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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 Bitnet: Jurnal Pendidikan 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 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.  
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.
PERANCANGAN DAN IMPLEMENTASI AWAL APLIKASI FINEME BERBASIS FLUTTER UNTUK PENCATATAN KEUANGAN PRIBADI DIGITAL Midayatul Arifin, Muhammad; Nabila Harahap, Salsa; Vega S. Meliala, Ruth Amelia; Noor, M Yazid; Perdana, Adidtya
PROGRESS Vol 18 No 1 (2026): April
Publisher : P3M STMIK Profesional Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56708/progres.v18i1.497

Abstract

Personal financial record keeping is often delayed or messy, making it difficult to summarize data, unconnected to budget limits, and easily overlooked. This research designed and built FineMe, a Flutter-based application for recording transactions, managing categories, setting total and per-category budgets, displaying daily charts, exporting data to CSV, and managing recurring transactions. The development followed a layered architecture software engineering approach with SQLite in the data layer, a repository for logic and aggregation, Riverpod for state synchronization, and a Material interface. Requirements were derived from daily usage scenarios and implemented iteratively, while functional testing assessed the accuracy of calculations and interface responsiveness. Results showed that the income, expense, and balance summaries were updated instantly, two separate daily charts were easy to read, budget progress was calculated accurately, valid CSV files were opened in a spreadsheet, and recurring transaction rules reduced repetitive input. These findings confirm the effective combination of Flutter, SQLite, and reactive state management for building a precise, responsive, and scalable financial recorder.
Implementasi Algoritma Shortest Path untuk Optimasi Rute pada Sistem Navigasi Lokasi Sirlia Sahid; Maissy Angelica Pakpahan; Rifqi Putra Winanda; Muhammad Raihansyah Lubis; Adidtya Perdana
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 4 No. 2 (2026): Mei : Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v4i2.841

Abstract

The increasing complexity of urban road networks demands intelligent navigation systems capable of determining optimal routes efficiently. This research implements the Dijkstra Shortest Path algorithm to optimize route search on a location navigation system in Medan City. The system models a road network as a weighted graph comprising 57 strategic locations and over 90 road connections, represented using adjacency list data structures. The Dijkstra algorithm, implemented in Python using the heapq module for priority queue management, achieves an optimal time complexity of O((V+E) log V). The system features five main functions: shortest route search, popular routes, location listing, dynamic location addition, and dynamic road connection addition. System testing using a case study from Kualanamu Airport to the University of North Sumatra (USU) yielded an optimal route of 16.5 km through 4 road segments. Results demonstrate that the system successfully determines the most efficient route, provides accurate distance and travel time information for multiple transport modes (motorcycle, car, walking), and presents step-by-step journey guidance. This research contributes as a practical reference for applying shortest path algorithms in urban areas and serves as a foundation for developing more complex navigation applications in the future.
Optimasi Alokasi Sumber Daya Bantuan Sosial : Pendekatan Algoritma Greedy dan Analisis Komputasi Maulana Al Nouri; Tia Risky Yasmin Saketang; Repi Meilani Putri; Paskal Arienda Epidonta Ginting; Adidtya Perdana
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 4 No. 3 (2026): Mei: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v4i3.1556

Abstract

The distribution of social assistance in Indonesia faces challenges such as inaccurate recipient data, overlapping programs, and limitations of traditional data management systems that lead to inaccurate targeting of aid. This study proposes a social assistance distribution optimization system using the Greedy algorithm that assesses recipient priorities based on economic conditions, number of family members, location, and urgency of needs with certain weights to produce objective rankings. This system is implemented in a JavaScript-based web application without external frameworks, making it lightweight and easily accessible. Simulations with 20 prospective recipients and a quota of 10 slots and validation with a dataset of 10,000 entries show that the Greedy algorithm produces identical results to Dynamic Programming but is much faster (669 times faster). In terms of complexity, this algorithm has O(n log n) time and O(n) space, and meets the requirements of the Greedy Choice Property and Optimal Substructure, making it a practical and efficient solution for managing large-scale social assistance distribution in Indonesia.
Analisis Komparatif Efisiensi Merge Sort dan Quick Sort Menggunakan Pendekatan Divide and Conquer pada Berbagai Kondisi Data Produk Sistem E-Commerce Gus Rosauli Pandiangan; Naufal Aqiilah Asra; Mohd. Rafiif Albani; Angelica Barus; Adidtya Perdana
MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika Vol. 2 No. 2 (2026): MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/murakom.v2i2.278

