Article Per Year (5 Year)
p-Index From 2021 - 2026
17.647
P-Index
This Author published in this journals
All Journal TEKNIK INFORMATIKA JURNAL SISTEM INFORMASI BISNIS Voteteknika (Vocational Teknik Elektronika dan Informatika) Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Explore: Jurnal Sistem Informasi dan Telematika (Telekomunikasi, Multimedia dan Informatika) Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika Jurnas Nasional Teknologi dan 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 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 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 JUKI : Jurnal Komputer dan Informatika Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Journal of Applied Computer Science and Technology (JACOST) 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 RJOCS (Riau Journal of Computer Science) SmartComp Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) JR : Jurnal Responsive Teknik Informatika Jurnal Responsive Teknik Informatika
Sarjon Defit
Universitas Putra Indonesia YPTK Padang
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

Published : 195 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 195 Documents
Search

 16   17   18   19   20 
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.
Accurately Determining Labor Test Results Using the Rough Set Method Devita, Retno; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7069

Abstract

An exam is something that must be done to test a person's ability or intelligence. The laboratory exam in the Computer Systems study program at Putra Indonesia University "YPTK" Padang consists of a digital systems exam, a fuzzy logic control exam, and a tool presentation. The Labor Exam must be passed by students who will take the comprehensive exam. In this study, laboratory exam data was taken for 20 students. So far, processing of student laboratory exam results has been done manually so it takes a long time to make decisions. To overcome this problem, a Rough Set method is used to determine laboratory test results. The Rough Set method is part of machine learning. This research produces 29 rules as knowledge, namely {Digital System} Or {A} = 3 rules, {Fuzzy Logic} Or {B} = 3 rules, {Tool Presentation} Or {C} = 3 rules, {Fuzzy Logic, Tool Percentage} Or {BC} = 6 rules, {Digital System, Fuzzy Logic} Or {AB} = 6 rules and {Digital System, Tool Percentage} Or {AC} = 8 rules. The Rough Set method can determine student laboratory exam results (pass or fail) accurately.
Machine Learning Predicts the Level of Disease Spread Saputra, Dhio; Wisky, Irzal Arief; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7070

Abstract

The aim of the research is predictive analysis of the spread of disease. Variable analysis at the population level in a region and the total disease events detected in the community. These variables can show the accuracy and certainty of the status of the resulting analysis. The concept of Machine Learning analysis is proposed to develop previous analysis models. The methods used include the K-Means cluster, Naïve Bayes, and Decision Tree (DT). There are two stages in the analysis process: pre-processing and classification. The discussion presented by K-Means provides a classification analysis pattern. The patterns obtained will be passed on to the classification process using Naïve Bayes and DT. Naïve Bayes results provide quite significant results with an accuracy rate of 83.33%. DT can also describe the results of information and knowledge analysis in the form of decision trees. DT produces decision trees that can provide knowledge and information analysis. The DT results provide an accuracy rate of 91.76% so these results can be used as consideration in decision making. The resulting information and knowledge can be used as a guide in making policies for handling health in the community.
Prediction of Graduation Accuracy Using the K-Means Clustering Algorithm and Classification Decision Tree Rahmawati, Sri; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7073

Abstract

Becoming a scholar at the right time for students is a very meaningful award for them if it is supported by seriousness and perseverance in their studies. Here, sample data was taken from 131 randomly taken in testing. Where there are still students who are not detected by the study program in completing their lectures, so research is carried out on clustering and classification with decision trees in determining the level of accuracy of lectures by clustering data, determining the initial centroid value and the centroid point. The results found were that there were 78 people grouped in cluster 0 and 53 people grouped in cluster 1, where those with potential for punctuality for their studies were in cluster 0 so they were students who could finish within the specified time. Meanwhile, students grouped in cluster 1 illustrate that these students need coaching and guidance both in the study program and with their supervisors. In the classification taken from the results of data clustering, two classes were obtained, namely class a and class b, with 73 and 58 data respectively, so that the results between clustering and classification did not differ too much in the data to predict the accuracy of a student's graduation.
Rought Set: Effective Method for Determining Scholarship Recipients Andin, Silfia; Defit, Sarjon
Jurnal Penelitian Pendidikan IPA Vol 10 No 4 (2024): April
Publisher : Postgraduate, University of Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/jppipa.v10i4.7088

