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All Journal Agrikultura Jurnal Teknik Mesin Journal of Engineering and Management in Industrial System Rekayasa Sipil Industria: Jurnal Teknologi dan Manajemen Agroindustri Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Aplikasia: Jurnal Aplikasi Ilmu-Ilmu Agama Jurnal INTECH Teknik Industri Universitas Serang Raya Buana Ilmu INTEGRITAS : Jurnal Pengabdian Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Industrial Engineering and Operation Management (JIEOM) Journal of Industial Engineering and Technology Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Teknika JUSTI (Jurnal Sistem dan Teknik Industri) JOSSE: Journal of Economics and Social Sciences Jurnal Teknologi dan Manajemen Sistem Industri Engineering Science Letter Journal of Novel Engineering Science and Technology De`Teksi : Jurnal Teknik Sipil Dedication : Jurnal Pengabdian Masyarakat Journal of Social Science and Economics
EKO WAHYU ABRYANDOKO
Univeritas Bojonegoro
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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Mathematical Modeling for Climate-Based Optimization of Rice Planting Schedules Moh Yusuf Dawud; Masahid Masahid; Eko Wahyu Abryandoko
Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Vol. 14 No. 6 (2025): December 2025
Publisher : The University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jtepl.v14i6.2285-2296

Abstract

The stability of rice production is greatly influenced by the dynamics of climate variability that changes rapidly and is unpredictable. This study developed a climate-based planting scheduling model that utilizes daily climate data and annual production data for the period 2016–2024. The predictive model was built through multiple linear regression to examine the effects of temperature, rainfall, humidity, and wind speed on crop yields and ARIMA to project climate and rice production until 2029. Data were obtained from BMKG, BPS, and related regional agencies, then processed to produce an adaptive planting schedule. The regression results showed high accuracy with R² = 0.99, Adjusted R² = 0.961, MAE = 5.980, and RMSE = 6.770. Rainfall showed a negative effect (p = 0.025) on rice production. The optimization model produced the two most profitable planting months each year and provided more stable yields than conventional planting patterns. Five-year production projections show fluctuations influenced by climate conditions, including a sharp decline in 2027 and a rebound in 2029. The development of an adaptive schedule model allows for alternative decision-making in areas vulnerable to climate change.
Development of an Ergonomic Based Adaptive Walker Design to Optimize Walking Mobility in the Elderly Novia Pramesti Dwi Cahyani; Eko Wahyu Abryandoko; Amalia Ma’rifatul Maghfiroh
Jurnal INTECH Teknik Industri Universitas Serang Raya Vol. 12 No. 1 (2026): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/intech.v12i1.11895

Abstract

The increasing elderly population has led to a higher prevalence of mobility limitations associated with reduced muscle strength, musculoskeletal disorders, and postural instability. Conventional walkers often fail to accommodate anthropometric variations and shock absorption needs, resulting in non-neutral postures and elevated ergonomic risk. This study developed an ergonomics-based adaptive walker integrating an adjustable handle mechanism and spring-based shock absorption. A pre- and post-intervention experimental approach involving five elderly participants was conducted using the Rapid Upper Limb Assessment (RULA) method. Initial RULA scores ranged from 5 to 7, indicating high to very high-risk levels. After using the adaptive walker, the scores decreased to 2–5, representing a 44.83% reduction in the average risk level. Improvements were observed in neck, trunk, and wrist posture, indicating enhanced ergonomic alignment and walking stability.
Pelatihan Penggunaan Mesin Pengering Padi Berbasis PLTS untuk Meningkatkan Efisiensi Pascapanen pada Petani di Duyungan Bojonegoro Amalia Ma'rifatul Maghfiroh; Eko Wahyu Abryandoko; Agus Fahmi
INTEGRITAS : Jurnal Pengabdian Vol 10 No 1 (2026): JANUARI - JULI
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat - Universitas Abdurachman Saleh Situbondo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36841/integritas.v10i1.7605

