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All Journal Riau Journal of Computer Science Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Mantik Penusa JMM (Jurnal Masyarakat Mandiri) JURNAL EDUCATION AND DEVELOPMENT J-SAKTI (Jurnal Sains Komputer dan Informatika) JURIKOM (Jurnal Riset Komputer) JURTEKSI Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Jurnal Mantik JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) TIN: TERAPAN INFORMATIKA NUSANTARA Jurnal Tunas Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JPM: JURNAL PENGABDIAN MASYARAKAT Jutsi: Jurnal Teknologi dan Sistem Informasi Journal of Informatics Management and Information Technology Journal of Social Responsibility Projects by Higher Education Forum Jurnal Sains Teknologi dan Sistem Informasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Bulletin of Information Technology (BIT) Aptekmas : Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Inovasi Jurnal Bangun Abdimas Jurnal Informatika dan Teknologi Informasi Jurnal Teknologi Pendidikan Jurnal Pengabdian Harapan Bangsa Jurnal Teknologi Ilmu Komputer Jurnal Teknologi Pendidikan International Journal of Informatics and Data Science
Jeperson Hutahaean
Sekolah Tinggi Manajemen Informatika Dan Komputer Royal Kisaran, Asahan
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PENGGUNAAN TEKNOLOGI MOBILE UNTUK PEMBELAJARAN INTERAKTIF DI LUAR RUANGAN Mulyani, Neni; Hutahaean, Jeperson; Putri Fahdrina, Jihan Aulia
JURNAL EDUCATION AND DEVELOPMENT Vol 13 No 1 (2025): Vol 13 No 2 Mei 2025
Publisher : Institut Pendidikan Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37081/ed.v13i1.6990

Abstract

Pembelajaran di luar ruangan memiliki potensi besar dalam meningkatkan keterlibatan dan pemahaman siswa terhadap materi pelajaran, namun masih banyak kendala dalam penerapannya, seperti keterbatasan sumber belajar, kurangnya panduan interaktif, serta kesulitan dalam mengukur efektivitas pembelajaran secara real-time. Teknologi mobile dapat menjadi solusi inovatif dengan menyediakan akses ke informasi, alat bantu interaktif, serta sistem evaluasi yang lebih fleksibel dan efektif. Oleh karena itu, penelitian ini berfokus pada pemanfaatan teknologi mobile dalam mendukung pembelajaran interaktif di luar ruangan dengan tujuan mengembangkan model pembelajaran berbasis teknologi mobile yang dapat meningkatkan efektivitas pembelajaran interaktif. Metode penelitian yang digunakan terdiri dari beberapa tahapan, yaitu studi literatur dan analisis kebutuhan, perancangan sistem aplikasi mobile dengan fitur interaktif seperti augmented reality (AR) dan kuis real-time, implementasi dan pengujian aplikasi di lingkungan sekolah, serta evaluasi efektivitas sistem berdasarkan hasil kuesioner, wawancara, dan analisis data penggunaan aplikasi. Luaran yang ditargetkan dalam penelitian ini mencakup pengembangan aplikasi mobile sebagai media pembelajaran interaktif, publikasi ilmiah terkait inovasi pembelajaran berbasis teknologi mobile, serta panduan implementasi bagi pendidik. Dengan tingkat kesiapan teknologi (TKT) yang berada pada level 4 hingga 6, penelitian ini diharapkan dapat memberikan kontribusi nyata dalam pengembangan model pembelajaran interaktif yang lebih inovatif dan adaptif dengan kemajuan teknologi.
Pemilihan Lahan Pertanian Padi Menggunakan Metode Topsis Hutahaean, Jeperson; Marpaung, Nasrun; Parini, Parini
Jurnal Teknologi Ilmu Komputer Vol. 3 No. 2: Juni 2025
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jtik.v3i2.376

