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All Journal TIN: TERAPAN INFORMATIKA NUSANTARA SKANIKA: Sistem Komputer dan Teknik Informatika
Nurjannah, Siti Laila
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Pengujian Kualitas Fungsional Website PERKASA Menggunakan Metode Black Box dengan Pendekatan Equivalence Partitioning Qonita, Vellisya Afifa; Sistamarien, Indira; Nurjannah, Siti Laila; Daulay, Silvia Ariani; Mindara, Gema Parasti; Wicaksono, Aditya
TIN: Terapan Informatika Nusantara Vol 6 No 7 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i7.8874

Abstract

This study was conducted by applying Black Box testing using the Equivalence Partitioning method to validate the functionality of the PERKASA website, a digital consultation platform in the fisheries sector designed to support fish farmers. The research was carried out in a controlled testing environment with a focus on several main features, namely expert consultation, account login, account registration, articles, and user profile editing. The Equivalence Partitioning method was used to categorize inputs into classes representing valid and invalid conditions. Each test case was executed to determine whether the system produced outputs that matched the expected results. A quantitative approach was applied to compare the actual outcomes with the predefined expected values. Test results showed that out of 54 scenarios, 46 performed as expected, resulting in a functional validation rate of 85,19%. Meanwhile, several other scenarios showed discrepancies in specific input validation. Validation was performed on both client and server sides to ensure data integrity and an optimal user experience. These findings demonstrate that Equivalence Partitioning can serve as a useful method for supporting the development of new websites such as PERKASA, while also contributing to future research on web based information systems.
PERBANDINGAN KINERJA ALGORITMA KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH JERUK MEDAN BERDASARKAN CITRA DIGITAL Putri, Fadilla Julianifa; Nurjannah, Siti Laila; Wati, Dwi Febrina; Daulay, Silvia Ariani; Sistamarien, Indira; Giri, Endang Purnama; Mindara, Gema Parasti
SKANIKA: Sistem Komputer dan Teknik Informatika Vol 9 No 1 (2026): Jurnal SKANIKA Januari 2026
Publisher : Universitas Budi Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36080/skanika.v9i1.3661

Abstract

As a regional flagship commodity with a promising selling value, the process of grouping the maturity level of Medan Orange is still dominated by manual visual techniques. This often triggers data inconsistency and requires a long duration of processing due to personnel subjectivity factors. This research aims to compare the performance of two machine learning algorithms, namely KNN and SVM, in classifying the maturity level of Medan Orange fruit based on digital images. The dataset used is a primary dataset collected directly from Medan Orange farmers in field conditions. The research stages include image acquisition, pre-processing, extraction of HSV-based color features and GLCM-based textures, as well as classification of maturity levels into three classes, namely raw, semi-cooked, and mature. The performance of both algorithms is evaluated using accuracy, precision, and recall metrics. The research results show that the KNN algorithm has a superior performance compared to SVM, with an accuracy rate of 96,25%, while SVM produces an accuracy of 91,25%. This result shows that KNN is effective and more suitable to be applied to the automation system of classification of the maturity of Medan Orange fruit based on digital images.
Co-Authors Aditya Wicaksono Daulay, Silvia Ariani Endang Purnama Giri Mindara, Gema Parasti Putri, Fadilla Julianifa Qonita, Vellisya Afifa Sistamarien, Indira Wati, Dwi Febrina