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All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Elektron Jurnal Ilmiah Jurnal Sains dan Teknologi Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Teknologi Informasi dan Ilmu Komputer Telematika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Prosiding Semnastek JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika Riau Journal of Computer Science JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research RABIT: Jurnal Teknologi dan Sistem Informasi Univrab INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Journal of Information Technology and Computer Engineering Jambura Journal of Informatics ComTech: Computer, Mathematics and Engineering Applications Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech International Journal of Informatics and Computation Dinasti International Journal of Education Management and Social Science Systematics Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sistim Informasi dan Teknologi Jurnal Informasi dan Teknologi Jurnal Informatika Ekonomi Bisnis Journal of Robotics and Control (JRC) Journal of Applied Engineering and Technological Science (JAETS) JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Ilmiah Manajemen Kesatuan Dinasti International Journal of Digital Business Management Indonesian Journal of Electrical Engineering and Computer Science JUKI : Jurnal Komputer dan Informatika Jurnal Perangkat Lunak Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) Jurnal Manajemen Sains Journal of Computer Scine and Information Technology Bulletin of Computer Science Research Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi Jurnal Administrasi Sosial dan Humaniora (JASIORA) Innovative: Journal Of Social Science Research e-Jurnal Apresiasi Ekonomi Jurnal Informatika Ekonomi Bisnis SATIN - Sains dan Teknologi Informasi RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
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Analisis Algoritma K-Means Clustering Dalam Pengelompokan Prestasi Belajar Siswa Menengah Atas (SMA) Dila, Rahmah; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.751

Abstract

The increased use of social media among high school students has a positive and negative impact on academic achievement. This can be seen from changes in learning patterns, concentration levels, and students' motivation in participating in learning activities. This study aims to classify student learning achievement based on the level of social media use using the K-Means Clustering algorithm. K-Means Clustering is one of the main methods in data mining.  which is a technique of grouping data based on the similarity of its characteristics. The parameters used in analyzing this study are Social Media Duration (X1), Active Time (X2), Main Platform (X3), Main Goal (X4), Social Media Access Time While Learning (X5), Social Media Addiction (X6), Social Media Addiction Level (X7), Number of Study Groups (X8) and Academic Average (X9). Based on the K-Means Clustering method, it has been proven to be able to group students based on the level of social media use. These results can be seen from the cluster category C0 (High) with 46 students, C1 (medium) with 80 students, and C2 (Low) with 72 students. The contribution of this research benefits students by helping them understand the relationship between social media usage habits and learning achievement, so as to encourage more effective time management.
Analisis Cluster Algoritma K-Means Untuk Pengelompokan Kondisi Gizi Balita Pada Posyandu Roza, Yesi Betriana; Defit, Sarjon; Arlis, Syafri
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.752

Abstract

Toddler health is a crucial indicator of community and national development. Integrated Service Posts (Posyandu) play a key role in monitoring the nutritional status of toddlers through routine weight and height checks. This study aims to analyze toddler nutritional status using the K-Means Clustering algorithm, a non-hierarchical method that groups data based on centroid proximity. The data came from 98 toddlers at the Posyandu in Manggung Village, North Pariaman District, Pariaman City, including weight, height, weight-for-age, height-for-age, weight-for-height, and weight gain. The K-Means results showed a distribution of three clusters: C0 (undernourished) with 37 toddlers, C1 (severely malnourished) with 17 toddlers, and C2 (well-nourished) with 44 toddlers. The majority of toddlers were categorized as well-nourished. This research contributes to the rapid identification of toddler nutritional problems, enabling Posyandu staff to take appropriate preventive and corrective measures.
Model Deep Learning Berbasis Multilayer Perceptron untuk Identifikasi Demam Berdarah Dengue dan Tifus Nurhadi, Nurhadi; Defit, Sarjon; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.754

