Sistem Pendukung Keputusan Prioritas Perbaikan Gedung Menggunakan Metode Analytic Hierarchy Process Dan Profile Matching

Gedung bertingkat memiliki banyak sub elemen, elemen, sub komponen dan komponen di tiap bagian struktur, arsitektur, dan utilitasnya. Kerna kompleksnya elemen dan sub elemen di dalam bangunan gedung bertingkat, sehingga diperlukan rencana kegiatan dan mekanisme yang terencana untuk menentukan prioritas perbaikan. Penelitian ini mengkaji implementasi Sistem Pendukung Keputusan (SPK) untuk menentukan prioritas perbaikan gedung berdasarkan aspek kerusakan pada sub elemen, elemen, dan komponen. Metode SPK yang digunakan yaitu Analytic Hierarchy Process (AHP) dan Profile Matching. Metode AHP digunakan untuk menentukan priority vector atau bobot prioritas sub elemen, elemen, dan komponen, sedangkan Profile Matching digunakan untuk menentukan perangkingan gedung yang menjadi prioritas perbaikan berdasarkan pengukuran volume kerusakan, jenis kerusakan, nilai pengurang dan faktor koreksi serta nilai Skala Indeks Kondisi Mckay pada sub elemen, elemen dan komponen gedung

A view of MCDM application in education

The effectiveness of the teaching and learning process by educators plays a significant role for countries to prepare students' potential in the forthcoming new industrial revolution (IR). However, the current COVID-19 pandemic and dynamic changes in the curriculum have created a significant shift of emphasis to educators. Hence, the teaching and learning process problems nowadays, including selecting appropriate effectiveness learning, have become a tough decision for educators. It can be solved using multi-criteria decision-making (MCDM) methods. The MCDM technique is widely applied and accepted in various fields but less in the teaching and learning context. This paper reviews and analyses the type of decision problems that were paid most attention to MCDM approaches, the adopted fuzzy set theory as well as inadequacies of those approaches. The purpose is to analyse and identify the literature review related to the applications of MCDM in education so new attributes and appropriate MCDM models for decision making can be suggested. The process involved comparing and analysing the MCDM application and fuzzy set theory in education by reviewing related articles in international scientific journals and well-known international conferences. Some improvements and more future works are recommended based on the inadequacies. The reviewed result may create an interest to the Ministry of Education (MoE) as it proposes teaching and learning process improvement, which will help to achieve greater satisfaction among educators and students

Classification of the effectiveness of ICT integration in teaching and learning using data mining based on AHP methods

Information and Communication Technology (ICT) is a medium that people rely on daily to receive information via a specific application. The Ministry of Education in Malaysia has widely integrated ICT into the education system since 1990s. However, ICT is still not fully implemented due to some obstacles such as lack of sufficient training, time, ICT resources and infrastructure. Therefore, the purpose of this research is to analyze and rank the factors that impede the integration of ICT in urban and rural secondary schools using the Analytic Hierarchy Process (AHP) approach, as well as to determine the significant difference between factors in urban and rural areas by using t-test analysis. In conjunction with the outputs, several predictive models are then developed to classify the students’ academic performance in urban and rural areas using Data Mining techniques. The respondents for this study are secondary school teachers in the Malaysian state of Kedah. For the first phase, 51 teachers were chosen to complete the questionnaire. A total of 238 teachers were chosen for the second phase. The hypothesis testing showed that there is no significant difference between the factors in both areas. The top three factors are workload (13.46%), lack of accessibility and network connection (13.25%), and lack of support assistance (12.09%). The result gained was used to develop several predictive models and the best model was selected for classifying the students’ academic performance. The Decision Tree (cross-validation) outperformed the other models with a misclassification rate of 32.98% (urban) and 16% (rural). The developed predictive model can be used by Ministry of Education or policymaker to plan national educational policies whilst implement a better strategy for educational development based on government policies and the Education Act