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Informatik : Jurnal Ilmu Komputer
Published by Universitas Pembangunan Nasional Veteran Jakarta
ISSN : 02164221     EISSN : 2655139X     DOI : -
Core Subject : Science,
Computer Science & IT
Informatik menerima artikel ilmiah dengan area penelitian pada area Internet Business & Application, Networking & Cyber Security, Statistics & Computation, Elearning & Multimedia, Robotics & Intelligene.
Arjuna Subject : -
Articles 212 Documents
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Developing Sorting Algorithm for SmartEdu Conveyor using Computer Vision Technology Ridwan; Yuliadi Erdani; Sarosa Castrena Abadi; Mochammad Dimas Anugrah; Abdur Rohman Harits Martawireja; Rizqi Aji Pratama
Informatik : Jurnal Ilmu Komputer Vol 21 No 3 (2025): December 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i3.12434

Abstract

This study aims to develop a sorting algorithm for the SmartEdu Conveyor using computer vision technology to enhance accuracy and efficiency in automated sorting systems. The system integrates a Raspberry Pi 4 as the main processing unit and employs the YOLOv8 object detection algorithm to classify geometric objects moving on a conveyor belt. Images captured by an overhead camera are processed in real time, and the results are transmitted through the MQTT protocol using the Paho MQTT library. Node-RED functions as the Human-Machine Interface (HMI), while a Programmable Logic Controller (PLC) drives double-acting pneumatic cylinders to perform the sorting mechanism. Experimental tests conducted at three conveyor speeds demonstrate that the system achieves an average accuracy confidence of 89.38% at 1 cm/s, 78.57% at 1.7 cm/s, and 59.28% at 2.3 cm/s. Further performance evaluation using the Precision–Recall curve yields a mean Average Precision (mAP) of 0.993 at an Intersection over Union (IoU) threshold of 0.5, indicating highly accurate object detection capability. The proposed YOLOv8-based sorting system demonstrates reliable real-time operation, high precision, and robust communication between vision and control modules. It will be implemented as a SmartEdu teaching aid prototype to support automation learning and industrial training applications. This work contributes to educational automation by integrating an open-source vision algorithm with industrial control architecture.
Design and Simulation of an IoT-Based Adaptive Control System for Urban Hydroponic Farming Nurhuda Maulana; Novi Trisman Hadi
Informatik : Jurnal Ilmu Komputer Vol 21 No 3 (2025): December 2025
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v21i3.12815

Abstract

Efficient water circulation is an essential requirement in urban hydroponic farming, yet many systems still depend on fixed timer control that cannot adjust to changing environmental conditions. This study develops an Internet of Things based adaptive pump control system that responds to real-time temperature and humidity data collected using DHT22 and DS18B20 sensors. An ESP32 microcontroller manages the sensing and control process, while MQTT and Blynk Cloud enable continuous monitoring and data exchange. The system is evaluated through a six hour hydroponic simulation on the Wokwi platform under three environmental scenarios: Normal, Heatwave, and Humid. Two control strategies are compared, the fixed interval mode (K1) and an adaptive mode (K2) based on threshold rules. The results show that the adaptive mode improves water efficiency by reducing pump operation by 23.4 percent on average while maintaining more stable temperature and humidity conditions. These findings indicate that lightweight IoT solutions can support responsive and efficient operation in urban hydroponic systems, offering a practical basis for further development of intelligent control in urban farming.
Developing Algorithm for Security System in Smart Door Lock and Fire Extinguishing Spray System Utilizing the CtrlX Platform Yuliadi Erdani; Mochammad Naufal; Muhammad Giriarda Abrari; Herman Budi Harja; Achmad Sambas; Siti Aminah
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12293

Abstract

Industries face high risks from unauthorized access and fire hazards, necessitating reliable and responsive safety systems. This research presents the design and evaluation of an IIoT-based smart door lock and fire extinguishing system utilizing the CtrlX Automation platform. The system integrates RFID and fingerprint-based access control with temperature, gas, and flame sensors supported by edge-AI processing. Experimental results from 12 trials demonstrated an average fire detection time of 344.25 ms, smoke detection accuracy of 92.21%, sprinkler actuation delay of 515.58 ms, evacuation door unlocking time of 811.50 ms, and cloud log reliability of 99.70%, with a 14.42% reduction in false alarms. Extended scenarios using various fire sources (paper, plastic, oil), sensing distances (1–3 m), and environmental interferences (airflow, smoke particles) confirmed the robustness of the system. Communication security was validated using MQTT over TLS with AES encryption, resulting in a latency overhead of only 8 ms, while the IDS demonstrated 96.2% detection accuracy against simulated DoS attacks. These findings indicate that the proposed system achieves fast detection, high accuracy, and reliable communication compared to conventional systems, thereby enhancing industrial and smart building safety.
Integration of Deep Learning for Optimization of Coconut Farming in North Sulawesi through Disease Detection Based on Hybrid CNN and LSTM Approaches Dyah Listianing Tyas; Andreuw Vandy Lengkong; Frendy Rocky Rumambi; Adrian Nicholas Lumowa
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12362