Abstract

Metode TOPSIS digunakan dalam penelitian ini untuk memilih lahan pertanian yang paling sesuai untuk berbagai kriteria seperti harga, kesuburan tanah, ketersediaan air, aksesibilitas dan iklim. Data ini diperoleh dari survei lapangan, analisis laboratorium tanah dan data sekunder dari badan pertanian lokal. Hasil penelitian menunjukkan bahwa metode TOPSIS efektif dalam menemukan lahan yang memenuhi kriteria pertanian optimal. Proses penilaian melibatkan pengumpulan data kualitatif dan kuantitatif, yang kemudian diolah menggunakan metode TOPSIS untuk menentukan peringkat lahan berdasarkan kedekatannya dengan solusi ideal. Metode ini juga menyediakan pendekatan pengambilan keputusan sistematis dan obyektif yang membantu petani dan pemangku kepentingan lainnya.
Stacking Ensemble with SMOTE for Robust Agricultural Commodity Price Prediction under Imbalanced Data Siagian, Yessica; Hutahaean, Jeperson; Mulyani, Neni
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.916

Abstract

The volatility of agricultural commodity prices presents a substantial obstacle in the agribusiness sector, especially in supporting timely and data-driven decision-making. This volatility is primarily caused by the imbalanced distribution of historical price data and the complex, often nonlinear nature of price patterns. To address this challenge, this study proposes a novel predictive modeling approach by integrating Stacking Ensemble Learning and Synthetic Minority Over-sampling Technique (SMOTE). The dataset used in this research consists of 5,558 records and 9 features, sourced from a publicly available Kaggle dataset. The target variable daily price was transformed into three classes: low, medium, and high, using a quartile-based discretization approach to enable multiclass classification. The main objective is to evaluate whether stacking combined with SMOTE can improve model performance compared to baseline models that use individual algorithms. A total of eight models were constructed and compared: four baseline models using SMOTE only, and four stacking models integrating SMOTE. The experimental results demonstrate that the proposed model Decision Tree Regression with Stacking and SMOTE achieved the highest performance, with 98.68% accuracy, an F1-score of 0.9868, Cohen’s Kappa of 0.9803, MCC of 0.9803, ROC-AUC of 0.9995, and a log loss of 0.0529. Other optimized models also performed well, such as Random Forest (98.37% accuracy) and Gradient Boosting (98.56%). In contrast, baseline models such as Linear Regression and Decision Tree without stacking achieved only around 67–68% accuracy, with log loss exceeding 0.97. The key contribution of this study is the empirical evidence that combining stacking and SMOTE significantly enhances classification accuracy and model robustness in imbalanced datasets. The novelty lies in applying a deep learning-optimized stacking framework specifically for agricultural commodity price classification, along with a comprehensive multiclass evaluation, offering new insights for practical implementation in agricultural decision support systems.
Comparative Analysis of Novel Deep Reinforcement Learning Methods for Food Distribution Optimization Hutahaean, Jeperson; Siagian, Yessica; Saputra, Endra
Journal of Applied Data Sciences Vol 6, No 4: December 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i4.956

Abstract

Uneven food distribution across various regions in Indonesia often results in supply-demand imbalances, leading to price surges, stock shortages, and overall market instability. This challenge is compounded by the limitations of conventional distribution systems, which are ill-equipped to respond to rapidly changing market dynamics. In response, this study introduces a novel, AI-driven approach by implementing Deep Reinforcement Learning (DRL) to optimize food distribution policies using real-world data. Specifically, we perform a comparative evaluation of four emerging DRL models—Double Deep Q-Network (Double DQN), Dueling DQN, Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C)—to determine their effectiveness in learning adaptive distribution strategies from national food logistics data provided by Indonesia’s Central Bureau of Statistics (BPS). Each model was trained within a custom simulation environment based on the Markov Decision Process (MDP) framework and evaluated using five core performance metrics: cumulative reward, average reward, success rate, sample efficiency, and best reward. The results reveal that A2C consistently outperformed the other models, delivering the highest average reward and most stable training performance, while PPO demonstrated strong efficiency and success rate. These findings underscore the potential of policy-gradient methods—particularly A2C—as robust and intelligent solutions for dynamic food logistics management. This research offers one of the first comparative benchmarks of DRL methods in the food distribution domain and highlights their applicability for future integration into national AI-powered logistics systems.
Film Popularity Analysis through Combined K-Means Clustering and Gradient Boosted Trees Agi Candra Bramantia; Desyanti; Jeperson Hutahaean; Erlin Windia Ambarsari
International Journal of Informatics and Data Science Vol. 2 No. 2 (2025): June 2025
Publisher : ADA Research Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64366/ijids.v2i2.81

