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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 30 Documents
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Home Anti Theft System Uses Based Telegram Bot Internet of Things Fadhlurrohman, Dimas; Basri, Mhd
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.36

Abstract

Often we hear cases of home theft and belongings valuable. This crime is difficult for the owner of valuables to know. Usually it will be known after the theft disaster. Circumstances like this certainly make us uncomfortable and feel restless about our valuables. Most people for their valuables security systems use CCTV (Closed Circuit TeleVision), which can record the movements of every person's activity. One of the disadvantages of using CCTV is that after we know there is a theft disaster, then we can only see from the image recordings that have occurred, and the perpetrators of the theft can be revealed. This of course still makes it difficult for us to solve these problems.
IoT Based Industrial Waste Monitoring System Design with Data Visualization on A Web Application Using The Supervised Learning Method Ritonga, Muhammad Nauval Asyqar Ridwan; Maulana, Halim
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.50

Abstract

Industrial waste management is a critical aspect of sustainable manufacturing, as improper handling can lead to severe environmental pollution and health hazards. Real-time monitoring of industrial waste parameters enables early detection of irregularities and supports informed decision-making for compliance with environmental regulations. This study presents the design of an IoT-based industrial waste monitoring system integrated with data visualization on a web application and enhanced by the supervised learning method for predictive analysis. The system utilizes IoT sensor nodes to measure key waste parameters such as pH level, temperature, turbidity, and chemical concentration. Sensor data is transmitted wirelessly to a cloud server, where it is stored, processed, and analyzed using supervised learning algorithms to classify waste quality and detect potential violations. The web application provides interactive dashboards, historical data tracking, and real-time alerts for stakeholders. Testing results demonstrate that the system achieves high accuracy in classifying waste conditions, offers user-friendly visual analytics, and enables proactive waste management. This research contributes to the development of intelligent environmental monitoring solutions, promoting efficiency, compliance, and sustainability in industrial operations.
Design and Implementation of a Web-Based Online Registration System for HIMATIF Using the Agile Development Method Senoaji, Arya Dimas; Akbar, Farid
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.51

Abstract

Efficient and well-organized registration processes are essential for supporting the activities and membership management of student organizations. The Information Technology Student Association (HIMATIF) requires a system that can streamline registration, reduce administrative workload, and improve data accuracy. This study focuses on the design and implementation of a web-based online registration system for Himatif using the Agile development method. The system features member registration, data validation, document uploads, activity selection, and administrative dashboards for managing registrant information. The Agile approach, specifically the Scrum framework, was applied to enable iterative development, continuous feedback, and rapid adaptation to changing requirements. The system was developed using PHP with the Laravel framework and MySQL for database management, ensuring secure and scalable performance. Testing results show that the application meets functional requirements, operates reliably across multiple devices, and significantly reduces the time required for registration compared to manual processes. This system enhances operational efficiency, improves data management, and provides a better user experience for both members and administrators.
Implementation of Linear Regression Algorithm in a Web-Based Major Prediction System for New Student Applicants at SMK N 1 Percut Sei Tuan Pulungan, Sabrina Meylani; Siambaton, Mhd. Zulfansyuri; Santoso, Heri
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.47

Abstract

This study aims to develop a web-based major prediction system by applying a linear regression algorithm to enhance transparency and accuracy in the selection process. The system predicts 14 available majors at SMK N 1 Percut Sei Tuan, including: Civil Construction and Housing Engineering, Modeling and Building Information Design, Geomatics Engineering, Electrical Installation Engineering, Electrical Power Network Engineering, Heating, Air Conditioning and Refrigeration Engineering, Audio Video Engineering, Machining Engineering, Welding Engineering, Light Vehicle Engineering, Motorcycle Engineering, Software Engineering, Computer and Network Engineering, and Television Production and Broadcasting. The system uses report card scores from the 5th and 6th semesters of junior high school as predictor variables, including Bahasa Indonesia, Mathematics, Science, and English. The system development method includes data collection through observation, literature study, and interviews, as well as system design using PHP, HTML, JavaScript, MySQL database, and XAMPP. System modeling was carried out using UML (Unified Modeling Language), which includes use case diagrams, sequence diagrams, and activity diagrams. The linear regression algorithm is implemented by calculating subject averages, regression coefficients, and intercepts to predict student acceptance. The results of the study, based on five student data samples, show that M. Dafi and Ahmad Suhendra were not eligible for any major. Adellya Saputri and Alfit Septian were accepted into one major, Television Production and Broadcasting. Meanwhile, Ummi qualified for five majors: Modeling and Building Information Design, Audio Video Engineering, Welding Engineering, Light Vehicle Engineering, and Television Production and Broadcasting.
A Decision Support System for Determining Optimal Concrete Quality Using the Simple Additive Weighting (SAW) Algorithm (Case Study: UISU Concrete Laboratory) Rianto, Muhammad Aulia Abdi; Siambaton, Mhd. Zulfansyuri; Santoso, Heri
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.48

