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All Journal Biospecies AL KAUNIYAH SPEKTRUM INDUSTRI TEKMAPRO Journal of Industrial Engineering and Management Inspiratif Pendidikan OPERATION EXCELLENCE: Journal of Applied Industrial Engineering PROZIMA (Productivity, Optimization and Manufacturing System Engineering) Jurnal Penelitian Pendidikan IPA (JPPIPA) JATI UNIK : Jurnal Ilmiah Teknik dan Manajemen Industri Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Metode : Jurnal Teknik Industri Jurnal Biologi Tropis Tibuana : Journal of Applied Industrial Engineering Industri Inovatif : Jurnal Teknik Industri Matrik : Jurnal Manajemen dan Teknik Industri Produksi Dedikasi: Jurnal Pengabdian Masyarakat Community Development Journal: Jurnal Pengabdian Masyarakat Indonesian Journal of Law and Economics Review Jurnal Senopati : Sustainability, Ergonomics, Optimization, and Application of Industrial Engineering Indonesian Journal of Innovation Studies BIOMA : Jurnal Biologi dan Pembelajarannya Jurnal Pengabdian Masyarakat Akademisi PELS (Procedia of Engineering and Life Science) Jurnal Teknologi dan Manajemen Industri Terapan Takuana: Jurnal Pendidikan, Sains, dan Humaniora Jurnal Karya Abdi Masyarakat Journal of Fish Health MIMBAR INTEGRITAS Jurnal Pengabdian Masyarakat Pinang Masak Innovative Technologica: Methodical Research Journal Physical Sciences, Life Science and Engineering Journal for Technology and Science IJOT Widya Teknik Academia Open
Tedjo Sukmono
Universitas Muhammadiyah Sidoarjo
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Fusing SVR with PSO Improves E-commerce Sales Prediction with 8.98% MAPE: Penggabungan SVR dengan PSO Meningkatkan Prediksi Penjualan E-commerce dengan MAPE 8,98% Angela, Fitrah Cornellya; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 2 (2024): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i2.1127

Abstract

This study explores the integration of Support Vector Regression (SVR) with Particle Swarm Optimization (PSO) to forecast clothing product sales at Nara Gallery Collection Boutique, addressing the challenge of achieving high forecast accuracy in e-commerce. Through literature review, direct observation, and interviews with a textile SME owner, SVR parameters are optimized using PSO. Results indicate a Mean Absolute Percentage Error (MAPE) value of 8.98% with optimized parameters (C = 34.3642, ε = 0.0110, σ = 0.3677, cLR = 0.1062, λ = 0.0117), enhancing decision-making in inventory management and strategic planning for e-commerce businesses. This research highlights the potential of integrating SVR with PSO for accurate sales forecasting and suggests avenues for further exploration in alternative forecasting methods and optimization techniques. Highlight: Enhanced Forecasting Accuracy: SVR and PSO integration improves e-commerce sales predictions. Parameter Optimization: PSO optimizes SVR parameters, reducing Mean Absolute Percentage Error. Strategic Inventory Management: Accurate forecasts aid in effective e-commerce inventory control. Keywoard: Support Vector Regression, Particle Swarm Optimization, Sales Forecasting, E-commerce, Inventory Management
Indonesia's Breakthrough in Efficient Shellfish Cleaning Transforms Seafood Industry: Terobosan Indonesia dalam Pembersihan Kerang yang Efisien Mengubah Industri Makanan Laut Setiawan, Ari Rio De; Jakaria, Ribangun Bamban; Sari , Indah Apriliana; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 2 (2024): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i2.1133

Abstract

This research aims to design an efficient cleaning tool for bloody cockles to help business actors meet high demand with limited production systems. Conducted in Sidoarjo, data was collected from literature, government reports, and interviews with local business actors, revealing that manual processing of 50 kg of cockles takes 6-8 hours. Using the morphological method, several design concepts were evaluated for maintenance ease, performance, safety, and durability. The selected design (concept 3) showed the highest efficiency with a 37.5% score. This tool is expected to reduce cleaning time and improve outcomes, enhancing productivity and quality in the seafood processing industry. Highlight: Efficient Design: Reduces cleaning time from 6-8 hours significantly. High Demand: Meets increased consumer demand with limited resources. Optimal Concept: Concept 3 excels in maintenance, safety, and efficiency. Keywoard: Bloody Cockles, Cleaning Tool, Morphological Method, Efficiency, Seafood Processing
Machine Learning Predicts Truck Breakdowns in Indonesia with 83% Accuracy: Machine Learning Memprediksi Kerusakan Truk di Indonesia dengan Akurasi 83% Rachman, Meisya Azzahra; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1156

