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Jurnal Informatika: Jurnal Pengembangan IT
Published by Politeknik Harapan Bersama Tegal
ISSN : 24775126     EISSN : 25489356     DOI : https://doi.org/10.30591
Core Subject : Science,
Computer Science & IT
The scope encompasses the Informatics Engineering, Computer Engineering and information Systems., but not limited to, the following scope: 1. Information Systems Information management e-Government E-business and e-Commerce Spatial Information Systems Geographical Information Systems IT Governance and Audits IT Service Management IT Project Management Information System Development Research Methods of Information Systems Software Quality Assurance 2. Computer Engineering Intelligent Systems Network Protocol and Management Robotic Computer Security Information Security and Privacy Information Forensics Network Security Protection Systems 3. Informatics Engineering Software Engineering Soft Computing Data Mining Information Retrieval Multimedia Technology Mobile Computing Artificial Intelligence Games Programming Computer Vision Image Processing, Embedded System Augmented/ Virtual Reality Image Processing Speech Recognition
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 9 Documents
 1 
Search results for , issue "Vol 11, No 1 (2026)" : 9 Documents clear
Optimasi Faktor Friksi dan Dinamis dengan Hibrida GA-ACO pada Estimasi Usaha Perangkat Lunak Agile Mahendri, Yusril; Paputungan, Irving Vitra; Setiani, Novi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10043

Abstract

Effort estimation remains a critical challenge in Agile Software Development due to the high dynamics of requirement changes and the reliance on friction factors (FF) and dynamic factors (DF) that are inherently subjective, often leading to significant deviations between estimated and actual project effort. This study aims to improve the accuracy of Agile software effort estimation by optimizing FF and DF parameters using a hybrid metaheuristic approach based on Genetic Algorithm and Ant Colony Optimization (GACO). The proposed method integrates a pheromone-based guided search mechanism from Ant Colony Optimization to generate high-quality initial populations, which are subsequently refined through the evolutionary process of Genetic Algorithm to achieve more stable and systematic parameter optimization. Experimental evaluation was conducted using two datasets, namely the Ziauddin dataset representing Agile projects and the Maxwell dataset encompassing cross-domain software projects. The results demonstrate that the GACO approach consistently outperforms the conventional Genetic Algorithm, as indicated by a substantial reduction in Mean Absolute Error from 616.38 to 354.81. Furthermore, statistical validation using the Wilcoxon Signed-Rank Test confirms that the performance difference between the two approaches is statistically significant. These findings indicate that integrating Ant Colony Optimization into Genetic Algorithm effectively enhances the accuracy, stability, and robustness of software effort estimation, thereby supporting more reliable resource planning in Agile software development.
Penerapan Transfer Learning VGG-16 untuk Mendeteksi Penyakit Mata Manusia Berbasis Citra Fundus Willy, Willy; Prabowo, Ary
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9291

Abstract

Eye disorders represent a serious global health issue that can lead to a decline in quality of life and even permanent blindness. Early diagnostic for eye diseases such as glaucoma, diabetic retinopathy, age-related macular degeneration, cataract, myopia, and hypertension is crucial to prevent more severe complications. The objective of this study is to develop an image classification model for fundus images using a transfer learning approach with the VGG-16 architecture. The dataset used is ODIR-5K, which includes eight classes of eye diseases. The research stages involve image preprocessing, data augmentation, class balancing using SMOTE, and CNN for training the model. The model training process was conducted over 80 epochs with a combination of freezing layers, fine-tuning, and hyperparameter tuning. Model evaluation was carried out using metrics such as accuracy, precision, recall, F1-score, confusion matrix, and ROC AUC curve. The results show that the developed model achieved an accuracy of 89% compared to the previous study which only reached 45%, with a macro average F1-score of 0.89. The model demonstrated excellent performance in classes such as Hypertension, Glaucoma, and Myopia, although challenges remain in distinguishing the Diabetes and Normal classes. Therefore, the VGG-16-based approach has proven effective for multi-class classification of fundus images, and the results of this study may serve as a foundation for developing deep learning-based diagnostic support systems in the field of ophthalmology.
Studi Komparatif Dampak Layanan Cloud Gaming terhadap Kinerja Jaringan Rumah Berbasis Ethernet dan WLAN Firnanda, Falentino; Putra, Yoga Gymnasti Prama; Yanti, Hesmi Aria; Zayandra, Ahmad Fauzi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9104

