Garuda - Garba Rujukan Digital

p-Index From 2021 - 2026

16.128

P-Index

This Author published in this journals

Anam, M Khairul

Universitas Samudra

Author-ID : 1573830

Religion Aerospace Engineering Humanities Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Other

Published : 132 Documents Claim Missing Document

Claim Missing Document

Articles

10 11 12 13 14

ANALISIS DAN PERANCANGNAN ARSITEKTUR TEKNOLOGI PADA PERGURUAN TINGGI MENGGUNAKAN FRAMEWORK TOGAF M Khairul Anam; Rivaldi Dwi Andhika; Khusaeri Andesa; Herwin Herwin; Agustin Agustin
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3459

In general, higher education is divided into two categories of benefits from the application of Information and Communication Technology (ICT). The first is referred to as core values and supporting values. The computer network at STMIK Amik Riau currently uses the Telkom Speedy Wireless Network provider in each building. Then each student is only given 1 account to access the Hotspot network on the STMIK Amik Riau campus which can only be used on 1 laptop that is registered and capacity that has been limited or shared by network operators. However, the use of Hotspots that students want to access is only found in a few places which if used are smooth. To overcome these problems a framework is needed, one of which is TOGAF. TOGAF is a framework that has a set of supporting tools for developing enterprise architectures. TOGAF is a method that can be adapted to all changes and needs during planning. The purpose of this research is to get the gaps in the existing technology architecture and then provide recommendations in the form of the proposed technology architecture so that the existing gaps can be repaired and can run well. The results of this study found that to improve service to students, 7 application modules were needed and the need for improvements to the existing network at STMIK Amik Riau.

STACKING ENSEMBLE MACHINE LEARNING MODEL FOR EARLY DETECTION OF CHRONIC KIDNEY DISEASE IN INDONESIA Agusviyanda; Hamdani; M. Khairul Anam; Agustin; M. Ikhsan Wibowo
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6501

Chronic Kidney Disease (CKD) is one of the major health problems that continues to rise and requires accurate early detection to prevent progression to end-stage renal failure. This study proposes a hybrid machine learning approach to automatically detect CKD by combining data balancing techniques, ensemble learning, and cross-validation. The dataset used was obtained from the Kaggle platform, consisting of 1,089 patient records, and was balanced using the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance. Three boosting algorithms—Adaboost, XGBoost, and LightGBM—were used as base models and combined through a stacking approach with Logistic Regression as the meta-classifier. Evaluation was conducted using a 5-fold cross-validation scheme with accuracy, precision, recall, and F1-score as performance metrics. The results show that the stacking model achieved an average accuracy of 99.40%, outperforming individual models (LightGBM: 98.87%; Adaboost: 98.76%; XGBoost: 98.61%) and exceeding the performance of several previous studies. These findings indicate that the stacking approach, when combined with SMOTE and cross-validation, significantly enhances classification performance for CKD detection.

Implementation of Cloud Computing Based on Infrastructure as a Service (IaaS) to Improve Transaction Quality (Case Study Shop of Central Mart Pekanbaru) Eva Yumami; Irfansyah Irfansyah; M Khairul Anam; Hamdani Hamdani
JURNAL TEKNOLOGI DAN OPEN SOURCE Vol. 6 No. 1 (2023): Jurnal Teknologi dan Open Source, June 2023
Publisher : Universitas Islam Kuantan Singingi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36378/jtos.v6i1.3127

An virtually infinite number of connected information and communication technology (ICT) resources can be found using a method known as cloud computing. Customers can use these resources on-demand over a network in the form of a public IP because both infrastructure and applications are fully owned and managed by third parties.To enhance staff performance and services in the context of transactions made by parties engaged in the buying and selling industry, a computer-based system is required, particularly for cashiers who handle customer payment transactions. There are still a lot of cashier programs available today that can only be accessed via a device linked to the same network or over the local network.In order to facilitate transactions and enable remote control, this research makes use of cloud computing technology that employs Infrastructure as a Service (IaaS) offerings. IaaS is a service that "rents" out fundamental information technology resources, such as storage space, computing power, memory, operating systems, network capacity, and others, so that customers can use them to execute their applications.Azure gives developers access to tools like Visual Studio and the ability to construct applications in a variety of languages, including.NET, Java, and Node.js. Because businesses don't have to worry about the expense of server equipment, implementing cloud computing can make it simpler for them to manage their business apps and finances. The ability for store administrators to use this program remotely (online) may then be aided or made simpler by this IaaS solution.