Abstract

Every year, higher education institutions receive a KIP Tuition scholarship quota that has been determined by Ristek Dikti through LLDIKTI which is given during the new student admissions process. The process of determining recipients is carried out manually resulting in inaccurate scholarship recipients being selected and the selection results may not be the same based on those who participated in making the decision. This research is motivated by the need for an algorithm for determining prospective scholarship recipients that is appropriate and effective because the recipient selection process often takes a long time because many high school and equivalent students register so that they exceed the quota limit while the quota given is limited. This research aims to use a system for scholarship recipients and provide rules and knowledge, namely rough set Theory and adapted to the Rosetta application, using prospective student data during the selection process for new students who apply for the KIP Kuliah scholarship in the 2020/2021 academic year. The resulting decision is the KIP Opportunity which consists of 4 (four) attributes, including parents' income, housing status, dependents, and parental status. The results of this research using sample data from 12 people produced 6 (six) rules and knowledge of 26 rules. This research is very supportive in identifying the eligibility of KIP Kuliah recipients.
Co-Authors Abdul Azis Said Adawiyah, Quratih Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Afriyadi, Iqbal Agus Perdana Windarto Agustin, Riris Ahmad Zaki Ahmad Zamsuri, Ahmad Akbar, Muhamad Rafi Akbar, Syifa Chairunnissa Deliva Am, Andri Nofiar Amran Sitohang Anam, M Khairul Andema, Henky Andin, Silfia Andri Nofiar Angga Putra Juledi Anthony Anggrawan Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bastola, Ramesh Bosker Sinaga Breinda, Engla Bufra, Fanny Septiani Daeng Saputra Perdana Dahria, Muhammad Daniel Theodorus Dayla May Cytry Dendi Ferdinal Deno Yulfa Ardian Deti Karmanita Devita, Retno Dhena Marichy Putri Dila, Rahmah Dinda Permata Sukma Dwi Utari Iswavigra Dwiki Aulia Fakhri Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Elda, Yusma eriwandi Fadlul Hamdi Faisal Roza Fajrul Islami Fanny Septiani Bufra Fatimah, Noor Fauzan Azim Fauzana, Rahmi Fauzi Erwis Febri Aldi Febri Hadi Febrina, Yerri Kurnia Firdaus, Muhammad Bambang Fitriani, Yetti Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Gunadi Widi Nurcahyo Gunadi Widi Nurcahyo, Gunadi Guslendra, Guslendra Hadiyanto, Tegas Halifia Hendri Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita, H Hendrik, Billy Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Hidayat, Rahmadani Honestya, Gabriela Huda, Ramzil Ikhbal Salam, Riyan Indah Savitri Hidayat Indhira, Sonia INTAN NUR FITRIYANI Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arief Wisky Ismail Virgo Jefdy Kurniawan Jeri Wandana Juansen, Monsya Jufri, Fikri Ramadhan Juledi, Angga Putra Junadhi, Junadhi Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Leony Lidya Lidya, Leoni Lubis, Fitri Amelia Sari Lubis, Siti Sahara Lusiana Lusiana M Syahputra M. Ibnu Pati M. Syahputra Mardayatmi, Suci 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 TAJUDDIN Muhammad, L. J. Mulyanda, Sandy Nadya Alinda Rahmi Nandan Limakrisna Nanik Istianingsih Nori Sahrun, Nori Novi Yanti Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhadi Nurhidayat Nursyahrina Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Parinduri, Rezti Deawinda Pati, Muhammad Ibnu Pebriyanti, Defi Pratiwi, Mutiana Pulungan, Akhiruddin Purnomo, Nopi Putra, Akmal Darman Putra, Rahman Arief Putra, Surya Dwi Putri, Adek Putri, Dhena Marichy Putri, Yozi Aulia Putut Wicaksono, Putut R Rahmiyanti Radillah, Teuku Rafika Sani Rafiska, Rian Rahmad Aditiya Rahmad Rahmad Rahmadani Hidayat Rahman Arief Putra Rahmi, Nadya Alinda Ramadhan, Mukhlis Ramadhanu, Agung Ramdani Bayu Putra Rani, Larissa Navia Refina Afindania, Pipin Resnawita, R Rezki - Rezki Rusydi Rian Kurniawan Rianti, Eva Rio Andika Malik Ritna Wahyuni Rizki Mubarak Roza Marmay Roza, Yesi Betriana Rusdianto Roestam Rustam, Camila S Sumijan Said, Abdul Azis Sandrawira Anggraini Sani, Rafikasani Saputra, Dhio Sari, Imrah Sari, Laynita Selfi Melisa Septiano, Renil Setiawan, Adil Sharon Shaza Alturky Siregar, Diffri Solihin Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S Suri, Ghea Paulina Surmayanti Surya Dwi Putra Suryani, Vivi Susandri, Susandri Susriyanti, Susriyanti Syafri Arlis Syafrika Deni Rizki, Syafrika Deni Syaljumairi, Raemon Syofneri, Nandel Tamaza, Muhammad Abyanda Teri Ade Putra Tesa Vausia Sandiva 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 Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yuhandri Yuhandri Yuhandri Yuhandri, Yuhandri Yulasmi Yulasmi, Yulasmi Yuli Hartati Yulihartati, Sandra Yunus, Yuhandri Yusma Elda Zakir, Supratman Zia Rahimi, Hadisha Zulvitri, Z Zuqron, M. Iqbal Zurni Mardian