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All Journal Jatilima : Jurnal Multimedia Dan Teknologi Informasi Cosmic Jurnal Teknik
Ginting, Masitha Putri Ardhana
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Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Pengujian Aplikasi Berbasis Web Data Ska Menggunakan Metode Black Box Testing Lubis, Alvin Sany; Ginting, Masitha Putri Ardhana
Cosmic Jurnal Teknik Vol 1 No 1 (2024): Februari
Publisher : Ali Institute or Research and Publication

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Abstract

Pengujian aplikasi merupakan fase kritis dalam siklus pengembangan perangkat lunak yang bertujuan untuk menjamin kualitas dan optimalitas kinerja. Salah satu pendekatan yang terbukti efektif dalam pengujian aplikasi adalah metode "Black Box", yang fokus pada pengujian fungsional tanpa memperhatikan implementasi internal aplikasi Dalam penerapan ini, black-box testing akan digunakan untuk menguji sebuah Sistem Informasi Data SKA. Pengujian ini akan dilakukan pada website Data SKA di kantor Disperindag dengan tujuan hanya untuk mengevaluasi apakah program tersebut berfungsi sesuai dengan yang diinginkan tanpa perlu mengetahui detail kode program yang digunakan. Kelebihan metode ini terletak pada kemampuannya untuk mengenali masalah pengguna pada tingkat tinggi tanpa memerlukan pengetahuan tentang implementasi internal. Penelitian ini memberikan kontribusi penting dalam memperdalam pemahaman efektivitas metode Black Box dalam konteks pengujian aplikasi, dan juga memberikan dasar untuk peningkatan proses pengujian fungsional di masa depan.
Prediction of Parents’ Satisfaction in Learning Methods Using K-Nearest Neighbor Algorithm Ginting, Masitha Putri Ardhana; Hasugian, Abdul Halim
Jurnal Multimedia dan Teknologi Informasi (Jatilima) Vol. 7 No. 03 (2025): Jatilima : Jurnal Multimedia Dan Teknologi Informasi
Publisher : Cattleya Darmaya Fortuna

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54209/jatilima.v7i03.1702

Abstract

Parental satisfaction in learning methods is an important indicator for evaluating the quality of education, especially in inclusive schools such as Smart Aurica School. This study aims to predict the level of parental satisfaction with learning methods using the K-Nearest Neighbor (K-NN) algorithm. The research employed a quantitative approach with data collected through questionnaires distributed to parents of students. The collected data were processed through several stages, including data cleaning, normalization, training and testing set division, and distance calculation using Euclidean Distance. The K-NN model was then applied to classify satisfaction levels based on the predetermined K value. The results indicate that the K-NN algorithm can provide accurate predictions of parental satisfaction, achieving a relatively high accuracy rate in testing. These findings demonstrate that K-NN is an effective approach to assist schools in evaluating learning methods and offering data-driven recommendations to improve educational quality. Therefore, this research contributes to the application of machine learning in providing a more objective and accurate evaluation of educational services, which can serve as a strategic basis for school decision-making.
Co-Authors Abdul Halim Hasugian Lubis, Alvin Sany