Abstract

PT. Varia Usaha Beton, a cement product company, faces frequent breakdowns of mixer trucks, reducing reliability from the target 90% to 60%. This study aims to predict truck breakdowns using a machine learning model based on the K-NN algorithm within the CRISP-DM framework. Data from the company's maintenance records were cleaned and split into training and testing sets. With k=20, the model achieved 90% accuracy on training data and 83% on testing data. These results can help improve maintenance scheduling and resource planning, enhancing truck reliability. Future research should compare other algorithms and consider different programming environments. Highlights: High Accuracy: K-NN model achieved 90% training and 83% testing accuracy. Maintenance Aid: Improves scheduling and resource planning for truck maintenance. Future Research: Compare algorithms and explore different programming environments. Keywords: Predictive Maintenance, Mixer Trucks, K-NN Algorithm, CRISP-DM, Machine Learning
Unlocking Global Efficiency with Enhanced Milling Machines Worldwide: Membuka Efisiensi Global dengan Mesin Penggilingan Ditingkatkan di Seluruh Dunia Lestari, Wiwik Puji; Wulandari, Indah Apriliana Sari; Sukmono, Tedjo; Jakaria, Ribangun Bamban
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1157

Abstract

This study investigates the effectiveness of six milling machines at PT. INKA, a railroad car manufacturing company, focusing on CNC Plano 133 and Horizontal Milling 142. Utilizing Overall Equipment Effectiveness (OEE) and Age Replacement methods, the research aims to optimize machine performance and propose preventive maintenance strategies. Data spanning from January 2021 to December 2022 were collected and analyzed. Results indicate that both CNC Plano 133 and Horizontal Milling 142 exhibit suboptimal OEE values, primarily due to breakdown losses. Applying Age Replacement within 10 days significantly enhances reliability, with post-replacement reliability reaching 100%. The findings underscore the importance of preventive maintenance in improving milling machine reliability and overall productivity, thereby enhancing competitiveness and sustainability in the manufacturing sector. Highlight: Improved productivity: Preventive maintenance boosts milling machines' reliability and effectiveness. Resource efficiency: Analyzing OEE minimizes downtime, optimizes production output. Competitive edge: Enhanced performance sustains competitiveness, strengthens manufacturing sector's sustainability. Keyword: Milling machines, Overall Equipment Effectiveness (OEE), Preventive maintenance, Age Replacement, Manufacturing industry
Indonesia's Breakthrough in Optimized Yarn Forecasting for Textile Demand Accuracy: Terobosan Indonesia dalam Peramalan Benang yang Dioptimalkan untuk Akurasi Permintaan Tekstil Lindyawati, Lely; Sari W, Indah Apriliana; Cahyana , Atikha Sidhi; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1164

Abstract

PT. XY, a textile company specializing in woven sarongs, faces fluctuating demand during Islamic religious celebrations, impacting production. In Ramadhan 2023, production increased by 30%, but warp yarn availability was insufficient. This study forecasts warp yarn production over twelve periods, comparing Double Exponential Smoothing Holt’s (DES) and Holt-Winter’s Exponential Smoothing (WES) methods, optimized using the golden section method. Using historical data from January 2021 to April 2023, WES with golden section parameters (α1 = 0.67387, β1 = 0.08756, γ2 = 0.85408) achieved the best accuracy with a MAPE of 5.5437%. The WES method is recommended for improving production planning at PT. XY, with future research suggested to explore production correlations and procurement costs. Highlight: Demand Fluctuation: PT. XY experiences significant demand changes during Islamic religious celebrations. Forecasting Methods: Comparing DES and WES methods for predicting warp yarn production. Optimal Accuracy: WES with golden section optimization achieved the lowest MAPE of 5.5437%. Keywoard: Textile Industry, Warp yarn forecasting, Production Planning, Holt-Winter's method, Golden section optimization
Revolutionizing Crystal Guava Production through Six Sigma and Kaizen Insights: Merevolusi Produksi Jambu Kristal melalui Wawasan Six Sigma dan Kaizen Mauli, Fajar Dwi; Cahyana , Atikha Sidhi; Sukmono, Tedjo; Jakaria, Ribangun Bamban
Indonesian Journal of Innovation Studies Vol. 25 No. 3 (2024): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i3.1169

