Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

23.969

P-Index

This Author published in this journals

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 Information System for Educators and Professionals : Journal of Information System Jurnal Penelitian Pendidikan IPA (JPPIPA) Indonesian Journal of Artificial Intelligence and Data Mining JITK (Jurnal Ilmu Pengetahuan dan Komputer) Rang Teknik Journal Sebatik 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 Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) 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 Jurnal Penelitian Inovatif Jurnal Ipteks Terapan : research of applied science and education Jurnal Pustaka Data : Pusat Akses Kajian Database, Analisa Teknologi, dan Arsitektur Komputer Jurnal Pustaka AI : Pusat Akses Kajian Teknologi Artificial Intelligence Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Komtekinfo Jurnal Sistim Informasi dan Teknologi INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System 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 Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Journal of Soft Computing Exploration Jurnal Perangkat Lunak

Sarjon Defit

Universitas Putra Indonesia YPTK Padang

Author-ID : 1898859

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 Environmental Science 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 : 255 Documents Claim Missing Document

Claim Missing Document

Articles

22 23 24 25 26

Adaptive Integration of Optuna Optimization and Stacking Ensemble Learning for Automated Work Competency Classification Mutiana Pratiwi; Sarjon Defit; Muhammad Tajuddin
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1228

Artificial intelligence and machine learning are increasingly used to automate analytical and decision processes, including the evaluation of human competencies. However, traditional models often face challenges in accuracy and generalization when applied to linguistic data from interviews. This study aims to develop a model that integrates Optuna optimization and stacking ensemble learning to enhance the accuracy and interpretability of competency classification. Interview transcript data were processed using natural language processing techniques such as cleaning, tokenization, case folding, stopword removal, and stemming to ensure textual consistency. The text was then transformed into numerical representations using term frequency inverse document frequency weighting. To handle class imbalance, the synthetic minority oversampling technique was employed. Optuna was applied to optimize the hyperparameters of base models, including support vector classifier, Naïve Bayes, random forest, gradient boosting, and XGBoost. These optimized models were combined through a stacking ensemble to form the final classifier. The proposed model achieved an accuracy of 94 percent and a precision of 95 percent with macro and weighted F1 scores of 0.94. The results demonstrate stable and balanced performance across all competency categories, including analytical thinking, initiating action, problem solving, and work standards. Comparative analysis with previous studies in sentiment analysis, medical diagnosis, and financial forecasting confirmed that the integration of Optuna and stacking produces more robust and generalizable outcomes. The integration of Optuna optimization and stacking ensemble learning effectively improves classification performance while maintaining interpretability. The model demonstrates strong potential for automated competency evaluation in recruitment and human resource analytics. This framework can be extended to other linguistic datasets to support transparent and data-driven decision-making in artificial intelligence applications.

An Integrated Text Analytics and Ensemble Machine Learning Framework for Fake Review Detection in Online Marketplaces Eka Praja Wiyata Mandala; Sarjon Defit; Gunadi Widi Nurcahyo
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1143

The increasing prevalence of fake reviews on e-commerce platforms undermines consumer trust and affects purchasing decisions, particularly for local products by limited visibility such as those by West Sumatra, Indonesia. This study proposes a hybrid approach combining text analytics and machine learning to enhance the detection of fake reviews. Four classification models—Naive Bayes, Random Forest, Logistic Regression, and K-Nearest Neighbor—were tested on a dataset of 1,500 labeled product reviews. Among these models, Random Forest had the highest starting accuracy of 0.8533. To enhance it, we created a better algorithm called EKAHypeRFor (Enhanced Knowledge Augmentation of Hyperparameter Random Forest). This method uses simple feature engineering and careful tuning of settings by RandomizedSearchCV. The enhanced model reached an accuracy of 0.8778, which is 2.45% higher than the original. It also includes a real-time review sorting tool, making it easy to use on online shopping sites. Tests by a confusion matrix and feature importance drawn the model works well and is easy to understand. This method is simple, fast, and accurate, helping to make online product reviews more trustworthy for small and medium businesses in the area.

Penerapan Metode K-Means Clustering Dalam Pengelompokan Penyakit Pada Ayam Kampung Unggul Balibangtan Iqbal Afriyadi; Sarjon Defit; 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. 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.

