Mekelle Institute of Technology

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    CRIME PATTERN DETECTION USING DATA MINING TECHNIQUES: CASE OF SHIRE TOWN POLICE OFFICE A
    (Mekelle University, 2025-01-24) TEKLAY LEMA
    Shire Town Police Office is not using any technology based system to make analysis of criminals’ activities to understand trends of previous years’ crimes and to identify the prevalent crime patterns occurred. This problem is not impacted only for the Shire Town Police Office but also for the region and the country. This research investigates the potential of data mining tools and techniques in developing models for crime pattern analysis to support the crime detection activities at the Shire Town Police Office-Tigray-Ethiopia. Out of more than 10,000 offenders’ record the researcher used only 9967 offenders’ recordS and 11 attributes data for this research. Utilizing clustering and classification algorithms, specifically K-means for clustering, J48 Decision Tree and NaïveBayes for classification, the research analyzes real offenders' data collected from the Shire Town police office. The results demonstrate that the J48 Decision Tree model, achieving an accuracy of 97.48% with 119 Number of Leaves and 157 Size of the tree, outperforms other models in detecting crime patterns based on the criteria that classifiers evaluated. Based on the findings of the J48 Decision Tree one sample is listed like: if the occurred crime type is at 2007 e.c, offenders who are grouped in the age group of Age2 (20 up to 32 years old), their Educational status is illiterate, and the crime occurred time is AM, then 109 offenders (97.32% of them) are classified as Male. There are 112 records. From which 3 records (2.67% of them) are incorrectly classified. This study highlights the significance of data mining in transforming raw crime data into actionable insights, thereby facilitating more effective decision-making in law enforcement. The findings emphasize the necessity of implementing modern data mining techniques to improve crime management strategies, ultimately contributing to enhanced public safety in the region.
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    FACTORS INFLUENCING ICT ADOPTION IN SECONDARY SCHOOLS: A CASE STUDY OF SECONDARY SCHOOLS IN WEREDA KEYH TEKLI, CENTRAL ZONE, TIGRAY REGION, ETHIOPIA
    (Mekelle University, 2025-07-24) Weldegerges Gebru
    The advent of Information and Communication Technology (ICT) has opened up tremendous opportunity and challenges in our quest for meeting the global demands of globalization and economic development. The purpose of this study was to determine factors that influence ICT adoption in secondary schools in wereda keyh tekli central zone of Tigray Region Ethiopia the specific objectives were as follows, To determine the influence of in-service support offered to teachers on adoption of ICT in teaching and learning in secondary schools in wereda keyh tekli to identify the influence of teachers‟ attitudes towards adoption of ICT in teaching and learning in secondary schools wereda keyh tekli and to examine if subject area is influences the adoption of ICT in teaching and learning in wereda keyh tekli.), which is used in studying individual’s technology adoption. This study adopted a descriptive methodology design where by quantitative tools and qualitative tools were used to collect data. The target populations of this study were 1156students and 4 public secondary schools, 7teachers and 7principalsand vice principals in wereda keyh tekli. The study used a sample of 2 secondary schools, 3 teachers and 297studentsselected using lottery system that means simple random sampling, 4principals(2directors and2vice directors)and supevisor selected by purposive sampling. Questionnaires were used to collect data from students and interview questionnaires also collected from teachers, principals and supervisors; Quantities data was analyzed using descriptive statistics. The findings from different data sets were synchronized during the presentation and discussion. From the foregoing, Study found out that adoption of ICT was a major In luence on teaching and learning in secondary schools, which was a clear indication that schools appreciated the role of ICT in education. However, there were a small number of respondents who felt that ICT had no major influence on teaching and learning. It is essential to note that the respondents only differed on the degree of influence of ICT on teaching and learning. The study found out that secondary schools offered different kinds of support to their teachers, especially those who have adopted ICT in their teaching and learning. There was a relatively even distribution in terms of area of support by schools to the teachers who have adopted ICT in teaching. The study revealed that the teacher’s attitude influenced their levels of adoption of ICT as a tool in teaching and learning. The study found out that thescience oriented subjects were most compatible in terms of curriculum with ICT adoption. The main conclusion from the study was that from the findings, the study found that adoption of ICT influences teaching and learning positively to secondary schools. Teachers were also supported in adopting ICT as a tool in teaching and learning. Teacher’s attitude influences their adoption of ICT where by their attitude is determined by their education levels. The use of ICT as a tool in subject’s areas was determined by the subject’s content and the study concluded academic performance followed by full ICT infrastructure were compatible with ICT. The study recommends that the institutions alongside the ministry of Education should find a way to increase the time period of class lesson with reliable resource materials. This will increase the teacher/ student exposure to the technology thus improves on the learning and teaching rate. The institutions environment should be designed to accommodate ICT. Infrastructure such as laboratories should be equipped well than the current status to be able to aaccommodate more number of students.
