Decision Support System for Targeting Higher Education

Chanwijit, Jaruwan; Lomwongpaiboon, Wilailux; Dowjam, Onjira; Tangworakitthaworn, Preecha

doi:10.1109/ict-ispc.2016.7519259

Cited by 8 publications

(3 citation statements)

References 1 publication

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“…DSS dapat diterapkan untuk membantu siswa dalam mengambil keputusan yang lebih baik untuk memilih jurusan. Sistem pendukung keputusan dalam penelitian ini untuk memecahkan masalah siswa pemengambilan keputusan dan keputusan yang diambil dapat menyesuaikan kurikulum (Chanwijit et al, 2016).…”

Section: B Sistem Pendukung Keputusanunclassified

Analisis Sistem Pendukung Keputusan Kelompok Dengan Model Ahp Dan Borda Dalam Mendukung Strategi Manajemen Pemasaran Minat Siswa Pada Universitas Swasta

Asbar

Sapnabiby²,

Pratama³

et al. 2022

Human Falah

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<p><em>Persaingan dalam menjaring mahasiswa universitas di aceh sangat tinggi. Oleh karena itu setiap universitas khususya fakultas harus mempersiapkan strategi managemen yang tepat dan disesuaikan dengan permintaan pasar. Pentingnya analisis stategi dalam melihat minat siswa memilih universitas berdasarkan jurusan yang diminati. Adapun variabel model decision making dan group meliputi Minat(k1), StatusAkreditasi(K2), Jurusan(K3), BidangKeilmuan(K4). Tujuan penelitian ini untuk mengetahui tingkat ketertarikan siswa dalam memilih jurusan pada masing-masing universitas swasta yang kemudian hasil tersebut dapat dijadikan rekomendasi bagi para pengambil keputusan dalam mempersiapkan promosi pada setiap jurusan. Metodelogi penelitian berupa survey pada masing-masing sekolah yang kemudian hasil kuisioner tersebut dimasukkan dalam variabel model AHP (Analytical Hierarchy Process) dan Borda. Hasil Penelitian ini Proses dari model AHP meliputi lamda max : 4.03, Consistency index : 0,01, rasio index " 0,9 dan Consistency ratio : 0,011. Hasil Perangkingan model AHP hasil nya Fakultas Keguruan dan Ilmu Pendidikan (A1) dengan nilai 0.3013, Fakultas Ilmu Komputer (A2) 0.2389, Fakultas Ekonomi 0.2377 (A3) dan terakhir Fakultas Ilmu Sosial dan Politik (A4) 0.222. Kemudian hasil nilai Borda adalah untuk A1 dengan nilai 14, hasil dari A2 dengan nilai 13, A3 adalah 8 dan terakhir A4 dengan nilai 5. Hasil penelitian ini dapat mendukung strategi-strategi dalam menjaring mahasiswa pada masing-masing fakultas.</em></p>

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Section: B Sistem Pendukung Keputusanunclassified

Analisis Sistem Pendukung Keputusan Kelompok Dengan Model Ahp Dan Borda Dalam Mendukung Strategi Manajemen Pemasaran Minat Siswa Pada Universitas Swasta

Asbar

Sapnabiby²,

Pratama³

et al. 2022

Human Falah

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“…A recent review by Cubukcu Cerasi et al ( 2022) revealed that most efforts had been put into designing educational DSSs that are either student-/instructor-or management-centred. Student-/instructor-centred DSSs focus either on supporting students in their learning journey and enhancing their educational experience (Almeida & Amoedo, 2018;Chanwijit et al, 2016;De Laet, 2023) or aim at improving learning processes and instructor performance (Oliva-Córdova et al, 2021;Quadri & Shukor, 2021). Management-centred DSSs are commonly dealing with general aspects of higher education administration and support existing institutional processes (Kasap et al, 2020;Nieto et al, 2019a;Ploywattanawong, 2016).…”

Section: Introductionmentioning

confidence: 99%

Supporting Decision-Making for Promoting Teaching and Learning Innovation

Kotorov,

Krasylnykova,

Pérez-Sanagustín

et al. 2024

Learning Analytics

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The quality of the data and the amount of correct information available is key to informed decision-making. Higher education institutions (HEIs) often employ various decision support systems (DSSs) to make better choices. However, there is a lack of systems to assist with decision-making to promote innovation in teaching and learning. In this study, we evaluate an analytic tool called PROF-XXI that supports strategic decision-making of teaching and learning centres (TLCs) by identifying their competencies in teaching and learning innovation. Through a multiple case study conducted with three Latin American universities and supported by quantitative and qualitative data, we observed how this tool is used and how it facilitates strategic decision-making. Our findings indicate that the tool is accessible, user-friendly, and effective in 1) initiating identification and systematic reflection of institutional competency levels in teaching and learning innovation, 2) enhancing understanding of strengths and weaknesses as well as identifying opportunities for innovation, 3) supporting TLCs with short- and long-term decision-making, and 4) continuously evaluating their strategies, programs, and initiatives. This research can benefit policymakers in higher education who are involved in measuring institutional competencies to improve teaching quality or in making strategic decisions related to teaching and learning innovation.

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“…Beside that, the data used to build DSS can also come from various domains [11] and various types of data [12]. One of important cases revealed by [13] was in DSS implementation at college to solve the problem of students' resignation before graduation. One of the causes was the curriculum that the students wanted to learn was not in line with their talent and interest.…”

Section: Introductionmentioning

confidence: 99%

The model of decision support system using hybrid method and actual weighting for the study program ranking

Amin,

Dwitayanti

2024

Bulletin EEI

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Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.

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Decision Support System for Targeting Higher Education

Cited by 8 publications

References 1 publication

Analisis Sistem Pendukung Keputusan Kelompok Dengan Model Ahp Dan Borda Dalam Mendukung Strategi Manajemen Pemasaran Minat Siswa Pada Universitas Swasta

Analisis Sistem Pendukung Keputusan Kelompok Dengan Model Ahp Dan Borda Dalam Mendukung Strategi Manajemen Pemasaran Minat Siswa Pada Universitas Swasta

Supporting Decision-Making for Promoting Teaching and Learning Innovation

The model of decision support system using hybrid method and actual weighting for the study program ranking

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