Abstract

Dengue Hemorrhagic Fever (DHF) and Typhus/Typhoid are two infectious diseases often found in tropical areas. In Indonesia, data shows that cases of DHF and typhoid are quite high, so a system is needed that can help doctors make faster and more accurate decisions based on blood test results. Based on the previous explanation, this study aims to apply the Deep Learning Multilayer Perceptron (MLP) method to be able to identify dengue fever and typhus. This study uses a Deep Learning-based Multilayer Perceptron approach for accurate classification of Dengue Fever, Typhoid Fever, and Normal cases using clinical blood parameters and selected symptoms. This methodology consists of several stages: dataset acquisition, preprocessing, model architecture design, training, and evaluation. The dataset was taken from Dumai City Hospital medical record data from 2023 to 2024, totaling 379 patient data used to identify Dengue Fever and Typhus using 7 clinical parameters as the main input obtained from laboratory examination results and patient clinical symptoms: Hemoglobin, Leukocyte, Platelet count, Hematocrit level, Headache, Abdominal pain, and diarrhea. Based on the results obtained, the application showed the best performance in classifying Dengue Fever, which is shown through the achievement of the model evaluation metrics as follows. The test results indicate that an increase in the amount of test data is directly proportional to the percentage of classification success achieved by the system. Based on the test results with 10% validation data, 70 % training data, and 20 % test data, the system showed very good performance with an overall accuracy of: 98.68% (Accuracy = 0.9868), which indicates a high level of success in classifying for the three classes, namely Normal, Dengue Fever, and Typhus.
Analisis Algoritma K-Means Clustering untuk Pengelompokan Rekomendasi Judul Proposal Tugas Akhir Mahasiswa Yulihartati, Sandra; Defit, Sarjon; Nurcahyo, Gunadi Widi
Bulletin of Computer Science Research Vol. 5 No. 5 (2025): August 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i5.755

Abstract

The academic process requires speed and accuracy in processing student data, such as submitting final project titles. In the context of final project title recommendations, many universities have not yet implemented the Data Mining approach optimally. Based on this, this study aims to recommend grouping of student final project proposal titles. The K-Means clustering method can be used in grouping data based on similarities between analyzed objects. With the K-Means method, the student grouping process utilizes grade data from the courses of Rock Mechanics, Drilling and Excavation Techniques, Underground Mining Methods, Reserve Modeling and Evaluation, Explosives and Blasting Techniques, Open Pit Mining, Mine Drainage Systems, Mapping Surveys, and Mineral Resources. The results of K-Means are strongly influenced by the k parameter and centroid initialization. The research variables include data mapping of course grades of students in the Mining Engineering Study Program. Based on the K-Means Clustering Method, it has been able to divide 104 student value data into 3 clusters, namely Natural Resource Exploration (C0), Geomechanics (C1) and Mining Environment (C2). The results of Cluster CO are 60, the results of Cluster C1 are 27 and the results of Cluster C2 are 17. The contribution of this research can provide fast, precise and accurate information in grouping recommendations for student final project proposal titles.
Development of extraction features for Detecting Adolescent Personality with Machine Learning Algorithms Wisky, Irzal Arief; Defit, Sarjon; Nurcahyo, Gunadi Widi
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3091

Abstract

This study aims to develop a Natural Language Processing (NLP)-based feature extraction algorithm optimized for personality type classification in adolescents. The algorithm used is TF-IDF + N-Gram Z, which combines Term Frequency-Inverse Document Frequency (TF-IDF) with the N-Gram Z technique to improve the feature representation of the analyzed text. TF-IDF functions to measure the importance of words in a document, while N-Gram Z enriches the context by considering the order of words that appear sequentially. The dataset in this study consists of 3,200 sentences generated by adolescent respondents through a survey designed to explore aspects of their personality. After the feature extraction process is complete, three variants of the Naïve Bayes method are applied for classification, namely Multinomial Naïve Bayes, Bernoulli Naïve Bayes, and Complement Naïve Bayes. Each variant has distinctive characteristics in handling certain data types, such as binomial and multinomial data. The results of the study show that the combined TF-IDF + N-Gram Z algorithm can produce highly representative features, as evidenced by high classification performance. The Multinomial Naïve Bayes and Complement Naïve Bayes variants each achieved 98% accuracy. These findings provide significant contributions to the development of NLP-based personality classification methods for Detecting Adolescent Personality. The combination of the TF-IDF + N-Gram Z algorithm with various Naïve Bayes variants produces an exceedingly high level of accuracy and can be applied in practice in the fields of psychology and adolescent education.
IMPLEMENTASI SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN ALAT KONTRASEPSI DENGAN METODE AHP DAN TOPSIS (STUDI KASUS DI PUSKESMAS GUNUNG LABU) Refina Afindania, Pipin; Defit, Sarjon; Sumijan
Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Vol 12 No 1 (2024): TEKNOIF APRIL 2024
Publisher : ITP Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21063/jtif.2024.V12.1.1-9