An Analysis of Public Satisfaction with Government Services: A Multi-Method Approach Using PCA, K-Means Clustering, and Linear Regression Abuzar Gafari; Sarjon Defit; Rini Sovia
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2742

Flawless performance evaluation results across all service dimensions may potentially obscure the identification of areas for improvement and diminish objectivity in decision-making. This study aims to identify the specific service attributes influencing public satisfaction and to segment respondents based on their satisfaction levels at the Office of the Ministry of Religious Affairs in Payakumbuh City. The research integrates Principal Component Analysis (PCA), K-means clustering, and linear regression. PCA was employed to reduce data dimensionality and establish principal components; K-means clustering was utilized to group respondents based on perceptual similarities regarding service quality; and linear regression was applied to identify the most significant factors influencing public satisfaction within each segment. The data were sourced from the Public Service Survey Information System (SISULAP) application of the Payakumbuh Ministry of Religious Affairs, spanning June 2024 to October 2025, with a total of 1,950 respondents. The findings reveal that service process and efficiency are the primary factors influencing all respondent segments, with the low-satisfaction segment identified as the top priority for service improvement. The regression models demonstrate robust performance across all segments. These findings provide an empirical foundation for data-driven policymaking to enhance public service quality.

Model Interpretation for Student Major Selection Using Principal Component Analysis and Random Forest Antoni Antoni; Sarjon Defit; Yuhandri Yuhandri
Sebatik Vol. 30 No. 1 (2026): June 2026
Publisher : STMIK Widya Cipta Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46984/sebatik.v30i1.2747

The development of information technology has had a significant impact on the education sector by providing data-driven tools to support the process of major selection. This process often causes confusion among students due to its crucial role in determining their academic and career futures. This study aims to develop an accurate and transparent recommendation system for major selection through the integration of Principal Component Analysis (PCA), Random Forest (RF), and SHAP. The research follows a systematic framework that includes data processing and model evaluation stages. PCA is applied to reduce the dimensionality of complex student data in order to improve computational efficiency and minimize information redundancy. Furthermore, the Random Forest algorithm is employed as a classification model to predict major recommendations such as Science, Social Sciences, and Religious Studies. The SHAP method is integrated to provide both mathematical and visual interpretations of the contribution of each academic feature to the model’s prediction results. The research data are obtained from the internal records of MAN 1 Payakumbuh covering the last three academic years (2022/2023–2024/2025). The dataset consists of 571 eleventh-grade students with tenth-grade academic scores and non-academic skill variables. The implementation of this model is able to provide more objective recommendations compared to conventional subjective assessments, achieving an accuracy of 88.70%. Visualization of feature contributions using SHAP enhances transparency and facilitates stakeholders’ understanding of the basis for each model decision. This study contributes to improving the efficiency of the major selection process and supports more accurate academic decision-making for students and educators.

Penerapan K-Means dan Algoritma C4.5 dalam Klasifikasi Ulasan Pengguna Aplikasi Mitsubishi SFID Dzil Hidayati; Sarjon Defit; Billy Hendrik
INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS : Journal of Information System Vol 11 No 1 (2026): INFORMATION SYSTEM FOR EDUCATORS AND PROFESSIONALS (Juni 2026)
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Bina Insani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3831

Pada penelitian ini dilatarbelakang oleh dimana banyaknya ulasan pengguna aplikasi SFID yang berisi keluhan dan penilaian dengan bahasa yang beragam sehingga sulit dianalisis secara manual, cepat dan objektif. Kondisi tersebut menyebabkan informasi penting terkait pengalaman pengguna, kendala teknis, dan kualitas layanan aplikasi belum dapat dimanfaatkan secara optimal sebagai dasar pengambilan keputusan pengembangan aplikasi. Dengan demikian, penelitian ini bertujuan untuk mengelompokkan dan mengklasifikasikan ulasan pengguna aplikasi Mitsubishi SFID secara sistematis guna mendukung proses pengambilan keputusan. Dalam penelitian ini, Metode K-Means Clustering digunakan untuk mengelompokkan ulasan berdasarkan tingkat kemiripan karakteristik dan tema, sedangkan algoritma C4.5 diterapkan untuk mengklasifikasikan ulasan pada setiap kelompok ke dalam kategori tertentu berdasarkan atribut yang relevan. Data yang dianalisis berasal dari ulasan pengguna aplikasi Mitsubishi SFID yang diperoleh melalui platform Google Play Store. Penelitian ini menunjukkan bahwa model klasifikasi yang dibuat berhasil memberikan tingkat akurasi yang baik serta hasil validasi menunjukkan bahwa model memiliki kinerja yang stabil dan dapat diandalkan dalam mengklasifikasikan ulasan pengguna dengan nilai akurasi 88,89%. Dampak dari penelitian ini adalah tersedianya informasi terstruktur mengenai permasalahan dan kebutuhan pengguna yang dapat dimanfaatkan sebagai dasar penentuan prioritas perbaikan fitur, peningkatan kualitas layanan aplikasi, serta membantu mengambil keputusan yang lebih tepat dan didasarkan pada data