Abstract

Pertanian merupakan sektor utama penopang perekonomian Indonesia, di mana sebagian besar penduduk bekerja sebagai petani. Untuk mendukung produktivitas pertanian, dibutuhkan teknologi tepat guna yang tidak hanya fokus pada tahap budidaya, tetapi juga pada proses pascapanen. Salah satu tahapan penting pascapanen adalah pengeringan padi guna menjaga kualitas gabah agar tetap bagus dan terhindar dari pembusukan akibar jamur. Selama ini, petani di Desa Duyungan masih mengandalkan metode tradisional berupa penjemuran di bawah sinar matahari yang sangat bergantung pada cuaca. Kondisi tersebut sering menimbulkan kerugian, terutama saat musim hujan. Melalui kegiatan pengabdian masyarakat, diperkenalkan inovasi mesin pengering padi tipe bed dryer berbasis Pembangkit Listrik Tenaga Surya (PLTS) dengan kapasitas 500 kg, dilengkapi pengaturan suhu 37–45 °C, dan blower bertenaga PLTS. Penggunaan mesin ini mampu menurunkan kadar air gabah dari 20–26% menjadi sekitar 14% dalam waktu 10–12 jam. Inovasi ini terbukti meningkatkan mutu panen, efisiensi kerja, serta kesejahteraan petani di Desa Duyungan Kecamatan Sukosewu Kabupaten Bojonegoro
Ensemble Machine Learning Models for Accurate Prediction of the Carbon Footprint of SCM-Blended Concrete Yulis Widhiastuti; Eko wahyu Abryandoko; Laily Agustina Rahmawati; Ocha Silvia Kencana; Putri Puja Pratiwi
Engineering Science Letter Vol. 5 No. 02 (2026): In Press - Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002078

Abstract

Concrete contributes approximately 8% of global CO₂ emissions. The incorporation of Supplementary Cementitious Materials (SCMs) as partial cement replacements is widely recognized as an effective strategy to reduce the carbon footprint of concrete. However, accurately quantifying the relationship between mix composition and carbon emissions remains challenging. This study develops a machine learning model to predict the carbon footprint of SCM-based concrete using material composition data. A global dataset comprising 1,456 mix designs collected from 136 publications across 27 countries was compiled, resulting in 1,294 valid samples after preprocessing. Four regression algorithms were evaluated: Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Gradient Boosting Regression (GBR), with hyperparameter tuning using 5-fold cross-validation. All models achieved high predictive accuracy (R² > 0.998), with GBR demonstrating the best performance (R² = 0.9996; RMSE = 1.7452 kg CO₂/m³; MAE = 1.2779 kg CO₂/m³). Feature importance analysis identified cement as the dominant contributor (>99.8%) to emissions. Sensitivity analysis confirmed a strong linear relationship between cement content and CO₂ emissions (~0.82 kg CO₂ per kg cement). These findings support emission-reduction strategies in sustainable concrete design.
Optimization of Concrete Mix Composition Containing Fly Ash and Slag Using a Machine Learning Algorithm for Compressive Strength Prediction Ichwan Hadi Saputra; Eko wahyu Abryandoko; Moh. Nurudduja
Engineering Science Letter Vol. 5 No. 02 (2026): In Press - Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.002300

Abstract

The modern construction industry faces significant challenges in developing sustainable concrete materials while maintaining structural quality requirements. Conventional trial-and-error methods for concrete mix design are time-consuming, costly, and often result in high variability in concrete quality. This study presents an integrated framework that combines machine learning techniques for concrete compressive strength prediction with genetic algorithm optimization to determine optimal mix compositions containing fly ash and blast furnace slag. Two predictive models were developed using the UCI Machine Learning Repository concrete dataset comprising 1,030 samples: Artificial Neural Network (ANN) Ensemble and Support Vector Regression (SVR). The ANN model demonstrated superior performance, achieving R² values ranging from 0.7475 to 0.8372, RMSE values between 6.11 and 7.94 MPa, and classification accuracy of 86.92% for concrete quality categorization across three classes (Class I: <20 MPa, Class II: 20-35 MPa, Class III: >35 MPa). In comparison, the SVR model achieved competitive but slightly lower performance with R² values of 0.7491-0.8378 and classification accuracy of 80.37%. The stability and generalizability of both models were confirmed through five-fold cross-validation. Subsequently, genetic algorithm optimization was applied to determine optimal mix compositions for each quality class while ensuring compliance with Indonesian National Standards (SNI 2847:2019, SNI 2461:2011, and SNI 8297:2016). The optimization process successfully produced concrete mix designs that achieved target compressive strengths of 14.95 MPa for Class I, 27.48 MPa for Class II, and 59.99 MPa for Class III. This framework demonstrates significant potential for developing sustainable concrete with optimal performance while meeting applicable technical standards, thereby contributing to a reduced carbon footprint in the construction industry through strategic utilization of supplementary cementitious materials.
Bridging Controlled Validation and Field Performance: Smartphone-Enabled YOLOv5–ANN for Visual Maturity Classification of Monthong Durian Abryandoko, Eko Wahyu; Ashari, Faisal; Bimananda, Ikhwan Sifa
Industria: Jurnal Teknologi dan Manajemen Agroindustri Vol 15, No 1 (2026): IN PROGRESS
Publisher : Department of Agro-industrial Technology, University of Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21776/ub.industria.2026.015.01.4

