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Anam, M Khairul

Universitas Samudra

Author-ID : 1573830

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Implementation of Cloud Computing Based on Infrastructure as a Service (IaaS) to Improve Transaction Quality (Case Study Shop of Central Mart Pekanbaru) Yumami, Eva; Irfansyah, Irfansyah; Anam, M Khairul; 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.

Framework for Analyzing Netizen Opinions on BPJS Using Sentiment Analysis and Social Network Analysis (SNA) Anam, M Khairul; Mahendra, Muhammad Ihza; Agustin, Wirta; Rahmaddeni, Rahmaddeni; Nurjayadi, Nurjayadi
INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Vol 6 No 1 (2022): February 2022
Publisher : Universitas Nusantara PGRI Kediri

The Social Security Administrative Body is a legal entity established to administer social security programs. News about BPJS policies is often found online and social media that has received responses from netizens as a form of public opinion on the policy. One of them is the opinion of netizens on social media Twitter. Ideas can be positive, neutral, or negative. These opinions are processed using the Support Vector Machine (SVM) method, in some SVM studies still getting unsatisfactory results, with rates below 60%. For this reason, it is necessary to have feature selection or a combination with the other methods to obtain higher accuracy. To see the actors who influence the opinion of netizens on the topic of BPJS, the Social Network Analysis (SNA) method is used. Based on the SVM Method's test results, the best accuracy results are obtained in combining the SVM Method with Adaboost, with an accuracy rate of 92%. Compared to the pure SVM method by 91%, the Combination of SVM Particle Swarm Optimization (PSO) by 87% and SVM using Feature Selection Genetic Algorithm (GA) by 86%.

Ambulance tracking system using GPS module and IoT based telegram messenger to find fastest route Akram, Rizalul; Novianda, Novianda; Atmaja, Teuku Hadi Wibowo; Anam, M. Khairul; Cut, Banta
International Journal of Advances in Applied Sciences Vol 14, No 4: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v14.i4.pp1322-1331

Traffic congestion in urban areas affects ambulance trips to hospitals. This research aims to find the fastest route for ambulances to travel. The fastest route has criteria such as road shape, road width, shortest distance traveled, and fewer road users. This detection system applies internet of things (IoT) technology to each ambulance equipped with global positioning system (GPS), NodeMCU, and Wi-Fi modem that can send GPS coordinates to the cloud server, which will then mark the shortest distance from its current location to the hospital through the place where the emergency call is raised. The components used in this research are Neo6M GPS, NodeMCU ESP8266, cloud computing, and smartphone. This system can provide realtime information on all ambulance positions via android applications and Telegram messenger. The results obtained can determine the fastest path, distance, and travel time. In addition, the operation of this system takes 2-3 minutes to find the GPS signal at the beginning, then there is a 1-2 second delay from the GPS Tracking movement. Testing the route accuracy of this system and google maps by driving by motorcycle shows the results of this GPS system are more accurate in terms of distance and travel time.

Benchmarking Graphics Rendering Capabilities: Java Processing vs. P5.js Firdaus, Muhammad Bambang; Darma, Adi Surya; Arifin, Zainal; Anam, M. Khairul; Halim, Muhammad Yusuf; Yunianta, Arda
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.

MYCD: Integration of YOLO-CNN and DenseNet for Real-Time Road Damage Detection Based on Field Images Yenni, Helda; Muzawi, Rometdo; Karpen, Karpen; Anam, M. Khairul; Kasaf, Michel; Hadi, Tjut Rizqi Maysyarah; Wahyuni, Dewi Sari
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.

