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All Journal Techno.Com: Jurnal Teknologi Informasi JITK (Jurnal Ilmu Pengetahuan dan Komputer) Journal of Information Systems and Technology Research Sisfo: Jurnal Ilmiah Sistem Informasi Bulletin of Network Engineer and Informatics (BUFNETS) Journal of Technology Informatics and Engineering
Andi Prayogi
Department Information Systems And Technology, Institut Teknologi Sawit Indonesia, Indonesia
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Library & Information Science Mechanical Engineering

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Low-Cost CCTV for Home Security With Face Detection Base on IoT Pane, Muhammad Akbar Syahbana; Saleh, Khairul; Prayogi, Andi; Dian, Rahmad; Siregar, Ratu Mutiara; Aris Sugianto, Raden
Journal of Information Systems and Technology Research Vol. 3 No. 1 (2024): January 2024
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v3i1.769

Abstract

Monitoring is a necessary part of Home surveillance that can be done through the internet network as a security measure. Many CCTV cameras on the market today continue to employ analog and conventional technology, specifically coaxial wire. As a result, extra expenditures for CCTV system wiring are required; besides being more expensive, the installation takes more handling, as the picture data cable and control signal cable cannot be merged. This project aims to develop a security system capable of detecting object movement in real-time utilizing a webcam camera attached to a raspberry pi. The findings of this study enable the development of a low-cost CCTV system that can be monitored remotely via the Internet of Things.
Direct implementation of AI-Based Facial Recognition for ITSI students Prayogi, Andi; Navea, Roy Francis; Dian, Rahmad; Pane, Muhammad Akbar Syahbana; Siregar, Ratu Mutiara; Sugianto, Raden Aris; Simbolon, Hasanal Fachri Satia
Journal of Information Systems and Technology Research Vol. 3 No. 3 (2024): September 2024
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v3i3.898

Abstract

The development of artificial intelligence (AI)-based facial recognition technology has become a significant research topic in the field of computing and security. At the Indonesian Palm Oil Institute (ITSI), AI-based facial recognition is introduced to students to improve their skills in developing AI-based applications. This study aims to implement and test a facial recognition system using a Python program by utilizing a dataset generated independently. This research method involves several stages, namely collecting ITSI students' facial data, data processing, creating a facial recognition model using a machine learning algorithm, and evaluating model performance. The dataset used was developed through a live shooting session involving active student participation. The facial recognition model was trained using a convolutional neural network (CNN) algorithm that was optimized to improve accuracy. The results of the study showed that the developed model was able to achieve high facial recognition accuracy, with an average accuracy rate of 92%. The discussion includes an analysis of factors that affect accuracy, such as variations in lighting and shooting angles, as well as the potential use of this technology in a campus environment, including for attendance and security purposes. The conclusion of this study shows that the implementation of AI-based facial recognition can be effectively applied in an academic environment, as well as providing students with practical experience in developing and testing AI applications. This study also opens up opportunities for further research on improving the performance of facial recognition systems and their application in various real-world scenarios.
Mobile Robot Detects Pests And Diseases In Palm Oil Nurseries Raden Aris Sugianto; Andi Prayogi; Hasanal Fachri Satia Simbolon
Journal of Technology Informatics and Engineering Vol 3 No 1 (2024): April : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i1.159

Abstract

The palm oil industry is an important economic pillar in various countries, making a significant contribution to global vegetable oil production. Despite its vital role, oil palm growth is often faced with serious challenges such as pest and disease attacks that can threaten crop yields and plant health. Oil palm seeding, as the initial stage in the plant growth cycle, is key in ensuring optimal quality and productivity in the future. The successful application of mobile robot technology in detecting pests and diseases in oil palm nurseries will not only increase production efficiency, but also support the principles of sustainability and wise resource management.
Penggunaan Random Forest dan Analisis Perilaku untuk Prediksi Serangan DDoS dalam Lingkungan Cloud Computing Prayogi, Andi; Pane, Muhammad Akbar Syahbana; Dian, Rahmad; Siregar, Ratu Mutiara; Sugianto, Raden Aris; Simbolon, Hasanal Fachri Satia
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.11317

Abstract

Dalam dunia komputasi awan yang semakin berkembang, ancaman serangan Distributed Denial of Service (DDoS) menjadi isu yang sangat krusial. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan model prediksi serangan DDoS menggunakan algoritma Random Forest dan analisis perilaku jaringan. Dataset CICIDS2017 digunakan sebagai sumber data utama untuk melatih dan menguji model prediksi yang dikembangkan. Pemilihan algoritma Random Forest didasarkan pada kemampuannya yang tinggi dalam menangani data besar dan kompleks serta kemampuannya dalam mengenali pola anomali yang sering menjadi indikasi serangan siber. Hasil pengujian menunjukkan bahwa model ini mencapai akurasi yang signifikan dengan precision sebesar 97,8%, recall sebesar 98,2%, dan F1-score sebesar 98,0%. Analisis perilaku jaringan yang diterapkan, melibatkan fitur-fitur dinamis seperti waktu antar paket (Inter-Arrival Time/IAT), ukuran rata-rata segmen, dan jumlah paket per detik, yang terbukti efektif dalam meningkatkan kemampuan deteksi model. Implementasi model dalam lingkungan komputasi awan menunjukkan bahwa metode ini dapat diintegrasikan dengan sistem deteksi intrusi (Intrusion Detection Systems/IDS) yang sudah ada untuk memberikan lapisan perlindungan tambahan terhadap serangan DDoS. Berdasarkan hasil yang diperoleh, penelitian ini merekomendasikan penggunaan kombinasi algoritma Random Forest dan analisis perilaku jaringan sebagai solusi yang efektif untuk mendeteksi serangan DDoS dalam lingkungan komputasi awan. Penelitian lanjutan disarankan untuk mengembangkan dan menguji model dengan dataset yang lebih beragam serta mengoptimalkan algoritma untuk meningkatkan performa deteksi.   Kata kunci: Random Forest, DDoS, Cloud Computing
DESIGN OF CONTROL SYSTEM AND TEMPERATURE IN COFFEE DRYER ARDUINO BASED AUTOMATIC USING FUZZY Ratu Mutiara Siregar; Budi Mulyara; Rahmad Dian; Maisarah Maisarah; Muhammad Akbar Syahbana Pane; Andi Prayogi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 10 No. 3 (2025): JITK Issue February 2025
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v10i3.6166

