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Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering)
Published by Universitas Lampung
ISSN : 2302 559X     EISSN : 2549 0818     DOI : -
Core Subject : Agriculture, Engineering,
Agriculture, Biological Sciences & Forestry Engineering
Jurnal Teknik Pertanian Lampung or Journal of Agricultural Engineering (JTEP-L) is a peer-reviewed open-access journal. The journal invites scientists and engineers throughout the world to exchange and disseminate theoretical and practice-oriented researches in the whole aspect of Agricultural Enginering including but not limited to Agricultural Mechanization, Irrigation, Soil and Water Engineering, Postharvest Technology, Renewable Energy, Farm Structure, and related fields. The first issue was published in October 2012 by Department of Agricultural Engineering, Faculty of Agriculture, University of Lampung. Jurnal Teknik Pertanian Lampung has ISSN number 2302 - 559X for print edition on October 10, 2012 then 2549 - 0818 for online edition on January 10, 2017. Jurnal Teknik Pertanian Lampung is issued periodically four times a year in March, June, September, and December. Jurnal Teknik Pertanian Lampung has been indexed by Google Scholar, Crossref, Directory Open Access Journals (DOAJ), and CABI. Since Volume 5 Issue 1 (2016) Jurnal Teknik Pertanian Lampung has been accredited as SINTA 3 by Directorate General of Higher Education (DIKTI). Starting Volume 10 Issue 3 (2021) the journal received accreditation SINTA 2.
Arjuna Subject : -
Articles 1,203 Documents
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A Effect of Straw and Cattle Manure Ratio on Bokashi Quality Based on C/N Balance and Nutrient Availability Yason Edisson Benu; Jemseng Carles Abineno; Marvin Jecson Pandu; Johny Agustinus Koylal; Micha Snoverson Ratu Rihi
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.1143-1151

Abstract

Bokashi is an anaerobically fermented organic fertilizer whose quality depends on the balance between carbon-rich and nitrogen-rich feedstocks. This study evaluated five bokashi formulations prepared from different proportions of leaf straw (JD) and cattle manure (PK), namely: JD100, JD75PK25, JD50PK50, JD25PK75, and PK100. Bokashi was fermented under anaerobic conditions for 21 days and assessed for temperature, moisture content, pH, organic carbon, C/N ratio, total nitrogen, P₂O₅, K₂O, and magnesium (Mg). The experiment was arranged in a Completely Randomized Design, while treatment performance was evaluated using descriptive statistics, including mean ± standard deviation, coefficient of variation, and trend analysis. Results showed that increasing the proportion of manure generally enhanced nutrient availability. Total nitrogen increased from 1.54% in JD100 to 2.72% in JD25PK75, while Mg concentration increased from 3,506 mg kg⁻¹ in JD100 to 10,999 mg kg⁻¹ in PK100. In contrast, straw-dominated formulations maintained higher organic carbon content (27.86%) and more neutral pH conditions (7.7). The C/N ratio declined from 18.10 in JD100 to 8.72 in PK100, indicating greater decomposition and compost maturity. Among the treatments, JD50PK50 exhibited comparatively balanced characteristics, combining relatively high organic carbon, moderate nutrient levels, near-neutral pH (6.8), and a moderate C/N ratio (11.48). These findings suggest that balanced straw–manure mixtures can improve bokashi maturity, nutrient retention, and chemical stability for sustainable organic fertilizer production.
The Optimization of Operating Parameters on the Reduction of Relative Humidity in a Dehumidifier Dryer Using Response Surface Methodology Iswahyono Iswahyono; Yossi Wibisono; Didiek Hermanuadi; Amal Bahariawan; Meta Fitri Rizkiana
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.1152-1163

Abstract

Relative humidity (RH) of the drying air is the main driving force in the drying process, as dry air has a greater capacity to absorb water vapor from the material; therefore, the lower the RH, the faster the drying rate. This study aims to determine the optimal RH point under varying airflow rate, evaporator temperature, and heater temperature, and to identify the most suitable RSM model equation. The research was carried out in three stages: (1) development of a dehumidifier dryer; (2) optimization and modeling using the Response Surface Methodology (RSM) with three factors—airflow rate, evaporator temperature, and heater temperature—and one primary response, namely the RH of drying air, along with an additional response of drying air temperature. Design Expert v13 software with the RSM Central Composite Design (CCD) was used to select optimum process conditions from the combination of operating parameters. The accuracy of heater temperature control was analyzed using relative error. The relationship between variables and the RH response of air entering the drying chamber was modeled as: Y = 14.02 + 0.8154A + 0.7625B – 2.75C – 0.4375AB – 0.0875AC – 0.2625BC + 2.48A² + 3.09B² + 9.83C² (where A = airflow rate, B = evaporator temperature, and C = heater temperature). The optimal RH response of the drying air was 20.263% at an airflow rate of 0.018 m³/s, evaporator temperature of 8.413 °C, and heater temperature of 39.49 °C. Air heating temperature control performed very well, with a relative error of 1.22%, which is below 5%.
Non-Destructive Detection of Coffee Bean Defects using Machine Vision and the YOLOv11 Algorithm Hary Kurniawan; Ince Siti Wardatullatifah S; Hanifah Ayu; Surya Abdul Muttalib; Sukmawaty Sukmawaty; Ansar Ansar; Rahmat Sabani; Murad Murad
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 15 No. 3 (2026): June 2026
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v15i3.1164-1179

Abstract

Advances in machine vision and deep learning offer a promising solution for automated, non-destructive quality assessment for high-quality coffee. This study evaluated the performance of five YOLOv11 variants (n, s, m, l, and x) for real-time detection of defective coffee beans and identified the most suitable model in terms of detection accuracy and computational efficiency. A conveyor-based machine vision system was developed to acquire top-view images of Robusta coffee beans under controlled illumination. A dataset of 3,500 images was prepared, comprising 3,000 annotated images for training and validation (80:20) and 500 images reserved for blind testing. All defective beans were grouped into a single defect class, and the YOLOv11 variants were evaluated using precision, recall, F1-score, mean Average Precision (mAP) at IoU thresholds of 0.5 and 0.5:0.95, and inference time. All YOLOv11 variants achieved high detection performance, with mAP@0.5 values exceeding 0.98. YOLOv11s showed the best overall balance, achieving the highest recall (0.954), mAP@0.5:0.95 (0.689), and F1-score (0.959), while maintaining low inference time and a compact model size. Larger variants, such as YOLOv11x, achieved slightly higher mAP@0.5 but required substantially greater computational resources, whereas YOLOv11n provided faster inference but lower robustness under stricter localization criteria. Blind testing revealed a performance gap relative to validation results, highlighting remaining challenges in model generalization. Overall, the results confirm the effectiveness of YOLOv11 for coffee bean defect detection and identify YOLOv11s as the most suitable variant for real-time inspection within the defined experimental scope.

Page 121 of 121 | Total Record : 1203

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