Abstract

The dynamic and competitive nature of the global film industry presents complex challenges in predicting film popularity, as success is shaped by the interplay of production investment, casting decisions, and audience preferences. This research addresses the limitations of previous studies that have focused primarily on direct relationships, such as budget versus box office returns, by introducing an integrated analytical framework that combines K-Means clustering and Gradient Boosted Trees (GBT) with explainable AI techniques. Utilizing the TMDB movie dataset and constructing features such as actor influence and studio power, the study segments films and predicts audience ratings while providing interpretable visualizations. The results reveal four distinct film clusters and demonstrate that actor influence and budget allocation are the most significant predictors of popularity. The proposed model achieves an R² score of 0.75 and a mean squared error of 0.35 in predicting audience ratings, while cluster analysis shows that Blockbuster films reach the highest average ratings (6.76), and Underperforming films the lowest (2.42). By integrating interpretable predictive modeling and interactive scenario tools, this research offers both theoretical advancement and practical value for industry stakeholders. However, the findings are limited by the available metadata and do not account for factors such as marketing or real-time audience trends, suggesting opportunities for future research to expand the analytical framework.
RASKIN RECIPIENT ELIGIBILITY DECISION SUPPORT SYSTEM USING THE AHP METHOD Azhar, Zulfi; Mulyani, Neni; Hutahaean, Jeperson; Sapriyanti, Sapriyanti
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 1 (2023): Desember 2023
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i1.2896

Abstract

Abstract: Some people in Sei Silau Timur Village in Buntu Pane, Kisaran, have low incomes with the existence of a government policy in the food management program that cooperates with Bulog to ease the burden on the community by distributing Raskin to villages where people have low incomes. And not all people get the chance to receive RASKIN because the quota is limited. There needs to be a selection in determining the eligibility of RASKIN for the right people and avoiding mistakes. The selection process can be completed using the application of computer science. Based on this, a decision support system is needed that can be used by the Sei Silau Timur village head's office staff in distributing RASKIN rice, which later this application can help and benefit the village community. This research uses the AHP method, which is carried out by comparing a matrix of several criteria and alternatives. The final assessment result is implementing the AHP method, where criterion 4, namely Total Income, is selected, with Alternative 1 named Selamet.            Keywords: AHP; public; RASKIN; selection; SPK Abstrak : Sebagian dari masyarakat di Desa Sei Silau Timur di kecamatan Buntu Pane, Kisaran mempunyai penghasilan yang rendah. Dengan adanya kebijakan pemerintah dalam program penanggulangan pangan yang bekerja sama dengan pihak Bulog untuk meringankan beban masyarakat dengan menyalurkan raskin ke desa-desa yang masyarakatnya berpenghasilan yang rendah. Dan tidak semua masyarakat mendapatkan kesempatan penerimaan RASKIN karena kuotanya terbatas. Perlu dilakukan dengan seleksi dalam penentuan pemberian kelayakan RASKIN kepada orang-orang yang tepat dan menghindari kekeliruan. Proses seleksi tersebut dapat diselesaikan dengan menggunakan penerapan secara ilmu komputer.  Bedasarkan hal tersebut maka diperlukan suatu sistem pendukung keputusan  yang dapat dipergunakan oleh petugas kantor Kepala Desa Sei Silau Timur dalam proses pembagian beras RASKIN, yang nantinya aplikasi ini dapat membantu dan bermanfaat bagi masyarakat desa tersebut. Penelitian ini menggunakan metode AHP,  dimana metode yang dilakukan dengan membandingkan matriks sejumlah kriteria dan alternatif. Hasil penilaian akhir yang terpilih merupakan implementasi dari metode AHP, dimana yang terpilih adalah kriteria 4 yaitu Jumlah Penghasilan  dengan alternatif 1 bernama Selamet. Kata Kunci: AHP; masyarakat; RASKIN; seleksi; SPK
A COMPARATIVE ANALYSIS OF MFEP AND SAW METHODS IN DECISION SUPPORT SYSTEMS FOR MAJOR SELECTION Hutahaean, Jeperson; Mulyani, Neni; Putri Fahdrina, Jihan Aulia
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 10 No. 4 (2024): September 2024
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i4.3442

