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All Journal IJISTECH (International Journal Of Information System & Technology) IJISTECH Jurnal Solusi Masyarakat Dikara
Fajriana, F
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Humanities Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Social Sciences Other

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Image Feature Extraction for Determining the Ripeness Level OF Oil Palm Fruits Using the K-Nearest Neighbor Algorithm Based on Color Features (Case PTPN IV Aceh Utara) Sudarti, Atrida; Ula, Munirul; Fajriana, F
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.391

Abstract

The availability of oil palm fruits at the appropriate ripeness level is crucial to achieving optimal oil production. Farmers often struggle to accurately determine fruit ripeness, resulting in inconsistent quality and reduced efficiency. This study aims to develop a classification system to determine the ripeness level oil palm fruits using the K-Nerest Neighbor (K-NN) algorithm based on color features extracted from fruits image. Color is a key indicator of maturity and directly influences oil yield. The data was collected through image acquisition and direct observation at the Cot Girek Palm Oil Mill (PKS) of PTPN IV, Aceh Utara. Image preprocessing was carried out to enhance and nomalize the data before feature extraction. The extracted color features were then used to classify the fruits into ripe and unripe categories using the K-NN algorithm. The results show that K-NN successfully classifies the ripeness level of oil palm fruits with an accuracy of 72.80%. This system provides a recommendation for fruit feasibility before processing, helping reduce production losses caused by immature or overripe fruits. Overall, this research contributes to improving decision-making in the palm oil industry through the application of image processing of machine learning techniques.
Image Feature Extraction for Determining the Ripeness Level OF Oil Palm Fruits Using the K-Nearest Neighbor Algorithm Based on Color Features (Case PTPN IV Aceh Utara) Sudarti, Atrida; Ula, Munirul; Fajriana, F
IJISTECH (International Journal of Information System and Technology) Vol 9, No 1 (2025): The June Edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v9i1.391

Abstract

The availability of oil palm fruits at the appropriate ripeness level is crucial to achieving optimal oil production. Farmers often struggle to accurately determine fruit ripeness, resulting in inconsistent quality and reduced efficiency. This study aims to develop a classification system to determine the ripeness level oil palm fruits using the K-Nerest Neighbor (K-NN) algorithm based on color features extracted from fruits image. Color is a key indicator of maturity and directly influences oil yield. The data was collected through image acquisition and direct observation at the Cot Girek Palm Oil Mill (PKS) of PTPN IV, Aceh Utara. Image preprocessing was carried out to enhance and nomalize the data before feature extraction. The extracted color features were then used to classify the fruits into ripe and unripe categories using the K-NN algorithm. The results show that K-NN successfully classifies the ripeness level of oil palm fruits with an accuracy of 72.80%. This system provides a recommendation for fruit feasibility before processing, helping reduce production losses caused by immature or overripe fruits. Overall, this research contributes to improving decision-making in the palm oil industry through the application of image processing of machine learning techniques.
Sharing Session Publikasi Ilmiah dan Sitasi dengan Aplikasi Mendeley: Workshop Pengabdian Kolaborasi Mursalin, M; Rohantizani, R; Fajriana, F; Azhari, Teuku; M, Deddy Satria
Jurnal Solusi Masyarakat Dikara Vol 3, No 3 (2023): DESEMBER 2023
Publisher : Yayasan Lembaga Riset dan Inovasi Dikara

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Publikasi ilmiah adalah proses pengungkapan pengetahuan, temuan, hasil penelitian, dan pemikiran ilmiah kepada masyarakat akademis dan umum melalui media tertentu yang diakui sebagai sumber informasi ilmiah. Definisi ini mencakup berbagai jenis publikasi, termasuk jurnal ilmiah, konferensi ilmiah, buku akademis, tesis, disertasi, laporan penelitian, dan lain sebagainya. Tujuan pengabdian ini adalah untuk memberikan pemahaman kepada mahasiswa semester akhir agar mampu menggunakan aplikasi Mendeley dalam menulis tugas akhir (Skripsi) serta artikel ilmiah untuk dipublikasikan dalam jurnal. Aplikasi Mendeley mampu memberikan kemudahan untuk mengatur dan mengelola referensi bagi penulis. Metode pelaksanaan pengabdian dilakukan dalam bentuk workshop tentang publikasi ilmiah dan sitasi dengan menggunakan aplikasi Mendeley. Materi worshop disampaikan dalam bentuk presentasi, diskusi dan praktek langsung di Laboratorium Komputer. Kegiatan pengabdian ini memiliki dampak yang besar terhadap pemahaman tentang apa itu publikasi ilmiah, bagaimana menulis skripsi, artikel dan sitasi dengan menggunakan aplikasi Mendeley, dimana tingkat pemahaman mahasiswa yang awalnya hanya ada 10% paham cara menggunakan aplikasi mendeley, kemudian mengalami kenaikan menjadi 95% mahasiswa mampu menggunakan aplikasi mendeley.
Co-Authors Deddy Satria M Munirul Ula Mursalin, M Rohantizani, R Sudarti, Atrida Teuku Azhari, Teuku