwaa Nafiisah Tambunan, Vivielda Farmawaty Tanjung, Elga Sari Tarigan, Yehezkiel Haganta Tasya Agustina Tampubolon, Putri Tia Risky Yasmin Saketang Vega S. Meliala, Ruth Amelia Wahyudi, Rizky Windy Aulia Yazid Noor, Muhammad Yessi Fitri Annisa Lubis Yolandari, Nezza Anggraini Yoseph Christian Sitanggang Yuda Advis Ambrosius Sitohang Yulita Molliq Rangkuti Yuyun Dwi Lestari Yuyun Dwi Lestari Zahra, Sofia Zai, Samuel Anaya Putra Zai, Yohanes Gerardus Haga Zidane, Muhammad Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta Zulfi, M. Fikri