Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Jurnal EECCIS Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Edu Elektrika Journal
Rizky Ajie Aprilianto
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Systematic Literature Review: Optimizing Broiler Chicken Cage Temperature and Humidity Fidel Lusiana Putri; Deva Kurnia Setiawan; Arsmanda Adi Nugraha; Feddy Setio Pribadi; Rizky Ajie Aprilianto
Jurnal EECCIS (Electrics, Electronics, Communications, Controls, Informatics, Systems) Vol. 18 No. 3 (2024)
Publisher : Faculty of Engineering, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/jeeccis.v18i3.1694

Abstract

Broiler production is highly dependent on environmental conditions, especially temperature and humidity in the cage. This research is a Systematic Literature Review that uses the PRISMA method to optimize the temperature and humidity of broiler cages. A total of 202 journals were found through Google Scholar and other repositories, with an additional 16 journals manually. After filtering, 38 journals were selected based on publication year 2019-2024. From the review of the selected literature, five research questions were derived that led to new understanding and solutions to the problems in the research topic. The results show that temperature and humidity significantly impact broiler performance, including health, welfare, and productivity. An IoT-based monitoring and control system is proven to assist with real-time monitoring and control of temperature and humidity, improving farm efficiency and effectiveness. Supporting factors for implementing this system include a friendly user interface, mobile applications, and real-time notifications. The application of IoT technology in broiler farming has the potential to provide significant benefits to farmers and the livestock industry as a whole.
Rancang Bangun Campus Shuttle Tracker Universitas Negeri Semarang Berbasis Modul GPS Smartphone Waskito, Deswal; Farah Syarifah, Dian; Muhammad Irfan Ardiansyah; Annuruddin Tsanasti Yassar; Ghesya Arnellia Brillian; Rizky Ajie Aprilianto; Apriansyah Wibowo
Edu Elektrika Journal Vol. 12 No. 2 (2024)
Publisher : LPPM Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/wkaf5949

Abstract

Universitas Negeri Semarang menerapkan konservasi di lingkungan kampus Sekaran melalui layanan Campus Shuttle yang telah beroperasi sejak tahun 2022. Namun, masih terdapat beberapa keterbatasan mengenai informasi jadwal operasional, lokasi shuttle, dan keterbatasan unit menjadi kendala yang menyebabkan fasilitas ini kurang diandalkan. Penelitian ini bertujuan memberikan solusi inovatif melalui pengembangan aplikasi Campus Shuttle Tracker berbasis web apps untuk meningkatkan akses layanan shuttle. Pengembangan aplikasi ini menggunakan teknologi GPS pada modul smartphone, diintegrasikan dengan framework Laravel berbasis Model-View-Controller (MVC). Proses penelitian berupa studi literatur, survei lapangan terhadap pengguna dan driver shuttle, serta analisis kebutuhan pengguna. Campus Shuttle Tracker menyajikan fitur utama yang disajikan berupa pelacakan posisi shuttle secara real-time, informasi drop-off, status ketersediaan kursi, notifikasi, dan pelaporan oleh pengguna. Penelitian ini menerapkan pengujian menggunakan metode ISO 9126 menunjukkan hasil sangat baik, dengan skor 91,6% pada aspek functionally, reliability, usability, efficiency, dan portability. Prototype berfungsi secara optimal pada lingkungan lokal, meskipun diperlukan penyempurnaan algoritma pelacakan dan konfigurasi sistem untuk penerapan di lingkungan produksi.
Stacking Ensemble Learning Model for Intrusion Detection in Electrical Substation Alam, Mohammad Mahruf; Pribadi, Feddy Setio; Rizky Ajie Aprilianto; Arvina Rizqi Nurul’aini
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 5 (2025): October 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i5.6502

Abstract

Electrical substations are crucial infrastructure in power transmission and distribution but are increasingly vulnerable to cyber threats. However, existing intrusion detection systems (IDS) face challenges such as high false positive rates, limited adaptability to emerging attack patterns, and imbalanced detection across different intrusion types. This study proposes a Stacking Ensemble Learning model to enhance intrusion detection accuracy in electrical substations. The proposed model integrates Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost (XGB) as base models with XGB acting as the meta-model. A real-world electrical substation IEC 60870-5-104 network traffic dataset comprising 319,949 instances with multiple attacks, such as DoS, Port Scan, NTP DdoS, IEC 104 Starvation, Fuzzy Attack, Flood Attack, and MITM, was used for this study. The results showed that the stacking model had the best accuracy (0.99990), precision (0.99990), recall (0.99990), and F1-score (0.99990), beating out the base, Bagging, and Boosting models. T-test results further confirmed statistical significance, with p-values of 0.00428 (LR), 0.04237 (SVM), 0.00000 (XGB), 0.00057 (KNN), 0.00549 (Boosting), and 0.00000 (Bagging) reinforcing the superiority of the Stacking Ensemble Learning approach. These findings highlight the effectiveness of Stacking Ensemble Learning in enhancing the detection accuracy of IDS for electrical substations and outperforming traditional models and other ensemble learning methods by minimizing false positives and false negatives.
Co-Authors Alam, Mohammad Mahruf Annuruddin Tsanasti Yassar Apriansyah Wibowo Arsmanda Adi Nugraha Arvina Rizqi Nurul’aini Deva Kurnia Setiawan Farah Syarifah, Dian Feddy Setio Pribadi Feddy Setio Pribadi Fidel Lusiana Putri Ghesya Arnellia Brillian Muhammad Irfan Ardiansyah Waskito, Deswal