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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 Dinasti International Journal of Education Management and Social Science Systematics 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)

Sarjon Defit

Universitas Putra Indonesia YPTK Padang

Author-ID : 2476220

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

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Enhancing U-Net for Wrist Fracture Segmentation in X-ray Images using Adaptive Callbacks and Weighted Loss Functions Radillah, Teuku; Defit, Sarjon; Nurcahyo, Gunadi Widi
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.v6i4.952

The detection of wrist fracture through medical imaging is causing considerable challenges due to the subtle and variable manifestation of such ruptures, necessitating precise and reliable segmentation methods. Therefore, this research aimed to propose an improved U-Net model for detecting wrist fracture. The model incorporated two innovations, namely adaptive callback training and weighted loss combination. The adaptive callback mechanism could be performed by dynamically adjusting the training parameters based on the model performance to prevent overfitting and accelerate convergence. At the same time, the loss function combined Dice Loss and Binary Cross-Entropy (BCE) Loss with linear as well as non-linear exponential weighting strategies, ensuring balanced optimization between region-based accuracy and pixel classification. During this analysis, a series of experiments were conducted on a curated wrist X-ray image dataset, and the results showed that the proposed method expressed superior performance in terms of segmentation accuracy when compared with previous U-Net and other state-of-the-art procedures. The proposed method achieved 91% accuracy, 87% precision, 86% recall, and 87% F1 score. Following this discussion, the findings showed the efficacy of the adaptive training design and loss function in improving the strength and sensitivity of the model in detecting wrist fracture

Addressing Class Imbalance in Machine Learning for Predicting On-Time Student Graduation at The Islamic University of Riau Efendi, Akmar; Defit, Sarjon
JURNAL TEKNIK INFORMATIKA Vol. 18 No. 2: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v18i2.45913

Timely graduation is an important indicator of academic performance in higher education. However, many students still fail to graduate on time, prompting the need for predictive models to support academic decision-making. This study aims to analyze the impact of class imbalance on machine learning algorithm performance in predicting student graduation at the Islamic University of Riau. Data were obtained through questionnaires and labeled into “graduated on time” and “not on time” classes, which were initially imbalanced. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied during preprocessing to balance the dataset. Four machine learning algorithms were compared: Decision Tree, Gaussian Naive Bayes, K-Nearest Neighbors, and Support Vector Machine. The evaluation was conducted with and without SMOTE, using accuracy, precision, recall, F1-score, and confusion matrix. Results showed significant performance improvements after applying SMOTE, with all models achieving around 99% accuracy. SVM achieved the most stable results across both conditions. The study highlights the effectiveness of SMOTE in improving classification fairness and reliability, especially in datasets with class imbalance. This work may assist universities in early intervention for students at risk of late graduation.

Penerapan Metode K-Means Clustering Dalam Pengelompokan Penyakit Pada Ayam Kampung Unggul Balibangtan Afriyadi, Iqbal; Defit, Sarjon; Sumijan, Sumijan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 14, No 4 (2025): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v14i4.8508

Penyakit ayam saat ini merupakan salah satu ancaman terbesar pada sebuah peternakan ayam. Penyakit pada ayam bisa disebabkan oleh virus dan bakteri.Â Ayam KUB merupakan salah satu jenis unggas yang dikembangkan oleh Badan Penelitian dan Pengembangan Pertanian Indonesia, dengan daya tahan tubuh yang baik dan produktivitas tinggi. Kendati demikian ayam KUB ini tetap rentan terhadap berbagai jenis penyakit yang dapat memengaruhi produktivitasnya. Pengelompokan penyakit pada ayam KUB penting untuk dilakukan guna mengidentifikasi pola serangan penyakit serta memberikan langkah preventif yang tepat bagi para peternak. Penelitian ini bertujuan untuk mengelompokkan penyakit yang menyerang Ayam Kampung Unggul Balitbangtan (KUB). Metode yang digunakan pada penelitian ini adalah penerapan machine learning dengan metode K-Means Clustering. Metode ini memiliki beberapa tahapan yaitu penyiapan data, normalisasi data, inisialisasi centroid, mengelompokkan data berdasarkan jarak terdekat, memperbarui centroid, iterasi sampai konvergensi, dan evaluasi hasil. Dataset yang diolah pada penelitian ini bersumber dari pengamatan langsung pada peternakan ayam ASA Farm Padang. Dataset yang digunakan dalam penelitian ini berjumlah 50 dataset yang berasal dari 50 ekor ayam KUB yang masuk kandang karantina pada peternakan tersebut. Pada penelitian ini menghasilkan kelompok penyakit ayam pada 3 kluster yaitu kluster 1 untuk ayam dengan penyakit gejala ringan dengan jumlah sebanyak 12 anggota, kluster 2 dengan penyakit gejala sedang dengan jumlah 14 anggota, dan kluster 3 dengan penyakit gejala tinggi sebanyak 24 anggota. Sehingga penelitian ini diharapkan dapat menjadi acuan bagi peternak, dokter hewan, peneliti selanjutnya atau pihak terkait dalam mengelompokan penyakit pada ayam kampung atau hewan ternak lainya.