Robust Predictive Model for Heart Disease Diagnosis Using Advanced Machine Learning Techniques Sovia, Rini; Anam, M. Khairul; Wisky, Irzal Arief; Permana, Randy; Rahmi, Nadya Alinda; Zain, Ruri Hartika
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.v7i1.1092

This study presents a hybrid ensemble learning framework designed to enhance the predictive accuracy, robustness, and generalizability of heart disease classification models. The framework integrates three base classifiers: Decision Tree (DT), Gaussian Naive Bayes (GNB), and K Nearest Neighbor (KNN), which are combined using a stacking ensemble method with Logistic Regression (LR) as the meta learner. Each classifier contributes a distinct analytical perspective: DT models nonlinear relationships, GNB provides probabilistic reasoning, and KNN captures similarity-based patterns. Logistic Regression aggregates their outputs to produce a unified predictive decision. To mitigate class imbalance commonly observed in clinical datasets, the Synthetic Minority Oversampling Technique (SMOTE) is applied to generate synthetic samples of the minority class, improving the model’s ability to recognize underrepresented cases. Hyperparameter optimization is performed using the Optuna framework, which applies the algorithm to efficiently explore parameter configurations. The proposed model was evaluated on a publicly available heart disease dataset and achieved an accuracy of 99.61%, precision of 99.62%, recall of 99.59%, F1 score of 99.60%, and specificity of 99.58%, corresponding to a false positive rate of only 0.42 percent. These results demonstrate the framework’s strong ability to accurately identify heart disease cases while minimizing misclassification. The integration of SMOTE, stacking, and Optuna optimization contributes to its superior performance and robustness. Consequently, this approach shows strong potential for integration into clinical decision support systems to assist healthcare professionals in reliable and timely diagnosis.

The Role of Machine Learning in Modern Football Analytics: A Systematic Review of Approaches and Their Implications Waskita, Ghozi Indra; Kurniawan, Haris; Yudhistira, Dewangga; Mohamad, Nur Ikhwan Bin; Anam, M. Khairul
JSES : Journal of Sport and Exercise Science Vol. 8 No. 2 (2025): September
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/jses.v8n2.p178-186

Purpose: Football has increasingly become a multidisciplinary field that integrates not only physical and tactical elements but also technological advancements to enhance decision-making. One of the prominent developments in this domain is the application of machine learning (ML) techniques to analyze match-related data, assess player performance, and optimize team strategies. This study aims to conduct a systematic literature review of contemporary research that employs machine learning algorithms within the context of football. Materials and Methods: A total of 50 scientific articles were initially retrieved from various reputable databases. Following a rigorous screening and eligibility assessment, 30 articles were selected for detailed analysis. Result: These studies employ diverse machine learning approaches, including Support Vector Machines (SVMs), Random Forests, XGBoost, Deep Learning, and clustering methods, for a wide range of purposes, such as match outcome prediction, player performance evaluation, injury detection, and playing position classification. The findings of this review underscore the potential of machine learning to contribute significantly to data-driven decision-making in football, providing valuable insights for coaches, performance analysts, and club management. Conclusion: Furthermore, this study identifies key challenges that remain, including data quality, data availability, and the interpretability of complex models. This review will serve as a critical reference for researchers and practitioners advancing intelligent technologies in sports, with particular emphasis on football.

Development of Knowledge Management System to Improve the Performance of the New Student Admission System for Higher Education Anam, M. Khairul; Fitri, Triyani Arita; Zoromi, Fransiskus; Junadhi, Junadhi; Nu'man, Nu'man
JISA(Jurnal Informatika dan Sains) Vol 5, No 2 (2022): JISA(Jurnal Informatika dan Sains)
Publisher : Universitas Trilogi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31326/jisa.v5i2.1443

The New Student Admission System (PMB) is the main door or core business of the University and requires a good management system. Every Academic Year STMIK Amik Riau forms a committee to carry out this PMB activity. The PBM committee consists of several parts, namely the promotion section, the registration section and the selection section. Each section carries out knowledge sharing or knowledge transfer in carrying out its duties. This knowledge sharing is only limited to informal or formal communication through meetings so that the knowledge sharing process has not been carried out optimally. The purpose of this study was (1) to measure the readiness of human resources in the application of knowledge sharing in terms of the dimensions of knowledge, culture, technology and dimensions and (2) to develop knowledge sharing features in the PMB system to support decision making quickly to increase the business value of the institution. The stages used in this KMS were The 10-Step Knowledge Management Roadmap while the evaluation of the application of KMS used the SECI model. The results obtained in this study are a system that helps new PMB officers learn the STMIK Amik Riau PMB system. so that the new PMB officer does not ask the old officer again.

