Article Per Year (5 Year)
p-Index From 2020 - 2025
1.665
P-Index
This Author published in this journals
All Journal Techno.Com: Jurnal Teknologi Informasi Jusikom : Jurnal Sistem Komputer Musirawas Jurnal Informatika Global Journal of Embedded Systems, Security and Intelligent Systems Journal of Intelligent Networks and IoT Global
Ricky Maulana Fajri
Universitas Indo Global Mandiri
Computer Science & IT Engineering

Published : 13 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 13 Documents
Search

 1   2 
Pengembangan Sistem Informasi Data Kesehatan Kabupaten Banyuasin Berbasis Model View Controller Ricky Maulana Fajri
Techno.Com Vol 16, No 3 (2017): Agustus 2017
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.089 KB) | DOI: 10.33633/tc.v16i3.1419

Abstract

Data kesehatan adalah sebuah data yang sangat penting bagi sebuah instansi dinas. Khususnya dinas kesehatan Banyuasin, setiap tahun data kesehatan digunakan sebagai pedoman penentuan kebijakan kabupaten Banyuasin. Namun, dikarenakan luasnya wilayah kerja dari dinas kesehatan sehingga mengakibatkan tidak seragamnya data yang diinput oleh setiap operator dari puskesmas. Hal ini berakibat pada tidak validnya data kesehatan dari dinas kesehatan. Sehingga penelitian ini bertujuan untuk membangun sebuah sistem informasi data kesehatan sehingga data yang sebelumnya tidak satu format menjadi sama dan menjadi terintegrasi. Adapun arsistektur model view controller dipilih karena arsitektur tersebut memiliki kelebihan seperti memudahkan sistem pengembangan dan memiliki modularitas yang tinggi. Hasil penelitian ini dapat digunakan oleh dinas kesehatan Banyuasin dalam menyusun laporan data kesehatan setiap tahunnya. Kata kunci—Sistem Informasi, Model View Controller, Modularitas
RANCANG BANGUNG APLIKASI ENKRIPSI DENGAN MENGGUNAKAN METODE RSA BERBASIS WEB Ricky Maulana Fajri
Jurnal Informatika Global Vol 7, No 1
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (353.571 KB) | DOI: 10.36982/jiig.v7i1.145

Abstract

The use of the internet has boost up the interaction of the data and information. The internet has made the transfer of information become faster and reliable. However, there are many malicious applications which consider would violate the information. Therefore, there is a need for a network security to make sure the security on the internet. Network security has three main goals, namely confidentiality, integrity and availability.  There are several techniques of an encryption, they are symmetric and asymmetric encryption. In symmetric encryption the key used to encrypt and decrypt a message is similar, while the key used in asymmetric encryption is different. There are many encryption types which are used in today life for example DES (Data encryption Standard), Triple DES and RSA. In this paper an application of encryption will be created. It will be created using PHP programming language and several stages of encryption for instance calculating the hash value using SHA-1, encrypt Hash value using RSA.  Finally, the decryption process will be explored to make the encrypted message readable to the user.Keyword: Encryption, RSA, SHA-, Hash.
RANCANG BANGUN SISTEM INFORMASI TRACER STUDY BERBASIS WEB STUDI KASUS FAKULTAS ILMU KOMPUTER UNIVERSITAS INDO GLOBAL MANDIRI Ricky Maulana Fajri
Jurnal Informatika Global Vol 7, No 2
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (853.163 KB) | DOI: 10.36982/jiig.v7i1.152

Abstract

The success of university could be measured by how much the alumni is working in the exact field of study. This could be analyzed using quistionare where the alumni filed the quistionare in the alumni website. However the data collected is not measurized how the alumni are accepted in the working place. This paper would disscuss how to implement information system of tracer study in the facultiy of computer science of the University of Indo Global Mandiri. The information system methodology would be the FAST framework as well as the ERD and DFD design mechanism, the research found out that the fast framework methodology is sufficient to develop the tracer study information system. Furthermore the information system of tracer study will present the result of tracer study by the alumni using graphical analysis for example pie chart. For the next development the researcher suggested to improve the security part of the information system. Keyword:Tracer study, ERD, DFD, Fast
APLIKASI CHATBOT UNTUK PENERIMAAN MAHASISWA BARU UNIVERSITAS INDO GLOBAL MANDIRI MENGGUNAKAN DEEP LEARNING Desrika Heryati; Zulkifli; Ricky Maulana Fajri
Journal Of Intelligent Networks and IoT Global Vol 1 No 1 (2023)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v1i1.3073

