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Cyberspace: Jurnal Pendidikan Teknologi Informasi
Published by Universitas Islam Negeri Ar-Raniry Banda Aceh
ISSN : 25982079     EISSN : 25979671     DOI : -
Core Subject : Science, Education,
Computer Science & IT Education Other
Cyberspace: Jurnal Pendidikan Teknologi Informasi is an open access, peer-reviewed journal that will consider any original scientific article that expands the field of information technology education and various other related applied computer sciences themes. The journal publishes articles of interest to Information technology teachers, researchers and practitioners. Manuscripts must be original and educationally interesting to the audience in the field. The goal is to promote concepts and ideas developed in this area of study by publishing relevant peer-reviewed scientific information and discussion. This will help information technology practitioners to advance their knowledge for greater benefit and output in their professional contexts.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 4 Documents
 1 
Search results for , issue "Vol 10 No 1 (2026)" : 4 Documents clear
PERFORMANCE ANALYSIS OF MACHINE LEARNING AND INDOBERT IN CLASSIFYING SENTIMENTS ON INDONESIA'S FREE NUTRITIOUS MEAL Maulyanda; Nazhifah, Sri Azizah; Pane, Syafrial Fachri; Irvanizam, Irvanizam
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33886

Abstract

Natural Language Processing (NLP) is a branch of artificial intelligence that is widely used to analyze whether a sentence contains positive, negative, or neutral sentiment, particularly in the context of expressing opinions in the online environment. This study compares several models to identify the most optimal one, namely Naïve Bayes, Support Vector Machine (SVM), XGBoost, and IndoBERT. The dataset used in this research was obtained from Kaggle and consists of 5,644 data points in the neutral class, 2,934 data points in the positive class, and 2,606 data points in the negative class. Prior to model implementation, the dataset underwent a preprocessing stage that included case folding, cleansing, tokenization, stemming, and stopword removal. Subsequently, the data were trained using the four aforementioned methods. The results indicate that Naïve Bayes achieved an accuracy of 75%, SVM reached 79%, XGBoost obtained 76%, while IndoBERT achieved the highest accuracy at 85%. Therefore, it can be concluded that, using this dataset, IndoBERT performed sentiment classification very effectively.
Optimasi Segmentasi Kepala Janin Berbasis U-Net Melalui Preprocessing Citra USG Putri Salsabila; Raihan Islamadina
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.33961

Abstract

Fetal Head Circumference (HC) measurement using Ultrasound (USG) imagery is a crucial biometric parameter for estimating gestational age and monitoring fetal growth rate. However, automated interpretation is often hindered by speckle noise, low contrast, and blurred object boundaries inherent in USG images. This study aims to optimize the performance of a U-Net architecture with backbone ResNet-34 for fetal head segmentation through image preprocessing and data augmentation techniques. The proposed method integrates Anisotropic Diffusion for noise reduction and CLAHE (Contrast Limited Adaptive Histogram Equalization) to enhance boundary features, alongside geometric augmentations (rotation, flip) and median blur. The model was trained on 799 training images and validation with 80:20 ratio and 200 test images from a public dataset. Results indicate that the proposed preprocessing significantly improves segmentation performance compared to the baseline. The Intersection over Union (IoU) score increased from 0.9440 to 0.9526, and the Dice Similarity Coefficient (DSC) get 0.9757. Although preprocessing visually intensified certain artifacts, it effectively enhanced feature distinctiveness for the model. Based on the segmentation output, biometric estimation was conducted using ellipse fitting. This study concludes that U-Net optimized with Anisotropic Diffusion and CLAHE preprocessing shows significant potential as an assistive tool for medical professionals, enabling faster biometric measurement while maintaining the necessity for clinical verification.
SIMULATION-DRIVEN METHOD FOR WATER COVERAGE MONITORING FROM MULTI-SENSOR SATELLITE IMAGERY Putri, Andriani; Huang, Chih-Yuan; Tseng, Kuo-Hsin; Lin, Tang-Huang; Maghfirah, Hayatun; Nazhifah, Sri Azizah; Ridho, Abdurrahman; Mutia, Cut
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.34380

Abstract

Monitoring water coverage in intertidal zones is challenging due to the lack of satellite sensors that simultaneously provide both high spatial and high temporal resolution. Landsat offers detailed spatial information but is limited by its 16-day revisit cycle, whereas Himawari-8 provides frequent observations but at coarse spatial scales. Existing multi-sensor fusion approaches, such as STARFM and ESTARFM, attempt to bridge this gap but rely on the assumption of linear or abrupt land cover changes, which is inadequate for capturing the gradual and non-linear dynamics of tidal environments. This study proposes a simulation-driven method to enhance water coverage monitoring by generating reference images that represent varying water height conditions. The approach integrates normalized Landsat OLI and Himawari-8 AHI imagery with digital elevation and tidal models to interpolate Modified Normalized Difference Water Index (mNDWI) values. Through linear interpolation, synthetic reference images are produced for low, medium, and high-water height scenarios, filling temporal gaps and providing additional input for fusion-based monitoring. Results from the Hsiang-Shan Wetland demonstrate that simulated reference images contribute more significantly to accurate water mapping than Himawari-8 data alone. The method improves temporal continuity, enhances the representation of tidal dynamics, and reduces discrepancies in fused outputs. Although the accuracy depends on DEM and tidal model quality, the findings highlight the potential of simulation-driven approaches to strengthen water monitoring frameworks. This method can be extended to support applications in flood mapping, wetland management, and coastal conservation.
SISTEM INFORMASI PENILAIAN KINERJA PEGAWAI BERBASIS WEB SEBAGAI SOLUSI DIGITALISASI SDM Kartikasari, Meivi; Syntia Widyayuningtias Putri Listio; Bagus Kristomoyo Kristanto; Mahdi Bashroni Rizal
CYBERSPACE: Jurnal Pendidikan Teknologi Informasi Vol 10 No 1 (2026)
Publisher : Universitas Islam Negeri Ar-Raniry Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/cj.v10i1.34627

Abstract

Employee performance assessment is a critical component of human resource management in any organization. However, many organizations still rely on conventional spreadsheet-based methods that result in calculation errors, delayed reporting, subjective evaluations, and data manipulation risks. This study presents the development of a web-based employee performance assessment information system using PHP and MySQL. The system accommodates three user roles: administrator, assessor, and employee. Black box testing across sixteen functional scenarios confirmed that all system features operated successfully. The system provides standardized evaluation criteria, automated scoring, real-time reporting, and centralized data storage, significantly improving objectivity, efficiency, and accountability in performance assessments.

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