Abstract

Abstract: The selection of majors at SMAS YPK Kedaisianam previously still used a manual system that was less effective in determining the right major for students. To overcome this, a new system that is easier and more accurate is needed. This system is expected to assist counseling guidance teachers in providing solutions for choosing majors to students. This study compares two methods, namely Multi Factor Evaluation Process (MFEP) and Simple Additive Weighting (SAW), which have similarities in weighting criteria to produce more effective rankings. The research methodology used is a quantitative approach with numerical data analysis. This study aims to describe the comparison of the two methods in the decision support system for choosing majors at SMKS DAAR Muhsinin. The results of the study show that the use of more effective methods in the application system can make decision-making easier. The conclusion of this study is that the application of MFEP methods can improve accuracy and efficiency in the course selection process.Keywords: decision support system; mfep and saw methods; major selection. Abstrak: Pemilihan jurusan di SMAS YPK Kedaisianam sebelumnya masih menggunakan sistem manual yang kurang efektif dalam menentukan jurusan yang tepat bagi siswa. Untuk mengatasi hal tersebut, diperlukan sistem baru yang lebih mudah dan akurat. Sistem ini diharapkan membantu guru bimbingan konseling dalam memberikan solusi pemilihan jurusan kepada siswa. Penelitian ini membandingkan dua metode, yaitu Multi Factor Evaluation Process (MFEP) dan Simple Additive Weighting (SAW), yang memiliki kesamaan dalam pembobotan kriteria untuk menghasilkan peringkat yang lebih efektif. Metodologi penelitian yang digunakan adalah pendekatan kuantitatif dengan analisis data berbasis angka. Penelitian ini bertujuan untuk mendeskripsikan perbandingan kedua metode tersebut dalam sistem pendukung keputusan pemilihan jurusan di SMKS DAAR Muhsinin. Hasil penelitian menunjukkan bahwa penggunaan metode yang lebih efektif dalam sistem aplikasi dapat mempermudah pengambilan keputusan. Simpulan dari penelitian ini adalah penerapan metode MFEP dapat meningkatkan akurasi dan efisiensi dalam proses pemilihan jurusan.Kata Kunci: metode mfep dan saw;  pemilihan jurusan; sistem pendukung keputusan.
MODELING CLOTHING ORDER SIZE GROUPING AT RIZKY CONVECTION USING THE K-MEANS METHOD Rahmadani, Putri; Hutahaean, Jeperson; Santoso, Santoso
JURTEKSI (jurnal Teknologi dan Sistem Informasi) Vol. 11 No. 2 (2025): Maret 2025
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Royal Kisaran

