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All Journal Jurnal Teknik Elektro dan Komputer TRIAC Jurnal Pengabdian UNDIKMA Journal of Renewable Energy and Smart Device
Deni Tri Laksono
Universitas Trunodjoyo Madura
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Design and Performance Analysis of an Automatic Plastic Mulch Cutting and Punching Machine Using PID Control Dian Neipa Purnamasari; Achmad Fiqhi Ibadillah; Achmad Ubaidillah; Adi Kurniawan Saputro; Deni Tri Laksono
Jurnal Teknik Elektro dan Komputer TRIAC Vol 13, No 1 (2026): Mei 2026
Publisher : Jurusan Teknik Elektro Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/triac.v13i1.33970

Abstract

Plastic mulch is widely used in horticulture to improve soil moisture retention, regulate temperature, and suppress weed growth. However, manual cutting and hole-punching processes are time-consuming, labor-intensive, and produce inconsistent results. This study presents the design and performance analysis of an automatic plastic mulch cutting and punching machine using a PID (Proportional–Integral–Derivative) control system. The system is based on an Arduino Nano. It integrates a DC motor for mulch feeding, a NEMA 17 stepper motor for the punching mechanism, a heater band controlled by an AC dimmer, and a photoelectric sensor for length feedback. PID parameters were initially determined using the Ziegler–Nichols method (Kp=13.5, Ki=2.8, Kd=7.1) and optimized experimentally to Kp=10, Ki=2, and Kd=1. Experimental results show that the optimized PID controller achieves a stable motor response with no oscillation and minimal overshoot, enabling accurate stopping at the desired set point. The system produces a consistent hole spacing of approximately 43.96 cm. Minor deviations were caused by sensor noise and environmental interference.
Implementation Solar PV Integrated Smart Greenhouse Technology for Urban Hydroponic Production at KWT Dorang Cinta Surabaya Monika Faswia Fahmi; Martha Laila Arisandra; Jihannuma Adibiah Nurdini; Deni Tri Laksono
Jurnal Pengabdian UNDIKMA Vol. 7 No. 4 (2026): November (IN PRESS)
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i4.23066

Abstract

KWT Dorang Cinta, a community-based farming group in Surabaya, Indonesia, faced several operational challenges in managing its Deep Flow Technique (DFT) hydroponic system. These included intermittent monitoring of pH and total dissolved solids (TDS) using portable instruments, manual nutrient dosing and pH adjustment based on members’ experience, and reliance on grid electricity to operate the water circulation pump. These limitations reportedly contributed to reduced crop yields and increased electricity costs, as indicated by interviews with group members. This community service program aimed to implement a solar photovoltaic (PV)-integrated smart greenhouse to improve hydroponic nutrient management and provide an alternative energy source for system operation. The intervention integrated pH, TDS, water-level, and temperature sensors with an ESP32-based controller, automated nutrient and pH dosing systems, smartphone-based monitoring, and a 300 Wp solar PV system equipped with a 12 V/100 Ah battery. The implementation process comprised field assessment, system development, installation, training, equipment handover, and evaluation. A five-point Likert-scale questionnaire was administered to 15 participants to assess their understanding, perceived usability, operational readiness, and perceived benefits of the training. The overall mean score was 3.97 out of 5, indicating generally positive participant perceptions. The highest rating was recorded for perceived productivity benefits (4.40 ± 0.51), whereas troubleshooting skills (3.33 ± 1.05) and knowledge of PV system operation (3.40 ± 1.12) received the lowest ratings. These findings demonstrate the technical feasibility of implementing the integrated system and its positive acceptance among participants. However, the relatively lower ratings for troubleshooting and PV operation underscore the need for continued technical mentoring and long-term performance evaluation to ensure effective system operation and sustainability.
Spatially Differentiated NPK Fertilizer Prescription with Crop Level Nitrogen Requirement Using Hybrid Gaussian Mixture Model Fuzzy Sugeno GIS Framework Monika Fahmi; Deni Tri Laksono; Adi Kurniawan Saputro; Yuni Nurfiana; Jean d’Amour Rukundo
Journal of Renewable Energy and Smart Device Vol. 4 No. 2 December 2026
Publisher : PT. Global Research Collaboration

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66314/joresd.v4i2.1355

Abstract

Agricultural productivity can be constrained by fertilizer management that does not account for spatial variability in soil nutrient conditions. This study develops a hybrid Gaussian Mixture Model (GMM)–Fuzzy Sugeno framework to transform probabilistic soil phosphorus (P) and potassium (K) patterns into spatially differentiated point-level fertilizer prescriptions within a Geographic Information System (GIS). Thirty sampling locations were evaluated in each paddy and maize cultivation field in Bangkalan Regency. Soil P and K were measured directly using a portable NPK sensor, whereas nitrogen (N) requirements were determined independently from crop-specific target-yield criteria and cultivation-area measurements. GMM parameters were estimated using the Expectation-Maximization (EM) algorithm, and candidate mixture orders were assessed using maximized log-likelihood, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and silhouette coefficients. The EM algorithm converged at iteration 42 for paddy and iteration 9 for maize. Both AIC and BIC selected a three-component model for paddy and maize, although the resulting component separation differed between crops, with silhouette coefficients of 0.16199 and 0.43309, respectively. The selected components were subsequently characterized by their P–K profiles, and posterior responsibilities were used as normalized firing strengths in the Fuzzy Sugeno inference layer. The hybrid framework generated spatially referenced, differentiated point-level prescriptions, with mean SP-36 and KCl rates of 72.334 and 68.432 kg/ha for paddy and 87.485 and 76.637 kg/ha for maize, respectively. The corresponding coefficients of variation were 26.45% and 30.13% for paddy and 16.34% and 33.07% for maize. In contrast, the conventional Fuzzy Sugeno formulation produced uniform SP-36 and KCl rates of 100 kg/ha across the investigated points. The results demonstrate that GMM posterior responsibilities can be propagated into the Sugeno inference layer to retain probabilistic component association and generate spatially differentiated fertilizer prescriptions. However, the study evaluates computational behaviour, probabilistic assignment, and prescription differentiation rather than agronomic effectiveness; the analysis is limited to two fields, one growing season, and 30 sampling locations per crop, without independent validation of yield response, nutrient uptake, fertilizer efficiency, or dose safety. Further multi-season and field-scale agronomic validation is therefore required before the generated prescriptions can be interpreted as validated fertilizer recommendations.
Co-Authors Achmad Fiqhi Ibadillah Achmad Ubaidillah Adi Kurniawan Saputro Adi Kurniawan Saputro Dian Neipa Purnamasari Jean d’Amour Rukundo Jihannuma Adibiah Nurdini Martha Laila Arisandra, Martha Laila Monika Fahmi Monika Faswia Fahmi Yuni Nurfiana