Abstract

Cloud gaming has transformed the digital gaming landscape by offloading rendering and computational processes to cloud servers, enabling users to play resource-intensive games on low-specification devices. However, in practice, there remains a critical issue regarding differences in performance and stability of home network connections in supporting cloud gaming services, particularly between Ethernet and Wireless Local Area Network (WLAN) connections. This study aims to analyze the impact of cloud gaming services, using NVIDIA GeForce NOW as a case study, on the performance of home networks under two different configurations: high-speed Ethernet and low-speed WLAN. Network traffic data were captured in real time using Wireshark over a total of 18 hours of gameplay sessions conducted across three days for each network type. Quality of Service (QoS) parameters, including latency, jitter, packet loss, and throughput, were extracted and analyzed using Python-based scripts. The results indicate that Ethernet connections provide more stable latency and jitter, experience no packet loss, and deliver more consistent throughput. In contrast, WLAN exhibits higher variability in latency and jitter, with fluctuating and less stable throughput. These findings confirm that while both network types can support cloud gaming under certain conditions, Ethernet offers superior performance and consistency. This study contributes practical insights for selecting and optimizing home network configurations to ensure a more reliable and seamless cloud gaming experience.
Adopsi dan Kehadiran Media Sosial Untuk Penanggulangan Bencana (Studi Pada Badan Penanggulangan Bencana Daerah (BPBD) Tingkat Provinsi di Indonesia) Brajawidagda, Uuf; Santiputri, Metta
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.9292

Abstract

Organisasi perlu mengadopsi dan aktif menggunakan media sosial agar dapat memanfaatkan sumber daya dengan masyarakat melalui media sosial sehingga dapat meningkatkan kinerja dalam mencapai tujuan organisasi. Walaupun studi mengenai partisipasi aktif masyarakat melalui media sosial dalam kegiatan penanggulangan bencana oleh lembaga kebencanaan tingkat nasional di Indonesia telah dilakukan, namun belum ada pemahaman menyeluruh mengenai tingkat adopsi dan penggunakan media sosial oleh lembaga kebencanaan tingkat provinsi. Untuk mengisi celah literature tersebut, kami menganalisis tahapan adopsi dan kehadiran di media sosial BPBD tingkat provinsi di seluruh Indonesia. Hasil analisis kami terhadap website dan akun media sosial 33 BPBD tingkat provinsi di Indonesia menunjukkan tingkat institusionalisasi media sosial yang cukup rendah dan adanya tingkat variasi kehadiran di media sosial. Kami menyajikan data terkini mengenai adopsi dan kehadiran BPBD tingkat provinsi di media sosial yang berguna bagi BNPB, BPBD dan pemerintah daerah untuk  meningkatkan kinerja organisasi dalam penanggulangan bencana.
Perbandingan Kinerja Algoritma Random Forest dan Convolutional Neural Network (CNN) Untuk Klasifikasi Citra Kucing Iwung, Hilaria; Rahman, Ben
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10156

Abstract

Cat breed classification is a significant challenge in the field of computer vision due to the high visual similarity between breeds (fine-grained classification) and pattern variations within a single breed. This study aims to compare the performance of two different machine learning approaches, namely Random Forest (RF) based on manual features and Convolutional Neural Network (CNN) based on automatic features. The research focuses on three cat breeds: Bombay, Siamese, and Persian. The research methodology uses a public dataset from Kaggle, divided in a ratio of 80:10:10. The RF pathway applies manual feature extraction through a combination of Histogram of Oriented Gradients (HOG) and Color Histogram. In contrast, the CNN pathway uses Transfer Learning techniques with the ResNet50V2 architecture. The test results show that CNN significantly outperforms RF with an accuracy of 93.33%, while RF only reaches 68.33%. The analysis shows that manual features in RF have difficulty capturing complex texture details in the Persian breed, while CNN is able to generalize well. It is concluded that the Deep Learning (CNN) approach is much more effective than traditional methods for animal breed classification.
Pipeline NLP End-to-End untuk Peringkasan Abstraktif dan Ekstraksi Entitas Berita Berbahasa Indonesia Berbasis Model Transformer Setia, Cuncun; Rukhviyanti, Novi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10030

Abstract

The rapid growth of online news content poses challenges for readers to capture the core information quickly and accurately. This research proposes and implements an automated end-to-end pipeline that integrates three main stages: data acquisition, abstractive text summarization, and Named Entity Recognition  (NER). The mT5 model is employed to generate coherent and concise summaries, while the BERT model is applied to extract key entities, including persons, organizations, and locations. The pipeline was evaluated using 100 news articles from the Egindo portal. Experimental results show that the system achieves an average text reduction of 62.47%, with a ROUGE-1 F1 score of 0.473. For NER tasks, the pipeline reached a Micro-F1 score close to 0.70, outperforming traditional approaches such as TextRank and CRF. These results demonstrate that the integration of Transformer-based models within a structured pipeline significantly improves summarization quality and entity extraction accuracy. The study contributes a practical NLP solution for the Indonesian language, providing a functional prototype that can be applied to online media analysis and media intelligence applications.
Pemanfaatan Teknologi Augmented Reality dengan Marker-Based Tracking sebagai Media Pengenalan Kabupaten Muara Enim Adeliani, Adeliani; Lestarini, Dinda; Seprina, Iin
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10051

