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All Journal Knowledge Engineering and Data Science Al-Ta'dib: Jurnal Kajian Ilmu Kependidikan Berkala Penelitian Agronomi
Nurhalimah, Sitti
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Agriculture, Biological Sciences & Forestry Computer Science & IT Education Engineering

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Aplikasi Mulsa Daun Pisang dan Pupuk Organik Cair (POC) Bonggol Pisang terhadap Pertumbuhan Tanaman Cabai (Capsicum annum L.) Nurmas, Andi; Adawiyah, Robiatul; Harjoni KW, Laode Muh.; Rakian, Tresjia Corina; Leomo, Sitti; Nurhalimah, Sitti
Berkala Penelitian Agronomi Vol 8, No 2 (2020)
Publisher : Universitas Halu Oleo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33772/bpa.v8i2.15176

Abstract

Rendahnya produktivitas tanaman cabai di Sulawesi Tenggara disebabkan kesuburan tanah yang rendah danketersediaan air yang terbatas akibat perubahan iklim terutama pada musim kemarau. Tanah-tanah di Sulawesi Tenggaradidominasi tanah marginal sehingga menjadi salah satu faktor penghambat pertumbuhan dan produksi tanaman. Salahsatu upaya yang dapat dilakukan dengan teknologi pemulsaan dan pemberian pupuk organik cair. Tujuan penelitianuntuk mengetahui pengaruh mulsa daun pisang dan POC bonggol pisang dalam meningkatkan pertumbuhan tanamancabai. Penelitian ini dilaksanakan di Laboratorium Lapangan Kebun Percobaan II Fakultas Pertanian UHO. yangberlangsung bulan Mei-Agustus 2019. Penelitian dilaksanakan menggunakan Rancangan Acak Kelompok (RAK) polafaktorial. Faktor I adalah mulsa daun pisang (M) terdiri atas 3 taraf yaitu kontrol (M0), Mulsa daun pisang 2 kg perpetak(M1) dan Mulsa daun pisang 4 kg perpetak (M2). Faktor II adalah POC bonggol pisang yang terdiri atas 3 taraf yaitukontrol (P0), POC 10 ml Lˉ1 air (P1) dan POC 20 ml Lˉ1 air (P1). Terdapat 9 kombinasi perlakuan dan masing-masingdiulang 3 kali sehingga diperoleh 27 unit percobaan. Variabel yang diamati yaitu tinggi tanaman, jumlah daun, jumlahcabang primer, jumlah cabang sekunder dan berat kering tanaman. Hasil penelitian menunjukkan bahwa perlakuan mulsadaun pisang dan pupuk organik cair (POC) bonggol pisang memberikan respon berbeda terhadap variabel pertumbuhantanaman cabai, baik secara mandiri maupun interaksi antar perlakuan.Kata Kunci: Mulsa Daun Pisang, Pupuk Organik Cair (POC) bonggol pisang, Tanaman Cabai
Strategi Kepala Sekolah dalam Pelaksanaan Ujian Nasional Berbasis Komputer (UNBK) di Sekolah Menengah Pertama Nurdin, Nurdin; Anhusadar, Laode; Herlina, Herlina; Nurhalimah, Sitti
Al-TA'DIB: Jurnal Kajian Ilmu Kependidikan Vol. 14 No. 1 (2021)
Publisher : Institut Agama Islam Negeri Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31332/atdbwv14i1.1901

Abstract

This qualitative case study aims to review and analyze the condition of the information and communication technology (ICT) facilities for the implementation of the computer-based national examination (UNBK), the principal's strategies in implementing UNBK, and the obstacles in implementing UNBK at one of junior high schools in Kota Kendari, Southeast Sulawesi, Indonesia. Data were collected by interview, observation and documentation. The result of data analysis revealed the lack of facilities for the implementation of UNBK in the school. The principal's strategies in implementing UNBK in the school cover various aspects, such as facilities and infrastructure, human resources, and pupil affairs. Two obstacles emerged i.e., technical and non-technical.
Feature Engineering and Anchor Optimization for Enhancing Faster R-CNN Detection of Low-Contrast Steel Surface Defects Darwis, Herdianti; Nurhalimah, Sitti; Azis, Huzain
Knowledge Engineering and Data Science
Publisher : citeus

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

Abstract

Detection of defects on low-contrast steel surfaces, especially crazing and rolled-in-scale, remains a major challenge due to their visual similarity to background patterns. Although state-of-the-art methods have achieved high accuracy through complex architectural adjustments, the contribution of preprocessing techniques has not been thoroughly investigated. This study investigates pre-processing-based improvements to Faster R-CNN by combining Bilateral Filtering to reduce noise, CLAHE to enhance local contrast, CIoU Loss for more effective bounding box regression, and customized anchor settings for irregular defect configurations. Evaluated using the NEU-DET dataset, our BF-CIoU Faster R-CNN model achieved a mAP@50 score of 72.32%, with an AP of 43.74% for crazing and 53.04% for rolled-in-scale. Although these results fall short of the performance of state-of-the-art architectures that utilize feature fusion and attention mechanisms (80.2% mAP), our approach demonstrates that preprocessing improvements alone can yield competitive baseline performance without additional architectural complexity. This study confirms the effectiveness of Bilateral Filtering and CLAHE in removing defective signals, while highlighting the need for more advanced feature-extraction modules to achieve higher accuracy. Further research will examine hybrid approaches that combine preprocessing with attention-based architectures for steel inspection systems in industry.
Co-Authors ANDI NURMAS, ANDI Darwis, Herdianti Harjoni KW, Laode Muh. Herlina Herlina LA ODE ANHUSADAR Leomo, Sitti Nurdin Nurdin Robiatul Adawiyah TRESJIA CORINA RAKIAN, TRESJIA CORINA