Abstract

This research aims to develop a palm leaf disease detection system based on a Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) integrated into a mobile application. The CNN model is used to extract visual features from leaf images, while the BiLSTM serves to capture sequential dependencies, thereby improving classification accuracy. The implementation was carried out by connecting the model, which is served via a Flask API, and accessed by the mobile application using Ngrok as a tunneling service for testing. Test results show that the system is capable of detecting healthy leaf conditions with an accuracy rate of up to 99.7%, and provides descriptive information about the leaf's condition and preventive treatment recommendations. The integration of the model into a mobile application enables real-time plant health monitoring, making it an innovative solution to support farmers in increasing productivity and preventing losses due to disease outbreaks.
Sales Information System Using FP-Growth Algorithm Based on Consumer Purchasing Patterns Erlan Fauzi Reza; Iin Ernawati
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12456

Abstract

The advancement of technology has driven businesses to innovate digitally to improve operational efficiency and effectiveness, including in the food and beverage industry. Two Much Coffee & Roastery is one of the cafes that still operates in a semi-digital manner, recording transactions manually using Microsoft Excel. The cafe faces challenges in optimizing sales strategies, particularly in implementing cross-selling strategies, as there is no system that provides automatic product recommendations. This study aims to implement data mining techniques using the FP-Growth algorithm to identify consumer purchasing patterns from historical transaction data. The algorithm was applied with a minimum support of 0.01 and a lift of 1.0, resulting in 30 association rules. These rules were integrated into a web-based system used by the cashier to support cross-selling strategies. The system not only records transactions but also provides product recommendations based on previous purchasing patterns, which is expected to effectively and efficiently increase the cafe’s sales.
Audit of the SIKS-NG Application System for Poverty Data at the Social Services Office of Southwest Sumba Regency Emeliana Kaka; Friden Elefri Neno; Paulus Mikku Ate
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12615

Abstract

The Next Generation Social Welfare Information System (SIKS-NG) is a national application developed by the Ministry of Social Affairs to manage integrated social welfare data (DTKS). This application is the main tool for local governments in collecting, verifying, and updating information on social assistance recipients. However, the effectiveness and reliability of its implementation in the regions is often not optimal, as evidenced by data inconsistencies, slow input processes, and limited user understanding. This study aims to audit the SIKS-NG system at the Social Service Office of Southwest Sumba Regency to assess the maturity level of the system and its compliance with good information technology governance principles. The research method used a qualitative descriptive approach with the COBIT 5 framework as the basis for the audit. Data was collected through observation, interviews, documentation, and questionnaires administered to system users and poverty data managers. The analysis was conducted on the domains in COBIT 5, namely Deliver, Service, and Support (DSS). The audit results showed that the maturity level of the SIKS-NG system was at a defined level (level 3), which means that the process was running according to procedure but was not yet fully documented and consistently monitored. Several aspects, such as data integration, user training, and information security, still need improvement. This research recommends strengthening IT governance policies, improving the quality of operator human resources, and optimizing real-time data-based monitoring and evaluation systems.
Navigasi UAV Otonom Multi-Landing Berbasis Finite State Machine Menggunakan Algoritma Waypoint Follower pada ROS2 Pipit Anggraeni; Hilda Khoirunnisa; Alif Prima Utama
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12680

Abstract

Challenges in medical logistics, such as geographical limitations, urban congestion, and ground transport risks, necessitate efficient and safe delivery solutions. Unmanned Aerial Vehicles (UAVs) offer a promising alternative. This research aims to develop and implement an autonomous UAV navigation system capable of complex multi-landing missions, validated for medical delivery scenarios. The system employs a hierarchical control architecture, integrating the Robot Operating System 2 (ROS2) as the high-level mission supervisor with the PX4 flight controller for low-level control. Mission logic, including navigation using the Waypoint Follower algorithm and landing sequences, is formally governed by a Finite State Machine (FSM) implemented in ROS2. Communication between ROS2 and PX4 is facilitated by Micro XRCE-DDS. System validation was performed via Software-in-the-Loop (SITL) simulation using Gazebo, featuring a mission scenario with four waypoints and one intermediate landing. Simulation results demonstrated that the system successfully completed the entire mission autonomously (100% success rate). Quantitative analysis of position accuracy revealed total position errors () between 1.35 m and 1.57 m during navigation phases, and an error of 0.84 m at the intermediate landing. Errors were found to be consistently dominant on the Y-axis. In conclusion, the proposed architecture, which separates mission logic (FSM in ROS2) from flight control (PX4), provides a robust and functional approach for autonomous UAV applications requiring complex mission interactions.
Comparative Analysis of Computer Vision Models for Detecting Nilam Plant Diseases: A Case Study of MobileNet vs. YOLO Jimmy Robot; Nancy Tuturoong; Ade Yusupa
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12800

