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All Journal JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) International Journal of Electronics and Communications Systems
Eriyani, Nindy Sri
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Computer Science & IT Education Electrical & Electronics Engineering

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IMPLEMENTASI JARINGAN SARAF TIRUAN BACKPROPAGATION UNTUK KLASIFIKASI TINGKAT KESEGARAN WORTEL BERBASIS PENGOLAHAN CITRA DIGITAL DISERTAI OPERASI MORFOLOGI Musdar, Devi Miftahul Jannah; Eriyani, Nindy Sri; Azis, Salsabila; Kaswar, Andi Baso; Sasmita, Sasmita
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 9, No 3 (2024)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v9i3.5672

Abstract

Penelitian ini bertujuan mengembangkan metode klasifikasi untuk menilai tingkat kesegaran wortel menggunakan algoritma Jaringan Saraf Tiruan (JST) dengan pendekatan backpropagation dan model morfologi matematika berbasis pengolahan citra digital. Fitur yang diekstraksi dalam proses klasifikasi meliputi warna (Hue, Saturation, Value), bentuk, dan tekstur (contrast, correlation, homogeneity). Model ini dibangun menggunakan 240 citra latih wortel yang dikategorikan menjadi tiga kelas: segar, kurang segar, dan tidak segar. Hasil pelatihan menunjukkan tingkat akurasi sebesar 95,83% dengan waktu komputasi rata-rata 323,47 detik per citra. Pengujian model menggunakan 60 citra uji mencapai tingkat akurasi 98,33% dengan waktu komputasi rata-rata 73,82 detik per citra. Kombinasi fitur warna HSV dengan tekstur (terutama correlation dan homogeneity) terbukti paling efektif dengan ruang warna HSV menghasilkan performa lebih baik dibandingkan ruang warna LAB dan RGB. Penelitian ini merekomendasikan eksplorasi model yang lebih modern dan memastikan konsistensi akuisisi citra untuk meningkatkan akurasi klasifikasi. Kesimpulannya, model JST yang dikembangkan menunjukkan efektivitas tinggi dalam klasifikasi kesegaran wortel dan berpotensi untuk diterapkan pada pengujian citra lainnya.
Outlier-Aware Clustering for Mapping Computer Science Students’ Career Readiness: A Hybrid DBSCAN–K-Means Approach Eriyani, Nindy Sri; Surianto, Dewi Fatmarani; Nurhidayat; Salam, Fitria Nur Dina; Muliadi
International Journal of Electronics and Communications Systems Vol. 6 No. 1 (2026): International Journal of Electronics and Communications System
Publisher : Universitas Islam Negeri Raden Intan Lampung, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/ijecs.v6i1.28865

Abstract

Career readiness assessment increasingly requires competency-based profiling beyond academic achievement, yet conventional clustering methods remain vulnerable to outliers that distort student classification. This study proposes an outlier-aware hybrid DBSCAN–K-Means framework to map the career readiness of computer science students using eight non-academic competency dimensions. Data were collected from 566 students across 21 Indonesian universities using a validated 35-item questionnaire covering leadership, collaboration, time management, self-directed learning, goal setting, adaptability, problem-solving, and technical skills. Cluster quality was evaluated using the Elbow Method, Silhouette Score, and Davies–Bouldin Index. DBSCAN identified 113 outliers (19.96%), and removing these observations improved clustering performance, increasing the Silhouette Score from 0.292 to 0.326 while reducing the Davies–Bouldin Index from 1.229 to 1.122. The hybrid approach identified four meaningful career readiness profiles, including highly prepared students, students requiring competency development, critically underprepared outliers, and exceptionally high-performing outliers overlooked by conventional clustering. These findings demonstrate that outlier-aware clustering produces more robust competency profiles and provides a replicable analytical framework for evidence-based career development strategies in higher education.
Co-Authors Andi Baso Kaswar Andi Baso Kaswar Azis, Salsabila Dewi Fatmarani Surianto Muliadi Musdar, Devi Miftahul Jannah Nurhidayat Salam, Fitria Nur Dina Sasmita Sasmita