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All Journal CommIT (Communication & Information Technology) JOIN (Jurnal Online Informatika) Sinkron : Jurnal dan Penelitian Teknik Informatika Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control GEMBIRA (Pengabdian Kepada Masyarakat) Jurnal Ilmiah Smart
Carles Juliandy
Department of Information Technology, Universitas Mikroskil
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain Andi Andi; Carles Juliandy; Robet Robet; Octara Pribadi
CommIT (Communication and Information Technology) Journal Vol. 16 No. 1 (2022): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v16i1.7958

Abstract

The rapid and dangerous spread of COVID-19 has forced governments in various countries to provide information on patients’ medical records to the public in the context of prevention. Meanwhile, patients’ medical records are vital and confidential because they contain patients’ privacy. Changing and falsifying a patient’s medical record leads to various dangerous consequences, such as mishandling which results in the patient’s death. From these problems, the research introduces a new model with a combination of blockchain technology and the Elliptic Curve Digital Signature Algorithm (ECDSA) to secure the medical records of COVID-19 patients. This model is an improvement from the model and framework proposed by previous researchers. The proposed model consists of two big parts (front and back end). Then, the simulations are carried out to measure and prove the level of security of blockchain technology in securing patient medical records. The research results show that the ECDSA algorithm can protect patients’ medical records from being opened by unauthorized parties. Then, blockchain technology can prevent changes or manipulation of patient medical records because the information recorded on the blockchain network is impossible to change and will be immutable. The research has successfully introduced a new model in securing patient medical records.
Game Development "Kill Corona Virus" for Education About Vaccination Using Finite State Machine and Collision Detection Andi; Juan Charles; Octara Pribadi; Carles Juliandy; Robet
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 4, November 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i4.1470

Abstract

COVID-19 is a disease caused by the coronavirus and causes the main symptoms in the form of respiratory problems. One way to overcome the COVID-19 pandemic is through the vaccination process. However, in practice, the public is still not educated about the importance of vaccination in preventing coronavirus infection, so it is necessary to develop a game that provides education to the public to vaccinate. This study chose games as educational media because there are many game enthusiasts and the delivery of education through games is more memorable than on other platforms. This study uses the Game Development Life Cycle (GDLC) method in the game development stage. In addition, to create intelligent coronavirus enemy NPC characters in this study, Finite State Machine (FSM) and Collision Detection methods will be implemented to detect the accuracy of players' shots. The results were obtained in the form of a game "Kill Corona Virus" which is used as a medium of education for the public about the importance of vaccination. Based on the results of the tests carried out, it was found that the implementation of the Collision Detection method in the game in detecting collisions was appropriate and quite accurate and the Finite State Machine method succeeded in creating coronavirus enemy NPCs with appropriate states. In addition, based on the results of processing respondents' answers, it is known that the ”Kill Corona Virus” game that was built can convey vaccination education messages well and make people interested in vaccinating.
Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm Andi Andi; Carles Juliandy; David David
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2023): Articles Research Volume 8 Issue 1, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i1.12145

Abstract

One of the trending topics in 2020 to 2022 is tweets about Coronavirus Disease 2019 (COVID-19). A large number of tweets regarding COVID-19 that have appeared have been mixed and not grouped properly, making it difficult for Twitter users to read and sort them based on the information they want. One solution that can be applied to overcome the problems described is through clustering of tweets information about COVID-19. In this study, researchers used quantitative research with the K-Means method, which is one of the clustering methods used in grouping data. The data used in this study is a dataset taken from Kaggle, namely Omicron-Covid-19 Variant Tweets, and also taken through a scraping process with Bright Data with a total of 4,103 datasets. The results showed that determining the best cluster using the Elbow method on the dataset produced empirical evidence that the best cluster was k = 5. The results of grouping tweets regarding COVID-19 using the K-Means Clustering method with k = 5 resulted in the largest number of cluster members being cluster 4 with 1,185 tweets, the second largest was cluster 1 with 1,047 tweets, the third largest was cluster 2 with 757 tweets, the fourth largest was cluster 3 as many as 744 tweets, and the smallest number of cluster members is cluster 5 as many as 370 tweets.
PENERAPAN SISTEM INFORMASI GEOGRAFIS PELAPORAN KECELAKAAN LALU LINTAS DI KOTA MEDAN BERBASIS WEB MENGGUNAKAN METODE HAVERSINE FORMULA Carles Juliandy; Andy Andy; Agus Siahaan
Jurnal Manajemen Akuntansi Dan Administrasi Bisnis Vol 5 No 1 (2021): Special Issue
Publisher : Sekolah Tinggi Manajemen Bisnis (STMB) MultiSmart