Implementation of a Medicine Distribution System to Determine Health Assistance Priorities for Flood-Affected Communities in Aceh Tamiang M. Khairul Anam; Tjut Rizqi Maysyarah Hadi; Michel Kasaf; Agusviyanda; M. Ikhsan Wibowo
JURIBMAS : Jurnal Hasil Pengabdian Masyarakat Vol 5 No 1 (2026): Juli 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juribmas.v5i1.979

Flooding in Kasih Sayang Village, Manyak Payed District, Aceh Tamiang Regency created challenges in the distribution of health assistance, particularly in data collection, prioritizing recipients, and recording medicine distribution. This community service activity aimed to implement a medicine distribution system to support the prioritization of health assistance for flood-affected communities. The methods used included initial visits, observation, interviews, questionnaires, program implementation, and partner response evaluation. The program was carried out through the symbolic handover of medicines and medical equipment to the village midwife and village head, followed by a trial of the medicine distribution application with partners. The results showed that the application could be used properly by partners and helped make recipient data management more organized, faster, and systematic. The evaluation involving the village head, village officials, and village midwife showed an average score of 3.72, which was categorized as very high. The highest score was found in the need for a system to determine aid recipient priorities at 3.90, followed by the urgency of post-flood health assistance at 3.85. Overall, this program provided practical benefits in supporting a more orderly, transparent, and accountable management of health assistance at the village level.

Benchmarking Graphics Rendering Capabilities: Java Processing vs. P5.js Muhammad Bambang Firdaus; Adi Surya Darma; Zainal Arifin; M. Khairul Anam; Muhammad Yusuf Halim; Arda Yunianta
Advance Sustainable Science Engineering and Technology Vol. 8 No. 1 (2026): November - January
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i1.2036

Rendering efficiency is a critical factor in cross-platform animation development. This study benchmarks the performance of Java Processing and P5.js by measuring frame rates and frame counts across six heterogeneous computing devices for 2D and 3D animation tasks. Each benchmark was executed under standardized conditions for 60 seconds, and performance data were collected at fixed intervals. Results indicate that Java Processing consistently achieves higher rendering efficiency, with up to 313% greater frame rates and 265% higher frame counts compared to P5.js, particularly in computationally intensive 3D scenarios. These differences are attributed to Java Processing’s compiled execution and direct OpenGL integration, while P5.js performance is constrained by browser-based execution and limited GPU utilization. The findings suggest Java Processing is preferable for high-performance simulations and complex visualizations, whereas P5.js remains effective for lightweight web-based 2D applications.

Pendidikan Darurat dan Pemulihan Psikososial Anak Pascabencana Banjir Bandang di Desa Tualang Baro Michel Kasaf; Tjut Rizqi Maysyarah Hadi; Jetno Harja; M Khairul Anam; Taufik Taufik; TM Rezaka Alfitra; Alfisyahrin Alfisyahrin
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/a1dvy087

Bencana banjir bandang yang melanda Desa Tualang Baro, Kecamatan Manyak Payed, Kabupaten Aceh Tamiang, memberikan dampak serius pada sektor pendidikan berupa kerusakan fasilitas fisik dan trauma psikologis yang menurunkan motivasi belajar anak. Masalah spesifik yang dihadapi mitra adalah hilangnya perlengkapan alat tulis siswa serta terbatasnya akses edukasi yang mampu memulihkan kesiapan mental anak pascabencana. Kegiatan Pengabdian kepada Masyarakat ini bertujuan untuk memulihkan kondisi psikososial, meningkatkan kembali semangat belajar, serta menjaga keberlanjutan pendidikan anak melalui intervensi darurat yang terencana. Metode yang digunakan adalah pendekatan partisipatif yang melibatkan dosen dan mahasiswa melalui tiga tahapan utama: persiapan kebutuhan logistik, pelaksanaan edukasi interaktif selama tiga hari, serta evaluasi respon partisipan. Hasil kegiatan menunjukkan bahwa distribusi paket alat tulis yang terdiri dari buku, alat gambar, dan perlengkapan sekolah lainnya berhasil memenuhi kebutuhan dasar belajar siswa. Indikator keberhasilan terlihat dari antusiasme dan partisipasi aktif anak-anak dalam sesi belajar bersama, di mana terjadi peningkatan kepercayaan diri dan pengurangan tanda-tanda trauma secara bertahap melalui pendekatan yang menyenangkan. Kesimpulannya, integrasi antara penyediaan sarana belajar fisik dan pendampingan edukatif merupakan strategi awal yang proporsional dan efektif dalam mendukung pemulihan pendidikan anak di situasi krisis.

