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Indah Purnama Sari
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Hanif Journal of Information Systems
Published by Ilmu Bersama Center
ISSN : -     EISSN : 30252342     DOI : https://doi.org/10.56211/hanif
Core Subject : Science,
Computer Science & IT Library & Information Science
Hanif journal of Information Systems aims to provide scientific literatures specifically on studies of applied research in information systems (IS)/information technology (IT) and public review of the development of theory, method and applied sciences related to the subject. Hanif Journal of Information Systems accepts manuscripts on the topics: E-Business/E-commerce E-Government E-Health E-learning Human-Computer Interaction Information Assurance & Intelligent Information Security & Risk Management IS/IT Operations Management IS/IT Organization & Human Resource Management IS/IT Strategic Planning IT Governance IT Investment Analysis IT Project Management Web Science Social Media in Business Multimedia Application Big Data Research New Technology Acceptance and Diffusion Green Information Systems Innovation Management/Technopreneurship Data Science And other topics relevant to Information Systems.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 50 Documents
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Predicting Non-Performing Loan Levels Using XGBoost and Explainable Data Mining Pastima Simanjuntak; Rika Harman
Hanif Journal of Information Systems Vol. 3 No. 2 (2026): February Edition
Publisher : Ilmu Bersama Center

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Abstract

Non-Performing Loans (NPLs) represent one of the most critical indicators in assessing the stability and financial health of banking institutions. An increasing NPL ratio may negatively affect profitability, liquidity, and overall banking performance, making accurate prediction models essential for effective credit risk management. This study proposes a predictive framework for estimating Non-Performing Loan levels using the Extreme Gradient Boosting (XGBoost) algorithm combined with Explainable Data Mining techniques. The dataset consists of historical banking and financial indicators that influence loan repayment behavior. Data preprocessing stages include data cleaning, feature selection, normalization, and handling of missing values to improve model performance. The XGBoost model was employed due to its ability to handle complex nonlinear relationships and high-dimensional data efficiently. To enhance model transparency and interpretability, Explainable Data Mining techniques based on SHapley Additive exPlanations (SHAP) were applied to identify the contribution and importance of individual features affecting NPL predictions. Experimental results demonstrate that the proposed XGBoost model achieves high predictive performance, outperforming conventional machine learning approaches in terms of accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Furthermore, the explainability analysis reveals the most influential factors contributing to NPL levels, providing valuable insights for financial institutions and policymakers in developing proactive risk mitigation strategies. The findings indicate that integrating XGBoost with Explainable Data Mining not only improves prediction accuracy but also enhances the interpretability and trustworthiness of credit risk assessment models. This approach can support data-driven decision-making processes and strengthen the sustainability of banking operations in increasingly complex financial environments.
Computer Vision-Based Lettuce and Plantweed Segmentation Using YOLO and Segment Anything for Precision Agriculture Akhmad Jayadi; Adi Ahmad Fauzi; Muhammad Ikhsan; Jaka Persada Sembiring; Ahmad Rofi'i
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

The presence of weeds in lettuce cultivation areas can reduce plant productivity because they compete for nutrients, water, and light. Manual weed identification requires considerable time and effort, so an automated system based on computer vision is needed . This study proposes the integration of the YOLO11s model and the Segment Anything Model (SAM) to detect and segment lettuce plants and weeds in agricultural environments. The dataset used consists of 741 training images , 212 validation images , and 106 testing images with two object classes, namely Lettuce and Plantweed . The YOLO11s model was trained for 100 epochs using an image size of 640 × 640 pixels and a batch size of 106 . 16. The training results show that the model obtained an mAP@50 value of 87.8%. And mAP@50–95 was 80.0% , indicating good object detection capability. Furthermore, the bounding box coordinates of the detection results were used as prompts in the Segment Anything Model to generate mask -shaped segmentations that follow the object contours more precisely. The experimental results show that the integration of YOLO11s and SAM is able to produce more detailed object representations compared to detection using bounding boxes alone. This approach has the potential to support various precision agriculture applications, such as plant morphology analysis, leaf area estimation, and the development of automated weed control systems .
Flood Prediction Model Using the Random Forest Algorithm in Padangsidimpuan City Alvi Nasution; Putri Maulidina Fadilah; Muhammad Hafiz
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v4i1.79