Abstract

This study aims to design a decision support system to determine the best concrete quality using the Simple Additive Weighting (SAW) algorithm. Concrete is the primary material in construction, possessing various mechanical properties and characteristics that define its quality. At the Concrete Laboratory of Universitas Islam Sumatera Utara (UISU), the determination of concrete quality is still conducted manually, relying on subjective experience, which can lead to inconsistencies in assessment. Therefore, developing a system based on the SAW algorithm is necessary to enhance efficiency and objectivity in selecting the best concrete. The research process begins with data collection on concrete samples, covering parameters such as compressive strength, water volume, setting time, cement content, and aggregate quantity. Each criterion is assigned a weight based on its importance, followed by normalization to align scale values. The SAW algorithm is then applied to calculate the final preference values for each concrete sample, ultimately generating a recommendation for selecting the highest-quality concrete. The study results show that Concrete C achieves the highest final score (0.94706), followed by Concrete A (0.88328) and Concrete B (0.76292). The study concludes that the SAW algorithm effectively enhances objectivity and accuracy in determining the best concrete quality.
Application of Data Mining to Determine the Performance of Family Planning Field Officers (PLKB) Using the C4.5 Algorithm Nasution, Perdinal; Azhari, Mulkan
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.52

Abstract

The effectiveness of family planning programs is closely related to the performance of Family Planning Field Officers (PLKB). Conventional performance evaluation methods often rely on manual assessments, which may lead to subjectivity and inconsistency. To overcome this issue, data mining techniques can be applied to analyze performance data systematically and objectively. This study employs the C4.5 decision tree algorithm to classify and evaluate the performance of PLKB. The dataset used in this research includes several indicators, such as service coverage, counseling frequency, reporting accuracy, and community participation. Prior to model construction, data preprocessing was performed to handle missing values and normalize attributes. The model performance was evaluated using accuracy, precision, recall, and F-measure. The findings indicate that the C4.5 algorithm successfully classified PLKB performance into three categories: high, medium, and low. The model achieved an accuracy of [insert % if available], demonstrating its effectiveness in identifying key determinants of officer performance. Moreover, the decision tree generated interpretable rules that highlight the most influential attributes affecting PLKB performance. The application of data mining using the C4.5 algorithm provides an objective and efficient method for evaluating PLKB performance. This approach not only enhances decision-making for supervision and training but also contributes to the improvement of family planning program implementation. Future research is suggested to compare the C4.5 algorithm with other classification methods to achieve higher accuracy and generalizability.
Comparison of Logistic Regression and K-Nearest Neighbor (KNN) Algorithms in a Heart Failure Prediction Dataset Nasution, Julia Namira; Azis, Zainal
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.53

Abstract

Heart failure is one of the leading causes of death worldwide. Early detection of heart failure risk is crucial to minimize its serious consequences. This study aims to compare the performance of two machine learning algorithms, namely Logistic Regression and K-Nearest Neighbor (KNN), in predicting heart failure using a dataset from the Kaggle platform. The research stages include data preprocessing, normalization, splitting into training and testing data, model implementation, and evaluation using a confusion matrix. Evaluation is based on accuracy, precision, recall, and F1-score metrics. The results show that Logistic Regression achieved an accuracy of 88.04% with an execution time of 0.022 seconds, while KNN achieved an accuracy of 85.51% with an execution time of 0.158 seconds. Logistic Regression outperformed in recall and F1-score, making it more effective for early detection of heart failure. Therefore, Logistic Regression is considered more optimal than KNN in the context of this study. However, Logistic Regression is not always superior to K-Nearest Neighbor, as prediction results highly depend on the characteristics of the specific case.
Application of Data Mining in Predicting Demand for Blood Bags Based on Blood Type Using the Single Moving Average Method (SMA): Case Study: PMI UDD, Langkat Regency Tahara, Aqilah; Irvan, Irvan
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.54

