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ANALISIS DISTRIBUSI KLOROFIL, NITROGEN, DAN KARAKTERISTIK DAUN SALAK SIDIMPUAN (Salacca sumatrana Becc.) BERDASARKAN POSISI DAUN DI KECAMATAN ANGKOLA BARAT Jumaria Nasution; Suswati; Syahbudin Hasibuan
Jurnal Sapta Agrica Vol 5 No 1 (2026): Jurnal Sapta Agrica
Publisher : Universitas Nias Raya

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Abstract

Sidimpuan salak (Salacca sumatrana Becc.) is a leading horticultural commodity in the Tapanuli region and is commonly cultivated under natural agroforestry systems beneath tree canopies. Leaf chlorophyll and nitrogen contents are important indicators of photosynthetic capacity and plant nutritional status, and their values may vary according to leaf position. This study aimed to analyze the distribution of chlorophyll, nitrogen, and leaf morphological characteristics of Sidimpuan salak based on leaf position (basal, middle, and apical) across three villages at different elevations, namely Sibakua Village (approximately 674 m above sea level), Hutakoje Village (approximately 550 m above sea level), and Sigumuru Village (approximately 470 m above sea level) in South Tapanuli Regency, North Sumatra, Indonesia. A total of 20 sample plants were selected from each location using purposive sampling. Chlorophyll and nitrogen contents were measured using a SPAD meter at the three leaf positions, while leaf area was calculated using the formula L = l × w × k (k = 0.7), where l represents leaf length and w represents leaf width. The data were analyzed using two-way analysis of variance (ANOVA), followed by Duncan's Multiple Range Test (DMRT). Location and leaf position had highly significant effects (p < 0.01) on chlorophyll and nitrogen contents, whereas their interaction was not significant. The highest chlorophyll and nitrogen contents were observed at the basal leaf position and gradually decreased toward the leaf apex. Among the study locations, the highest chlorophyll and nitrogen contents were recorded in Sigumuru, while the lowest values were found in Hutakoje. In contrast, the largest leaf area was observed in Hutakoje. The basal leaf position is therefore recommended as the most representative sampling position for assessing the chlorophyll and nitrogen status of Sidimpuan salak plants.
Sosialisasi Pemanfaatan IoT Berbasis Machine Learning pada Deteksi Penyakit Tanaman Sawit untuk Pertanian Berkelanjutan di Dusun I Bukit Gantung Desa Sumber Mulyo Hartono; M. Khahfi Zuhanda; Sayuti Rahman; Retna Astuti Kuswardani; Suswati; Muhammad Zen; Erianto Ongko; Lili Suryati
Dedikasi Sains dan Teknologi (DST) Vol. 5 No. 2 (2025): Artikel Pengabdian Nopember 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/dst.v5i2.7148

Abstract

Kegiatan Pengabdian kepada Masyarakat (PkM) ini dilaksanakan untuk meningkatkan literasi digital petani sawit di Dusun I Bukit Gantung, Desa Sumber Mulyo, melalui sosialisasi pemanfaatan Internet of Things (IoT) berbasis machine learning untuk smart agriculture dalam deteksi dini penyakit tanaman sawit. Pelatihan dirancang dalam empat tahapan, yaitu persiapan perangkat, penyampaian materi konseptual, praktik instalasi IoT, penerapan machine learning, serta pendampingan lapangan. Evaluasi dilakukan menggunakan pre-test dan post-test untuk mengukur perubahan kompetensi peserta terhadap konsep IoT, machine learning, instalasi sensor, dan interpretasi hasil deteksi. Berdasarkan analisis, evaluasi hasil pelatihan menunjukkan peningkatan sebesar 47%, dengan kenaikan tertinggi pada keterampilan instalasi sensor dan membaca hasil aplikasi (+54%). Penerapan teknologi ini membantu petani melakukan deteksi penyakit lebih cepat dan akurat sehingga penggunaan pestisida dapat ditekan melalui penyemprotan selektif. Selain menghasilkan peningkatan kompetensi teknis, kegiatan ini meningkatkan keberterimaan teknologi di kalangan petani, terbukti dari 14 dari 15 kelompok yang secara konsisten menggunakan perangkat IoT pascapelatihan. Secara sosial, program ini mendorong perubahan perilaku kolektif menuju praktik budidaya yang lebih aman, efisien, dan berbasis data. Implementasi ini menunjukkan bahwa integrasi IoT–machine learning mampu memperkuat keberlanjutan pertanian sawit sekaligus meningkatkan kualitas pengelolaan kebun masyarakat, serta memberikan dasar penting bagi pengembangan sistem monitoring kesehatan tanaman yang lebih komprehensif dan adaptif pada skala komunitas.
Co-Authors Erianto Ongko Hartono Jumaria Nasution Lili Suryati Muhammad Khahfi Zuhanda Muhammad Zen Rahman, Sayuti Retna Astuti Kuswardani Syahbudin Hasibuan