Abstract

Most prospective new students want to get fast and accurate information. Prospective new students certainly want to get a lot of information about the procedure for registering new students at each tertiary institution, especially Indo Global Mandiri University. Information on new student admissions at Indo Global Mandiri University only uses websites and social pages as general information. To overcome this problem, a new student acceptance chatbot application was created at Indo Global Mandiri University to provide information that can summarize well and display the information to users. To create this chatbot using the Python programming language using Deep learning. The deep learning algorithm model that is applied in making chatbots uses the Artificial Neural Network model. The application of the Artificial Neural Network model can recognize question patterns quickly and accurately so as to get an appropriate response. The model was then tested with 15 different conversations and successfully answered with a chatbot accuracy value of 86% and 13% error
SISTEM BUKA TUTUP TERPAL SECARA OTOMATIS PADA PENJEMURAN GABAH BERBASIS TELEGRAM BERDASARKAN SENSOR BH1750 (SENSOR CAHAYA) DAN RAIN DROP SENSOR (SENSOR HUJAN) Nonik Wulantika; Tasmi; Ricky Maulana Fajri
Journal Of Intelligent Networks and IoT Global Vol 1 No 1 (2023)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v1i1.3078

Abstract

Tujuan dari penelitian ini adalah untuk membantu meringankan pekerjaan petani, dan agar gabah yang dijemur dapat menghasilkan kualitas yang baik. Dikarenakan masih banyaknya petani di daerah-daerah tertentu yang melakukan proses penjemuran gabah secara manual bergantung pada sinar matahari yang tidak bisa diprediksi. Dilakukan percobaan sensor hujan akan mendeteksi turunya hujan Ketika menerima tetesan air dengan nilai kurang dari 1000 RH dan tidak mendeteksi hujan bila lebih dari 1000 RH, Sedangkan sensor BH1750 akan mendeteksi terang bila nilai lebih dari 70 lux dan mendeteksi mendung bila kurang dari 70 lux. Hasil Kedua sensor dapat bekerja dengan baik, sensor BH1750 dapat mendeteksi adanya perubahan cahaya dari terang ke gelap atau sebaliknya dan sensor hujan dapat mendeteksi adanya tetesan air hujan. Dari pengujian membuktikan bahwa sensor BH1750 dan sensor hujan berkerja dengan baik dalam mendeteksi cuaca dan motor DC bergerak sesuai dengan nilai yang telah diinput
PENYORTIRAN BUAH TOMAT BERDASARKAN TINGKAT KEMATANGAN MENGGUNAKAN SENSOR WARNA TCS3200 Anggraini, Dea; Antony, Fery; Fajri, Ricky Maulana
Journal Of Intelligent Networks and IoT Global Vol 2 No 2 (2024)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v2i2.4957

Abstract

Penelitian ini mengembangkan sebuah prototipe untuk mendeteksi dan memisahkan tomat berdasarkan tingkat kematangannya, yaitu tomat matang, setengah matang, dan mentah, dengan memanfaatkan warna sebagai parameter utama. Alat ini menggunakan sensor warna TCS3200 dan teknologi NodeMCU ESP32 untuk mengotomatisasi proses penyortiran. Ketika tomat ditempatkan pada alat ini, sensor warna TCS3200 mendeteksi warna tomat, kemudian data warna tersebut dikirimkan ke NodeMCU ESP32 untuk diproses lebih lanjut. Berdasarkan hasil pemrosesan, tomat akan diarahkan ke wadah yang sesuai, tergantung pada kematangan, baik untuk tomat matang, setengah matang, maupun mentah. Pengujian dilakukan menggunakan 15 tomat, yang terdiri dari 8 tomat matang, 4 tomat setengah matang, dan 3 tomat mentah. Hasil pengujian menunjukkan bahwa alat ini memiliki akurasi sebesar 73,3% dalam mendeteksi dan memisahkan tomat sesuai dengan tingkat kematangannya.
Perancangan Sistem Pakar Diagnosis Penyakit Kulit Dengan Menggunakan Algoritma Forward Chaining NI Wayan Priscila Yuni Praditya; Purnamasari, Evi; Fajri, Ricky Maulana
Jurnal Ilmiah Informatika Global Vol. 16 No. 1: April 2025
Publisher : UNIVERSITAS INDO GLOBAL MANDIRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jiig.v16i1.5167

Abstract

The skin is the largest organ of the human body and has an important role to protect the internal organs of the human body from direct external interference such as sunlight and scratches from sharp or blunt objects. Without skin, our bodies will not be formed perfectly. Just like other organs of the body, if the skin is not properly cared for, it will cause skin diseases, although skin diseases are not too dangerous in human life, skin diseases can make humans feel uncomfortable. This skin disease has many types that are almost the same, therefore an expert system is needed to find out what type of skin disease we are likely to experience. This application is made web-based. The method used by this expert system is the Forward Chaining method, in this expert system it will show several questions or symptoms experienced, then after the user answers the available questions, the diagnosis results will appear. Based on the design of the creation and implementation of an expert system to diagnose skin diseases using the forward chaining algorithm, it can be concluded that the expert system for diagnosing skin diseases using the forward chaining algorithm can help in diagnosing skin diseases suffered or experienced by someone, this expert system for diagnosing skin diseases is very useful for many people to find out what type of skin disease they are suffering from considering the many types of skin diseases whose symptoms are almost similar, this expert system is very dependent on the ability of experts (doctors) who are the source of knowledge in research on this skin disease, and the expert system designed is web-based, making it easier for users to diagnose their diseases anywhere and anytime.
Applying Few-Shot Learning with Graph Neural Network (GNNs) For Fraud Detection Ricky Maulana Fajri; Antony, Fery; Rachmansyah
Journal of Embedded Systems, Security and Intelligent Systems Vol 6, No 2 (2025): June 2025
Publisher : Program Studi Teknik Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59562/jessi.v6i2.8319

