Android malware is an application that targets Android devices to steal crucial data, including money or confidential information from Android users. Recent years have seen a surge in research on Android malware, as its types continue to evolve, and cybersecurity requires periodic improvements. This research focuses on detecting Android malware attack patterns using deep learning and convolutional neural network (CNN) models, which classify and detect malware attack patterns on Android devices into two categories: malware and non-malware. This research contributes to understanding how effective the CNN models are by comparing the ratio of data used with several epochs. We effectively use CNN models to detect malware attack patterns. The results show that the deep learning method with the CNN model can manage unstructured data. The research results indicate that the CNN model demonstrates a minimal error rate during evaluation. The comparison of accuracy, precision, recall, F1 Score, and area under the curve (AUC) values demonstrates the recognition of malware attack patterns, reaching an average of 92% accuracy in data testing. This provides a holistic understanding of the model's performance and its practical utility in detecting Android malware.

M.T., Ph.D., IPU., ASEAN Eng.

Prof. Ir.

Deris Stiawan

Detection of android malware with deep learning method using convolutional neural network model

Detection of attacks on internet of things (IoT) networks is an important challenge that requires effective and efficient solutions. This study proposes the use of various machine learning (ML) techniques in classifying attacks using unidirectional, bidirectional, and packet features. The proposed methods that implement decision tree (DT), random forest (RF), extreme gradient boosting classifier (XGBC), AdaBoost (AB) and linear discriminant analysis (LDA) work perfectly with all kinds of datasets and includes. It also works very well with data type-based feature selection (DTBFS) and correlation-based feature selection (CBFS). The experiment results show a significant improvement compared to previous studies and reveals that unidirectional and bidirectional features provide higher accuracy compared to packet features. Furthermore, ML models, particularly DT, and RF, have faster computing times compared to more complex deep learning models. This analysis also shows potential overfitting in some models, which requires further validation with different datasets. Based on these findings, we recommend the use of RF and DT for scenarios with unidirectional and bidirectional features, while AB and LDA for packet features. The study concludes that using the right ML techniques along with features that work in both directions can make an intrusion detection system for IoT networks becomes very accurate.

Revolutionizing internet of things intrusion detection using machine learning with unidirectional, bidirectional, and packet features

FC-PoW: A Machine Learning-Based Blockchain Consensus for Efficient Order Processing in Manufacturing

This study proposes a new method to assess researcher expertise using publication data. The quality of research publications is an important indicator in the ranking of universities that are undergoing diversification. Research publications have become an important indicator in the university ranking system and have a major impact on the reputation of universities as a lens for the study of expertise and prestige for human resources. Expertise is often difficult to verify objectively, as a result, many people claim to be experts or are considered experts without evidence and correct data. To ensure the expertise of researchers, it must be proven with valid data support through measurable and presentable expertise parameters. The model built uses the cosine similarity and association rule approaches. The publication variables attached to the researcher are formulated in the collaboration of the algorithm to assess the level of researcher expertise. Validation of important points of publications as parameters for measuring expertise has been identified as the main factor contributing to the measurement of researcher expertise and its impact on university reputation. The model built successfully validated researcher expertise up to 72% which is relevant to its support for university rankings up to 75%.

New approach to measuring researcher expertise using cosine similarity algorithm and association rules

Mental health, encompassing emotional, psychological, and social well-being, plays a pivotal role in individuals’ academic and personal success. Among undergraduate students, the prevalence of mental health disorders such as anxiety, depression, and stress has escalated in response to academic pressures, lifestyle transitions, and socioeconomic factors. Early detection is vital for mitigating severe repercussions, including diminished academic performance and increased risk of self-harm. Leveraging the Depression, Anxiety, and Stress Scale-42 (DASS-42), this study employs machine learning (ML) methods—specifically Support Vector Machine (SVM) and Random Forest (RF)—to analyze mental health patterns under a two-class scenario (“Good” vs. “Counseling”). A balanced dataset was obtained through the Synthetic Minority Over-Sampling Technique (SMOTE), ensuring fairer representation of minority “Counseling” cases. The results demonstrate that both SVM and RF excel in detecting depression, each achieving near-perfect accuracy, precision, and recall (0.97). This performance suggests that the discriminative power of the features for depression is sufficiently robust to enable clear classification boundaries with minimal misclassification. For anxiety, SVM attains a slightly higher accuracy (0.91) compared to RF (0.90), largely driven by SVM’s superior precision (0.94) when identifying “Good” cases. In stress prediction, SVM again outperforms RF (0.95 vs. 0.93 accuracy), although RF showcases near-perfect precision and recall for “Counseling.” Despite these successes, both models occasionally falter when distinguishing borderline cases, indicating that feature overlap and remaining class imbalance still pose challenges. In conclusion, SVM and RF provide promising avenues for early mental health detection in university settings, highlighting the potential of ML-driven tools to inform targeted interventions. Future research should focus on expanding feature sets (e.g., integrating behavioral or physiological data) and refining sampling strategies to enhance minority-class detection.

S.Kom., M.T., Ph.D.

