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All Journal SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Fadilla Julianifa Putri
IPB University
Computer Science & IT Control & Systems Engineering Engineering

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PERBANDINGAN KINERJA ALGORITMA KNN DAN SVM DALAM KLASIFIKASI KEMATANGAN BUAH JERUK MEDAN BERDASARKAN CITRA DIGITAL Fadilla Julianifa Putri; Siti Laila Nurjannah; Dwi Febrina Wati; Silvia Ariani Daulay; Indira Sistamarien; Endang Purnama Giri; Gema Parasti Mindara
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.
Sistem Pengelolaan Toko Hewan Peliharaan Petify Berbasis Website Zafira A’idah Gunawan; Fadilla Julianifa Putri; Raina Disa Wibowo; Cahaya Elisabet Butarbutar; Hanin Putri Sholiha; Aditya Wicaksono; Gema Parasti Mindara; Inna Novianty; Lathifunnisa Fatonah; Endang Purnama Giri
Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Vol. 7 No. 01 (2026): Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI)
Publisher : Program Studi Teknik Informatika, FTIK, Universitas Indraprasta PGRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30998/jrami.v7i01.590

Abstract

Manual operational data management in pet shops often causes problems, such as inaccurate stock data, difficulty in tracing transaction history, and inefficient services. The goal of this study is to create and build a website-based pet shop management system called Petify. This system was developed to provide an integrated platform for administrators to centrally manage product data, grooming services, scheduling, transactions, and suppliers. The system development method used is the Waterfall model, which includes the stages of requirements analysis, system design, implementation, and testing. In the system design stage, system modeling was carried out using ERD and DFD, as well as user interface (UI/UX) designs tailored to the needs of administrators. The result of this research is a website-based information system that can replace manual recording and simplify store operations. Based on system testing using the Black Box Testing method with the Equivalence Partitioning approach, all functional features of the system were proven to work well and valid according to user needs. The implementation of Petify is expected to improve store management efficiency and service quality for customers.
Co-Authors Aditya Wicaksono Cahaya Elisabet Butarbutar Dwi Febrina Wati Endang Purnama Giri Gema Parasti Mindara Hanin Putri Sholiha Indira Sistamarien Inna Novianty Lathifunnisa Fatonah Raina Disa Wibowo Silvia Ariani Daulay Siti Laila Nurjannah Zafira A’idah Gunawan