Abstract

The problem that is often faced is that many mothers of couples of childbearing age do not understand how to choose a contraceptive method that is suitable for use. To address this problem among couples of reproductive age in choosing the most appropriate contraceptive method, the Analytical Hierarchy Process  (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is proposed to be utilized. It is expected to be beneficial in aiding the selection of a suitable contraceptive method for users. The objective of this research is to implement the AHP-TOPSIS method in a decision support system for choosing contraceptive methods for couples of reproductive age at the Gunung Labu Community Health Center. The results of the analysis using the AHP-TOPSIS method indicate that the appropriate contraceptive methods for couples of reproductive age are Implan, IUD, Birth Control Injection, and Birth Control Pills. The combination of AHP-TOPSIS in contraceptive method selection yields the conclusion that the Decision Support System (DSS) built in this research is expected to facilitate midwives in recommending contraceptive methods for couples of reproductive age. AHP method is employed to calculate the weights of each contraceptive method criterion. The results of the priority weight calculations for all criteria used in this study yielded a Consistency Index (CI) of 0.07. The analysis using the AHP-TOPSIS method resulted in Implan, IUD, Birth Control Injection, and Birth Control Pills being identified as the appropriate contraceptive methods for couples of reproductive age.
Segmentasi Tunggakan Pelanggan Menggunakan Algoritma K-Means Cluster pada Perusahaan Air Minum Daerah Akbar, Syifa Chairunnissa Deliva; Defit, Sarjon; Hendrik, Billy
Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence) Vol 5 No 2 (2025): Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence)
Publisher : Pustaka Galeri Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55382/jurnalpustakaai.v5i2.1215

Abstract

Perusahaan Air Minum Daerah (Perumdam) Tirta Anai is a Regional Elected Business Entity providing clean water services to customers, but based on the BPKP performance report, this company is categorized as an unhealthy BUMD. One of the factors causing this is due to the high arrears of customers which have an impact on the company's revenue, while efforts in the form of late fines have not been able to provide a deterrent effect to customers. Based on this, this research was carried out with the aim of segmenting customer arrears at the Tirta Anai Regional Drinking Water Company. Segmentation is carried out using the K-Means Clustering algorithm. K-Means Clustering is a data mining algorithm used in grouping data based on its similarity in characteristics. The data in this study is sourced from the database of customers who are in arrears at the Tirta Anai Regional Drinking Water Company as of May 2025 which focuses on the Household group, with as many as 20,646 customer arrears data. From this population, samples were taken using the Slovin formula with an error rate of 5% so that 392 data were analyzed. The parameters used in analyzing this study are the number of months of customer arrears and total customer arrears. Based on the K-Means Clustering method, it is proven to be able to group customers based on their payment patterns. The results are divided into C0 (Low) containing 327 data, C1 (High) containing 6 data, and C2 (Medium) containing 59 data. The contribution of this research has an impact on companies in taking strategies for handling customer service in managing existing connections.
Implementasi Algoritma Apriori dalam Data Mining untuk Optimalisasi Stok Obat di Apotik Parinduri, Rezti Deawinda; Defit, Sarjon; Nurcahyo, Gunadi Widi
Jurnal KomtekInfo Vol. 11 No. 3 (2024): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v11i3.544