Abstract

Efisiensi pengurutan data produk pada platform belanja daring menjadi faktor penting bagi kepuasan pengguna. Walaupun Merge Sort dan Quick Sort secara teori punya kompleksitas yang sama namun performa realitanya sangat bergantung pada kondisi awal data yang diolah. Penelitian ini membandingkan efisiensi kedua algoritma menggunakan simulasi Python pada skenario data acak, terurut, dan terbalik hingga 100.000 elemen. Hasil pengujian membuktikan Quick Sort lebih unggul pada data acak dengan efisiensi waktu 19,2% karena penggunaan memori yang lebih optimal. Di sisi lain Merge Sort menunjukkan kestabilan tinggi karena tidak terpengaruh oleh pola distribusi data bahkan 42,5% lebih cepat daripada Quick Sort pada kondisi data terurut terbalik. Kesimpulannya Quick Sort paling disarankan untuk pengolahan data acak sedangkan Merge Sort menjadi solusi terbaik jika sistem memerlukan kestabilan performa pada data berskala besar atau memiliki pola tertentu.
Analisis Perbandingan Kompleksitas Waktu dan Ruang pada Algoritma Quick Sort, Merge Sort, dan Heap Sort M. Fahmi Arafat; Nuriana Sipahutar; Adidtya Perdana; Riski Immanuel Situmorang; Raja Ansel Hartama Sihombing
MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika Vol. 2 No. 2 (2026): MUARA KOMPUTER : Jurnal Ilmiah Ilmu Komputer & Elektronika
Publisher : CV MUARA EDUKASI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64365/murakom.v2i2.281