Abstract

Flooding is a complex environmental phenomenon influenced by meteorological, temporal, and urban conditions. This study aims to develop a flood risk prediction model in Padangsidimpuan City using the Random Forest algorithm by integrating meteorological and temporal variables. The data set consists of historical observations from Padangsidimpuan City, including rainfall, temperature, humidity, wind direction, and flood occurrence status. The data were processed and divided into training and testing sets to evaluate model performance. The results indicate that the Random Forest model achieves strong classification performance, with an accuracy of 0.98 and specificity of 0.99, demonstrating a high capability in correctly identifying non-flood conditions. The model also shows good discrimination ability, with a ROC-AUC value of approximately 0.87. However, the recall value of 0.50 suggests that only half of the actual flood events are correctly detected, primarily due to class imbalance between flood and non-flood data. Feature importance analysis reveals that short-term meteorological variables, particularly rainfall and temperature, along with temporal patterns, are the most influential factors in flood prediction. In addition, spatial interpretation shows that flood-prone areas in Padangsidimpuan City are concentrated in densely populated urban zones with limited drainage capacity, highlighting the influence of urban environmental conditions on flood risk.Overall, the Random Forest model provides a strong foundation for flood risk prediction in Padangsidimpuan City. However, further improvements in data balancing and model optimization are required to enhance sensitivity and support a more reliable flood early warning system.
Development of Augmented Reality Technology as a Learning Medium for Hijaiyah Letters and Makharijul Huruf Based on Android (Case Study: SD IT Cendekia Darussalam) aini athiva; Sarini Vita Dewi
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

The Quran is a holy book for Muslims that serves as an important guide in all aspects of life, including education. The ability to read the Quran is honed from an early age, especially for students at the elementary school level, they require mastery of the hijaiyah letters, makhorijul huruf and the application of the rules of tajwid correctly. However, in its implementation, several obstacles are still encountered, including the lack of innovative teaching media, low visualization power, and the lack of student enthusiasm for learning amidst technological developments. This study aims to develop an interactive learning tool by utilizing Augmented Reality (AR) technology to help students recognize the hijaiyah letters and understand how to pronounce them through the ABATA method. The method applied in this study is Research and Development (RnD) which includes the stages of needs analysis, product development design and feasibility testing. The results show that the resulting product is able to display a visual representation of the hijaiyah letters in an interactive three-dimensional format equipped with audio pronunciation according to tajwid standards. Research has shown that the use of AR-based media can stimulate interest and strengthen students' understanding of the Quran, particularly in letter recognition and pronunciation accuracy. Furthermore, this media can create a more interactive, engaging, and effective learning environment compared to traditional methods. Therefore, the use of AR-based learning media has the potential to be an innovative alternative to support a more effective and enjoyable Quran learning process at the elementary level.
VIRTUAL REALITY ANIMATION DESIGN OF DHARMAWANGSA UNIVERSITY BUILDING Fikri Ananda; Welnof Satria
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

This research aims to design and build a Virtual Reality (VR) animation of the physical environment of the Dharmawangsa University Building as an Android-based interactive media. The development of this application was driven by the need for more innovative and attractive promotional and campus introduction media, replacing conventional methods such as brochures and static videos. The methods used include three-dimensional (3D) modeling using Blender software for building object creation, as well as integration into Unity 3D for developing VR applications compatible with mid-range Android devices. The scope of the application is focused on the visualization of the main building, parking area, and open space in the campus environment. Evaluations were carried out on aspects of application performance, device compatibility, and user experience in navigation and interaction in the virtual environment. The results of the study show that the developed VR application is able to provide an immersive and interactive campus exploration experience, and runs stably on Android devices with mid-spec specifications. Thus, this application is expected to be an effective solution in supporting promotions, new student orientation, and digital visualization of the campus environment.
Design of Interactive Animated Learning Media as a Stimulus for Improving Fine Motor Skills of Children Aged 5–6 Years at TK Bungong Jeumpa putri nahrisah PUTRI NAHRISAH; Raihan Islamadina
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

