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Journal Innovations Computer Science
Published by Yayasan Kawanad
ISSN : 29619718     EISSN : 2961970X     DOI : https://doi.org/10.56347/jics
Core Subject : Science,
Computer Science & IT
Journal Innovations Computer Science (JICS) is a peer-reviewed, twice-annually published international journal that focuses on innovative, original, previously unpublished, experimental or theoretical research concepts. Journal Innovations Computer Science (JICS) covers all areas of computer & information science, applications & systems engineering in computer & information science. JICS core vision is to be an innovation platform in information technology and computer science. Articles of interdisciplinary nature are particularly welcome. All published article URLs will have a digital object identifier (DOI).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 137 Documents
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Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery Achmad Fahrezi, Irgy; Poerwandono, Edhy
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.339

Abstract

This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
Analysis of Enterprise Network Performance Using the SNMP (Simple Network Management Protocol) Method Alwanto, Hilmi; Yel, Mesra Betty
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.346

Abstract

This study examines the implementation of the Simple Network Management Protocol (SNMP) integrated with the Cacti monitoring platform to evaluate enterprise network performance within a simulated environment using PNETLab. A quantitative approach was applied through continuous data collection and measurement of key performance indicators such as throughput, packet loss, delay, and availability. The experiment utilized virtual Mikrotik routers connected to an Ubuntu-based Cacti server configured for SNMP polling and RRDTool data storage. Real-time visualization enabled efficient tracking of network behavior and early detection of anomalies. The results showed that under normal conditions, the network achieved stable performance with throughput between 70–90% of link capacity, zero packet loss, latency below 150 milliseconds, and availability above 99%, meeting ITU-T/TIPHON Quality of Service (QoS) standards. When faults were simulated, the system accurately detected and displayed traffic interruptions, allowing rapid identification and resolution of network issues. Compared with other monitoring tools such as Zabbix and Nagios, the SNMP–Cacti integration proved simpler to configure while maintaining analytical precision and reliability. These findings confirm that Cacti, supported by SNMP, provides an efficient, scalable, and low-overhead solution for enterprise network monitoring. Future development may incorporate SNMPv3 for enhanced security and automated alert systems or predictive analytics to improve responsiveness and proactive maintenance in larger infrastructures.
Implementation of Automatic Chicken Coop Temperature Controller Using DHT11 Sensor and Servo Motor Mukminin, Mukminin; Rasiban
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.363

Abstract

This study presents the design, development, and evaluation of an automatic temperature control system for broiler chicken coops using NodeMCU and DHT11 sensors integrated within an Internet of Things (IoT) framework. The system was designed to maintain coop temperature stability automatically, minimizing manual intervention and optimizing environmental conditions for broiler productivity. Using a Research and Development (R&D) approach, the system was constructed with hardware components including NodeMCU ESP8266, DHT11 sensor, servo motor, relay, lamp, and cooling fan, while the software utilized Arduino IDE, Python, and Telegram Bot API for real-time monitoring. The seven-day experimental testing, with thirty readings per day, demonstrated that the system maintained an average temperature of 27.6°C (±0.8°C), achieving 98.5% accuracy compared to manual thermometers, with an average error of 0.65%. The actuators exhibited an average response time of 1.8 seconds, ensuring quick adaptation to environmental changes and preventing heat stress in broilers. The automation reduced manual monitoring time by 80% and inspection frequency by 83%, while lowering energy consumption by approximately 40% through temperature-based device activation. These results confirm that low-cost IoT automation enhances environmental stability, animal welfare, and operational efficiency, aligning with the global trend toward precision livestock farming. Future improvements should focus on integrating multi-node systems, adaptive control algorithms, and humidity regulation to expand scalability, reliability, and sustainability in poultry management.
Application of IoT Technology in Designing and Building an Automatic Plant Watering System Using Graph Chart and Blynk NodeMCU ESP8266 Sapta, Lerry Salasi; Sumantri, Erno
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.364