Abstract

AbstractAssessment of the ripeness and quality of Durio zibethinus 'Monthong' commonly relies on human experience, making harvesting and sorting susceptible to subjectivity, lighting conditions, observation angles, and differences in interpreting external fruit characteristics. Although nondestructive visual methods have shown high performance under controlled conditions, evidence of smartphone-based applications in field environments remains limited, particularly for spiny fruits with complex surface morphology. This study developed a hybrid YOLOv5–ANN framework to classify the visual ripeness of durian as a proxy for sweetness, where sweet and unsweet labels represent visual ripeness rather than direct sugar-content measurements. A total of 154 smartphone images were processed through data cleaning, resizing, normalization, augmentation, and segmentation before analysis using YOLOv5 for object localization and feature extraction and ANN for binary classification. The model was integrated into a web/smartphone inference system and evaluated using accuracy, precision, recall, and F1-score. Under controlled conditions, the model achieved 92.70% accuracy, whereas smartphone implementation in field conditions achieved 67.50% accuracy with an inference time of 83 ms. Performance declined due to variations in lighting, background, viewing angle, distance, focus, fruit position, and visual similarity. These results demonstrate the potential of the system as a rapid, nondestructive initial sorting tool while highlighting the need to improve field generalization.Keywords: initial sorting, Monthong durian, smartphone inference, visual maturity, YOLOv5–ANN AbstrakPenilaian kematangan dan kualitas durian Monthong bergantung pada pengalaman manusia sehingga hasil panen dan sortasi rentan berubah akibat subjektivitas, variasi pencahayaan, sudut pengamatan, dan perbedaan penafsiran ciri eksternal buah. Pendekatan visual nondestruktif menunjukkan performa tinggi dalam lingkungan terkontrol, sedangkan bukti kinerjanya melalui smartphone pada kondisi lapang masih terbatas, terutama untuk buah berduri dengan morfologi permukaan kompleks. Penelitian ini mengembangkan kerangka hibrida YOLOv5–ANN untuk mengklasifikasikan kematangan visual durian sebagai proksi kemanisan, sedangkan label manis dan tidak manis diperlakukan sebagai proksi yang berkaitan dengan kematangan visual, bukan pengukuran langsung kadar gula. Pembersihan, perubahan ukuran, normalisasi, augmentasi, dan pembagian data dilakukan pada 154 citra smartphone sebelum diproses melalui YOLOv5 sebagai pelokalisasi objek sekaligus ekstraktor ciri dan ANN sebagai pengklasifikasi biner. Model kemudian diintegrasikan ke sistem inferensi web/smartphone dan dievaluasi menggunakan akurasi, presisi, recall, dan F1-score. Evaluasi terkontrol menghasilkan akurasi 92,70%, sedangkan penerapan smartphone mencapai 67,50% dengan waktu inferensi 83 ms. Penurunan performa terjadi saat model menghadapi variasi pencahayaan, latar, sudut, jarak, fokus, posisi buah, dan kemiripan visual antarkelas. Temuan ini menunjukkan potensi sistem sebagai alat bantu sortasi awal yang cepat dan tidak merusak, sekaligus memperlihatkan batas translasi model dari data terkontrol menuju penggunaan nyata dan perlu penguatan generalisasi lapang.Kata kunci: durian Monthong, inferensi smartphone, kematangan visual, sortasi awal, YOLOv5–ANN 
Optimasi Kinerja Excavator Menggunakan Metode Overall Equipment Effectiveness (OEE) Eko Wahyu Abryandoko; Alfi Wijiatin; Nova Nevila Rodhi
Journal of Industrial Engineering and Technology Vol. 6 No. 1 (2025): Desember 2025
Publisher : Universitas Muria Kudus