Penerapan Na ÌˆÄ±ve Bayes Classifier, K-Nearest Neighbor (KNN) dan Decision Tree untuk Menganalisis Sentimen pada Interaksi Netizen dan Pemeritah M. Khairul Anam; Bunga Nanti Pikir; Muhammad Bambang Firdaus; Susi Erlinda; Agustin Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 21 No. 1 (2021)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v21i1.1092

Pemerintah Pekanbaru saat ini sudah menerapkan teknologi dalam sistem pemerintahan, penerapannya saat ini masih mendapat keluhan dari masyarakat seperti layanan publik command center yang hanya sebagian masyarakat mengetahuinya dan penerapan cctv yang ada di Alat Pemberi Isyarat Lalu Lintas (APILL) yang belum berfungsi dengan baik. Penerapan teknologi lainnya oleh Pemerintah Pekanbaru dapat kita lihat dari keberadaan portal-portal web situs resmi Pemerintah. Sedangkan untuk melihat beragam komentar netizen dari twitter. Twitter menjadi tempat untuk mendapatkan data yang diungkapkan masyarakat melalui tweets yang diposting ke timeline. Analisa sentimen dilakukan untuk melihat pendapat atau kecenderungan opini netizen terhadap pemerintah Pekanbaru yang mengandung sentimen positif, negatif, dan netral. Data yang digunakan adalah tweet dengan jumlah dataset sebanyak 150 tweets. Data tersebut kemudian di analisa agar menjadi informasi. Analisa dilakukan menggunakan metode data mining yaitu NaÃ¯ve Bayes Classifier, K-Nearest Neighbor (KNN), dan Decision tree. Penggunaan ketiga pendekatan ini berupaya untuk mengkategorikan hasil komentar netizen terkait penggunaan teknologi yang telah melalui proses analisis sentimen dan membandingkan keakuratan ketiga cara tersebut. Hasil akurasi yang didapatkan cukup beragam yaitu dari metode NaÃ¯ve Bayes akurasi 100%, metode KKN akurasi 98,25%, dan metode decision tree akurasi 62,28%.

The Application of Usability Testing to Analyze the Quality of Android-Based Acupressure Smart Chair Applications M. Khairul anam; Esi Tri Emerlada; Susi Erlinda; Tashid Tashid; Torkis Nasution
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 22 No. 2 (2023)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v22i2.2312

A smart chair is a reflection smart chair that utilizes waste tires as an alternative to acupuncture. Smart chairs are designed for people who are phobic about acupuncture needles by replacing these needles with waste tires. Acupuncture smart chairs also make it easier for users without having to go to the acupuncture practice place. This smart chair is equipped with an application that is directly connected to android. The smart chair application is an android-based remote control where users can control the application remotely. However, this application has not been tested so it is not yet known how effective and efficient the use of the application is. Therefore, researchers would conduct testing by using the usability testing method. The usability testing method is a method carried out to measure the ease of the application that has been made. The analysis in this method used five evaluation components, namely learnability, efficiency, memorability, errors, and satisfaction. This research would make instruments based on usability testing and then distribute instruments to samples by using sampling techniques. The results of this study showed a variable learnability value was 65% while the efficiency variable got a value of 74%. In terms of memorability, its value was 59%, then the Errors variable value was 74%, and the last variable, namely satisfaction, reached a value of 74%.

