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All Journal Jurnal Ilmu Komputer Jurnal SASAK : Desain Visual dan Komunikasi Teknomatika: Jurnal Informatika dan Komputer International Journal of Artificial Intelligence in Medical Issues
Ery Setiyawan Jullev Atmadji
Politeknik Negeri Jember
Arts Computer Science & IT Dentistry Health Professions Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health

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KOLABORASI MULTI AGENT SISTEM DAN WEBSERVICE PADA PROSES PENGAMBILAN DATA SUHU Ery Setiyawan Jullev Atmadji
Jurnal Ilmu Komputer Vol 10 No 2 (2017): September 2017
Publisher : Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (743.035 KB) | DOI: 10.24843/jik.2017.v10.i02.p01

Abstract

Webservice adalah salah satu teknologi yang sedang berkembang akhir-akhir ini, webservice merupakan salah satu contoh dari SOA (Service-Oriented Architecture). Hal inilah yang mengakibatkan webservice banyak digunakan dalam komunikasi cross platform application. Selain itu dalam pengambilan data pada webservice umumnya masih menggunakan model sinkronisasi atau request pada saat tertentu, oleh karena itu pada paper ini akan menjelaskan bagaimana penggabungan multi agent system akan membantu dalam pengambilan data di webservice secara autonomous. Perangkat lunak berbasis intelligent agent ini dirancang menggunakan metodologi Prometheus, dan dikembangkan melalui platform JADE dengan menggunakan bahasa pemrograman Java. Pengembangan model berfokus pada bagaimana agen dapat melakukan pengambilan data pada webservice, mematakan data berdasarkan metadata serta menyampaikan hasil pengambilan data tersebut. Konsep yang diterapkan pada agen dalam pengambilan data berdasarkan pada WSDL (Web Service Definition Language), dengan jenis data adalah suhu,humadity yang berada pada banyak ruangan di waktu tertentu. Evaluasi dilakukan terhadap 10, 100 dan 200 data yang didapatkan dari webservice dan sensor.
Kombinasi Port Knocking Dan VPN Guna Pengamanan Akses Secure Shell Pada Cloud Computing Ery Setiyawan Jullev Atmadji; Bekti Maryuni Susanto; Khusnul Hotima
Jurnal Teknomatika Vol 12 No 1 (2019): TEKNOMATIKA
Publisher : Fakultas Teknik dan Teknologi Informasi, Universitas Jenderal Achmad Yani Yogyakarta

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

Abstract

Salah satu bentuk cloud computing yang lazim digunakan adalah virtualisasi, teknologi virtualisasi memungkinkan melakukan share resource pada satu komputer server sehingga akan menekan biaya implementasi sebuah sistem informasi. Isu keamanan data merupakan salah satu aspek yang menjadi fokus dalam pengembangan sistem informasi dan cloud computing, dua ancaman keamanan pada cloud computing yaitu kehilangan atau kebocoran data dan pembajakan account atau service. Guna mencegah dua celah keamanan tersebut maka dibuatlah beberapa model keamanan salah satunya adalah port knocking pada jaringan VPN yang merupakan pengembangan dari keamanan jaringan guna mencegah pencurian data menggunakan port 22. Penggunaan VPN dapat mengurangi penyadapan paket dari pihak luar yang ingin mengambil data, serta memanfaatkan iptables dapat menolak ip address yang tidak terdaftar saat mengaksess port 22 serta melakukan remote server. Port knocking digunakan sebagai pintu masuk client untuk melakukan remote server melalui port 22 dengan menggunakan ketukan yang telah dikonfigurasi oleh server. Mekanisme ini memungkinkan client dapat mengakses port 22 walau dalam keadaan port 22 tertutup, karena port knocking juga memiliki prinsip “ buka port jika klient membutuhkan dan tutup port kembali jika klient sudah selesai”. Dengan demikian menggunakan ip dari tun0 vpn server dan port knocking akan sedikit menyulitkan pihak luar untuk melakukan pengaksesan server.
Design of Interactive Augmented Reality Learning Media Using theVAKT Approach for Early Childhood Vegetable Recognition Wahyu Kurnia Dewanto; Zilvanhisna Emka Fitri; Achmad Amreza Alfarit; Arizal Mujibtamala Nanda Imron; Reski Yulina Widiastuti; Ery Setiyawan Jullev Atmadji; Fatimatuzzahra Fatimatuzzahra
Jurnal SASAK : Desain Visual dan Komunikasi Vol. 8 No. 1 (2026): SASAK (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/sasak.v8i1.6317