Abstract

Detecting fraudulent transactions in financial systems presents a major challenge due to the scarcity of fraud instances and the limited availability of labeled data. This study explores the use of few-shot learning techniques combined with Graph Neural Networks (GNNs) to address these constraints. We evaluate four GNN architectures—Graph Convolutional Network (GCN), GraphSAGE, Graph Attention Network (GAT), and Simplified Graph Convolutional Network (SGCN)—on four real-world fraud detection datasets: Bank Fraud, IEEE-CIS, PaySIM, and ECommerce. Graph-based representations are constructed for each dataset, and models are trained using only 0%, 1%, 5%, and 10% of labeled data to simulate few-shot conditions. Experimental results show that GNNs, particularly GAT and GraphSAGE, maintain strong performance even with minimal supervision. Notably, GAT and GCN achieved an F1-score of 0.88 on the PaySIM dataset with just 10% labeled data, and GraphSAGE reached 0.25 on the highly imbalanced IEEE-CIS dataset. ROC curve analysis further demonstrates the discriminative capabilities of each model under different label settings. These findings highlight the potential of GNNs for effective fraud detection in low-resource and imbalanced environments, offering a practical solution for financial institutions aiming to enhance security with minimal labeled data.
STUDI PERFORMA DAN EXPLAINABILITY JARINGAN SYARAF TIRUAN UNTUK PERAMALAN CUACA MENGGUNAKAN METODE LIME ,, Rachmansyah; Fajri, Ricky Maulana; Tasmi, Tasmi; Hamim, Sumi Amariena
Jusikom : Jurnal Sistem Komputer Musirawas Vol 10 No 1 (2025): Jusikom : Jurnal Sistem Komputer Musirawas JUNI
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusikom.v10i1.2655

Abstract

Accurate weather prediction is crucial to support various sectors such as agriculture, transportation, and disaster mitigation. Artificial Neural Networks have been proven to improve the accuracy of weather forecasts through their ability to capture complex nonlinear patterns in atmospheric data. However, the complexity of these artificial neural networks architectures often results in decisions that are non-transparent and difficult for end users to understand. To address this issue, this study examines the effectiveness of the Local Interpretable Model-agnostic Explanation (LIME) method in providing local explanations for weather predictions generated by the artificial neural networks. The study uses historical meteorological data and evaluates the interpretability of prediction results for several key weather variables. Experimental results show that LIME is capable of identifying the most influential features affecting the model's decisions, as well as providing human-understandable insights into the prediction logic. These findings reinforce the importance of integrating explainability methods into artificial neural network-based weather prediction systems to enhance user trust and support more informed decision-making.
KLASIFIKASI KANKER PARU PARU MENGGUNAKAN CNN DENGAN 5 ARSITEKTUR Vierisyah, Aldo; Tasmi; Fajri, Ricky Maulana
Journal Of Intelligent Networks and IoT Global Vol 1 No 2 (2023)
Publisher : Universitas Indo Global Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jinig.v1i2.3643

Abstract

Kanker paru-paru merupakan penyakit mematikan yang membutuhkan deteksi dini dan penanganan yang tepat. Pada penelitian ini, metode klasifikasi kanker paru-paru menggunakan Convolutional Neural Network (CNN) dengan 5 arsitektur yang berbeda, yaitu VGG16, VGG19, Resnet50, Resnet101, dan Xception. Tujuan dari penelitian ini adalah untuk meningkatkan akurasi klasifikasi kanker paru dengan membandingkan performa dari kelima arsitektur tersebut. Hasil eksperimen menunjukkan bahwa arsitektur Resnet101 dan VGG16 dan VGG19 memiliki kinerja terbaik dengan akurasi klasifikasi masing-masing 93,4% dan 92,5%, sedangkan arsitektur Resnet50 dan Xception memiliki akurasi klasifikasi yang rendah. Penelitian ini memberikan bukti bahwa penggunaan CNN dengan arsitektur yang tepat dapat meningkatkan akurasi klasifikasi kanker paru-paru
Co-Authors ,, Rachmansyah Aditya Pratama Alvira, Dwi Okta Anggraini, Dea Antony, Fery Chatarina Umbul Wahyuni Desrika Heryati Hamim, Sumi Amariena NI Wayan Priscila Yuni Praditya Nonik Wulantika Nurwijayanti Purnamasari, Evi Putri, RA Martasya Rachmansyah Sunardi, Hastha Tasmi Tasmi Vierisyah, Aldo Zulkifli