Abstract

This research developed an Internet of Things (IoT)–based automatic watering system designed to support the maintenance of Aglaonema ornamental plants. The system utilizes a NodeMCU ESP8266 microcontroller connected to a soil-moisture sensor and a DHT11 temperature sensor, all integrated with the Blynk mobile platform for real-time monitoring and remote control. By combining sensor feedback with automated logic, the system activates irrigation when soil moisture falls below 40 percent and stops it when levels exceed 60 percent. Laboratory and field testing confirmed that the prototype operated reliably within a six-meter Wi-Fi range, maintaining stable communication and accurate sensor readings. The device reduced water consumption by about 37 percent compared with manual watering and provided timely notifications through both the LCD display and the Blynk application. Although system performance depends on Wi-Fi connectivity and lacks backup power, its overall operation demonstrates that IoT-based automation can significantly improve water efficiency and convenience for small-scale plant owners. Future enhancement may include solar-powered modules, additional sensors, and cloud data storage to strengthen system reliability and expand smart-agriculture applications.
Implementation of Edge Computing for Optimizing Sensor Data Collection in Smart Buildings Fajri, T. Irfan; Ningsih, Liasulistia; Octiva, Cut Susan; Hakim, Muhammad Lukman; Hasma, Nur Amalia
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.369

Abstract

The development of the Internet of Things (IoT) has driven the implementation of smart buildings that rely on real-time sensor data collection and analysis. However, cloud computing-based systems often face problems of high latency and large network loads. This research implements an edge computing architecture to optimize sensor data collection in smart buildings. A prototype was built using edge nodes (Raspberry Pi) that process data from temperature, humidity, light, and motion sensors locally before sending it to the cloud. Test results show that edge computing can reduce latency by up to 45% and reduce data traffic to the cloud by 60%, while also improving the energy efficiency of sensor devices. Thus, edge computing has been proven to effectively improve the performance and efficiency of data collection systems in smart buildings
Review of Comsoft Aeronautical Data Access System Information Management & Service in the NOTAM Flow at Palembang Regional Aeronautical Information Service Puspa Aryani, Dian; Sadiatmi, Rini; Saulina, Martha
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.379

Abstract

This study aims to describe how the Comsoft Aeronautical Data Access System (CADAS) is used in the NOTAM workflow at Palembang Aeronautical Information Service and how operators behave when the system is not stable. This research was conducted with a qualitative case study approach, which combined interviews, participatory observations, and document analysis to capture daily operational practices and problems that occurred in real conditions. The results of this study indicated that CADAS is still the main system for NOTAM drafting, verification, and transmission. However, its performance is often disturbed by irregular system errors such as sudden shutdowns, delays in data processing, and intermittent failures to send or receive raw NOTAM information. These disturbances create obstacles in fulfilling time-sensitive procedural requirements especially the ten-minute verification standard as stated in SOP.012. In case of system unresponsiveness, operators switch to alternative procedures through email distribution, Web Flight Plan submission, and manual logbook documentation so as not to cause delays; however, these manual steps require continuous follow-up to avoid gaps in recording. In general, this study underlines the importance of increasing CADAS reliability and ensuring that backup procedures are consistently applied so that continuity can be maintained even if there is an interruption in the system.
Enhancing Real-Time Face Recognition Robustness against Low Lighting through Dynamic Feature Enhancement Rival, Muhammad; Mulyana, Dadang Iskandar
Journal Innovations Computer Science Vol. 4 No. 2 (2025): November
Publisher : Yayasan Kawanad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56347/jics.v4i2.380

Abstract

Low-light conditions continue to challenge real-time face recognition because dim illumination often produces noisy and low-contrast images that weaken the quality of feature extraction. This study investigates how different preprocessing strategies influence the performance of a dlib-ResNet–based recognition system under such conditions. Two reference dataset sizes—33 and 1000 images—were used to observe how reference variation affects embedding stability. Enhancement was applied either offline to the reference dataset or in real time to incoming video frames, and both approaches were also tested in combination. The experiments show that offline preprocessing provides the most reliable improvement. Enhancing reference images raised the F1-Score by 7.28% (small dataset) and 7.50% (large dataset) without reducing processing speed, indicating that clearer embeddings at registration contribute to more stable matching. Real-time preprocessing, however, produced inconsistent results. While slight gains appeared in specific cases, the added computation and occasional distortion of facial structure reduced accuracy in other scenarios. The combined method produced the weakest performance, with the large dataset showing a 33.71% decline, suggesting that excessive modification disrupts structural consistency between reference and test images. Overall, the results highlight the importance of maintaining coherent facial features rather than applying aggressive adjustments to every frame. Offline enhancement is the most practical strategy for low-light deployments, whereas real-time enhancement should be used selectively. Future work may explore adaptive illumination adjustment capable of tuning enhancement parameters automatically to match varying lighting conditions.

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