Abstract

Blood stock availability at the Blood Donation Unit (UDD) PMI Langkat Regency is often disrupted due to unpredictable demand and reliance on voluntary donors, leading to risks of shortages or surpluses, especially for certain blood groups. This study applies a data mining method using the Single Moving Average (SMA) approach to predict the demand for blood bags based on blood groups (A, B, AB, and O) over the past year. The forecasting process calculates the average demand over a specific period and measures accuracy using MAPE, MSE, and MAD. The results show that the SMA method provides reasonably accurate predictions, with blood group O having the highest average demand of 161 bags per month, followed by blood groups A, B, and AB. An average MAPE value below 10% indicates that this method is effective for blood stock planning at UDD PMI Langkat, helping to optimize blood inventory management and minimize the risk of shortages or surpluses.
Detecting Zero-Width Characters Obfuscated in Phishing URLs using the XGBOOST Algorithm Asadel, Ahmad; Zulherry, Andi
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.56

Abstract

Phishing attacks represent one of the most common and damaging cyber threats, with techniques continuously evolving to become more sophisticated and harder to detect. One of the latest evasion methods of concern is the use of Zero-Width Characters (ZWC)—invisible Unicode Characters inserted into URLs to deceive traditional detection systems and human visual perception. This research aims to develop and evaluate an effective and reliable machine learning model to detect phishing URLs that have been obfuscated using ZWC. The eXtreme Gradient Boosting (XGBoost) algorithm was chosen for its proven superiority in handling complex data and its performance optimization capabilities. This study utilized a public dataset from Kaggle consisting of 11,430 URL samples, which was then modified through a feature engineering process. Specifically, 50% of the phishing URLs were injected with one of five types of ZWC (ZWSP, ZWNJ, ZWJ, RLM, LRM), and a dedicated binary feature was created to flag the presence of these Characters. Initial training revealed signs of minor overfitting. Consequently, a hyperparameter tuning process was conducted by adjusting the max_depth and min_child_weight parameters to create a more robust model. The final model was evaluated on 20% of the test data and demonstrated exceptionally high performance, achieving an Accuracy of 97.24%, Precision of 97.03%, Recall of 97.37%, and an AUC score of 0.9972. The high Recall value is particularly crucial, proving the model's reliability in minimizing the risk of missed threats. This research successfully proves that an XGBoost-based approach with targeted feature engineering can be an effective solution against advanced phishing attacks.
Development of Virtual Reality-Based Computer Assembly Simulation Learning Media Prastia, Ilham; Azhari, Mulkan
Hanif Journal of Information Systems Vol. 3 No. 1 (2025): August Edition
Publisher : Ilmu Bersama Center

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56211/hanif.v3i1.66

Abstract

The development of Virtual Reality (VR) technology provides great opportunities in creating interactive and immersive learning media that can simulate hands-on practice more realistically. In computer assembly learning at vocational schools, limited availability of laboratory equipment often becomes a major obstacle, resulting in students not gaining optimal direct practice experience. This study aims to develop a Virtual Reality-based computer assembly learning simulation as an interactive, safe, and engaging alternative learning tool. The research employed a Research and Development (R&D) method using the ADDIE model, consisting of the stages of analysis, design, development, implementation, and evaluation. Computer component assets were modeled using Blender 3D and then integrated into Unity to build an interactive VR-based simulation. The testing phase involved Black Box Testing and Application Testing with 10 respondents, consisting of 7 vocational students and 3 alumni from the Computer and Network Engineering major. The results show that all interactive features performed according to the expected scenarios, and the feasibility assessment through Application Testing achieved a score of 87.2%, indicating that the simulation is suitable, easy to use, and effective in improving students’ understanding of computer assembly procedures. Additionally, the VR media was considered to provide a more realistic learning experience, reduce the potential for errors during real practice, and increase student engagement throughout the learning process. Therefore, this VR-based learning media can serve as a solution to laboratory limitations and a foundation for further development of VR-based practical learning materials in the field of Computer and Network Engineering.

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