Abstract

Early childhood education requires adaptive media to facilitate cognitive and linguistic development. This study aims to evaluate the integration of the Visual-Auditory-Kinesthetic-Tactile (VAKT) frame work into an Augmented Reality (AR) application for vegetable recognition, balancing technical engineering with user-centric design paradigms. This research used a method developed with Unity and the Vuforia SDK; the application architecture incorporates real-time marker tracking, a dual-language localization database, and automated dynamic scoring algorithms. Usability was quantitatively measured with 12 respondents using the Technology Acceptance Model (TAM) and the System Usability Scale (SUS) frameworks. The results of this research yielded a high TAM utility score of 80.63% andan ”Excellent” (B+) SUS rating. Technically, the system effectively maps software constraints onto cognitive stimuli, utilizing single-story sans-serif typography and high-saturation color rendering to sustain attention and support emergent literacy. While nearly 90% of respondents affirmed the application’s learning efficacy, empirical logs exposed a critical friction point: asynchronous background audio caused sensory overstimulation for 41.67% of users. These findings support the Split Attention Effect theory, suggesting that in AR environments, multi-sensory inputs must be hierarchically ordered to prevent cognitive overload. This study concludes that a synchronous multimodal hierarchy is essential for successful interactive multimedia environments.
Leakage-Aware and Explainable Machine Learning for Healthcare Claim Fraud Detection Using Imbalanced Medical Insurance Data Dian Hafidh Zulfikar; Ery Setiyawan Jullev Atmadji; Bagus Satrio Wahyu Poetro
International Journal of Artificial Intelligence in Medical Issues Vol. 4 No. 1 (2026): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/z3207345

Abstract

Healthcare insurance fraud is a critical challenge in health systems because fraudulent claims may cause financial losses, increase administrative burden, and reduce trust in healthcare services. This study proposes an explainable machine learning approach for detecting fraudulent healthcare insurance claims using imbalanced medical claim data. The dataset consisted of 10,000 healthcare insurance claim records with 20 attributes, including patient information, provider characteristics, claim-related financial variables, medical codes, temporal features, and fraud labels. Fraudulent claims represented only 8.29% of the dataset, indicating a clear class imbalance problem. Several machine learning models were evaluated, including Logistic Regression, Decision Tree, Random Forest, Extra Trees, and AdaBoost, under different imbalance handling strategies, namely baseline learning, class weighting, and SMOTE. In addition, two feature scenarios were compared: a full-feature scenario and a leakage-aware scenario that excluded potentially post-decision variables such as claim status and approved amount. The experimental results showed that the best full-feature model was Logistic Regression without additional imbalance handling, achieving an accuracy of 0.9900, precision of 0.9740, recall of 0.9036, F1-score of 0.9375, ROC-AUC of 0.9989, and PR-AUC of 0.9896. The model correctly detected 150 out of 166 fraudulent claims in the test set. However, the best leakage-aware model achieved a lower F1-score of 0.6983, indicating that potentially leaked variables may substantially affect model performance. Feature importance analysis showed that claim amount, approved amount, claim submission delay, claim status, and provider-related variables were among the most influential predictors. These findings demonstrate that explainable machine learning can support healthcare claim fraud detection, but careful attention must be given to class imbalance, data leakage, and operational deployment context
Co-Authors Achmad Amreza Alfarit Arizal Mujibtamala Nanda Imron Bagus Satrio Wahyu Poetro Bekti Maryuni Susanto Dian Hafidh Zulfikar Fatimatuzzahra Fatimatuzzahra Khusnul Hotima Reski Yulina Widiastuti Wahyu Kurnia Dewanto Zilvanhisna Emka Fitri