Optimized Ensemble Learning Using Boosting, Stacking, and Voting for Early Stunting Risk Prediction Munawir Munawir; M. Khairul Anam; Liza Fitria; Nurul Fadillah
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026 (in progress)
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7107

Stunting remains a pressing health issue in Indonesia’s coastal communities, where uneven access to nutrition services and maternal–child care can limit early prevention efforts. This study develops a machine learning framework to estimate stunting risk among children under five in the eastern coastal region of Aceh. The modeling process began with data cleaning and preparation, followed by class rebalancing with SMOTE and automated parameter tuning using Optuna. Four standalone classifiers were evaluated: Logistic Regression, Gaussian Naïve Bayes, Support Vector Machine, and Random Forest. Their outputs were then extended through three ensemble configurations, namely XGBoost-assisted boosting for each baseline model, a stacking scheme named Stackstun with Logistic Regression as the final learner, and an optimized weighted soft-voting model referred to as Votsstun. Performance was measured using accuracy, precision, recall, and F1-score. Among the single classifiers, Logistic Regression achieved the best result, with an accuracy of about 0.93. The strongest overall performance was obtained by the boosted Random Forest model, which reached an accuracy of 0.9952 and produced almost perfect class-level precision, recall, and F1-score. Votsstun also performed consistently, recording an accuracy of approximately 0.986, while its macro and weighted F1-scores approached 0.99. These results indicate that the combined use of class rebalancing, automated optimization, and ensemble learning can improve the robustness of stunting-risk classification. The proposed framework may assist local health agencies in identifying vulnerable children earlier, prioritizing limited intervention resources, and strengthening prevention programs in coastal communities.

Sistem Prediksi Permintaan Barang Berat dengan Memanfaatkan Data Historis Penjualan Menggunakan Long Short-Term Memory (LSTM)TERM MEMORY (LSTM) Ginda Maruli Andi Siregar; Khairul Anam; Saiyaratul Mawaddah
Journal of Informatic and Information Security Vol. 7 No. 1 (2026): Juni 2026
Publisher : Program Studi Informatika, Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/w0zqt762

Inventory management is an important aspect in maintaining the effectiveness of business operations, particularly in building material stores where demand fluctuations can affect stock availability. Inaccurate inventory planning may lead to overstock or stockout conditions, resulting in increased operational costs and reduced customer satisfaction. This study aims to develop a demand forecasting system for building materials using the Long Short-Term Memory (LSTM) method based on historical sales data at Toko Bangunan Beu Sukses. The dataset used consists of daily sales data from January 2024 to October 2025 covering six products, namely steel, cement, paint, pipes, zinc roofing, and plywood. Data preprocessing was performed through logarithmic transformation, differencing, normalization using MinMaxScaler, and sequence formation using the sliding window method. The LSTM model was trained and evaluated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The evaluation results indicate that the proposed model achieved a high level of forecasting accuracy, with all products obtaining MAPE values below 2%. Furthermore, the developed model was successfully integrated into a web-based application to support inventory management and decision-making processes. The results demonstrate that the LSTM method can effectively predict building material demand and support more efficient inventory management.

MYCD: Integration of YOLO-CNN and DenseNet for Real-Time Road Damage Detection Based on Field Images Helda Yenni; Rometdo Muzawi; Karpen Karpen; M. Khairul Anam; Michel Kasaf; Tjut Rizqi Maysyarah Hadi; Dewi Sari Wahyuni
Journal of Applied Data Sciences Vol 7, No 1: January 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i1.1040

Road damage such as cracks, potholes, and uneven surfaces poses serious risks to transportation safety, logistics efficiency, and maintenance budgeting in Indonesia. Manual inspection is time consuming, labor intensive, and prone to error, motivating the use of reliable computer vision solutions. This study proposes MYCD, a hybrid and mobile ready architecture that combines the fast detection ability of YOLO with the dense feature reuse of DenseNet, enhanced by the Convolutional Block Attention Module (CBAM) for spatial and channel focus and Spatial Pyramid Pooling (SPP) for multi scale context understanding. The system detects and classifies the severity of road damage into minor, moderate, and severe categories using images captured by standard cameras. MYCD was trained and validated on 1,120 field images using an 80/20 split to simulate realistic deployment. Validation achieved 64 percent accuracy, with the highest per class precision of 0.72 for minor damage and mAP@0.5 = 0.677. The confusion matrix showed that most errors occurred in the moderate category because of visual similarity with minor and severe damage. Unlike earlier studies that extended YOLO with heavy backbones such as ResNet or EfficientNet, MYCD focuses on feature propagation (DenseNet), attention precision (CBAM), and multi scale fusion (SPP) optimized for real time operation on standard hardware. Efficiency profiling confirmed its deployability. After compression, the model size is 46.8 MB and it requires 3.7 GFLOPs per inference at 640×640 resolution. On a mid-range Android device (Snapdragon 778G, 8 GB RAM), MYCD runs at 19 frames per second with 1.2 GB peak memory. Compared with YOLOv8 WD (68 MB; 5.2 GFLOPs), MYCD reduces computation by 31 percent while maintaining similar accuracy. Overall, MYCD achieves a practical balance of speed, accuracy, and efficiency, providing a deployable and reproducible framework for real time road damage detection in resource limited settings.