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

Abstract

Excavator is the main tool in the mining process. If the excavator is damaged, it will affect the mining process, reduce production targets, the cost of repairing damage will be high, and in the end the company will suffer losses. At PT. United Tractors Semen Gresik, based on the company's historical data from January to December 2022, the effectiveness of the Excavator unit in Mine Operation Area 1 (Tuban site) averaged 57%, of the minimum OEE standard set by the company of 75%. So it is necessary to improve to meet company standards. The purpose of this research is to optimize the performance of excavators for limestone and clay mining at PT. United Tractors Semen Gresik. The method approach in this study is carried out by calculating availability, utilization and productivity idex, then the value of overall equipment effectiveness (OEE) can be identified. After obtaining the factors that affect excavator performance, optimization is carried out using fishbone diagram analysis. The results of the research conducted show that the average total value of Overall Equipment Effectiveness (OEE) is 70%, which is still below the OEE standard set by the company, which is 75%. The factors that have the most influence on the low effectiveness of the Excavator unit using the Overall Equipment Effectiveness (OEE) method analysis are the loss time, downtime, and actual productivity factors.
Integrated Prediction Model for Normal and Recycled Aggregate Concrete Strength Using Ensemble Learning Techniques Sujiat; Eko wahyu Abryandoko; Ocha Silvia Kencana; Nayla Farikha Zahra
Journal of Novel Engineering Science and Technology Vol. 5 No. 02 (2026): Journal of Novel Engineering Science and Technology
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/jnest.v5i02.2092

Abstract

Recycled aggregate concrete (RAC) is a sustainable alternative construction material to reduce natural resource exploitation and manage construction and demolition waste. However, predicting the mechanical performance of RAC remains a challenge due to the high variability of recycled aggregate properties. The purpose of this study is to develop a machine learning model to predict the compressive strength of recycled aggregate-based concrete and compare its performance with normal concrete. The dataset used consists of 2165 samples (1600 normal concrete and 565 recycled aggregate concrete) collected from various scientific publications. Three tree-based machine learning algorithms (Random Forest, XGBoost, and LightGBM) were implemented and optimized using RandomizedSearchCV with 5-fold cross-validation. The results showed that LightGBM provided the best performance with R² = 0.92, MAE = 2.45 MPa, and RMSE = 3.52 MPa on the test set. This model is able to predict the compressive strength of normal concrete (R² = 0.92) and recycled aggregate concrete (R² = 0.91) with almost the same accuracy, indicating strong generalization. Feature importance analysis revealed that curing age, cement content, and water content are the most important factors in compressive strength prediction, while for RAC, recycled aggregate water absorption (WRCA) also makes a significant contribution. Error analysis shows that residuals are random and normally distributed without systematic bias. This model can reliably predict concrete compressive strength in the range of 20-60 MPa with an average error of ±3-4 MPa and can be integrated into mix proportioning design software to improve the efficiency of the design process and support the use of sustainable construction materials.
Co-Authors Agus Fahmi Ahmad Alfianul Karim Ahmad Ridho Ramadhani Ahmad Wiji Barokah Ahmad Wiji Barokah Alfi Wijiatin Amalia Ma’rifatul Maghfiroh Ardana Putri Farahdiansari Ary Angga Fachreza ASHARI, FAISAL Bimananda, Ikhwan Sifa Budi Laksono, Galih Faisal Ashari Faisal Ashari Faisal Ashari Ichwan Hadi Saputra Khasanah, Naim Munah khoiriah, miftakhul Laksono, Galih Budi Lukmana, Priagung Maghfiroh, Amalia Ma’rifatu Masahid Masahid meckel rifaldo, mohammad Moh. Nurudduja Moh. Yusuf Dawud Muhammad Rozi Mushthofa Nayla Farikha Zahra Nirwanto, Bambang Nova Nevila Rodhi Novia Pramesti Dwi Cahyani Nungki Dio Febriansa Ocha Silvia Kencana Pamuji, Imam Putri Puja Pratiwi Rahmadan, Eki Agtrin Rahmawati, Laily Agustina Rizky Stighfarrinata Sifa Bimananda, Ikhwan ST.,MT, Suji'at Sudirnanto, Sudirnanto Sujiat Yulis Widhiastuti