Abstract

Penelitian ini bertujuan untuk menganalisis dan membandingkan kinerja algoritma pengurutan Quick Sort, Merge Sort, dan Heap Sort berdasarkan waktu eksekusi dan penggunaan memori. Metode yang digunakan adalah pendekatan kuantitatif dengan melakukan pengujian terhadap ketiga algoritma menggunakan dataset dengan variasi ukuran dan kondisi data, yaitu acak, terurut, dan terbalik. Parameter yang diukur meliputi waktu eksekusi yang diperoleh melalui proses eksperimen menggunakan bahasa pemrograman Python, serta analisis penggunaan memori yang dilakukan secara teoritis. Hasil penelitian menunjukkan bahwa waktu eksekusi meningkat seiring dengan bertambahnya jumlah data. Quick Sort memiliki performa yang unggul pada kondisi data terurut dan terbalik, serta tetap kompetitif pada data acak. Merge Sort menunjukkan kestabilan performa dan cenderung lebih optimal pada data acak berukuran besar. Sementara itu, Heap Sort memiliki waktu eksekusi yang relatif lebih tinggi, namun lebih efisien dalam penggunaan memori. Dengan demikian, pemilihan algoritma pengurutan yang optimal perlu mempertimbangkan karakteristik data serta kebutuhan sistem, baik dari segi efisiensi waktu maupun penggunaan memori.
Co-Authors Ade Zulkarnain Adinda Soleha Adryan Rachmadsyah Ryan Afrrahman S. Effendi, Ali Agus Waruwu, Stefen Al Khowarizmi 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 Andly Sofian Hasugian Angelica Barus Angelina S. Saragih Anggi Silalahi Anwar Shaleh Lbn Gaol Aqilah Defiyanti Arief Budiman Arief Budiman Arion Pardede Arnita AS Mansur Ashillah, Salma Asro Harahap, Fatima Audy Priscilia, Selfi Augis Dinanti Aurela Khoiri Nasution Ayu Amelia Pwrtiwi Azhara Amelia H Azima Lubis, Fauzan Azis Kurniadi Bicanro Gebriyan Panjaitan Br. Hutagalung, Fhadillah Budi Akbar, Muhammad Bunga Dwi Febrianti Bush Henrydunan, John Callysa Elistia Calvin Syahputra Christian Nicholas Sinaga Damayanti, Nina Afria Dea Anggraini Dedy Kiswanto Delvita Aulia Artika Dian Septiana DIdi Febrian Dinda Syafitri Dwi Syaputra Ega Pratama Ega Zuhairi Ramadhan Elga Sari Tanjung Erika Nia Devina Br Purba Evanthe, Hansel Evelyn Keisha Silalahi Eviyona Laurenta Br Barus Fadilah, Putri Maulidina Farezi, Nazwar Farizky Aulia Lubis 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 Hamidah Nasution Hapzi Ali Hasibuan, Ade Zulkarnain Henrydunan, John Bush Hermawan Syahputra Ichwanul Muslim Karo Karo Ilyasyah Drilanang, Muhammad Imelda, Yusmita Impana Manik, Kristin Insan Pratama Siagian, Raihan Insan Taufik Irya Shakila Syukron, Ananda Isa Hidayati Jhon Gabriel Simarmata Josua Anugrah Deo Tampubolon Juhraini Helfiana Lexa Juliana, Feby Kana Saputra S Kayla Amelia Putri Khairany Zuhriyyah Jinan Hsb Khairul Fahmi Sagala Khodotun Hadawiyah Margolang Khoiriah, Najwatul Kokod, Mario Maysan Lasker Pangarapan Sinaga Lastri Putri Silaban Lestari, Yuyun Dwi M. Fahmi Arafat M. Faris Al Rafiq 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 Hidayatul Arifin Muhammad Iqbal Fahrezzi Muhammad Khoiruddin Harahap Muhammad Kurniawan Muhammad Raffi Akbar Tanjung Muhammad Raihansyah Lubis Muhammad Rivai Muhammad Rois Lukman Damanik Muhammad Yazid Noor Muhammad Zidane Al-Kautsar Muslim Sinaga, Rizal Nabila Harahap, Salsa Najwatul Khoiriah Nasution, Afifah Naila Natasha Patricia Nainggolan Naufal Aqiilah Asra Nayla Anjani Nasution 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, Clara Kresensia Paskah Abadi Simanullang Paskal Arienda Epidonta Ginting Pebiana Putri, Fahra Peter Tymoty Hutabarat Prana Walidin, Adamsyach Pratama, Ega Priscilia, Selfi Audy Purba, Jogi Putra Paskah Halawa, Sovantri Putri Handayani, Agata Putri Mauliidna Fadilah Raffi Akbar Tanjung, Muhammad Raihan Insan Pratama Siagian Raisya Putri 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 Rizky Wahyudi Rossy Pratiwy Sihombing Rut Kezia Imburi Ruth Amelia Vega S Meliala Sahara Lani Lestari Salsa Nabila Harahap Sapta Warman Zai, Tri Sarah Putri Nasutio. Sembiring, Febe Gracia Sevta Triwana Simamora Shaleh Lbn Gaol, Anwar Shaqila Rahmayani Gultom Simanjuntak, Yesy Simbolon, Agata Putri Handayani Sinaga, Rizal Muslim Sirlia Sahid Sirus Daniel H Nababan Siti Haliza Zamili Sofia Zahra Sri Dewi Stefen Agus Waruwu Sukma, Ayman Human Suleho, Febrina Syahri, Alfin Syti Salwaa Nafiisah Tambunan, Vivielda Farmawaty Tasya Agustina Tampubolon, Putri Tia Risky Yasmin Saketang Vega S. Meliala, Ruth Amelia Wahyudi, Rizky Windy Aulia Windy Aulia Yazid Noor, Muhammad Yehezkiel Haganta Tarigan Yessi Fitri Annisa Lubis Yohanes Gerardus Haga Zai Yolandari, Nezza Anggraini Yoseph Christian Sitanggang Yuda Advis Ambrosius Sitohang Yulita Molliq Rangkuti Yuyun Dwi Lestari Yuyun Dwi Lestari Zai, Samuel Anaya Putra Zevan Irfandi Surbakti Zidane, Muhammad Zulfahmi Indra, Zulfahmi Zulfahrizan, Atta Zulfi, M. Fikri