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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 431 Documents
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Perancangan dan Evaluasi Desain Antarmuka Pengguna pada E-Commerce Peralatan Medis Berbasis User Centered Design Wardhanie, Ayouvi Poerna; Effendi, Pradita Maulidya; Prasetyo, Muhammad Farid Alif
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8816

Abstract

In today’s rapidly evolving digital era, e-commerce platforms play a key role in distributing information, marketing, ordering, and online product sales. However, many platforms still struggle with suboptimal user interface and user experience design, such as unclear product and company information, complex ordering and payment processes, and visually unappealing interfaces, such as layout, colour, images, and typography. This study aims to designing the UI/UX design of a healthcare e-commerce platform at PT. Wanbass Timur Persada is using the User Centered Design (UCD) approach. UCD emphasizes active user involvement throughout the design process to ensure that the final product aligns with user needs, preferences, and behaviours. The research involved observing business process flows, conducting competitor UI/UX analysis, creating user personas and journey maps, developing prototypes, and evaluating the design. The result is a website-based e-commerce prototype that enhances navigation, improves information search efficiency, and features a clean and readable interface. User testing and in-depth interviews indicate that the prototype improves business process clarity and efficiency, while also meeting user expectations regarding interaction control and perceived security.
Implementasi Algoritma Support Vector Regression untuk Prediksi Harga Emas Berdasarkan Data Historis Hidayatulloh, M Rizqi; Yuwono, Dwi Purbo
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9233

Abstract

Amidst global economic volatility, accurate forecasting of gold prices remains a crucial and challenging task for investors and financial policymakers, as gold functions as a vital safe-haven asset and a hedge against inflation. This study focuses on gold price prediction utilizing the Support Vector Regression (SVR) algorithm, with the main objective of improving forecast accuracy. The relevance of this prediction is underpinned by the dynamic characteristics of gold prices, which is essential for decision-making by various stakeholders. Historical gold price data were obtained from the investing.com platform. The SVR implementation was carried out utilizing the Radial Basis Function (RBF) kernel. The SVR parameter optimization process employing Grid Search successfully identified the optimal values, namely C=1000, ϵ=0.5, and γ=0.01. To ensure model robustness and generalization capability, validation was performed using 5-Fold Cross Validation, which yielded an average Mean Absolute Percentage Error (MAPE) of 0.66%. The very high level of SVR accuracy, alongside its consistency across each fold, stability, and reliability, indicates that the optimized SVR model is a prospective solution for gold price forecasting in the commodity market.
Pengembangan Aplikasi Chatbot Untuk Layanan Penerimaan Mahasiswa Baru Berbasis Natural Language Processing Setiyorini, Agustin
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9406

Abstract

Abstrak – Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan sistem chatbot berbasis Natural Language Processing (NLP) untuk mendukung layanan informasi Penerimaan Mahasiswa Baru (PMB) di Universitas Janabadra. Layanan PMB selama ini masih bergantung pada interaksi manual yang terbatas pada jam kerja. Oleh karena itu, diperlukan solusi digital yang mampu memberikan informasi secara cepat, akurat, dan real-time. Sistem dikembangkan menggunakan framework CodeIgniter 4 dan memanfaatkan algoritma Naive Bayes untuk klasifikasi intent serta Levenshtein Distance untuk pencocokan kemiripan teks. Dataset pelatihan disusun berdasarkan kumpulan pertanyaan umum calon mahasiswa. Hasil evaluasi menunjukkan bahwa chatbot mampu menjawab 70% dari 500 pertanyaan secara otomatis dengan akurasi 92% dan waktu respons rata-rata 0,5 detik. Selain itu, chatbot mampu menurunkan beban kerja staf administrasi hingga 30%. Survei terhadap 100 pengguna menunjukkan bahwa 85% responden merasa puas terhadap kecepatan dan keakuratan respons sistem. Sistem ini juga mendukung penyimpanan konteks percakapan dan integrasi langsung dengan informasi PMB universitas. Penelitian ini menyimpulkan bahwa chatbot berbasis NLP dapat menjadi solusi efektif dalam meningkatkan efisiensi layanan informasi pendidikan tinggi. Pengembangan lanjutan diarahkan pada perluasan dataset, adopsi model NLP berbasis Transformer, serta integrasi lintas platform komunikasi untuk memperluas jangkauan layanan. Kata Kunci: Chatbot, Natural Language Processing, Naive Bayes, Levenshtein Distance, Penerimaan Mahasiswa Baru.
Pengembangan Aplikasi Prediksi Harga Emas Berbasis Web Menggunakan Model Time Series Abdullah, Fikrian Nur; Nurardian, Ridwana Septian; Liya, Amel; Saputra, Ari Setia; Saputra, Atio Wahyudi; Bismi, Waeisul
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9165

Abstract

High gold price volatility due to global economic instability poses challenges in investment decision-making. This research aims to develop a web-based gold price prediction application using a time series model, focusing on the Gated Recurrent Unit (GRU) algorithm. This application is designed to present real-time, accurate, and easily accessible gold price predictions, thereby increasing the efficiency and transparency of information for investment decision making. The development process starts from collecting and preprocessing daily gold price data for the period 2013-2023, then comparing four predictive models: LSTM, GRU, ARIMA, and XGBoost. Evaluation is performed using MAE, RMSE, and R² metrics. Results showed that GRU provided the best performance with an RMSE value of 17.76 and R² of 0.9410. The GRU model is integrated into a web application using the Flask framework, with an interactive HTML-based interface and Chart.js visualization. This application presents real-time gold price predictions and can be accessed by general users and investors. The results of this study show that the time series approach with GRU is effective in projecting gold prices, and can be a relevant tool in supporting data-based investment decisions.
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.
Enhancing PCOS Classification with Weighted Loss-Based Neural Network on Imbalanced Data Maslakhah, Amanda; Hakim, Lukman
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.9449

Abstract

Polycystic Ovary Syndrome (PCOS) represents a multifaceted endocrine–metabolic condition that poses a significant risk to reproductive health in women of childbearing age. The disorder is influenced by various contributing factors and is commonly associated with clinical features such as disrupted ovulation, hormonal imbalance due to excess androgens, and morphological changes in the ovaries. In automated PCOS classification, a major limitation arises from the disproportionate distribution of data samples, in which instances without PCOS considerably outnumber affected cases. This imbalance tends to bias predictive models toward the dominant class, thereby reducing the detection capability for minority instances and increasing the likelihood of missed PCOS diagnoses. To address this issue, this study proposes the incorporation of a Weighted Loss Function into a Neural Network-based classification framework aimed at improving sensitivity to PCOS cases. The research workflow comprises data preprocessing, neural network architecture construction, integration of class-weighted loss, and systematic experimentation across multiple architectural designs and training configurations. The experimental findings demonstrate that applying a Weighted Loss Function with manually assigned class weights of 1:2, a learning rate of 0.001, five hidden layers, and 50 training epochs delivers optimal classification performance. Under these settings, the model achieves high values across evaluation metrics, including precision, recall, F1-score, and overall accuracy, reaching up to 99%. The results confirm that the proposed approach effectively mitigates majority-class bias and enhances the model’s ability to identify PCOS cases. This improvement is further reinforced through careful hyperparameter tuning and comprehensive experimental evaluation.
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.

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