Android Malware Threats: A Strengthened Reverse Engineering Approach to Forensic Analysis

The widespread adoption of Android devices has rendered them a primary target for malware attacks, resulting in substantial financial losses and significant breaches of user privacy. Malware can exploit system vulnerabilities to execute unauthorized premium SMS transactions, exfiltrate sensitive data, and install additional malicious applications. Conventional detection methodologies, such as static and dynamic analysis, often prove inadequate in identifying deeply embedded malicious behaviors. This study introduces a systematic reverse engineering framework for analysing suspicious Android applications. In contrast to traditional approaches, the proposed methodology comprises six distinct stages: initialization, decompilation, static analysis, code reversal, behavioral analysis, and reporting. This structured process facilitates a comprehensive examination of an application’s internal mechanisms, enabling the identification of concealed malware functionalities. The findings of this study demonstrate that the proposed method attains an overall effectiveness of 84.3%, surpassing conventional static and dynamic analysis techniques. Furthermore, this research generates a detailed list of files containing specific malware indicators, thereby enhancing the effectiveness of future malware detection and prevention systems. These results underscore the efficacy of reverse engineering as a critical tool for understanding and mitigating sophisticated Android malware threats

Uncovering Insights in Spotify User Reviews with Optimized Support Vector Machine (SVM)

The rapid growth of user-generated reviews on platforms like Spotify necessitates efficient analytical techniques to extract valuable insights.  This study employs a Support Vector Machine algorithm, optimized using Forward Selection, Backwards Elimination, Optimized Selection, Bagging, and AdaBoost, to effectively classify user reviews. A dataset of approximately 10,000 Spotify reviews was compiled from diverse online sources, ensuring a representative sample. The analysis reveals sentiment patterns across positive, negative, and neutral categories, with positive reviews dominates the landscape. These patterns help highlight Spotify’s strengths while identifying areas for improvement. However, the SVM algorithm faces challenges in classifying minority classes, particularly negative sentiments, due to class imbalance. To address this, advanced optimization techniques are utilized to enhance classification precision and recall. Preprocessing steps, including data cleansing, tokenization, stemming, and stopword removal, refine the dataset, while TF-IDF converts text into numerical features for effective feature selection. The results show that the Optimized Selection method achieves the highest accuracy of 84.5%, outperforming other approaches. This research contributes significantly to developing balanced sentiment analysis models. Future studies may explore deep learning techniques to further improve classification accuracy and mitigate current limitations in data representation

Pandangan Islam Terhadap Kemajuan Teknologi Informasi

Advances in information technology have brought about major changes in various aspects of human life. Rapid developments in this field include innovations in communications, education, health, economics and industry. Information technology has enabled fast and efficient access to information, increased productivity, and opened up new opportunities in an increasingly connected global world. However, behind all this progress, there are also challenges related to data security, privacy and ethics in the use of technology. In the Islamic view, information technology, like all forms of science, is a neutral tool. Islam views that technological advancements should be used for good purposes and in line with the moral and ethical values taught by religion. Technology should be utilized to strengthen social relations, spread goodness and improve the welfare of mankind, not to damage or cause destruction. The principles of sharia and maqashid sharia (the objectives of sharia) can serve as a guide in utilizing information technology to stay within the framework that is in accordance with Islamic teachings. A review of the benefits and harms of information technology shows that there are two sides that must be considered. On the one hand, this technology provides many benefits, such as increasing access to knowledge, accelerating communication, and facilitating various daily activities. On the other hand, possible harms include misuse of information, the spread of hoaxes, over-dependence, and potential moral damage if not balanced with proper control and guidance. Therefore, it is important to manage and utilize information technology wisely, in accordance with Islamic teachings that emphasize the balance between benefits and potential damage

Analisis Efektivitas Metode Filtering dan Intersection dalam Analisis Data Permukaan Bangunan dengan QGIS

This study evaluates the efficiency of two methods for processing geospatial building surface data, namely Filtering and Intersection, using a case study in Blitar Regency. The data for this research was obtained by comparing two sources: OpenStreetMap (OSM), which has a data completeness rate of 60%, and Google Open Building, with a data completeness rate of 90%. From these two sources, the data with the highest completeness, which is from Google Open Building, was selected for further analysis. The data processing was carried out using QGIS software, chosen for its capability to support various geospatial analysis methods. The comparison of the two methods was based on three main criteria: processing time, resource efficiency, and scalability. The results showed that the Filtering method outperforms in all these aspects. Filtering can complete processing in an average of 1.6 seconds, significantly faster than the Intersection method, which requires an average of 7 minutes and 50 seconds. In terms of resource efficiency, Filtering is also more economical, with an average CPU usage of 18.85% and memory usage of 121.4 MB, compared to the Intersection method’s 34.05% CPU usage and 236.4 MB of memory. Additionally, the Filtering method demonstrated better scalability, capable of handling larger datasets with fewer resources and less time. Therefore, the Filtering method is recommended for geospatial data processing that prioritizes speed, efficiency, and the ability to handle large and complex datasets

Arsitektur Microservice untuk Optimalisasi Aplikasi Eco-Maps dalam Mendukung Kampus Ramah Lingkungan

The implementation of environmentally friendly campus concepts has become increasingly crucial in addressing global environmental challenges. Eco-Maps is an application designed to visualize and manage sustainability efforts on campus, including energy management, waste management, and sustainable transportation initiatives. To enhance efficiency and flexibility, this study discusses the application of a microservice architecture in Eco-Maps. This architecture supports faster and more efficient development, testing, and deployment, while enabling horizontal scalability to manage high complexity and large data volumes. By separating application functions into independent services, microservices facilitate maintenance and updates while minimizing the impact of failures in individual services. This study also reviews the integration of containerization technologies, such as Docker and Kubernetes, to support microservice implementation. Through these technologies, the application can be deployed quickly and consistently across various environments, from development to production. System testing was conducted using load testing and stress testing methods, as shown in Tables 3 and 4. The results demonstrate that the average response time across ten iterations was 745.9 ms, with an average CPU usage of 44.38%. These findings confirm that processing load directly affects CPU efficiency and overall system performance

