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Taqwa Hariguna
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IJIIS: International Journal of Informatics and Information Systems
Published by Bright Publisher
ISSN : -     EISSN : 25797069     DOI : https://doi.org/10.47738/ijiis
Core Subject : Science,
Computer Science & IT
The IJIIS is an international journal that aims to encourage comprehensive, multi-specialty informatics and information systems. The Journal publishes original research articles and review articles. It is an open access journal, with free access for each visitor (ijiis.org/index.php/IJIIS/); meanwhile we have set up a robust online platform and use an online submission system to ensure the international visibility and the rigid peer review process. The journal staff is committed to a quick turnaround time both in regards to peer-review and time to publication.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
 1 
Search results for , issue "Vol 8, No 4: Regular Issue: December 2025" : 5 Documents clear
Predicting Demand for MSME Products Using Artificial Neural Networks (ANN) Based on Historical Sales Data Endahti, Les; Faturahman, Muhammad Shihab
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.288

Abstract

Accurate demand forecasting plays a crucial role in supporting inventory and sales strategies, particularly for Micro, Small, and Medium Enterprises (MSMEs) that often face resource constraints. This study aims to develop a predictive model using Artificial Neural Networks (ANN) to forecast product demand based on historical sales data. The ANN model is trained and evaluated using a structured experimental approach, adjusting parameters such as the number of hidden layers, learning rate, and epochs to identify the best-performing architecture. Evaluation metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) are used to measure model performance. The results demonstrate that the ANN model is capable of capturing complex nonlinear relationships in multidimensional data and producing accurate demand forecasts. The model particularly performs well in predicting demand trends for products in the Electronics and Household categories. These findings provide valuable insights for MSME stakeholders in optimizing inventory planning and making data-driven business decisions.
Customer SCustomer Segmentation Using an Enhanced RFM–K-Means Framework on The Online Retail Datasetegmentation Using Enhanced K – Means Clustering Agus, Isnandar; Hasibuan, MS
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.289

Abstract

Effective customer segmentation is crucial for online retailers to enhance marketing strategies and boost profitability. However, analyzing transactional data often reveals challenges, such as noisy records and incomplete temporal patterns, which hinder accurate customer profiling. This paper proposes a robust methodology combining RFM (Recency, Frequency, Monetary) analysis with enhanced K-means clustering to segment customers of a UK-based online retailer, using data from December 2010 to December 2011. We preprocess the data to handle anomalies, engineer RFM features, and optimize cluster selection using the Elbow Method and Davies-Bouldin score, identifying four distinct segments: Best Customers, Loyal Customers, Almost Lost, and Lost Cheap Customers. Results show a 5% improvement in segmentation accuracy compared to baseline methods, with actionable insights for targeted marketing. This approach not only advances customer segmentation techniques but also offers practical value for retail businesses aiming to improve customer retention and sales.
A Text Classification Approach for Detecting Cyberbullying Risk on Twitter Using Support Vector Machine with Naive Bayes and Random Forest Comparison Yarsasi, Sri; Iskoko, Angga
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.290

Abstract

The rapid development of social media as a means of digital interaction also presents serious challenges in the form of the spread of negative content, including cyberbullying. Cyberbullying is a form of verbal violence committed online and has a significant impact on mental health, especially in adolescents. This research aims to develop a text classification model to detect the risk of cyberbullying using the Support Vector Machine (SVM) algorithm. The data used comes from a collection of cyberbullying-themed tweets. The research stages include text preprocessing (normalization, cleaning, tokenization, stopword removal, and stemming), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF), data division into training and testing sets, and model training using linear kernel of SVM. The model was evaluated using accuracy, precision, recall, and F1-score metrics. The results show that this approach is able to identify risky comments quite accurately, with optimal performance on the linear kernel. This research contributes to the development of automated detection systems to create a safer and healthier digital ecosystem, and supports preventive efforts in mitigating cyberbullying online.
A Comparative Analysis of Machine Learning Classifier of Anemia Diagnosis Based on Complete Blood Count (CBC) Data Putri, Nadia Awalia; Mukti, Bayu Priya
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.286

Abstract

Anemia is a prevalent hematological condition that requires accurate and timely diagnosis to ensure effective treatment. This study aims to compare the performance of several machine learning algorithms Random Forest, Support Vector Machine (SVM), Naive Bayes, and XGBoost in classifying different types of anemia based on Complete Blood Count (CBC) data. The dataset includes three diagnostic categories: Healthy, Normocytic hypochromic anemia, and Normocytic normochromic anemia. After preprocessing and normalization, each model was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. The results show that XGBoost achieved the highest overall performance with 99% accuracy and a perfect AUC of 1.00, followed closely by SVM and Naive Bayes. Naive Bayes showed lower performance, particularly in identifying normocytic normochromic anemia. These findings suggest that machine learning, especially ensemble-based models, holds strong potential in supporting clinical diagnosis of anemia using CBC data.
An Analysis of the Relationship Between Social Media Usage Intensity and Anxiety Levels Among University Students Using a Quantitative Approach Guballo, Jayvie Ochona
International Journal of Informatics and Information Systems Vol 8, No 4: Regular Issue: December 2025
Publisher : International Journal of Informatics and Information Systems

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/ijiis.v8i4.287

Abstract

The rapid development of social media has significantly influenced students' communication patterns and daily habits. While it offers ease in accessing information and interacting with others, excessive use of social media can negatively affect mental health, particularly anxiety. This study aims to analyze the relationship between social media usage intensity and anxiety levels among university students. A descriptive-correlational quantitative approach was applied using secondary data. The analysis was conducted using the Python programming language through several stages, including data cleaning, descriptive statistics, data visualization, and Pearson correlation testing. The results show a significant positive relationship between the duration of social media usage and students' anxiety levels, with a correlation coefficient of 0.52 and a p-value of 0.003. These findings indicate that the more time students spend on social media, the higher their reported anxiety levels. This study is expected to serve as a basis for promoting digital literacy and raising awareness of the importance of mental health among university students.

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