Abstract

The development of digital technology has increased the demand for more interactive information media, including those used to present regional potential. Muara Enim Regency is rich in culture, industry, and tourism, all of which need to be introduced through more engaging media for both younger generations and the wider community. This study aims to develop an Augmented Reality–based application for introducing Muara Enim Regency using the Marker-Based Tracking method as a response to the need for more immersive and accessible information media. The development process follows the Multimedia Development Life Cycle (MDLC) method, which includes the Concept, Design, Material Collecting, Assembly, Testing, and Distribution phases. The application is implemented using Unity and Vuforia, integrating 3D objects, information panels, and an interactive quiz feature. Functional testing through Black-box Testing shows that all features operate according to specifications without significant issues. User Acceptance Testing (UAT) produced results categorized as very good, indicating that the application is positively received in terms of operational ease, informational clarity, stability, and interaction experience. Therefore, this application is considered suitable as an alternative medium for introducing the potential of Muara Enim Regency and has promising opportunities for further development through additional content and enhanced interactivity.
Smart Finance: Desain dan Implementasi Sistem Keuangan Cerdas Real-Time Berbasis IoT untuk UMKM Wardana, Bendra; Sitompul, Pretty Naomi
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10006

Abstract

The development of Internet of Things (IoT) technology and real-time data analytics provides opportunities to improve financial management efficiency for Micro, Small, and Medium Enterprises (MSMEs). However, most MSMEs in Indonesia still rely on manual bookkeeping, which is inefficient, prone to errors, and limits access to formal financing. This study aims to design and implement Smart Finance, an IoT-based intelligent financial system capable of processing transaction data automatically in real time. The research method includes system requirement identification, system design and device integration, application implementation, and system performance testing. The system was developed as a web-based application integrated with IoT devices such as ESP32-CAM to support automatic transaction recording, cash flow visualization, and digital financial report generation. The testing results indicate that the system can automatically record transactions with good accuracy, provide real-time financial dashboards, and deliver transaction notifications, thereby helping MSME owners monitor their financial conditions more quickly and transparently. The main contribution of this study lies in integrating IoT devices with a web-based financial recording system that enables automatic and real-time transaction recording, an approach that is still rarely implemented in MSME financial management. Although challenges related to internet connection stability remain, the developed system demonstrates potential in improving efficiency, transparency, and the quality of financial decision-making among MSMEs. This study concludes that Smart Finance can serve as a practical and adaptive digital financial solution to support the sustainability and competitiveness of MSMEs in the digital era.
Penerapan Metode Canny Edge Untuk Deteksi Pelat Nomor Kendaraan Area Parkir PLN Mabar Hakim nainggolan, Ihsanul; Fakhriza, M.
Jurnal Informatika: Jurnal Pengembangan IT Vol 11, No 1 (2026)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v11i1.10121

Abstract

Vehicle license plate detection is an essential component in modern parking management, particularly in institutional areas like PLN Mabar, which necessitate fast and accurate identification systems. This study focuses on applying the Canny Edge Detection method to accurately identify the edges of vehicle license plates under specific environmental settings, specifically lighting conditions of 100–115 lux and a camera height of 40 cm, evaluated across various threshold levels. Widely regarded as an optimal edge detection algorithm, the Canny Edge method offers significant advantages, including high-precision edge detection, robust noise interference minimization, and the generation of clear object boundaries. The research findings demonstrate that this method delivers excellent performance for vehicle detection in parking facilities when operating under controlled lighting and camera parameters. Specifically, the test results reveal that within a threshold range of 50 to 500, the system achieves a flawless 100% detection accuracy. This highlights the method's effectiveness in capturing crucial object edges under the tested conditions. Conversely, increasing the threshold beyond 500 leads to a gradual decline in system accuracy, dropping to 20% at a threshold of 900–1000. This decline indicates that excessively high threshold values cause the system to discard vital contours necessary for accurate detection. Ultimately, the system successfully detects license plate edges with a high success rate and stable processing times, proving its viability for practical implementation within the vehicle identification system at the PLN Mabar parking area.

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