Abstract

Data Mining memainkan peran penting dalam mengelola dan menganalisis data besar untuk menemukan pola tersembunyi yang mendukung pengambilan keputusan strategis. Algoritma Apriori, yang dikenal untuk menemukan aturan asosiasi dalam data, menjadi alat yang sangat penting di berbagai sektor, termasuk sektor kesehatan. Dalam pengelolaan stok obat di apotek, terdapat tantangan signifikan seperti kelebihan stok, kekurangan stok, dan risiko kedaluwarsa obat, yang semuanya memerlukan solusi yang tepat dan canggih. Penelitian ini bertujuan untuk menerapkan Algoritma Apriori dalam Data Mining guna meningkatkan efektivitas pengelolaan stok obat, dengan fokus pada beberapa aspek kunci: pertama, memantau dan menganalisis pola pembelian obat secara mendalam; kedua, meningkatkan tata kelola stok melalui penerapan sistem monitoring otomatis yang terintegrasi dengan algoritma tersebut; dan ketiga, mengurangi tingkat kedaluwarsa obat melalui analisis data transaksi yang lebih komprehensif. Data transaksi yang digunakan dalam penelitian ini berasal dari PT Enseval Putera Megatrading Tbk. Cabang Padang, yang meliputi periode 3-7 Juni 2024. Data ini dianalisis menggunakan Microsoft Excel 2010 untuk pengolahan awal dan disimulasikan lebih lanjut dengan RapidMiner untuk memvalidasi hasil. Algoritma Apriori diterapkan untuk menentukan stok obat yang optimal melalui proses yang mencakup penentuan minimum support sebesar 3% dan confidence sebesar 40%, serta eliminasi itemset yang tidak relevan atau yang tidak memenuhi kriteria. Hasil dari analisis ini berhasil menemukan enam aturan asosiasi yang dapat digunakan untuk meramalkan stok obat secara lebih efektif dan efisien. Implementasi Algoritma Apriori diharapkan dapat secara signifikan meningkatkan efisiensi dalam manajemen stok obat, mengurangi risiko kelebihan atau kekurangan stok, serta meminimalkan masalah kedaluwarsa obat. Lebih dari itu, penelitian ini juga berkontribusi pada pengembangan pengetahuan ilmiah dalam bidang Data Mining dan manajemen stok obat, serta memberikan landasan yang kuat bagi penelitian lanjutan dan aplikasi praktis dalam konteks yang serupa. Dengan demikian, hasil penelitian ini tidak hanya memberikan solusi praktis untuk masalah pengelolaan stok obat, tetapi juga memperluas cakrawala pengetahuan dalam penggunaan teknik Data Mining untuk tujuan manajerial di bidang kesehatan.
Quickly Assess the Acceptability Sentiment of White Paracetamol Intake Using KNN-SMOTE Based On Receptive Deciding Rio Andika Malik; Faizal Riza; Sarjon Defitb
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 01 (2024): Vol. 15, No. 01 April 2024
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i01.p05

Abstract

This research aims to develop a fast and adaptive sentiment evaluation approach related to the use of white paracetamol using a combination of the K-Nearest Neighbors (KNN) algorithm, Synthetic Minority Over-Sampling Technique (SMOTE), and the Receptive Deciding concept. Imbalances in the dataset, where positive sentiment may predominate, are addressed using SMOTE to synthesize minority class samples. The KNN algorithm is applied to build a sentiment classification model, while Receptive Deciding is used to provide adaptive intelligence to changes in sentiment. The SMOTE oversampling process is carried out to achieve class balance, while KNN is used to classify sentiment. Receptive Deciding is applied to increase the model's adaptability to changes in sentiment. The research results show that integrating the SMOTE, KNN, and Receptive Deciding methods effectively assesses sentiment accurately and adaptively. The developed model can recognize changes in sentiment over time and provide balanced evaluation results. These findings are expected to contribute to understanding public sentiment towards using white paracetamol and be the basis for developing more effective health communication strategies.
The Role of Customer Trust as a Mediator in Building Loyalty to Agung Toyota After-Sales Service Rahmadani Hidayat; Sarjon Defit; Yulasmi
Jurnal Ilmiah Manajemen Kesatuan Vol. 13 No. 4 (2025): JIMKES Edisi Juli 2025
Publisher : LPPM Institut Bisnis dan Informatika Kesatuan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37641/jimkes.v13i4.3477