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v11i2.3774

Abstract

Abstract: Rizky Convection Business is a sportswear production company based on Jalan Elang Lestari Kisaran. Every day they receive orders from schools and agencies that need sportswear. However, Rizky Convection often faces challenges, especially in managing raw material inventory, allocating production time and processing orders that come in large quantities. For this reason, order data needs to be grouped to make it easier for employees to work on it. The data that will be processed in this study is order data from 2022-2024. One way to do this is to apply data mining techniques, one of which is the K-Means Clustering method. The purpose of this study is to model the use of K-Means Clustering to improve production management and procurement of raw materials for fabrics at Rizky Convection. K-Means Clustering is the grouping of a number of data into clusters (groups) so that each cluster will contain data that is as similar as possible. The results of K-Means Clustering grouping with 3 clusters, namely cluster 1, the large order group has 25 order data, cluster 2, the small order group has 361 order data and cluster 3, the medium order group has 100 order data.Keywords: data mining; order data; K-Means; sportswear manufacturing. Abstrak: Usaha Konveksi Rizky merupakan perusahaan produksi pakaian olahraga yang berpusat di Jalan Elang Lestari Kisaran. Setiap hari mereka menerima pesanan dari sekolah dan instansi yang membutuhkan pakaian olahraga. Namun Konveksi Rizky sering menghadapi tantangan terutama dalam pengelolaan persediaan bahan baku kain, pengalokasian waktu produksi dan pemrosesan pesanan yang datang dalam jumlah banyak. Untuk itu, data pesanan perlu dikelompokkan untuk mempermudah karyawan dalam mengerjakannya. Data yang akan diproses pada penelitian ini adalah data pesanan dari tahun 2022-2024. Salah satu cara untuk hal tersebut adalah dengan menerapkan teknik data mining, salah satunya metode K-Means Clustering. Tujuan penelitian ini adalah untuk memodelkan penggunaan K-Means Clustering untuk meningkatkan manajemen produksi dan pengadaan bahan baku kain di Rizky Konveksi. K-Means Clustering adalah pengelompokan sejumlah data ke dalam cluster (group) sehingga setiap dalam cluster tersebut akan berisi data yang semirip mungkin. Hasil pengelompokkan K-Means Clustering dengan 3 cluster yaitu cluster 1 kelompok pesanan banyak memiliki 25 data pesanan, cluster 2 kelompok pesanan sedikit memiliki 361 data pesanan dan cluster 3 kelompok pesanan sedang memiliki 100 data pesanan.Kata kunci: data mining; data pesanan; K-Means; konveksi.
Pemanfaatan Blog Sebagai Media Pengetahuan Dan Bisnis Pada PKK Desa Sei Silau Timur Mulyani, Neni; Hutahaean, Jeperson; Azhar, Zulfi; Yuliana, Siska; Farenza, Dinda Novri
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 7 No. 1 (2024): Januari 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v7i1.2861

Abstract

The existence of blog media in disseminating information is a means of storing information online that can be used by staff so that they can provide all information without space and time limits. The PKK of East Sei Silau Village, Asahan Regency, has several active activities carried out in several places in its environment. In carrying out these activities, they still use devices manually to provide information in their activities. The aim of carrying out this service activity is to find out the use of blogs in the business and economic category, to find something unique about blogs that can be published to various parties regarding the use of business and economic blogs so that they can be used effectively and more usefully. The activity implementation method used was in the form of training, interactive discussions with the entire PKK team in East Sei Silau Village, Asahan Regency. This activity uses theoretical and practical knowledge in the room. As a result of the final assessment of the activity, the participants were able to understand the material presented based on the results of the assessment scores obtained from the posttest test results. The activity material given to participants can be used and utilized in creating a blog. The material presented to participants reached 90% of all the concepts involved in creating blogs. The delivery of the material consists of several sessions, namely: explanation, practice, and discussion.
Penerapan Metode WP Dan Metode Maut Pada Pemilihan Kafe Bagi Mahasiswa Hutahaean, Jeperson
Jurnal Teknologi Ilmu Komputer Vol. 2 No. 2: Juni 2024
Publisher : PT. Bangun Harapan Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56854/jtik.v2i2.244