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    ENHANCING BANKING SERVICES THROUGH DATA MINING: A CASE STUDY OF WUKRO CITY , TIGRAY REGION.
    (Mekelle University, 2025-07-24) ABRHALEY KORKOS
    Banking services play a crucial role in supporting economic development and financial inclusion by providing essential products such as savings, loans, fund transfers, mobile banking, and customer support. However, in developing regions like Wukro City, Tigray, banks face persistent challenges in delivering efficient, reliable, and customer-oriented services. These challenges include long service times, limited personalization, weak customer relationship management, and gaps in decision-making processes. To address these issues, this study explores how data mining models can be applied to enhance banking services, thereby improving service quality, customer satisfaction, and operational efficiency. The research employed a mixed-methods approach, integrating both quantitative and qualitative techniques. Survey data were collected from banking customers and employees to identify service gaps and customer expectations. Quantitative data were analyzed using WEKA software to develop predictive. The qualitative data, gathered through interviews and focus group discussions, provided contextual insights into customer experiences and perceptions of banking services in Wukro City. The findings revealed that data mining models can significantly enhance banking services by enabling banks to segment customers effectively, predict loan repayment behaviors, identify cross-selling opportunities, and detect service inefficiencies. The study also highlighted that customer satisfaction is closely linked with digital service adoption, personalized banking products, and reduced waiting times. Furthermore, the results suggest that integrating data-driven decision-making into banking operations can strengthen competitiveness and trust in the local financial sector. This research contributes to the growing field of technology-driven financial services by demonstrating the applicability of data mining in a developing regional context. For Wukro City banks, the study provides a practical framework to adopt data mining techniques in order to deliver more customer-focused, efficient, and innovative services. Ultimately, the study concludes that leveraging data mining not only enhances banking performance but also supports broader financial inclusion and sustainable economic development in the Tigray region.
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    CRIME PATTERN DETECTION USING DATA MINING TECHNIQUES: CASE OF SHIRE TOWN POLICE OFFICE
    (Mekelle University, 2025-01-24) TEKLAY LEMA
    Shire Town Police Office is not using any technology based system to make analysis of criminals’ activities to understand trends of previous years’ crimes and to identify the prevalent crime patterns occurred. This problem is not impacted only for the Shire Town Police Office but also for the region and the country. This research investigates the potential of data mining tools and techniques in developing models for crime pattern analysis to support the crime detection activities at the Shire Town Police Office-Tigray-Ethiopia. Out of more than 10,000 offenders’ record the researcher used only 9967 offenders’ recordS and 11 attributes data for this research. Utilizing clustering and classification algorithms, specifically K-means for clustering, J48 Decision Tree and NaïveBayes for classification, the research analyzes real offenders' data collected from the Shire Town police office. The results demonstrate that the J48 Decision Tree model, achieving an accuracy of 97.48% with 119 Number of Leaves and 157 Size of the tree, outperforms other models in detecting crime patterns based on the criteria that classifiers evaluated. Based on the findings of the J48 Decision Tree one sample is listed like: if the occurred crime type is at 2007 e.c, offenders who are grouped in the age group of Age2 (20 up to 32 years old), their Educational status is illiterate, and the crime occurred time is AM, then 109 offenders (97.32% of them) are classified as Male. There are 112 records. From which 3 records (2.67% of them) are incorrectly classified. This study highlights the significance of data mining in transforming raw crime data into actionable insights, thereby facilitating more effective decision-making in law enforcement. The findings emphasize the necessity of implementing modern data mining techniques to improve crime management strategies, ultimately contributing to enhanced public safety in the region.