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62262/jmab.v5i1.223

Abstract

Menurut data yang dirilis oleh Badan Pusat Statistik (BPS) kecelakaan lalu lintas yang terjadi di Provinsi Sumatera Utara pada tahun 2017 – 2019 meningkat 5-7% setiap tahunnya. Terkait hal ini, diperlukan pelaporan kecelakaan lalu lintas yang terjadi di suatu lokasi tertentu ke kantor polisi terdekat, sehingga pihak kepolisian mengetahui bahwa ada kejadian di lokasi tersebut dan mengambil tindakan lanjut terhadap pelaporan tersebut. Kondisi saat itu ketika terjadi kecelakaan, korban atau masyarakat di sekitarnya tidak mengetahui kantor polisi terdekat untuk melakukan pelaporan kasus kecelakaan lalu lintas. Keadaan seperti ini membuat respon dari kepolisian tidak bisa secepat seharusnya yang membuat beberapa kerugian karena lamanya bantuan datang, sehingga dibutuhkan sebuah sistem informasi berupa layanan pelaporan kecelakaan lalu lintas ke kantor polisi terdekat. Dibuat sebuah model sistem informasi geografis berbasis website pelaporan kecelakaan lalu lintas ke kantor polisi terdekat dengan menggunakan pemanfaatan map leaflet API dan metode Haversine Formula. Hasil dari penelitian skripsi ini adalah sebuah model sistem informasi pelaporan kecelakaan lalu lintas yang dapat mempermudah pengguna dalam melakukan proses pelaporan serta dapat membantu pihak kepolisian dalam memproses laporan-laporan tersebut
PENGENALAN DAN PENERAPAN ENTERPRISE RESOURCE PLANNING (ERP) DAN SOFTWARE AS A SERVICE (SAAS) MELALUI DEWATALKS Darwin; Carles Juliandy; Ng Poi Wong; Bryan Febrian
Jurnal Gembira: Pengabdian Kepada Masyarakat Vol 2 No 01 (2024): FEBRUARI 2024
Publisher : Media Inovasi Pendidikan dan Publikasi

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

Abstract

Dewatalks merupakan platform penyelenggara webinar gratis setiap minggunya yang memberikan ilmu langsung dari expert yang terpercaya. Dengan harapan para peserta juga bisa dapat terinspirasi dan mengetahui lebih lanjut pengalaman di bidang-bidang tertentu, sehingga ilmu tersebut bisa digunakan untuk langsung diterapkan atau bahkan dibagikan kepada lingkungan sekitarnya yang membutuhkan. Adapun permasalahan yang dihadapi mitra adalah dalam menyelenggarakan webinar secara rutin dan topik yang berbeda-beda, sehingga selalu membutuhkan pembicara yang dapat membagikan pengetahuan dengan topik yang diminta dan di waktu yang sudah ditetapkan. Solusi untuk menyelesaikan masalah tersebut adalah dengan memberikan sharing pengetahuan pada tanggal yang sudah ditetapkan oleh Dewatalks untuk menyelenggarakan webinar tersebut. Adapun luaran yang ditargetkan adalah publikasi hasil pengabdian pada dan publikasi melalui media sosial salah satu surat kabar secara opsional
Modeling Face Detection Application Using Convolutional Neural Network and Face-API for Effective and Efficient Online Attendance Tracking Carles Juliandy; Ng Poi Wong; Darwin
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1203

Abstract

The pandemic of Covid-19 emergency has ended, but it gives us a new lifestyle every aspect of life and also in the education aspect has changed. At that moment as one of the ways to prevent pandemic infection, many governments give the policy to close the offline class and continue with online classes. The online class system encountered several problems and one of those problems was to track the students’ attendance to ensure all the students were attending the class. The teacher needed extra effort to track it because they needed to call the students one by one which is wasting time and sometimes would miss the presence of the students who attend the class. To make it effective efficient accurate and time-consuming when tracking attendance in online classes for teachers, we proposed the face detection model which combines face-api.js and CNN to detect and recognize the students’ faces to help teachers track attendance by just uploading the screenshot image of the online meeting application. We tested our model with accuracy and speed testing. With 3 images of every student’s face as training data, our model was able to recognize the face with 100% accuracy in just 41,65 seconds which is faster than calling students one by one that need almost 3 to 5 minutes if there are many students. Future research can be done by focusing research on improving the model to detect the students’ faces with different brightness, contrast, and saturation because students may not have the same place and condition when joining an online meeting class.
Co-Authors Agus Siahaan Andi Andi Andi Andi Andi Andy Andy Bryan Febrian Darwin Darwin David David Juan Charles Ng Poi Wong Ng Poi Wong Octara Pribadi Octara Pribadi Robet Robet Robet