Abstract

Bidang usaha kuliner di Indonesia, khususnya kafe, mengalami perkembangan pesat. Di Kabupaten Asahan, meningkatnya jumlah mahasiswa luar daerah membuka peluang bagi bisnis kafe untuk menjadi tempat favorit bagi mahasiswa dalam menghilangkan rasa bosan, belajar, atau bersosialisasi. Namun, banyak kafe yang tutup karena tidak mampu bersaing atau kurang strategis. Mahasiswa sering kesulitan menemukan kafe yang tepat tanpa menghabiskan banyak waktu. Oleh karena itu, diperlukan sistem pendukung keputusan (SPK) untuk membantu mahasiswa memilih kafe yang sesuai. Metode Weighted Product (WP) dan Multi Attribute Utility Theory (MAUT) digunakan dalam penelitian ini. Metode WP memungkinkan penetapan bobot berbeda untuk setiap kriteria yang dianggap penting, sedangkan metode MAUT memproses data dengan tingkat akurasi tinggi tanpa memerlukan normalisasi. Penelitian ini bertujuan untuk memberikan rekomendasi kafe terbaik berdasarkan kriteria suasana kafe, luas parkiran, harga menu, kecepatan wifi, serta jarak. Diharapkan SPK ini dapat membantu mahasiswa dalam menentukan kafe yang nyaman untuk belajar atau bersantai, dengan memberikan rekomendasi yang objektif dan sesuai kebutuhan.
Co-Authors Aandanu Aandanu Abdul Karim Ade Mayhaky Afdawiah, Rabiatul afrisawati, Afrisawati Agi Candra Bramantia Agus Perdana Windarto Ahmad Zein Hasibuan Akmal Nasution Alpionita , Ella Ananda, Dewi Anggara, Nadia Ayu Agustri Arridha Zikra Syah Aulia Kartika Aulia Kartika Aulia Khairani Nasution Aulia khairani Nasution Ayu Ambarwati Ayu Handayani Azmi Dwi Andira Putri, Aulia Badaruddin, Muliati Bella Putri Cahyani Ben Rahman Cecep Maulana Cecep Maulana Dandi Irwansyah dermawan, ari Desyanti Dewi Harwini Efendi Hutagalung, Jhonson Eka Saputra, Anton Endra Saputra Erlin Windia Ambarsari Eska, Juna Eva Solita Pasaribu Evi Ariyanti Purba Farenza, Dinda Novri Fitri Hadanyani Gogor Christmass Setyawan Guntur Maha Putra Handayani, Masitah Haryansyah Sitorus Hasian, Irene Hetty Rohayani Hommy Dorthy Ellyany Sinaga Hutagalung, Juniar Indah Kurnia Irawati, Novica Irianto Irianto Jasmir Jhonson Efendi Hutagalung Jumaryadi, Yuwan Kifti, Wan Mariatul Marpaung, Nasrun Maulana, Cecep Muh Saleh Malawat Muhammad Amin Muhazir, Ahmad Muthia Dewi Nazrul Azizi Neny Mulyani Nofri Yudi Arifin Nofriadi Nofriadi Novita Sari Nuriadi Manurung Parini, Parini Parwan Harahap Putri Fahdrina, Jihan Aulia Putri Rahmadani, Putri Rachmad Andri Atmoko Rahayu, Elly ramadhani ramadhani Ramadhani, Andrew Riski Afdhalis Syahreza Rolly Yesputra Roslidar Salsabila, Adinda Saludin Saludin SANTOSO SANTOSO Sapriyanti, Sapriyanti Saputra, Endra Sartini Sartini Setiawansyah Setiawansyah Siagian, Yessica Siti Nuraisyah Suci Dewi Maharani Sianipar Sitorus, Lamhot Sri Amelia, Sri Sri Rezki Maulina Azmi Sri Wahyuni Sufi Lubis, Syafaat Suparmadi S Sussolaikah, Kelik Syafira, Shella Syahrial Tengku Riza Zarzani N Tia Zulaika Aulia Pane Tiara Aninditha Umbari Putri, Lia Wibowo, Gentur Wahyu Nyipto William Ramdhan Wily Julitawaty Yesica Siagian Yessica Siagian Yuliana, Siska Yuwaldi Away Yuwaldi Away Zulfi Azhar Zulika Maduri