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All Journal Nusantara Hasana Journal Kohesi: Jurnal Sains dan Teknologi
Samsuriah, Samsuriah
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Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Economics, Econometrics & Finance Education Energy Engineering Nursing Other

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IMPLEMENTASI BUSINESS INTELLIGENCE UNTUK OPTIMALISASI MONITORING KONTRAK LAYANAN LAND AFFAIR MELALUI DASHBOARD POWER BI Muhammad Fitrah Rivan; Samsuriah, Samsuriah
Kohesi: Jurnal Sains dan Teknologi Vol. 7 No. 10 (2025): Kohesi: Jurnal Sains dan Teknologi
Publisher : CV SWA Anugerah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.3785/kohesi.v7i10.12860

Abstract

Dalam era digitalisasi, kebutuhan akan pengelolaan data yang efisien dan transparan menjadi hal krusial bagi perusahaan, khususnya dalam pemantauan kontrak layanan. Penelitian ini membahas implementasi Business Intelligence menggunakan Power BI untuk merancang dashboard visualisasi data kontrak, dengan fokus pada layanan Land Affair. Proses pengembangan mengikuti Design Science Research Methodology (DSRM), yang mencakup identifikasi masalah, perancangan solusi, pengembangan dashboard, demonstrasi, dan evaluasi. Dashboard ini bertujuan untuk mempermudah pemangku kepentingan dalam melakukan monitoring kontrak secara real-time, dengan menyajikan data seperti nilai kontrak, status aktif, mitra kerja, dan tenggat waktu. Hasil evaluasi menunjukkan peningkatan efisiensi dan transparansi dalam pengambilan keputusan berbasis data.
PENERAPAN METODE K-NEAREST NEIGHBOAR (KNN) DALAM KLASIFIKASI KELULUSAN SISWA Samsuriah, Samsuriah; Asriyadi, Asriyadi
Nusantara Hasana Journal Vol. 5 No. 8 (2026): Nusantara Hasana Journal, January 2026
Publisher : Yayasan Nusantara Hasana Berdikari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59003/nhj.v5i8.1898

Abstract

Student graduation determination is an important aspect of the educational process that requires objective and accurate decision-making. Along with the development of information technology, the utilization of academic student data can be conducted through data mining approaches to support decision-making processes. This study aims to apply the K-Nearest Neighbor (KNN) method to classify student graduation status based on attendance data and final scores. The dataset consists of five student records as training data and two student records as testing data. The research stages include data preprocessing, distance calculation using Euclidean Distance, and class determination based on the majority of the nearest neighbors with a K value of 3. The results show that student F is classified as graduating because most of its nearest neighbors belong to the graduating class, while student G is classified as not graduating due to a greater number of nearest neighbors from the non-graduating class. Therefore, it can be concluded that the K-Nearest Neighbor (KNN) method is able to provide fairly accurate student graduation classification results and can be used as a decision support tool in the education sector.
Co-Authors Asriyadi Asriyadi Muhammad Fitrah Rivan