Sistem Pakar Menggunakan Metode Forward Chaining Untuk Mendeteksi Kerusakan Jaringan Internet (Studi Kasus : Di Layanan Internet Diskominfotik Sumatera Barat) Zaki, Ahmad; Defit, Sarjon; Sumijan, Sumijan; Fauzana, Rahmi
Jurnal Nasional Teknologi dan Sistem Informasi Vol 9 No 3 (2023): Desember 2023
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v9i3.2023.227-236

Sistem informasi yang interaktif dapat membantu kinerja pegawai dalam mendukung program SPBE (Sistem Pemerintah Berbasis Elektronik. Dinas Kominfotik Sumatera Barat berperan penting dalam memberikan layanan internet kepada OPD-OPD di bawah lingkup Pemerintahan Provinsi Sumatera Barat. Pembangunan sistem jaringan internet yang sudah baik tidak dapat dijamin bahwa jaringan tersebut terbebas dari gangguan dan kerusakan. Gangguan terhadap akses internet akan berdampak terhadap produktifitas bekerja pegawai dan pelayanan kepada masyarakat. Kurangnya pemahaman PIC OPD dan pengguna dalam menangani permasalahan gangguan jaringan internet, maka dibutuhkan keahlian pakar dalam melakukan identifikasi kerusakan pada jaringan internet berdasarkan gejala-gejala yang terjadi, serta diberikan solusi perbaikan pada gangguan yang ada. Pengumpulan data dilakukan melalui wawancara dan observasi lapangan. Metode yang digunakan untuk pengolahan data pada Sistem Pakar ini yaitu metode forward chaining. Forward Chaining adalah sebuah strategi pencarian dalam system pakar yang dimulai dari sekumpulan data atau fakta, dari data-data tersebut, system akan mencari suatu kesimpulan yang menjadi solusi dari permasalahan yang dihadapi. Berdasarkan hasil pengujian Sistem Pakar menggunakan metode forward chaining untuk mendeteksi gangguan jaringan internet menghasilkan tingkat akurasi sebesar 100 % menggunakan 29 data uji. Berdasarkan hasil yang didapatkan dari Sistem Pakar dengan metode forward chaining, system tersebut dapat digunakan untuk mendeteksi kerusakan jaringan internet di Layanan Internet Diskominfotik Sumatera Barat.

Hybrid Data Mining with the Combination of K-Means Algorithm and C4.5 to Predict Student Achievement Ramadhanu, Agung; Defit, Sarjon; Kareem, Shahab Wahhab
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : Universitas Dharma Wacana

Getting academic achievement is the dream of every student who studies at higher education, especially undergraduate level. Undergraduate students aspire to the highest achievement (champion) at the last achievement of their studies. However, students cannot predict whether these students with the habits that have been done and the current conditions will make them excel or not. Apart from that, of course, students also want to know what factors and conditions influence the achievement the most. The objective to be achieved in this research is how to predict which number of students among them are predicted to excel (champion) at the end of the semester with a combination of the K-Means and C4.5 methods. Besides, the purpose of this study reveals how the K-Means algorithm performs data clustering of student data who will excel or not and how the C4.5 algorithm predicts students who have been grouped. Data processing in this study uses the Rapid Miner software version 9.7.002. The result of this research is that it is easier to group data in numerical form than data in polynomial form. Other results in this study were that out of 100 students, 27 students (27%) were predicted to excel (champions) and 73 (73%) did not achieve (not champions).

Factors Influencing Customer Satisfaction and Their Impact on Customer Loyalty Septiano, Renil; Defit, Sarjon; Yulasmi, Yulasmi; Limakrisna, Nandan; Lusiana, Lusiana
International Journal of Artificial Intelligence Research Vol 8, No 1.1 (2024)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1.1351

Workshop customers, known as unit entry by the automotive world, are one of the sources of revenue for automotive companies. Workshop unit entry is related to customer loyalty. The study aims to determine the relationship between perceived price on customer loyalty through customer satisfaction in auto repair shops customers. A quantitative approach is used as a research approach. Data were collected by questionnaire. The collected data were analyzed to determine the effect between variables, and the analysis technique was Partial Least Square (PLS). The research was conducted at Auto2000 West Sumatra. The research subjects are Auto2000 consumers who use workshop services. The object of research is a review of the influence of perceived price on customer loyalty with customer satisfaction as mediation. The sampling technique is purposive sampling. The results showed that Customer satisfaction can mediate the effect of perceived price on customer loyalty of consumers who use Auto2000 workshop services.

