cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
register@ft.unipdu.ac.id
Editorial Address
Kompleks Pondok Pesantren Darul Ulum, Rejoso, Peterongan, Jombang, East Java, Indonesia, 61481
Location
Kab. jombang,
Jawa timur
INDONESIA
Register: Jurnal Ilmiah Teknologi Sistem Informasi
Published by Universitas Pesantren Tinggi Darul ulum
ISSN : 25030477     EISSN : 25023357     DOI : https://doi.org/10.26594/register
Core Subject : Science,
Computer Science & IT
Register: Scientific Journals of Information System Technology is an international, peer-reviewed journal that publishes the latest research results in Information and Communication Technology (ICT). The journal covers a wide range of topics, including Enterprise Systems, Information Systems Management, Data Acquisition and Information Dissemination, Data Engineering and Business Intelligence, and IT Infrastructure and Security. The journal has been indexed on Scopus (reputated international indexed) and accredited with grade “SINTA 1” by the Director Decree (1438/E5/DT.05.00/2024) as a recognition of its excellent quality in management and publication for international indexed journal.
Arjuna Subject : -
Articles 228 Documents
 19   20   21   22   23 
Development of an Interactive Augmented Reality-Based Weather Forecast Simulation with Automated BMKG Data for Enhanced TV Broadcasting Moh. Zikky; Robby Alexander; Zakha Maisat Eka Darmawan; Fitrah Maharani Humaira; Laily Asna Safira
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5117

Abstract

The weather has a significant impact on human activities. Weather data are used in various industries. Recently, Augmented Reality (AR) technology has been widely used to enhance content representation through multidimensional visualization. Television stations utilize AR to capture viewers' attention in the programs they broadcast; AR has the potential to enhance communication between viewers and the content. Our research on developing an AR application for an engaging immersive reality implementation aims to simulate a virtual stage in TV broadcasts, displaying real-time weather data from the Indonesian Meteorology, Climatology, and Geophysics Agency (BMKG) using the latest SQL database. The implementation of the AR system as a multimedia mode in delivering weather information increases the attractiveness and engagement of viewers. This system operates by translating weather data from SQL into key parameters for pre-built 3D visualizations, which appear at marked coordinates. This application runs on the Windows platform and can be managed via Android, allowing presenters to convey engaging scenarios. Research on East Java BMKG data showed that the design function to transform BMKG data into a virtual weather animation data was significantly successful in both synchronizations.
From Clicks to Cradles: Mapping the Digital Landscape of Maternal Support through Bibliometric Analysis Rita Wong Mee Mee; Hanim Mohamad Ismail; Belinda Marie Balraj; Lim Seong Pek; Ali Derahvasht; Yang Mingmei
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5437

Abstract

Online platforms have emerged as crucial parenting information sources in the digital age, revolutionizing how moms seek help, make choices, and deal with the difficulties of raising children. This change demonstrates the increasing scholarly interest in comprehending how digital resources affect the experiences of mothers. With an emphasis on performance and co-citation analyses, this study offers a bibliometric analysis of studies on mothers' interactions with online parenting resources. After a thorough screening process, 453 studies were eventually included out of the 1,352 records that were first found using the Web of Science database. While co-citation analysis finds thematic clusters like maternal mental health, digital parenting literacy, and online community engagement, performance analysis identifies important contributors, such as top authors, organizations, and nations. The findings show that, especially in reaction to the COVID-19 pandemic, academics are increasingly focusing on digital platforms as the main sources of parenting information. The results indicate that although mothers can benefit greatly from online resources, problems with disinformation and inequalities in digital literacy still exist. Since it highlights the necessity of trustworthy digital parenting resources to support maternal mental health and child development, this study is in line with Sustainable Development Goal (SDG) 3: Good Health and Well-Being. For a more comprehensive understanding of digital parenting practices, future research should incorporate qualitative methods and investigate cross-cultural viewpoints. A systematic road map for upcoming studies, the creation of policies, and real-world applications in the rapidly changing field of online parenting support is offered by this bibliometric analysis.
Analytical Estimation of Jitter for the MMPP-N Traffic Model Hicham Magri; Mohamed Rachdi; Mohamed Azzouazi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 11 No 2 (2025): July
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v11i2.5679

Abstract

Nowadays, consumers demand powerful and adequate communication techniques to meet their desires for communication and exchange. These techniques, such online chats, VoIP, video conferencing, and AR/VR services, require well-defined Quality of Service (QoS) properties, such as minimal transmission delay, high throughput, low inter-arrival time, and low packet delay variation. Among these, jitter is an important QoS parameter in IP networks that can affect the performance of real-time applications and services. In this paper, we provide an overview of jitter and relevant studies. We propose an analytical calculation of jitter for the MMPP-2 and MMPP-N traffic models and analyze the jitter behavior for voice traffic.
Data Augmentation of Sperm Images Using Generative Adversarial Networks (WGAN-GP) I Gede Susrama Mas Diyasa; Hajjar Ayu Cahyani Kuswardhani; Mohammad Idhom; Prismahardi Aji Riyantoko; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5954