Abstract

The coffee bean drying process is a crucial stage in ensuring the final quality of coffee products. Conventional drying methods, which rely on sunlight, face several challenges, such as dependence on weather conditions and prolonged drying times. This study proposes the design of a control and temperature system for an automatic coffee dryer based on the Arduino Mega 2560, aimed at enhancing the efficiency and consistency of the drying process. The system utilizes a semi-enclosed drying technology equipped with DHT22 temperature and humidity sensors, controlled by Arduino-Uno and Fuzzy Logic. This control system monitors temperature and humidity in real-time, maintaining the drying conditions at 55°C and 15% RH. If the temperature or humidity exceeds the set limits, the system activates an LED and buzzer alarm, indicating that the drying process has reached optimal conditions. The prototype was tested under various conditions, and the results demonstrate that the system has a high accuracy level in controlling temperature and humidity, significantly accelerating the drying process compared to traditional methods. By implementing this technology, the coffee industry in Indonesia is expected to achieve the Coffee Drying Operational Standards in accordance with SNI, maintain flavor quality, optimize the use of drying land, and reduce drying duration. This development offers an innovative solution that can enhance the quality and productivity of coffee processing, providing significant economic benefits to farmers and coffee industry stakeholders.
IOT-BASED SMART HELMET PROTOTYPE FOR FIELD SECURITY SAFETY IN OIL PALM PLANTATIONS Rasyid Abdullah Habib Syaban; Andi Prayogi; Muhammad Akbar
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/737748

Abstract

Oil palm plantation security officers face serious risks including occupational accidents, physical assaults during Fresh Fruit Bunch (FFB) theft incidents, and communication limitations in vast and remote field areas. Existing surveillance systems remain manual, lacking real-time position tracking, anomaly detection, and visual verification capabilities. This research develops an IoT-Based Smart Helmet Prototype integrating a GPS NEO-M8N module for real-time location tracking, an IMU MPU6050 sensor for four-level impact classification (NONE/LOW/MEDIUM/HIGH), and dual OV3660 cameras on two ESP32-S3 CAM WROOM N16R8 microcontrollers for simultaneous front and rear RTSP video streaming. All data is transmitted over a 4G modem to a VPS running Mosquitto MQTT broker, MediaMTX media server, Redis cache, Node.js REST API, and a Next.js web dashboard with interactive Leaflet mapping. Functional testing at the Institut Teknologi Sawit Indonesia (ITSI) Experimental Plantation demonstrated a GPS coordinate error of less than 0.0015%, IMU impact classification consistency of 98% across 50 trials, streaming latency of 3–5 seconds via HLS, MQTT average latency of 28 ms, and panic button response under 1 second. The system provides a viable IoT solution for improving field security monitoring in oil palm plantations.
Android-Based Digitalization of Fresh Fruit Bunch Harvest and Supply Chain with Geo-Tagging and Online-Offline Cloud Synchronization Ifan Gultom; Ratu Mutiara Siregar; Andi Prayogi
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 10 No. 2 (2026): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2026 (In Press)
Publisher : Universitas Malikussaleh

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

Abstract

Background: Manual recording of Fresh Fruit Bunch (FFB) harvest activities at Kebun Tanah Putih PTPN IV Regional 3 results in reporting delays of up to 24 hours, a human error rate of approximately 15–20% per entry, inability to verify harvest locations spatially, and absence of real-time monitoring under limited network connectivity. Objective: This study develops HarvestTrack, an Android and web-based mobile information system for FFB harvest recording and supply chain monitoring, aimed at improving data accuracy, operational efficiency, and transparency in oil palm plantation management. Method: A Research and Development (R&D) methodology with a prototyping approach was employed, covering requirements analysis, system design, implementation, and testing. The system was built with Flutter, Firebase Firestore (cloud backend), and SQLite (local storage) using an offline-first architecture. Delta synchronization via WorkManager and geo-tagging via Fused Location Provider API (≤10 m accuracy) were implemented. Testing included functional, performance, and User Acceptance Testing (UAT). Results: Functional testing confirmed 100% success for offline data recording and automatic cloud synchronization (<5 seconds/entry). Geo-tagging achieved ≤10 m accuracy in 95% of 40 field test locations. Last-Write-Wins (LWW) conflict resolution attained an error rate below 2%. Three role-based user modules (KCS, Foreman, Admin) were fully implemented with differentiated access controls. Contribution: HarvestTrack is the first integrated system combining offline-first architecture, real-time geo-tagging, automated conflict handling, and web-based monitoring dashboard for FFB supply chain digitalization in Indonesia's oil palm sector.
Co-Authors Hasanal Fachri Satia Simbolon Ifan Gultom Khairul Saleh Maisarah Muhammad Akbar Muhammad Akbar Syahbana Pane Mulyara, Budi Navea, Roy Francis Raden Aris Sugianto Rahmad Dian Rasyid Abdullah Habib Syaban Ratu Mutiara Siregar Simbolon, Hasanal Fachri Satia