Co-Authors -, Tashid Abrar Hadi Ade Riyanda Putra Agustin Agustin Agustin Agustin Agustin Agustin Agusviyanda Agusviyanda Agusviyanda Agusviyanda Ahmad Ihsan Ahmad Zamsuri Ahmad Zamsuri, Ahmad Aisum Aliyah Sari Akram, Rizalul Al Amin Fadillah Sani Alfa Saleh Alfisyahrin, Alfisyahrin Alfitra, TM Rezaka Alkadri Masnur Ambiyar, Ambiyar Andesa, Khusaeri Andhika, Imam 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 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 Darma, Adi Surya Daryanto, Diki Dea Safitri Dedy Irfan Devi Yuliana Dewi Sari Wahyuni Didik Sudyana Didik Sudyana Diki Daryanto Diky Daryanto Dona Wahyuning Laily Eddy Kurniawan Pradana Efrizoni, Lusiana Elangga Sony Widiharsono Elva, Yesri Emerlada, Esi Tri Erlin Erlin Erlinda, Susi Ersan Fadrial, Yogi Esi Tri Emerlada Fadli Suandi Fahrul Yamani Faisol Mas’ud 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 Fuquh Rahmat Shaleh Gendhy Dwi Harlyan Gubtha Mahendra Putra Gunadi Gunanti Mahasri Gunawan, Chichi Rizka Habibi Ulayya Hadi Asnal, Hadi Hairah, Ummul Halim, Muhammad Yusuf Hamdani Hamdani - Hamdani . Hamdani Hamdani Hamdani Hamdani Hamdani Hamdani Handayani, Nadya Satya Hanif Aulia Happy Yugo Prasetiya Haris Kurniawan, Haris Harja, Jetno Hartomi, Zupri Henra Hasan J. Alyamani Haviluddin Haviluddin Hazira, Nadila Helda Yeni Helda Yenni, Helda Hendra Saputra Hendrawan, Riki hendri, nofri Herianto Herianto Herwin Herwin Ika Purnamasari Ike Yunia Pasa Ikhsan Ikhsan Indah Mukhlis Tamara Indra Prayogo Indra Prayogo Indri Febrianti Irfan Putra Pratama Irfansyah Irfansyah Irfansyah Irfansyah Irsyad, Akhmad Irwanda Syahputra Irwanda Syahputra Irzal Arief Wisky Istianah Istianah Jamaris, Muhamad Jamaris, Muhammad Jasmarizal Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Kadek Mirnawati Karfindo, Karfindo 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 Lia Oktavia Ika Putri Lilis Cahaya Septiana Liza Fitria Lucky Lhaura Van FC Lucky Lhaura Van FC, Lucky Lhaura Lusiana Lusiana Efrizoni Lusiana Lusiana M Syauqi Hafizh M. Ikhsan Wibowo Machdalena Mahamad, Abd Kadir Mahendra, Muhammad Ihza Mahessya, Raja Ayu 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 Budi Saputra muhammad Fuad Muhammad Ikhsan Wibowo Muhammad Nur Ihwan Muhammad Wisdan Pratama Putra Munawir Munawir Munawir N.A, Randi Nadila Rahmadhani Nadya Alinda Rahmi Nanda, Novianda Nanda Nariza Wanti Wulan Sari Nasrul Sani Neci Nirwanda Nina Nurmalia Dewi Nisa, Aida Nora Lizarti Novi Yona Sidratul Munti Nu'man, Nu'man Nurhuda, Agus Tri Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurjayadi Nurkholifah Dwi Rahayu Nurul fadillah, Nurul Nurul Indriani Nurwijayanti Pandu Pratama Putra, Pandu Pratama Paradila, Dinda Parlindungan Kudadiri Permana, Randy Pradipta , Rahman Pranata, Angga Pratiwi, Mutiana 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, Rometdo Ruri Hartika Zain Saiful Bukhori Salman Aldo Alfaresi Salsabila Rabbani Salsabila Rabbani Saon, Sharifah Saputra, Eko Ikhwan Sari Irma Yani Sitorus Sari, Atalya Kurnia Sarjon Defit Setyantini, Woro Hastuti 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 Torkis Nasution Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari Tri Putri Lestari, Tri Putri Triyani Arita Fitri Ulfah, Aniq Noviciate Wahyudianto, Mochamad Rizky Wahyuni, Dewi Sari Waksito, Alan Zulfikar Waskita, Ghozi Indra Wifra, Rizki Wirta Agustin Wirta Agustin Yaakub, Saleh Yansyah Saputra Wijaya Yenni, Heda Yesaya Twin Situmorang Yogi Ersan Fadrial Yogi Yunefri, Yogi Yoyon Efendi Yuda Irawan Yudhistira, Dewangga Yumami, Eva Zainal Arifin Zeki Kurniadi zeki Kurniadi

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