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All Journal Jurnal Algoritma TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Riki Andri Yusda
Universitas Royal
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance

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Analisa Model Clustering Untuk Pemetaan Kualitas Lulusan Mahasiswa Berdasarkan Dataset Tracer Study Riki Andri Yusda; Risnawati Risnawati; Santoso Santoso; Putri Zakiyah Maharani Siregar; Widiya Putri Nurani
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 4 No 2(SEMNASTIK) (2024): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akunt
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol4No2(SEMNASTIK).pp18-23

Abstract

Graduate data from the tracer study process is critical in assessing the quality of a university's graduates. From this data, universities can see an objective picture to measure and evaluate the curriculum, materials, and the achievement of learning competencies so far whether they are following what is expected by graduate users. This will provide input to university management in making strategies and policies to improve quality. However, the problem is that the amount of data available so far has not been maximized properly to assist management in making decisions. Data on graduates and users of existing graduates are only processed into semester and annual reports and there is no in-depth analysis. So management does not get information that helps improve graduates' quality in the future. Optimization of clustering methods using the elbow method with a comparison of other distance formulas such as Euclidean Distance, Mahalanobis Distance, and Manhattan City Distance to improve the performance of mapping results. The DBI result obtained is 1.89 for the number of 6 clusters.
Sistem Pendukung Keputusan Berbasis Metode SAW Untuk Pemilihan Biji Kopi Berkualitas Sebagai Bahan Minuman di Kopi Luar Dalam Nurhidayah; Riki Andri Yusda; Sudarmin
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3319

Abstract

Selecting high-quality coffee beans is a critical factor in maintaining flavor consistency in the coffee beverage industry. At Kopi Luar Dalam, the raw material selection process is still conducted subjectively, which can lead to inconsistent decisions. This study aims to design and implement a Decision Support System using the Simple Additive Weighting (SAW) method to determine the best coffee beans based on four criteria: cost, aroma, bean color, and physical form. The study employs a quantitative approach involving decision matrix normalization and preference value calculation. The results show that the African Blue Mountain Arabica (A8) alternative obtained the highest preference value of 0.80 and is therefore recommended as the best raw material. Methodologically, this study demonstrates that the application of SAW is capable of producing an objective, structured, and transparent selection process in the context of raw material selection for the SME-scale coffee industry.
Sistem Pemberian Reward Karyawan Dengan Pembobotan AHP dan MOORA pada CV. Putra Karya Logam Sukses Venty; Riki Andri Yusda; Akmal
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3320

Abstract

Determining objective employee rewards is a challenge for manufacturing companies because manual processes are prone to subjectivity and inconsistencies in evaluation. This study aims to develop a Decision Support System based on the integration of the Analytic Hierarchy Process (AHP) and MOORA to improve the accuracy and transparency of performance evaluations. AHP is used to determine the weights of five criteria through a consistency test (CR < 0.1), while MOORA is used for the ranking process of 69 employee alternatives. The results show that the system produces the best alternative with the highest preference value of 0.1474 and demonstrates ranking stability based on a weight sensitivity test (correlation coefficient 0.92). A comparison with the SAW and TOPSIS methods demonstrates the consistency of the best alternative; however, the AHP–MOORA approach provides more structured weight validation and better ranking stability. Scientifically, this study confirms the superiority of integrating weighting and optimization methods in producing a robust and verified decision-making model, particularly in the context of employee reward evaluation in the manufacturing sector.
Co-Authors Akmal Nurhidayah Putri Zakiyah Maharani Siregar Risnawati Risnawati Santoso Santoso Sudarmin Venty Widiya Putri Nurani