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51211/isbi.v11i1.3831

Hybrid Text Mining for Hate Speech Detection in Indonesia: A Naïve Bayes-Based Approach Muhammad Habib Yuhandri; Halifia Hendri; Richi Andrianto; Sarjon Defit
Khazanah Informatika : Jurnal Ilmu Komputer dan Informatika Vol. 12 No. 1 (2026): April 2026
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Hate speech (HS) is defined as speech that conveys hateful meaning and intent. In contemporary times, the prevalence of hate speech has surged in the virtual realm, particularly on social media platforms. Among these platforms, Twitter, now renamed X, stands out as one of the most widely used and a significant medium for the dissemination of hate speech. Hate speech can be categorized into various levels of severity, including HS_Weak, HS_Moderate, and HS_Strong. This study utilizes a dataset comprising 13,169 tweets from the social media application X from Indonesia users in 2023 to investigate hate speech detection. The research employs a novel hybrid approach that integrates image input with five preprocessing techniques: data cleaning, case folding, tokenization, stop-words removal, and stemming. Following preprocessing, the study applies Natural Language Processing (NLP) techniques in conjunction with Naïve Bayes classification. The combination of these NLP methods proves to be highly effective for the classification of text data. The key findings of this research demonstrate that the hybrid method significantly enhances hate speech detection accuracy. The evaluation of the classification model, based on training and validation, reveals an accuracy rate of 80%, a precision value of 85%, a recall value of 75%, and an F1-score of 80%. These results indicate substantial improvement over previous research outcomes. The findings suggest that the hybrid method is robust and effective for hate speech detection on social media platforms. Future research should explore the comparison of this hybrid approach with other classification methods to further validate its efficacy and potential applications in various domains of text classification.

Training on the Use of EduStory-Gen for Developing Interactive Storytelling among Kindergarten and Early Childhood Education Teachers Junadhi Junadhi; Sarjon Defit; Okfalisa Okfalisa
SPEKTA (Jurnal Pengabdian Kepada Masyarakat : Teknologi dan Aplikasi) Vol. 7 No. 1 (2026)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/spekta.v7i1.15957

Background: Early childhood education plays an important role in supporting children’s cognitive, social, and emotional development. However, many kindergartens and early childhood education teachers continue to rely on conventional storytelling approaches and have limited experience in utilizing digital and artificial intelligence (AI)-based technologies for learning activities. Contribution: This community service program contributes to teacher professional development by introducing EduStory-Gen, an AI-assisted storytelling platform that integrates narrative generation and visual illustration creation. Method: The program employed a participatory training approach involving 30 kindergarten and early childhood education teachers. The activities included needs analysis, training preparation, pre-assessment, hands-on training, mentoring, and post-training evaluation. Results: The results showed substantial improvements in teachers’ competencies related to digital storytelling and AI-assisted educational tools. The average competency score increased from 2.9 in the pre-assessment to 4.4 in the post-assessment. The highest improvement was observed in teachers’ knowledge of AI-based tools (+1.7). Conclusion: The EduStory-Gen training program effectively enhanced teachers’ digital storytelling competencies and AI literacy while fostering positive learning experiences. The findings highlight the potential of AI-assisted storytelling tools to support innovative teaching practices and strengthen technology integration in early childhood education.

Optimizing Naive Bayes for Sentiment Analysis of M-Passport Reviews Using N-Gram and Synthetic Minority Over-sampling Technique Kartika, Devia; Defit, Sarjon
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5853

The diverse user perceptions and increasing number of negative reviews of the M-Passport application indicate the need for sentiment analysis-based evaluation to more accurately measure the quality of digital immigration services. This study aims to analyze user sentiment towards the M-Passport application using an optimized Naïve Bayes classification model. Review data was obtained through web scraping from various digital platforms and processed using text preprocessing, TF-IDF feature extraction, N-Gram representation, and the Synthetic Minority Over-sampling Technique (SMOTE) technique to address data representativeness. The proposed model classifies user reviews into positive, neutral, and negative sentiment categories. Test results show that optimization using N-Gram and SMOTE successfully improved model performance, with accuracy increasing from 61% to 77.51%, precision from 0.75 to 0.78, recall from 0.53 to 0.78, and F1-score from 0.50 to 0.77. These results demonstrate that the combination of feature engineering and data balancing can improve text context representation and sentiment classification stability across multiple classes. Furthermore, sentiment analysis successfully identified key factors contributing to user dissatisfaction, such as technical constraints, feature limitations, and application difficulty. These results demonstrate that the proposed approach is effective in supporting data-driven evaluation to improve the quality of digital immigration services.

