Predictive Analytics for Employability in Malaysian TVET with a Hybrid of Regression and Clustering Methods

Graduate employability remains a high concern for Technical and Vocational Education and Training (TVET) institutions, particularly within Malaysia’s Technical University Network (MTUN), where producing industry-ready graduates is a central goal. While machine learning has transformed fields like healthcare and finance, its application in vocational education remains underexplored—particularly for employability prediction. This study addresses this gap by hybridizing decision trees and clustering to uncover non-linear patterns in student survey data. Guided by Human Capital Theory and SERVQUAL, which inform variable selection (e.g., technical skills as productivity investments), this study integrates multiple linear regression, decision tree regression, and K-Means clustering to identify significant predictors and uncover latent student groupings. Using a publicly available dataset of Likert-scale responses from MTUN students, technical skills and supervisory support consistently emerged as the most impactful employability predictors. Communication showed moderate influence, while training delivery and problem-solving exhibited variable effects depending on the modelling approach. Unlike regression, decision trees revealed non-linear interaction thresholds. For example, students with SVR < 3.5 and TS < 4.0 had 40% lower employability scores, suggesting targeted mentoring could yield disproportionate improvements. Clustering revealed three distinct student profiles, which could support data-driven interventions. This hybrid framework demonstrates the potential for integrating machine learning into institutional analytics for proactive support of employability

CMIS: Crime Map Information System for Safety Environment

Abstract Crime Map is an online web based geographical information system that assists the public and users to visualize crime activities geographically. It acts as a platform for the public communities to share crime activities they encountered. Crime and violence plague the communities we are living in. As part of the community, crime prevention is everyone's responsibility. The purpose of Crime Map is to provide insights of the crimes occurring around Malaysia and raise the public's awareness on crime activities in their neighbourhood. For that, Crime Map visualizes crime activities on a geographical heat maps, generated based on geospatial data. Crime Map analyse data obtained from crime reports to generate useful information on crime trends. At the end of the development, users should be able to make use of the system to access to details of crime reported, crime analysis and report crimes activities. The development of Crime Map also enable the public to obtain insights about crime activities in their area. Thus, enabling the public to work together with the law enforcer to prevent and fight crime.</jats:p

An Approach to Automatic Garbage Detection Framework Designing using CNN

This paper proposes a system for automatic detection of litter and garbage dumps in CCTV feeds with the help of deep learning implementations. The designed system named Greenlock scans and identifies entities that resemble an accumulation of garbage or a garbage dump in real time and alerts the respective authorities to deal with the issue by locating the point of origin. The entity is labelled as garbage if it passes a certain similarity threshold. ResNet-50 has been used for the training purpose alongside TensorFlow for mathematical operations for the neural network. Combined with a pre-existing CCTV surveillance system, this system has the capability to hugely minimize garbage management costs via the prevention of formation of big dumps. The automatic detection also saves the manpower required in manual surveillance and contributes towards healthy neighborhoods and cleaner cities. This article is also showing the comparison between applied various algorithms such as standard TensorFlow, inception algo and faster-r CNN and Resnet-50, and it has been observed that Resnet-50 performed with better accuracy. The study performed here proved to be a stress reliever in terms of the garbage identification and dumping for any country. At the end of the article the comparison chart has been shown

Comparative Analysis of Distance Functions on DBSCAN Algorithm: Mapping Malnourished Toddlers in Medan City, Indonesia

Medan City is one of Indonesia\u27s largest cities and faces fundamental challenges in addressing malnourished toddlers. It had a stunting prevalence of 19.9% in 2022. The high rates necessitate a practical approach to identifying and managing high-risk areas. This study aims to map districts in Medan City based on the spatial data of public health center locations and malnutrition data for toddlers, using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. DBSCAN is a popular clustering algorithm because of its ability to group data based on density and detect outliers as noise. However, using the Euclidean distance function in DBSCAN may not be appropriate for all geospatial cases. The novelty lies in comparing five distance functions (Euclidean, Manhattan, Minkowski, Cosine, Chebyshev) within DBSCAN to determine which produces the most meaningful clustering in a geospatial health context. The study shows that DBSCAN with the Chebyshev distance function cannot effectively map the malnutrition problem in toddlers, as indicated by a Silhouette index (SI) value below 0.25. The clustering quality using Minkowski and Cosine distance functions in DBSCAN is not superior to that of the classical DBSCAN, with all three producing weak and unclear structures. The most effective mapping results come from using the Manhattan distance function in DBSCAN, which yields an SI value of 0.51045, two clusters, and optimal parameters of Minpts = 6–9 and ε = 6.98–7.8. The first cluster includes two districts (Medan Labuhan and Marelan), while the remaining districts form the second cluster. The analysis of different distance functions provides new insights into how selecting the appropriate distance measure can influence clustering quality in a geospatial context with DBSCAN. The similarity of the clusters is expected to inform decision-making in addressing toddler malnutrition issues in Medan City

BBIS: Beacon Bus Information System

Abstract Lack of bus information for example bus timetable, status of the bus and messy advertisement on bulletin board at the bus stop will give negative impact to tourist. Therefore, a real-time update bus information bulletin board provides all information needed so that passengers can save their bus information searching time. Supported with Android or iOS, Beacon Bus Information System (BBIS) provides bus information between Batu Pahat and Kluang area. BBIS is a system that implements physical web technology and interaction on demand. It built on Backend-as-a-Service, a cloud solution and Firebase non relational database as data persistence backend and syncs between user client in the real-time. People walk through bus stop with smart device and do not require any application. Bluetooth Beacon is used to achieve smart device's best performance of data sharing. Intellij IDEA 15 is one of the tools that that used to develop the BBIS system. Multi-language included front end and backend supported Integration development environment (IDE) helped to speed up integration process.</jats:p