Fine motor skills play a significant role in early childhood development, particularly in supporting children’s ability to perform activities that require coordination between the eyes and small muscles of the hands and fingers. Adequate stimulation is essential during the ages of 5–6 years, as this developmental stage influences children’s readiness for various learning activities. Preliminary observations conducted at TK Bungong Jeumpa indicated that several children experienced difficulties in activities involving hand–eye coordination, object manipulation, and control of finger movements. In addition, classroom learning activities were still dominated by conventional media that provided limited opportunities for active interaction. This study aimed to design an interactive animated learning media to stimulate the fine motor skills of children aged 5–6 years at TK Bungong Jeumpa. The study employed a Research and Development (R&D) approach using the ADDIE model, consisting of the Analyze, Design, and Development stages. Data were collected through observations, interviews with teachers, and literature review to identify learning needs and determine appropriate media characteristics for early childhood education. The result of this study is an interactive animated learning media developed with the theme “Feeding Animals.” The media integrates animated visual elements, audio instructions, and interactive activities such as drag-and-drop games, line tracing, shape matching, and simple puzzle arrangements. The interface was designed using colorful graphics, child-friendly characters, and straightforward navigation to encourage active participation and engagement during learning activities. The developed media provides an alternative technology-based learning resource that supports fine motor stimulation through interactive experiences. By combining visual, auditory, and kinesthetic elements, the media is expected to assist teachers in creating more engaging learning environments and support the development of fine motor skills among children in early childhood education settings. The validation results indicated that the developed media was highly feasible for use in learning activities. The media expert validation obtained a feasibility score of 100%, while the material expert validation obtained a feasibility score of 95%, both categorized as very feasible. These findings indicate that the developed interactive animated learning media is appropriate for supporting fine motor skill stimulation among children aged 5–6 years. Keywords: Interactive Animated Learning Media, Fine Motor Skills, Early Childhood Education, Multimedia Learning, ADDIE Model.
The Impact of Dark UX on E-Commerce Consumer Decisions: A Qualitative Analysis and Ethical Design Recommendations Hendri Purnomo; Firmansyah; Ari Rudiyan
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v4i1.93

Abstract

Dark UX is an interface design practice that manipulates users into taking actions that benefit the service provider, often at the expense of the consumer. This study explores how dark UX influences consumer decision-making in e-commerce applications, identifies user issues, and develops recommendations for developers. A qualitative approach with purposive sampling was used to select 15 informants aged 25-45 who actively shop through e-commerce applications. Data were collected through in-depth semi-structured interviews and document analysis, then analyzed using thematic coding using Miles and Huberman's interactive model. Findings indicate that misleading urgency (limited stock notifications), sneak-into-basket (automatic item additions), and roach motel patterns (cancellation barriers) induce significant psychological distress, particularly the fear of missing out, which drives impulse purchases. Hidden costs that emerge only at the checkout stage and manipulated product reviews also erode user trust. The study recommends the implementation of ethical design principles, including up-front price transparency, easy-to-understand terms and conditions, real-time behavioral interventions, and regular dark UX audits. Stronger regulations and digital literacy programs are needed to create a healthy and sustainable e-commerce ecosystem.
Design of a Web-Based Savings Recording Application at Usaha Bersama Multifungsi (UB. MUFI) Koja, North Jakarta Faqih Matuz Sahro; Thomas Budiman; Hidayatul Ichwan
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