CLaGAtt: A Hybrid CNN-LSTM-GRU-Attention Model for Stunting Classification Based on Anthropometric Sequences Sofiansyah Fadli; Ahmad Tantoni; Novia Arista; M. Khairul Anam; Muhammad Bambang Firdaus
Journal of Applied Data Sciences Vol 7, No 3: September 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i3.1454

Stunting is a chronic nutritional problem that requires accurate early identification because it affects child growth, cognitive development, and long-term human capital. This study adapts the CLaGAtt model, a hybrid CNN–LSTM–GRU–Attention architecture, for stunting classification using anthropometric sequence data. Rather than proposing a new deep learning architecture, the main contribution of this study lies in adapting the existing CLaGAtt framework through an integrated preprocessing pipeline, sequence construction strategy, class balancing using SMOTE, and an evaluation protocol specifically designed for stunting prediction. The preprocessing pipeline included irrelevant-column removal, data transformation, label encoding, standard scaling, class balancing using SMOTE, and sequence generation with a time step of five and a step of one. Three train–test split scenarios were evaluated, namely 90:10, 80:20, and 70:30. Experimental results showed that the 90:10 split produced the best performance, with 91.42% accuracy, 91.50% precision, 91.50% recall, and 91.43% F1-score. The 80:20 and 70:30 scenarios achieved 87.14% and 85.71% accuracy, respectively, indicating that larger training proportions improved model generalization in the available dataset. These findings suggest that the adapted CLaGAtt framework can effectively integrate convolutional feature extraction, sequential learning, and temporal attention for stunting classification from structured anthropometric data. Future work should validate the model on external datasets and integrate regional visualization to support priority intervention mapping.