Muhammad Qurhanul Rizqie

Early Mental Health Detection with Machine Learning: A Practical Approach to Model Development and Implementation

Haystack is Facebook's unique way of managing large amounts of user-generated content like photos. The architecture prioritizes performance, reliability, and scalability to overcome network-attached storage system bottlenecks. Haystack speeds data access and ensures data integrity during hardware failures by using physical and logical volumes. This study examines the architecture of Facebook's Haystack data storage system and its effects on scalability and efficiency in handling large photo data. According to the study, the store, directory, and cache functions work together to reduce input/output (I/O) operations and improve metadata processing, which traditional network-attached storage systems cannot do. Haystack manages massive photo data storage and retrieval, solving network-attached storage (NAS) limitations. It balances throughput and latency by minimizing disk operations and optimizing metadata processing. Each store, directory, and cache contribute to this ecosystem. The Haystack architecture reduces disk operations and metadata processing bottlenecks with distributed caching. A cache allows instant access to frequently requested images and balances read and write operations across the system. We should study advanced storage system architectures based on Facebook's Haystack architecture. This could involve investigating faster metadata processing algorithms, using artificial intelligence (AI) to improve fault detection and repair systems, and assessing the economic impact of distributed caches.

S.Kom., M.T.

Dr. Ir.

Ahmad Heryanto

Haystack-based Facebook’s data storage architecture: store, directory, and cache

The KIP-Kuliah program is an Indonesian government program to increase access to higher education for prospective students from underprivileged families, in line with the 4th goal of the Sustainable Development Goals (SDGs) on Quality Education. However, the manual selection process causes inaccuracy in targeting KIP-Kuliah recipients and also low accuracy in the selection process so that a more objective and efficient data-based approach is needed. This study aims to propose a text analysis-based recommendation system with a hybrid machine learning approach to improve the accuracy, efficiency and objectivity of the selection of KIP-Kuliah recipients using Natural Language Processing (NLP) with Bag of Words and TF-IDF text representations to process applicant descriptions and five Gaussian Naïve Bayes algorithm models, Logistic Regression, Random Forest, Support Vector Machine and Gradient Boosting Machine. To overcome data imbalance and feature optimization, this study explores the use of SMOTE and PCA (Principal Component Analysis). The results showed that Logistic Regression with TF-IDF without SMOTE and PCA provided an accuracy performance of 80.82% followed by Random Forest with the same accuracy but higher execution time. These findings indicate that the combination of NLP and hybrid machine learning can improve the accuracy of KIP Kuliah recipient selection, thereby supporting a fairer, more efficient and quality education system and supporting the development of achieving SDGs targets.

A Text-Based Recommendation System Analysis Using a Hybrid Machine Learning Model

In scientific research, the identification process of finding various relevant methods is carried out through in-depth analysis of various literatures. This analysis aims to evaluate the contribution of previous research novelties and extract valuable information from existing literature. This study focuses on the development of an abstract text extraction model from scientific literature using a machine learning model. The scientific literature abstract text dataset used in this study was obtained from the Garuda National Aggregator. The extraction models used include Conditional Random Field (CRF), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) to read and extract abstract text from scientific literature, as well as identify important entities in unstructured text. The results of the study show that the Bi-LSTM model is able to provide good performance in extracting entities from abstract text with Precision, Recall, and F1-Score values with a score of 0.92 which indicates its ability to recognize and classify entities with high accuracy and extract information in unstructured abstract text.

The New Model Text Abstract Extraction with Machine Learning

This study explores the integration of Integrated Marketing Communication (IMC) and digital marketing strategies within the context of event tourism to enhance tourist visits. Utilizing a systematic literature review (SLR) approach, the study synthesizes findings from 87 peer-reviewed articles obtained from reputable academic databases. The research examines key theoretical foundations, empirical evidence, and the practical use of digital tools and strategic communication to attract and engage tourists in event-based tourism. Theoretical frameworks underpinning the study include communication theory, the marketing mix, and audience engagement models, complemented by middle and applied theories related to IMC, digital marketing, and event tourism. The results highlight IMC as a central theme in the literature, with growing emphasis on tourist engagement, experience personalization, and authentic messaging. Social media and data-driven marketing are found to play significant roles in influencing tourist decision-making and increasing destination appeal. This review contributes to the academic discourse by identifying gaps in research and suggesting future empirical directions, especially regarding the application of advanced technologies like artificial intelligence and virtual reality in promoting event tourism. Practically, the study offers strategic insights for tourism stakeholders to design integrated marketing efforts that effectively boost tourist attendance and support sustainable growth in event destinations.

IMC and Digital Marketing in Event Tourism to Enhance Tourist Visits: A Systematic Review

Mental health issues among individuals, particularly in counseling contexts, require practical tools to understand and address emotional states. This study explores the application of machine learning models for emotion detection in mental health counseling conversations, focusing on four algorithms: Bernoulli Naive Bayes, Decision Tree, Logistic Regression, and Random Forest. The dataset, derived from transcribed counseling sessions, underwent preprocessing, including stemming, stopword removal, and TF-IDF vectorization to create structured inputs for classification. Emotional categories such as "Depresi" (Depression), "Kecewa" (Dissapointed), "Senang" (Happy), "Bingung" (Confused) and "Stres" (Stress) were analyzed to evaluate model performance. Results indicated that Logistic Regression achieved the highest accuracy at 82%, showcasing its reliability and scalability, followed closely by Random Forest with 81%, demonstrating robustness in handling complex data structures. Bernoulli Naive Bayes performed competitively at 80%, excelling in computational efficiency, while Decision Tree recorded the lowest accuracy at 70%, reflecting its limitations in managing overlapping features and high-dimensional data. These findings highlight the potential of machine learning in addressing the increasing demand for scalable mental health support tools. The study underscores the importance of model selection, balanced datasets, and feature engineering to improve classification accuracy. Future work includes developing AI-driven chatbots for real-time emotion detection and integrating multimodal data to enhance interpretability. This research contributes to advancing automated solutions for mental health care, offering new pathways for timely and personalized interventions.

Publikasi Ilmiah