Abstract

This study aims to analyze the role of customer trust as a mediator in building loyalty in Agung Toyota's after-sales service. Customer loyalty is an important aspect of business sustainability and service quality is considered a factor that influences it. However, in practice, the effect of service quality on customer loyalty is not always direct, but can be mediated by customer trust factors. This study uses a quantitative approach with a survey method. Data were collected by distributing questionnaires to 200 respondents who are Agung Toyota Pekanbaru Harapan Raya customers. The data analysis technique used is Structural Equation Modeling (SEM) with the help of SmartPLS software. The results of the study indicate that service quality has a positive and significant effect on customer trust. Furthermore, customer trust is proven to have a positive and significant effect on customer loyalty. Interestingly, service quality also has a direct effect on customer loyalty, but the effect becomes stronger when mediated by customer trust. This indicates that trust acts as a partial intervening variable in the relationship between service quality and customer loyalty. The implications of this study indicate that companies need to consistently improve the dimensions of service quality to build customer trust, which will ultimately increase their loyalty. By focusing on creating trust through superior service, companies can build long-term, profitable relationships with customers.
Co-Authors Abdul Azis Said Abulwafa Muhammad Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zaki Ahmad Zamsuri, Ahmad AHMADI Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Ali Ikhwan Alkhairi, Putrama Alvi Dwi Wahyuni Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andri Nofiar Angga Putra Juledi Anisya Anisya Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Billy Hendrik Bob Subhan Riza Bosker Sinaga Boy Sandy Dwi Nugraha.H Breinda, Engla Brestina Gultom Bufra, Fanny Septiani Chairun Nas Cyntia Trimulia Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Defi Pebriyanti Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devia Kartika Devita, Retno Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dila, Rahmah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma Elfiswandi Elfiswandi eriwandi Fadillah, Riszki Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza Fajrul Islami Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febi Nur Salisah Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus Firdaus Firdaus, Muhammad Bambang Fitri Safnita Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Ghea Paulina Suri Gunadi W Nurcahyo Gunadi Widi N. Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo Guslendra Guslendra Guslendra, Guslendra Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Istianingsih, Nanik Iswandi Saputra Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Jufriadif Na`am, Jufriadif Juledi, Angga Putra Julius Santony Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Lengga S. Sandy Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Iqbal Zuqron M. Syahputra Mardayatmi, Suci Mardian, Zurni Mardison Mardison Mardison Marfalino, Hari Meilinda Sari Meilinda Sari Melissa Triandini Menhard, Menhard Mhd Hary Kurniawan Miftahul Hasanah Miftahul Hasanah, Miftahul Mike Zaimy Monsya Juansen Muhammad Dahria Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Ramdani Bayu Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rafki, Rafnelly Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Ramadhanu, Agung - Randy Permana Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Rianti, Eva Rico Anggara Rini Sovia Rini Sovia Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila S Sumijan S Sumijan Sabil, Muhammad Said, Abdul Azis Saiful Nurarif Sandrawira Anggraini Sani, Rafikasani Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Silfia Andin Sintia Sintia Siregar, Diffri Solihin Siregar, Fajri Marindra Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sukardi Sulastri Sulastri Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Surmayanti, Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva Tukino, Tukino tukino, tukino Veri, Jhon Veza, Okta Virgo, Ismail Vitriani, Vitriani Wahyu, Fungki Wanto, Anjar Wenni Afrodita Weri Sirait Y Yuhandri Yamin, Abdul Yamin Yemi, Leonardo Yenila, Firna Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z