K-Means and K-NN Methods For Determining Student Interest Guslendra, Guslendra; Defit, Sarjon; Bastola, Ramesh
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Putra Indonesia University 'YPTK' Padang's Department of Information Systems, Faculty of Computing Science has three specializations, namely Information Technology Management, Business Information Systems, and Industrial Information Systems. In the fifth semester, the acquisition of specializations takes place. In the next semester, the selection of specialist programs will be determined. The option of the degree is adapted to students' needs and capacities. The acquisition of results generated in the previous semester can be seen. The objective of this survey is to provide students with suggestions for the collection of degrees. The study was performed using K-Means and K-Nearest Neighbor methods to obtain the classification of students and the correlation between recent cases and past cases. This analysis uses 13 characteristics, of which 12 are predictors and 1 is the option. The test results can be used as a way to suggest the student preferences based on preset attributes through the K-Means and K-NN methods.

Determination Potential Experts by Application The Apriori Algorithm and the K-Means Algorithm Sovia, Rini; Defit, Sarjon; Fatimah, Noor
International Journal of Artificial Intelligence Research Vol 6, No 1 (2022): June 2022
Publisher : Universitas Dharma Wacana

Experts are people who have special expertise who provide services based on their expertise.Â The company has experts in handling projects that will be carried out for the progress of the company.Â The importance of the quality of experts in the company can improve the quality of human resources.Â The Apriori algorithm is a data mining method that has the aim of looking for association patterns based on the project being carried out so that they can be identified by experts who are often used in handling projects.Â Furthermore, a data mining approach is needed to classify experts with the K-means algorithm used.Â This study combines theÂ AprioriÂ and K-means algorithms, by grouping experts based on the handling of the project they are working on.

Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth Rani, Larissa Navia; Defit, Sarjon; Muhammad, L. J.
International Journal of Artificial Intelligence Research Vol 5, No 1 (2021): June 2021
Publisher : Universitas Dharma Wacana

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes

Indeks Kesiapan Perguruan Tinggi dalam Mengimplementasikan Smart Campus Zakir, Supratman; Defit, Sarjon; Vitriani, Vitriani
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 6 No 3: Juni 2019
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Keberhasilan perguruan tinggi memanfaatkan Teknologi Informasi dan Komunikasi (TIK) atau sering dikenal dengan istilah smart campus sebagai upaya kompetitif dan bernilai saing pada hakekatnya terletak pada sejumlah indicator seperti technoware, infoware, orgaware dan humanware. Upaya pencapaaian tujuan penggunaan smart campus tersebut dibutuhkan skema perencanaan yang matang dan need analysis yang menyeluruh. Banyak perguruan tinggi yang gagal mengimplementasikn smart campus disebabkan beberapa hal, seperti perencanaan yang tidak baik, tenaga ahli yang tidak siap, sarana prasarana yang kurang memadai, biaya awal pengembangan yang tidak tersedia dan kebijakan yang tidak konsisten. Penelitian ini bertujuan untuk melihat kesiapan perguruan tinggi dalam mengimplemetasikan smart campus. Penelitian ini menggunakan jenis penelitian deskriptif kuantitatif. Hasil penelitian memperlihatkan bahwa pengembangan cyber campus pada komponen ICT Use yang mencakup dimensi kebutuhan dan keselarasan serta dimensi proses dan tata kelola baru memasuki tahap kurang siap. Komponen ICT Readiness yang mencakup dimensi sumber daya teknologi pada kategori hampir berhasil. Komponen ICT Capability yang mencakup dimensi komunitas memasuki kategori belum berhasil. Komponen ICT impact sudah memasuki kategori hampir berhasil. Secara keseluruhan komponen pengembangan cyber campus dikategorikan hampir berhasilAbstractThe triumph of higher education in utilizing ICT or commonly termed as smart campus as a competitive strategy depends on some indicator like technoware, infoware, orgaware and human ware. To achieve such goal is not an easy task, it needs to have a good planning and a holistic needs analysis. Many educational institutions fail to implement smart campus due to some factors for instance bad planning, unready human resources, unsporting infrastructure, lack of fund and inconsistent policy. The research aims at revealing readiness index of higher education in utilizing ICT. The research uses descriptive quantitative approach research that describes an object as it is with research stages including presurvey, leterature studies, questionnaires, data analysis and empirical findings. Data were obtained by using a research questionnaire. The finding reveals that developing cyber campus in the component ICT Use which covers need, harmony, and process dimensions and governance is categorized less-ready. Component of ICT Readiness which covers dimension of technology resources is categorized as successful, meanwhile component of ICT capability of community dimension is not successful yet. Component of ICT impact comes to category near to successful. As a whole, the components of developing cyber campus are in the success category.

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 Elfiswandi Elfiswandi 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 Fristi Riandari Fuad El Khair Gaja, Rizqi Nusabbih Hidayatullah Gunadi Widi Nurcahyo 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. 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 TAJUDDIN Muhammad Tajuddin Muhammad, L. J. Mulyanda, Sandy Mutiana Pratiwi 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 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 Rafnelly Rafki 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 Siswahyudianto Sitanggang, Sahat Sonang Slamet Riyadi Sofika Enggari Sovia, Rini Sri Dewi Sri Dewi Sri Dewi, Apriandini Sri Rahmawati Suci Mardayatmi Suhefi Oktarian Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan Sumijan, S 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

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