Abstract

This study analyzes the use of WGAN-GP for data augmentation in the analysis of sperm morphology. WGAN-GP has been the focus in this study for generating sperm microscopy images, which in turn aims to mitigate the problem of data scarcity in medical imaging. A heterogeneous dataset with mixed object categories was initially employed, leading to an FID score of 134, which in turn reflected a high incidence of mode collapse. For this reason, the dataset was divided into subcategories of Normal, Abnormal, and Non-Sperm identifications, with the scores of the subcategories being 59.19, 74.92, and 83.56, respectively, and showing better balanced model stability. This study's primary contribution is the use of WGAN-GP for the first time for sperm image data augmentation and the generation of more realistic synthetic images. Furthermore, this study illustrates the first understanding of the intricacies of data distribution's complexity and its effect on the model's performance, indicating the possibility of improvement using class-based techniques and sophisticated architectures for the generator. The innovation of this study is the application of WGAN-GP to sperm morphology datasets, improving image quality and the stability of the results, coupled with extensive model performance analysis and providing a further understanding of the field of medical image data augmentation.
Unsupervised Optimization of Boundary Information Based on the Coefficient of Variation to Improve Image Segmentation Cahyo Crysdian
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.4725

Abstract

The automatic retrieval of boundary information from image objects suffers from the problem of under and over-segmentation, where the former leads to missed object detection, while the latter delivers an improper object shape. A method to optimize the automatic retrieval of complete and proper boundary information is proposed in this research based on an unsupervised approach. The strategy is to utilize the trade-off between the coefficient of variation from shape distribution against the mean of entropy contribution from segmented regions. This mechanism relies on the assumption that the segmentation result of a natural image contains a prominent main object representation with its details which are presented as a normal distribution of segmented regions. The research also enhances the entropy-based segmentation evaluation by redefining the computation of image entropy and segmentation entropy. The experiment shows that the proposed approach is capable of reducing over-segmentation by 57.20% compared to the existing algorithm, while at the same time reducing the consumption time by 85.26%. The empirical evaluation shows that the proposed approach delivers the highest accuracy among other evaluated methods. Qualitative validation based on groups of human observers shows that the proposed approach is the most desired algorithm for producing boundary information and measuring segmentation quality. These findings suggest that the trade-off between the mean of entropy contribution from the segmented regions and the coefficient of variation from shape distribution becomes an effective feature for unsupervised retrieval of boundary information.
Enhanced Rice Yield Prediction in Indonesia with Integrated Climate and Agricultural Data Using Decision Tree Regression Tegar Arifin Prasetyo; Samuel Jefri Siahaan; Usman Efendi; Mesya Angeliqa Hutagalung; Amalia Nur Alifah
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5352

Abstract

Rice is central to Indonesia’s food security, yet provincial yields are highly sensitive to climatic variability, making reliable forecasting essential for national planning and improving farmer welfare. Most prior Indonesian yield models rely on rainfall and temperature data and omit sunlight exposure duration, which is a limiting factor for photosynthesis in the humid tropics where solar radiation, not temperature, often constrains productivity. This study develops a province-level rice yeild prediction model based on Decision Tree Regression (DTR) that integrates climate data from the Meteorology, Climatology, and Geophysics Agency (BMKG) with agricultural statistics from the Central Statistics Agency (BPS). The dataset comprises data from 34 provinces covering the period from 2018 to 2023 (204 province-year observations), with year, harvested land area, rainfall, and sunlight exposure duration as predictors and rice production as the target variable. The dataset was partitioned into training and testing subsets using an 80:20 ratio. Hyperparameter tuning was performed using k-fold cross-validation, and model performance was performed using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The model attained an average RMSE of 93,046 tons (MAE ≈ 51,824 tons; MAPE ≈ 6.8% on the 2023 hold-out year). A key finding is that absolute RMSE is strongly scale-dependent. When evaluated in relative terms, the highest-producing Java provinces were among the most accurately predicted (relative RMSE ≈ 2.3–2.5%), whereas several small or structurally volatile provinces showed relative errors above 40%. The study contributes to the literature by providing (i) the explicit integration of sunlight exposure into Indonesian rice yield modeling, (ii) a province-disaggregated error analysis that reframes accuracy in scale-independent terms, and (iii) an interpretable decision-support tool for food-policy stakeholders such as Bulog and the Ministry of Agriculture.
Agroecological Zoning of Bangkalan Regency Using K-Means and HDBSCAN Based on Integrated Soil Fertility and Climate Features Wahyudi Agustiono; Giraldo Stevanus; Yoga Dwitya Pramudita; Wahyudi Setiawan; Deshinta Arrova Dewi
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.5969