Abstract

Nilam Plant (Patchouli plant) located in Minahasa Regency can be affected by several diseases that can significantly reduce their essential oil capacity. The common practice of monitoring crops for diagnosis relies on labor-intensive methods that can be variable in accuracy, depending on previous experience of the tended. The main goal of the study is to develop and to compare model performance using Deep Learning computer vision in monitoring conditions of patchouli plants with respect to different conditions (healthy, bacterial wilt, viral, and budok). This study assess and compare MobileNet (a lightweight classifier) against Deep Learning based object detectors (YOLOv5, v8, v11) using an underlying dataset that was created unimpeachably in a natural patchouli field setting, consisting of 3,000 images which contain 3,820 annotated bounding boxes, across 4 classes (Healthy, Bacterial Wilt, Viral, Budok). Evaluation reveals a clear trade-off between the two models. MobileNet finds (nearly perfect) classification accuracy of 94.7% (F1-score>0.90 for all classes), while the faster YOLOv8l yields an 88.2% mAP50. Both models had the hardest time dealing with the "Viral" class due to visual similarities it shared with the healthy class (F1: 0.90, mAP: 0.81). MobileNet produced better accuracy (94.7%) but had slower inference time (3.0s). YOLOv8l provided real-time detection (1.4s) but lower mAP (88.2%). Our recommendation is a hybrid 2-stage system (YOLO-drone scan; MobileNet-farmer confirmation) as an operational approach for Precision Agriculture in Patchouli farming. Overall, the main takeaway is that: MobileNet is intended for diagnostic application and YOLOv8 superior for real-time video-based field monitoring.
Mengidentifikasi Faktor Penentu Keberhasilan Kritis untuk Meningkatkan Kepatuhan SLA pada Managed Print Services: Studi Kasus Berbasis ITIL di Sebuah Organisasi Sektor Publik Irwan Bengkulah; Teguh Raharjo
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.12850

Abstract

The adoption of Managed Services (MS) has become a key strategy for public sector organizations seeking to enhance operational efficiency and service quality. However, maintaining consistent Service Level Agreement (SLA) compliance remains a major challenge, particularly due to vendor delays and limited service visibility. This study aims to identify and prioritize the Critical Success Factors (CSFs) influencing SLA compliance within the Managed Print Services (MPS) environment of a public sector organization. Using a mixed-method approach, the research applies the Analytic Hierarchy Process (AHP) to evaluate four service success criteria and eight corresponding CSFs, validated through expert judgment from echelon-4 IT officials. The results indicate that Vendor Responsiveness (36.0%) and User Satisfaction (27.7%) are the most influential criteria, reflecting an organizational emphasis on external service quality and user value. At the CSF level, strategic management factors dominate: Planning (20.2%), Evaluation (18.3%), and Leadership (15.8%) emerge as the top contributors, whereas technical elements such as Troubleshooting (7.8%) and Team Performance (5.8%) rank lowest. These findings highlight that sustainable SLA compliance in the public sector relies primarily on strengthening governance and strategic oversight rather than operational execution. Accordingly, the study recommends enhancing structured planning and continuous evaluation mechanisms to ensure alignment between service delivery and user value, consistent with ITIL principles.
Developing a Machine Learning Model to Shorten Emergency Department Length of Stay: Model Testing and Nurses Acceptance Laksita Barbara; Neny Rosmawarni; Arief Wahyudi Jadmiko
Informatik : Jurnal Ilmu Komputer Vol 22 No 1 (2026): April 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i1.13031

Abstract

This study aimed to develop and evaluate a portfolio of ML models to predict ED LoS and examine nurses’ acceptance of AI-based clinical decision support. Secondary data from two public hospitals in Jakarta were analysed using three ML algorithms Random Forest, Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) to classify ED LoS into short, medium, and prolonged categories. Predictor variables included triage level, arrival time, referral source, disposition, number of diagnostic tests, and consultations. Model performance was assessed using precision, recall, and F1-scores across training, testing, and blind validation datasets. Additionally, nurses’ readiness to adopt ML tools was evaluated using a survey. Across 687 ED cases, XGBoost achieved the best overall performance (precision, recall, and F1-score = 1.00), indicating excellent discrimination and balance between sensitivity and specificity. SVM also demonstrated strong external validation (blind-test F1 = 1.00), confirming robust generalisation across hospital sites. High-performance metrics across all models indicate consistent accuracy and calibration. Most nurses (89.3%) expressed high performance expectancy, and 95.7% high effort expectancy toward technology adoption. The developed ML framework accurately predicts ED LoS in Jakarta’s hospital settings, providing a foundation for data-driven resource management.

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