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    THE IMPACT OF INFORMATION TECHNOLOGICAL INNOVATION ON FINANCIAL PERFORMANCE OF CBE (COMMERICAL BANK OF ETHIOPIA) IN Aksum, TIGRAY REGION
    (Mekelle University, 2025-08-24) ZEMENAWIT HAILEMARYAM
    This thesis investigates the relationship between technological innovation and the financial performance of Commercial Bank of Ethiopia in the Aksum, Tigray Region. The study evaluates key financial metrics, including return on assets (ROA), return on equity (ROE), and net profit margin (NPM), to assess the impact of innovations like mobile banking, ATMs, and electronic fund transfers (EFT) on profitability, efficiency, and risk management. The banking sector has been significantly transformed by technological advancements, redefining how services are delivered and managed. While global studies suggest a positive impact of these innovations, this research focuses on a local context where the relationship between technology and financial outcomes has been underexplored. A quantitative research design was used, with data collected from 4 Commercial Bank of Ethiopia, employing multivariate regression analysis to determine the effects of technological innovation on financial performance. The findings indicate a positive correlation between technological innovation and financial performance, particularly in the areas of mobile banking and ATMs, which have significantly boosted ROA and profitability. However, challenges such as the cost of technology implementation and regulatory pressures limit the extent of these benefits. In conclusion, technological innovation is essential for enhancing the financial performance of Commercial Bank of Ethiopia. Recommendations include increased investment in customer-centric technologies and addressing barriers to innovation. The study provides insights for Commercial Bank of Ethiopia, regulators, and policymakers in maximizing returns from technology adoption.
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    The Effect of Information Literacy Skills on the Utilization of E-Information Resources: The Case of Akaki kaliti Sub-City Secondary School Teachers
    (Mekelle University, 2025-09-24) YAMRAL BAYE
    The study emphasized on investigating the effect of information literacy skill on utilization of e-resources: the case of Akaki kaliti sub-city secondary schools’ teachers. It assesses the level of information literacy skill among social science department and natural science department teachers, the types of ILS challenges they were facing, their knowledges about basic ILS components, the ILS acquiring methods, and what supports and enabling situations that the school administrative bodies were arranged to facilitate utilization of electronic information resources for efficient and effective teaching learning goals attainments. The sample was taken from three secondary schools in Akaki kaliti sub-city using stratified random sampling techniques to select teacher respondents and purposive sampling for school directors and library staffs, and the quantitative data was analyzed using descriptive survey that include frequency, mean, correlation, and regression analysis. The study revealed that teachers in both social and natural science have lower information literacy skill which was about (M= 2.41) mean score and consequently their e-resources utilization was also below the mean mid value, account about (M=2.43) mean score. All the three secondary school teachers have poor basic skills of computer, network, and digital literacy which was about only 24%, 16% and 18% respondents were have these skills respectively. Majority of the teachers have not got opportunities to learn about information literacy skill, and the school administrator support was insignificant in facilitating e-resources utilization of Akaki kaliti sub-city secondary school teachers. In addition, the correlation r= 0.904 and regression analysis R= 0.87 showed that e-resource utilization have positive and strong relationship with ILS and significantly affected by ILS. The study recommended importance of inclusion of ILS in teachers training curriculum as a compulsory course, and within the CPD program in the schools. Facilities to access computers, internet service and digital libraries in schools must be arranged to create enabling situations to utilize electronic information resources.