Purpose. This study aims to design and build a web-based savings recording application at Usaha Bersama Multifungsi (UB. MUFI) Koja, North Jakarta, to replace the manual recording that still relies on books and Microsoft Excel. Method. The study uses a qualitative approach with data collection through observation, interviews, and literature study, and applies the Waterfall software development method. The application was built using the PHP programming language, a MySQL database, and the Bootstrap framework, then tested using the Black Box Testing method. Findings. The resulting application is able to manage member data, deposit and withdrawal transactions, calculate balances automatically, store transaction histories, and generate reports by period. All main functions (register, login, member management, transactions, history, report printing, and change password) worked as expected during testing. Conclusion. This web-based application makes the savings recording process faster, more accurate, and more transparent, while reducing the risk of errors and data loss.
Comparative Evaluation of Remote Sensing, Socioeconomic, and Integrated Data for Predicting Regional Economic Growth in North Sumatra Using Machine Learning Dian Septiana; Sisti Nadia Amalia; Fahmi Ashari S Sihaloho
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

This study evaluates the predictive performance of remote sensing variables, socioeconomic indicators, and their integration for estimating regional economic growth across 33 districts and municipalities in North Sumatra, Indonesia. A quantitative cross-sectional design was employed using three predictor scenarios: remote sensing variables (Nighttime Light, NDVI, Built-up Area, and Land Surface Temperature), socioeconomic indicators (Human Development Index, Open Unemployment Rate, Fiscal Capacity Index, and Disaster Risk Index), and an integrated dataset. Four regression algorithms (Linear Regression, Support Vector Regression, Random Forest, and K-Nearest Neighbors) were optimized using RandomizedSearchCV and validated through Leave-One-Out Cross Validation. Model performance was evaluated using RMSE, MAE, and R², while permutation importance assessed predictor contributions. Support Vector Regression achieved the best predictive performance across all predictor scenarios. The socioeconomic dataset yielded the highest prediction accuracy (RMSE = 0.625, MAE = 0.480, R² = 0.198), outperforming the remote sensing-only dataset (RMSE = 0.685, MAE = 0.471, R² = 0.038) and the integrated dataset (RMSE = 0.647, MAE = 0.492, R² = 0.140). Although the R² values were relatively low, they reflect the complexity of regional economic growth and the influence of factors beyond those included in this study. Permutation importance identified the Human Development Index and Disaster Risk Index as the most influential predictors. These findings indicate that socioeconomic indicators are stronger predictors of regional economic growth, while remote sensing variables provide complementary spatial information. Although integrating remote sensing variables did not improve predictive accuracy, it offers valuable environmental context for more comprehensive data-driven regional development planning.
Implementation of K-Means Clustering for Inventory Item Classification in Inventory Management Febry Yusmita
Hanif Journal of Information Systems Vol. 4 No. 1 (2026): August Edition
Publisher : Ilmu Bersama Center

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Abstract

This study aims to implement the K-Means Clustering algorithm to classify inventory items at PT. Deli Farm Indonesia in order to improve inventory management efficiency and support managerial decision-making based on sales data patterns. This research employed a data mining approach using the Knowledge Discovery in Databases (KDD) framework. The research stages consisted of data collection, preprocessing, data transformation, clustering using the K-Means algorithm, and evaluation of clustering results. The implementation process was carried out using RapidMiner software with inventory transaction data obtained from PT. Deli Farm Indonesia. The implementation of the K-Means Clustering algorithm successfully classified inventory items into several clusters according to their sales characteristics and stock movement. The clustering results provide useful information for identifying fast-moving and slow-moving products, enabling the company to optimize inventory control, reduce excess stock, and improve inventory planning efficiency. The K-Means Clustering algorithm can be effectively applied to inventory management by providing meaningful classifications of inventory items. The resulting clusters can support strategic decision-making in warehouse management and contribute to more efficient inventory control at PT. Deli Farm Indonesia.

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