Abstract

Agroecological heterogeneity poses challenges for agricultural planning in Bangkalan Regency, Indonesia. This study aimed to delineate agroecological zones by integrating soil fertility, climate, and topographic variables using K-Means clustering and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN). A total of 11,000 geospatial observations obtained from Google Earth Engine were aggregated into 277 village-level units. The dataset included soil nutrients (nitrogen, phosphorus, and potassium), the Soil Quality Index, temperature, rainfall, humidity, elevation, and slope. Data preparation, modeling, and evaluation were performed as the primary methodological steps. Min-Max Scaling was applied to normalize the data. The optimal K-Means configuration (K = 3) achieved a Silhouette Score of 0.2668, an Inertia value of 294.5529, and a Calinski-Harabasz Index (CHI) of 75.8821. The resulting clusters were classified as High-Potential (52 villages), Moderate-Potential (142 villages), and Environmental-Constraint (83 villages) zones. HDBSCAN was used to validate clustering patterns and detect environmental anomalies. The optimal HDBSCAN configuration identified two density-based clusters and five noise villages. These villages showed exceptionally high nitrogen, phosphorus, and Soil Quality Index values, indicating localized agroecological hotspots. The integration of K-Means and HDBSCAN offers a comprehensive framework for agricultural planning, resource allocation, and sustainable land management.
AI Ethics in Indonesian Higher Education: A Systematic Review of Algorithmic Bias, Privacy, and Accountability Yomi Agung Susanto; Eko Hariadi; Lilik Anifah; Ratna Suhartini; Purwoko Ajie
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 12 No 1 (2026): January (In Progress)
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v12i1.6353

Abstract

Artificial Intelligence (AI) is increasingly integrated into higher education to enhance personalised learning, automate assessment, and improve institutional efficiency. However, its rapid adoption also raises ethical concerns related to algorithmic bias, data privacy, and accountability, particularly in Indonesia, where regulatory frameworks and digital infrastructure remain underdeveloped. Despite growing global discussions on AI ethics, limited studies have systematically examined these challenges within Indonesian higher education. This study analyses the ethical implications of AI integration by focusing on algorithmic fairness, data privacy, and governance accountability. Employing a narrative review approach supported by the PRISMA 2020 framework, this study systematically reviews 56 studies retrieved from the Scopus database between 2021 and 2025. Article screening was conducted using Covidence, while VOSviewer was utilised to identify research trends and thematic gaps. The findings reveal three major ethical concerns: (1) algorithmic bias in AI-driven assessment and admissions systems; (2) risks to data privacy and student surveillance associated with learning analytics; and (3) limited transparency and accountability in AI-based decision-making. The study further identifies significant gaps in Indonesia’s policy readiness and institutional governance. As its contribution, this study proposes a culturally grounded approach to AI governance and recommends the development of a National AI in Education Ethics Charter to support responsible and equitable AI integration in higher education.

Page 23 of 23 | Total Record : 228

 19   20   21   22   23 

Filter by Year

2015 2026


Reset
Filter By Issues
All Issue Vol 12 No 1 (2026): January (In Progress) Vol 11 No 2 (2025): July Vol 11 No 1 (2025): January Vol 10 No 2 (2024): July Vol 10 No 1 (2024): January Vol 9 No 2 (2023): July Vol 9 No 1 (2023): January Vol 8 No 2 (2022): July Vol 8, No 1 (2022): In progress (January) Vol 8 No 1 (2022): January Vol 7, No 2 (2021): July Vol 7, No 1 (2021): January Vol 6, No 1 (2020): January-June (Articles In progress 4/7) Vol 6, No 1 (2020): January-June (Articles in press 2/7) Vol 6, No 2 (2020): July Vol 6, No 1 (2020): January Vol 6, No 1 (2020): January-June Vol 5, No 1 (2019): January-June (In progress: 1/7) Vol 5, No 2 (2019): July-December Vol 5, No 2 (2019): July Vol. 5 No. 1 (2019): January Vol 5, No 1 (2019): January-June Vol 5, No 1 (2019): January Vol 4, No 2 (2018): July-December Vol 4, No 2 (2018): July Vol 4, No 1 (2018): January Vol 4, No 1 (2018): January-June Vol 3, No 1 (2017): Januari-Juni (3/7) Vol 3, No 2 (2017): July Vol 3, No 2 (2017): July-December Vol 3, No 1 (2017): January Vol 2, No 2 (2016): Juli-Desember Vol 2, No 2 (2016): July Vol 2, No 1 (2016): January Vol 2, No 1 (2016): Januari-Juni Vol 1, No 1 (2015): Januari-Juni Vol 1, No 1 (2015): January More Issue