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All Journal Jurnal Kridatama Sains dan Teknologi Communication and Information Journal
Raehan, Muhamad
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Religion Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences

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Penilaian Kinerja Dosen Menggunakan Metode Simple Additive Weighting (SAW) Valerian, Kumara Davin; Anas, Muhamad Abdul; Farrasanto, Akram; Raehan, Muhamad; Anshor, Abdul Halim
Jurnal Kridatama Sains dan Teknologi Vol 6 No 02 (2024): JURNAL KRIDATAMA SAINS DAN TEKNOLOGI
Publisher : Universitas Ma'arif Nahdlatul Ulama Kebumen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53863/kst.v6i02.1458

Abstract

Lecturer performance evaluation is an important component in efforts to improve the quality of education, research, and community service in higher education. Effective lecturer performance depends not only on academic competence, but also on pedagogical ability, discipline, and contribution to the development of science and technological innovation. This study aims to design a decision support system based on the Simple Additive Weighting (SAW) method to produce objective and measurable lecturer performance. Research data were collected through observation by reviewing the head of the study program and informatics engineering students at Pelita Bangsa University. The evaluation process includes normalizing the decision matrix that includes several main criteria, such as attendance, discipline, course learning objectives, and semester learning plans. Each criterion is given a weight according to its level of importance. The results of the alternative calculations show that the highest score is selected as the contribution with the best performance. In this study, data from 20 doses were analyzed, resulting in a ranking based on the calculated scores. The second alternative (TIF2) obtained the highest score (V2 = 0.95), followed by the fifth alternative (TIF5) with a score (V5 = 0.94). These findings indicate that the SAW method can efficiently identify the best dose performance based on predetermined criteria. This system not only provides objective results but also facilitates the decision-making process in higher education institutions. The system designed through this research is expected to be an effective tool in transmitting and improving lecturer performance. With proper implementation, universities can improve the quality of teaching, research, and community service sustainably
Klasterisasi Pola Nilai Impor Migas Bulanan Berdasarkan Pelabuhan Bongkar di Indonesia Tahun 2024 Menggunakan Algoritma K-Means Farrasanto, Akram; Raehan, Muhamad; Alhafiz, Muhammad Verdy Hasan
Comit: Communication, Information and Technology Journal Vol. 3 No. 2 (2025): Comit: Communication and Information Journal
Publisher : IAI Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47467/comit.v3i2.8899

Abstract

Indonesia’s oil and gas (O&G) distribution relies on imports through various unloading ports with different monthly patterns. This study aims to cluster O&G ports in 2024 based on monthly import values using the K-Means algorithm. The method follows Knowledge Discovery in Databases (KDD) stages: data selection, preprocessing, transformation, clustering, evaluation, and visualization. Analysis was conducted in Google Colab using Python with Scikit-learn, Pandas, and Matplotlib. Results show three main clusters: ports with high, medium, and low import volumes. Evaluation using Elbow Method and Silhouette Score confirmed that three clusters offer optimal separation. PCA visualization clearly shows cluster distribution. These findings support more efficient energy logistics planning and port infrastructure development based on data-driven insights.
Co-Authors Abdul Halim Anshor Alhafiz, Muhammad Verdy Hasan Anas, Muhamad Abdul Farrasanto, Akram Valerian, Kumara Davin