Co-Authors Abdul Azis Said Abuzar Gafari Adawiyah, Quratih Ade, Ade Puspita Sari Adek Putri Adi Gunawan Adi Gunawan, Adi Adyanata Lubis Aflili Sari Afriosa Syawitri Agung Ramadhanu 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 Antoni Antoni Arda Yunianta ardialis Ariandi, Vicky Arif Budiman Arif Budiman Arika Juwita Z Asri Hidayad Ayunda, Afifah Trista Bambang Supperianto 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 Dhena Marichy Putri Dhio Saputra Dicky Novriansyah Dinda Permata Sukma Dinul Akhiyar Dwi Utari Iswavigra Dwiki Aulia Fakhri Dwiprihatmo, Mohammad Reza Dzil Hidayati Efendi, Akmar Efendi, Muhamad Efrizoni, Lusiana Eka Praja Wiyata Mandala Eka Sofianti Elda, Yusma Elfiswandi, Elfiswandi eriwandi Eva Rianti Fadlul Hamdi Faisal Roza Faizal Riza Faizal Riza 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 Firna Yenila 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 Gunadi Widi Nurcahyo, Gunadi Guslendra Habdi Habdi Habdi, Habdi Hadiyanto, Tegas Halifia Hendri Hamsir hamsir Handika, Yola Tri Haris Kurniawan Hartati, Yuli Hasmaynelis Fitri Haviluddin Haviluddin Hazlita Hendro Budiantoro Hengki Juliansa Henky Andema Hermanto Hidayad, Asri Honestya, Gabriela Huda, Ramzil Ibnu Putra Ika Melinia Sapitri Fitriyanti Ikhbal Salam, Riyan Ikhsan, Naufal Indah Savitri Hidayat Indhira, Sonia Intan Nur Fitriyani Iqbal Afriyadi Ira Nia Sanita Irsyad, As'Ary Sahlul Irzal Arif 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 Junadhi, Junadhi Kamelia Sari, Rima Kareem, Shahab Wahhab Khairul Azmi Kurniawan, Jefdy Kurniawan, Mhd Hary Larissa Navia Rani, Larissa 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 Habib Yuhandri Muhammad Tajuddin MUHAMMAD TAJUDDIN Muhammad, Abulwafa Muhammad, L. J. Mukhlis Santoso Mulyanda, Sandy Mutiana Pratiwi Nadya Alinda Rahmi Nandan Limakrisna Nori Sahrun Nori Sahrun, Nori Novi Yanti Nur Aini Nurcahyo, Gunadi Nurcahyo, Gunadi Widi Nurdin, Yogi K Nurhidayat Nursyahrina Okfalisa Okfalisa Okfalisa, - Okmarizal, Bisma Olivia, Ladyka Febby Pandu Pratama Putra, Pandu Pratama Pati, Muhammad Ibnu Pipin Refina Afindania 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 Rahmadani Hidayat Rahman Arief Putra Rahmi Fauzana Rahmi, Nadya Alinda Rakhmad Pribowo Hariputra Ramadhan, Mukhlis Randy Permana Randy Permana Refina Afindania, Pipin Resnawita, R Retno Devita Rezki - Rezki Rusydi Rezti Deawinda Parinduri Rian Kurniawan Richi Andrianto Rico Anggara Rio Andika Malik Riszki Fadillah Ritna Wahyuni Rizki Mubarak Roza Marmay Ruri Hartika Zain Rusdianto Roestam Rusdianto Roestam Rustam, Camila 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, 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 Yerri Kurnia Febrina Yetti Fitriani Yogi K. Nurdin Yoni Aswan Yuda Irawan Yudha Aditya Fiandra Yudha Aditya Fiandra Yuhandri Yuhandri, Yuhandri Yul Antonisfia Yulasmi Yuli Hartati Yusma Elda Z Zulvitri Zakir, Supratman Zia Rahimi, Hadisha Zulharbi Zulharbi Zulvitri, Z

Title

Found 255 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 255 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 255 Documents
Search