Co-Authors -, Tashid Abrar Hadi Ade Riyanda Putra Adi Surya Darma Agustin Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Agusviyanda Ahmad Ihsan Ahmad Tantoni Ahmad Zamsuri Ahmad Zamsuri, Ahmad Aisum Aliyah Sari Akram, Rizalul Al Amin Fadillah Sani Alfa Saleh Alfisyahrin Alfisyahrin Alkadri Masnur Ambiyar, Ambiyar Andesa, Khusaeri Andi Supriadi Chan, Andi Supriadi Anwar, Reksi Aprillian Kartino Arba, Muhammad Hendra Arda Yunianta Arda Yunianta Arief Hidayat Arita Fitri, Triyani Arsyah, Ulya Ilhami Atalya Kurnia Sari Atmaja, Teuku Hadi Wibowo Ayu Mahessya, Raja Bambang Kurniawan Br.Situmorang, Elisabet Sinta Romaito Budiman, Edy Budiman, Edy Bunga Nanti Pikir Bunga Nanti Pikir Chatarina Umbul Wahyuni Cut, Banta Damar Sanggara Habibie Daryanto, Diki Dea Safitri Dedy Irfan Devi Yuliana Dewi Sari Wahyuni Dewi Sari Wahyuni Didik Sudyana Didik Sudyana Diki Daryanto Diky Daryanto Eddy Kurniawan Pradana Efrizoni, Lusiana Emerlada, Esi Tri Erlin Erlin Erlinda, Susi Ersan Fadrial, Yogi Esi Tri Emerlada Fadli Suandi Fahrul Yamani Fajar Arifandi Fajrizal Fatdha, T.Sy. Eiva Faza Alameka Fernando Elda Pati Fika Felanda Ardelia Firdaus, Muhammad Bambang Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Fryonanda, Harfebi Gendhy Dwi Harlyan Gubtha Mahendra Putra Gunadi Gunawan, Chichi Rizka Habibi Ulayya Hadi Asnal, Hadi Hairah, Ummul Hamdani Hamdani Hamdani - Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Hanif Aulia Happy Yugo Prasetiya Haris Kurniawan, Haris Hasan J. Alyamani Haviluddin Haviluddin Hazira, Nadila Helda Yeni Helda Yenni Helda Yenni, Helda Hendra Saputra Hendrawan, Riki hendri, nofri Herianto Herianto Herwin Herwin Herwin Herwin, Herwin Ike Yunia Pasa Ikhsan Ikhsan Imam Andhika Indah Mukhlis Tamara Indra Prayogo Indra Prayogo Indri Febrianti Irfan Putra Pratama Irfansyah Irfansyah Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Irzal Arif Wisky Istianah Istianah Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Jetno Harja Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Karfindo, Karfindo Karpen Karpen Kartina Diah K. W. Kharisma Rahayu Khusaeri Andesa Khusaeri Andesa Kresnapati, I Nyoman Bagus Aji Kudadiri, Parlindungan Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah Lathifah, Lathifah Latifah Liza Fitria Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Lusiana Lusiana Efrizoni Lusiana Efrizoni Lusiana Lusiana M Syauqi Hafizh M. Ikhsan Wibowo Machdalena Mahamad, Abd Kadir Mahendra, Muhammad Ihza Mardainis Mardainis Mardainis Martilinda Panjaitan Mega Susanti Mega Susanti Melda Royani Michal Dennis Michel Kasaf Mi`rajul Rifqi Mohamad, Nur Ikhwan Bin Muhaimin, Abdi Muhamad Jamaris Muhamad Sadar Muhamad Sadar, Muhamad Muhammad Bambang F Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Bambang Firdaus Muhammad Budi Saputra muhammad Fuad Muhammad Ikhsan Wibowo Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Muhammad Yusuf Halim Munawir Munawir Munawir Munawir Munawir Mutiana Pratiwi N.A, Randi Nadila Rahmadhani Nadya Alinda Rahmi Nadya Satya Handayani Nanda, Novianda Nanda Nariza Wanti Wulan Sari Nasrul Sani Neci Nirwanda Nisa, Aida Nora Lizarti Novi Yona Sidratul Munti Novia Arista Nu'man, Nu'man Nurhuda, Agus Tri Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurul Fadillah Nurul fadillah, Nurul Nurul Indriani Nurwijayanti Pandu Pratama Putra, Pandu Pratama Paradila, Dinda Parlindungan Kudadiri Permana, Randy Pradipta , Rahman Pranata, Angga Purwanto Putra, Ryanda Satria Rahmaddeni Rahmaddeni Rahmaddeni Rahmaddeni Rahmi, Nadya Alinda Rahmiati Rahmiati Rahmiati Rebecca La Volla Nyoto Refni Wahyuni Reksi Anwar Rini Yanti Rini Yanti Rini Yanti Rinno Hendika Putra Rio Andika Malik Rivaldi Dwi Andhika Rohana Yola Parastika Hutasoit Rohmat Romadhoni Rometdo Muzawi Ruri Hartika Zain Saiful Bukhori Saiyaratul Mawaddah Salsabila Rabbani Salsabila Rabbani Saon, Sharifah Saputra, Eko Ikhwan Sari Irma Yani Sitorus Sari, Atalya Kurnia Sarjon Defit Silvyana Dwi Putri Sofiansyah Fadli Sofiansyah Fadli Soni Sovia, Rini suaidah suaidah Sumijan Sumijan Susandri, Susandri Susanti Susanti Susanti Susanti Susanti Susanti Susanti, Mega Susanti, Susanti SUSI ERLINDA Susi Erlinda Susi Erlinda Syam, Salmaini Safitri Syamsiar, Syamsiar T. Sy. Eiva Fatdha Taruk, Medi Tashid Tashid Tashid Tatang Hidayat Taufik Taufik Tejawati, Andi Tengku Alvin Firdaus Teri Ade Putra Tjut Rizqi Maysyarah Hadi TM Rezaka Alfitra Torkis Nasution Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari, Tri Putri Triyani Arita Fitri Ulfah, Aniq Noviciate Wahyudianto, Mochamad Rizky Waksito, Alan Zulfikar Waskita, Ghozi Indra Wifra, Rizki Wirta Agustin Wirta Agustin Yaakub, Saleh Yansyah Saputra Wijaya Yenni, Heda Yesaya Twin Situmorang Yesri Elva Yogi Ersan Fadrial Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yudhistira, Dewangga Yumami, Eva Zainal Arifin Zeki Kurniadi zeki Kurniadi Zupri Henra Hartomi

Title

Found 132 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 132 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 132 Documents
Search