Analisis Cluster untuk Pengelompokan Kemampuan Penguasaan ICT Menggunakan K-Means dan Autoencoder

Information and Communication Technology (ICT) skills are essential in today’s digital age. However, numerous new students possess varying levels of ICT proficiency and may lack the necessary skills expected by universities. ICT training is essential for enhancing students’ ICT skills. Nevertheless, delivering the same training to all students proves to be less effective. Therefore, grouping students’ ICT skills is crucial to ensure that the training provided aligns with the fundamental abilities of the students. Cluster analysis is a common method for grouping data. This study employs k-Means and an autoencoder for cluster analysis, with the autoencoder utilized to reduce data dimensions and k-Means to perform the clustering process. The Elbow method is utilized to identify the ideal number of clusters. The optimal number of clusters determined was three. Model evaluation was conducted using the Silhouette coefficient and the Davies-Bouldin Index (DBI). The evaluation results revealed that the combination of k-Means and autoencoder yields superior performance compared to using k-Means alone, as evidenced by a higher Silhouette value and a lower DBI value

The Impact of Algorithms on Decision-Making in Daily Life: A Polling Study of Technology Users

Algorithms have become an integral part of everyday life, particularly in entertainment, shopping, and navigation. This study examines how algorithms influence individual decision-making. Data were collected through an online poll involving 200 respondents, selected using a statistical sampling method. The results indicate that 55% of respondents perceive algorithms as having a significant influence on their decisions, while 28% report a moderate impact. A confidence interval analysis (95%) has been included to ensure statistical accuracy. The study highlights the importance of digital literacy in mitigating algorithmic bias and suggests future research on how socio-cultural factors shape algorithmic perceptions. This research contributes to understanding the extent of algorithmic influence on daily decision-making and raises user awareness of technology’s impact. The implications include the importance of digital literacy to mitigate dependency and bias in algorithm usage and the potential to develop more transparent and ethical algorithmic systems. Future research could explore the relationship between users' awareness of algorithms and their behaviors in various contexts and evaluate ways to enhance public understanding of how algorithms function in the evolving digital ecosystem

Belajar Matematika dalam Perspektif Islam

This study examines the importance of learning mathematics from an Islamic perspective, arguing thatthis discipline is not only a cognitive tool, but also a spiritual means of understanding the greatness ofAllah SWT. In Islam, mathematics has a unique position as the Qur’an implicitly refers to order, balanceand careful calculation in the creation of the universe, as seen in the movement of celestial bodies or thedivision of inheritance. Hadiths of the Prophet Muhammad also encourage the pursuit of knowledge andthe use of reason, which broadly includes the study of mathematics as a foundation for the advancementof science. Through the library research method, the researcher collected and analyzed various literaturesources, including journal articles, academic books, books of tafsir, and Hadith, both in digital and printformats. To ensure validity and depth of analysis, the researcher applied data triangulation, comparing andconfirming findings from various sources. The results of the analysis show that learning mathematics canfoster tafakur (contemplation) and tadabbur (deep appreciation) of Allah’s creation, thus increasing faith andpiety. The study concludes that mathematics, from an Islamic perspective, is a way to uncover the secrets ofthe universe and witness the signs of divine power, making it a worshipful activity with a spiritual dimension

Enhancing Diabetes Classification Using a Relaxed Online Maximum Margin Algorithm

Diabetes mellitus is a growing global health concern that requires accurate and reliable classification models for early diagnosis and effective management. Traditional machine learning models often struggle with class imbalance, generalization limitations, and high false-positive rates, leading to misdiagnoses and delayed interventions. This study enhances the Relaxed Online Maximum Margin Algorithm (ROMMA) to improve the accuracy of diabetes classification. Using a publicly available dataset from Kaggle, which contains 768 medical records with nine health attributes, the model’s performance was evaluated through a confusion matrix and classification metrics. The Enhanced ROMMA achieved an accuracy of 92%, significantly improving upon the Standard ROMMA’s 85% accuracy. The recall for diabetes detection increased from 0.83 to 0.94, reducing false negatives and ensuring more accurate patient identification. While slight misclassification still exists, this improvement enhances the model’s reliability for clinical applications. Future research should incorporate larger datasets and advanced techniques to enhance robustness and generalizability. This study contributes to the development of more accurate machine learning models for diabetes prediction, ultimately supporting better healthcare decision-making

Price Forecasting of Chili Variant Commodities Using Radial Basis Function Neural Network

This study addresses the challenge of price instability in chili markets, which can lead to economic losses and inflation. To mitigate this issue, we propose a machine learning model using Radial Basis Function Neural Networks (RBFNN) to predict prices of various chili variants. Our quantitative approach involves a comprehensive data preparation process, including preprocessing and normalization of time series data collected from 2018 to 2022. The RBFNN model is constructed with K-Means clustering for optimal hidden layer configurations and evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The results demonstrate promising accuracy, with MAPE error rates below 20% and relatively low RMSE values for large red chili (10.37%, 4484) and curly red chili (14.77%, 5590). Our findings indicate the potential for creating a reliable forecast model for predicting chili prices over 7 days, enabling better supply and demand management. The study's results also suggest that increased training data enhances forecasting accuracy. This research contributes to the development of effective price forecasting models, providing valuable insights for policymakers and stakeholders in the chili industry