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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 Indonesian Journal of Artificial Intelligence and Data Mining Sarwahita : Jurnal Pengabdian Kepada Masyarakat 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 Other

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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.
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.
Pengenalan Computer Vision Berbasis Convolutional Neural Network melalui Dewatalks Juliandy, Carles; Darwin, Darwin
Bahasa Indonesia Vol 21 No 01 (2024): Sarwahita : Jurnal Pengabdian Kepada Masyarakat
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/sarwahita.211.4

Abstract

The meaning of computer vision itself is to create how computers can see and understand what they see, just like humans perceive things. Some applications of computer vision around us include the use of face sensors by mobile devices to unlock phones, the use of cameras in public places to support electronic traffic tickets, and many other applications. However, in daily life, many people are still not aware of the implementation of computer vision around us. Therefore, in this community service, computer vision with the CNN algorithm is introduced to the public to better understand the potential that can be generated from computer vision. The results of this community service have achieved the initial goal of introducing computer vision to the community. Feedback from participants also indicates that 3 participants were very dissatisfied, 4 participants were dissatisfied, 14 participants were neutral, 54 participants were satisfied, and 18 participants were very satisfied. Based on the feedback provided by the participants, future advanced training will be conducted with a longer duration and more in-depth material in each session. Abstrak Makna dari computer vision sendiri adalah membuat bagaimana komputer dapat melihat dan memahami apa yang dilihat sama seperti manusia melihat sesuatu. Beberapa pemanfaatan computer vision di sekitar kita seperti penggunaan sensor wajah oleh perangkat ponsel untuk membuka kunci ponsel, pemanfaatan kamera di tempat umum untuk mendukung e-tilang, dan masih banyak pemanfaatan lainnya. Akan tetapi dalam keseharian, masih banyak masyarakat yang belum mengenali adanya penerapan computer vision di sekitar kita. Oleh karena ini pada pengabdian kali ini diperkenalkan computer vision dengan algoritma CNN ini kepada masyarakat untuk dapat lebih memahami potensi yang dapat dihasilkan dari computer vision. Hasil dari pengabdian ini mencapat tujuan yang sudah ditetapkan di awal yaitu mengenalkan computer vision kepada masyarakat. Feedback dari peserta juga menunjukkan bahwa peserta yang merasa sangat tidak puas sebanyak 3 orang, kurang puas sebanyak 4 orang, biasa saja 14 orang, puas 54 orang, sangat puas 18 orang. Berdasarkan feedback yang sudah diberikan oleh para peserta kedepannya akan dilakukan pelatihan lanjutan dengan durasi yang lebih panjang dan materi yang lebih mendalam di setiap pelatihannya.
Clustering Analysis of Tweets About COVID-19 Using the K-Means Algorithm Andi, Andi; Juliandy, Carles; David, David
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 1 (2023): Articles Research Volume 7 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.
Classification of Big Data Stunting Using Support Vector Regression Method at Stella Maris Medan Maternity Hospital Chen, Kelvin; Adriansyah, R. A. Fattah; Juliandy, Carles; Sinaga, Frans Mikael; Liko, Frederick; Angkasa, Aswin
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.31112

Abstract

This study aims to classify big data related to stunting using the Support Vector Regression (SVR) method at Stella Maris Maternity Hospital, Medan. Stunting, a condition of impaired growth in children due to chronic malnutrition and repeated infections, affects physical and cognitive development. With increasing health data, big data processing methods are essential for accurate information. SVR was chosen for handling high-dimensional and non-linear data, providing precise results. The study uses medical information, nutritional history, and socio-economic factors collected from hospital patients. The research process includes data collection, pre-processing to address missing values and outliers, normalization, and SVR application. Final results use SVR with Voting Classifier combining Support Vector Classifier (SVC), Random Forest (RF), and Gradient Boosting (GB), achieving an accuracy of 91.67%. This approach effectively identifies main stunting factors, aiding clinical decision-making and intervention programs. The study showcases big data and machine learning's potential in healthcare, serving as a model for improving health services and monitoring children's health conditions.
Forecasting Climate Change Patterns to Improving Rice Harvest Using SVR for Achieving Green Economy Juliandy, Carles; Kelvin, Kelvin; Halim, Apriyanto; Pipin, Sio Jurnalis; Sinaga, Frans Mikael; Lestari, Wulan Sri
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.32393

Abstract

The consistently declining rice harvest will cause several economic and environmental problems. The unstable and unpredictable climate change was believed as the main problem of the declining rice harvest. We proposed a method for forecasting climate change to help the farmer in their rice cultivation. We used Support Vector Regression (SVR) to improve algorithm steps such as normalizing the data and applying an Adaptive Linear Combiner (ALC) to optimize the dataset before we processed it with the algorithm. Our model gets 95% accuracy as measured with the confusion matrix. We believe our model will help the farmers in their rice cultivation with good climate forecasting. A further benefit of this research we belief that with the well-forecasted climate, the usage of pesticides will decrease and will help the vision of the Indonesian government with a green economy
Co-Authors Adriansyah, R. A. Fattah Agus Siahaan Andi Andi Andi Andi Andy Andy Angkasa, Aswin Apriyanto Halim Bryan Febrian Chen, Kelvin Darwin David David Juan Charles Kelvin Kelvin Lestari, Wulan Sri Liko, Frederick Ng Poi Wong Octara Pribadi Octara Pribadi Pipin, Sio Jurnalis Robet Robet Robet Sinaga, Frans Mikael