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    Developing a Law Enforcement Framework Using a Data Mining Approach: A Case Study on Criminal Justice in Gindeberet Woreda Court, West Shoa Zone
    (Mekelle University, 2025-10-24) TOLASA GARAMA
    Crime is a person‟s behavioral disorder that result instability of citizens‟ normal life in all circumstances; social, economic, environmental, and political means. Today, security is the world‟s major concerns and becomes a primary agenda along the world. Because, the issue grows in intensity and complexity, following its dramatic peak of existence. Analysis of such large collection of criminal data, to make appropriate decision becomes difficult and complex. To overcome such problems available technologies are being recommended to use. Here the researcher tried to model the integration of DM, to recommend and assist the stakeholders in decision making related to crime incidents. To generate rules, decision tree and rule induction algorithms were selected as these techniques are widely used to represent real world problems in the form of rule. PART rule induction algorithm recorded best and it used to acquire knowledge. Because, PART rule and PROLOG follows contradictory algorithm, forward chaining and backward chaining respectively, the researcher develop a model that change PART rule to PROLOG supported syntax form for implementation. The performance of the system was evaluated using the test cases prepared for this purpose. Twenty-five test cases were prepared for system performance test and provided to domain experts. Users were trained for conducting user acceptance test and were asked to fill test forms provided. Generally, the system has scored 70% overall performances which is a promising result.
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    A predictive Modeling Framework for Identifying Key Factors Influencing Students’ Academic Performance in Secondary Schools Using Machine Learning
    (Mekelle University, 2025-08) Solomon Tsegay
    This study develops a predictive modeling framework to identify key factors influencing student academic performance in public secondary schools. Using a dataset comprising socio-economic, demographic, and academic variables, three machine learning algorithms like Linear Regression, Random Forest, and XGBoost were evaluated. Feature selection was conducted using Linear Regression coefficients, Random Forest importance, and XGBoost importance to extract the most impactful predictors. The models were assessed using Root Mean Squared Error (RMSE) and the coefficient of determination (R²). Results indicate that the XGBoost feature selection combined with Linear Regression yielded the highest performance (RMSE = 40.182, R² = 0.331), demonstrating improved predictive accuracy compared to other combinations. The findings highlight the significance of factors such as study hours, attendance rate, teacher quality, and assignment completion in determining student outcomes. This research contributes to data-driven educational decision-making, enabling stakeholders to target interventions more effectively. Recommendations for policy, practice, and future research are also provided
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    IMAGE PROCESSING AND DEEP LEARNING BASED CLASSIFICATION OF COFFEE LEAF DISEASE
    (Mekelle University, 2025-07-24) NETSANET ADUGNA
    Coffee leaf diseases are a major threat to coffee production in Ethiopia and worldwide. Early detection and treatment of diseases are essential to prevent crop losses. Convolutional neural networks (CNNs) are a powerful machine learning technique that can be used for image classification. In this research report, we explore the use of CNNs for coffee leaf disease identification. We show that CNNs can be used to achieve high accuracy on this task, even with a relatively small dataset. We also show that AlexNet is a good choice for the base architecture of CNNs for coffee leaf disease identification. The approach is based on AlexNet architecture, and it achieved an accuracy of 97.5% on a dataset of 12600 coffee leaf images. Our research has several implications for the use of CNNs for coffee leaf disease identification. First, it suggests that CNNs are a promising new approach for this task. Second, it suggests that AlexNet is a good choice for the base architecture of CNNs for this task. Third, it suggests that the use of larger datasets can further improve the accuracy of CNNs for this task. Our research also has several limitations. First, our dataset was relatively small. This means that the models we trained may not be able to generalize well to new data. Second, we only evaluated our models on a single type of coffee leaf disease. It is possible that the models would not perform as well on other types of coffee leaf diseases. Despite these limitations, our research provides a good foundation for future research on the use of CNNs for coffee leaf disease identification. We believe that CNNs have the potential to revolutionize the way that coffee leaf diseases are identified and managed.
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