Abstract

This study addresses the quality issues in crystal guava production at UD Bumiaji Sejahtera using the Six Sigma and Kaizen methodologies. By analyzing factors like non-uniform maturity, black spots, fruit rot, shape defects, and seed defects, this research aims to identify key areas for improvement. Results indicate that non-uniform maturity contributes significantly to defects, highlighting the need for improved pest control, efficient application methods, regular maintenance, material inspection, and enhanced standards for farmers and agricultural workers. These findings offer practical insights for enhancing crystal guava production quality and reducing defects at UD Bumiaji Sejahtera. Highlight: Six Sigma and Kaizen for Crystal Guava Quality Enhancement. Non-uniform Maturity Identified as Key Defect Contributor. Recommendations: Improved Pest Control, Application Methods, and Standards for Farmers. Keyword: Crystal Guava Production, Six Sigma, Kaizen, Quality Improvement, Defect Reduction
Markov Method Revolutionizes Paper Industry, Slashing Maintenance Costs Globally: Metode Markov Merevolusi Industri Kertas, Memangkas Biaya Pemeliharaan Secara Global Syaifullah, Dikril Ilham; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 4 (2024): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i4.1176

Abstract

Paper manufacturing industries rely on efficient machinery for production processes, yet neglecting maintenance can lead to substantial costs. This study addresses the maintenance cost optimization challenge in a Pasuruan paper company, where maintenance expenses exceed 20% of the standard. Utilizing the Markov chain method, the research identifies optimal paper machine maintenance costs, achieving a 32% cost reduction. By analyzing data from company documents and literature reviews, specific machine states are identified, recommending overhaul strategies to enhance operational efficiency. The study's implications underscore the method's effectiveness in reducing costs and improving productivity for paper manufacturing industries, offering valuable insights for similar sectors. Highlights: 1. Neglecting maintenance in paper manufacturing leads to substantial costs.2. Markov chain method optimizes paper machine maintenance, reducing expenses.3. Implementation of optimal maintenance strategies significantly enhances financial sustainability. Keywords: Paper manufacturing, Maintenance optimization, Markov chain method, Cost reduction, Operational efficiency.
EOQ Optimization Revolutionizes Inventory Management, Delivering Cost Savings Globally: Optimalisasi EOQ Merevolusi Manajemen Inventaris, Menghasilkan Penghematan Biaya Secara Global Wardhani, Devira Kusuma; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 4 (2024): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i4.1177

Abstract

Efficient inventory management is crucial for companies to minimize costs while ensuring adequate supply to meet demand. PT Samawa Tirta Alam, a bottled sweet drinking water company, faces significant inventory management issues, notably with refined sugar raw materials. High transportation costs and fluctuating demand contribute to excessive inventory accumulation and increased storage costs, necessitating an optimization strategy. This study implements the Economic Order Quantity (EOQ) method to address these challenges. Through a mixed-method approach, qualitative data were gathered via observations and interviews, while quantitative analysis utilized historical data to calculate optimal order quantities, frequencies, safety stock levels, and reorder points. Results demonstrate substantial cost savings, with optimal order quantities determined, reducing order frequencies from 12 to 2 times per year, and minimizing safety stock levels. The findings underscore the effectiveness of the EOQ method in enhancing cost-effectiveness and operational performance in raw material inventory management for similar companies. Highlights: 1. Cost Efficiency: EOQ method reduces total inventory costs effectively.2. Operational Precision: Minimizing safety stock enhances operational efficiency.3. Strategic Insight: Integrated approach provides practical solutions for inventory management challenges. Keywords: Inventory Management, Economic Order Quantity (EOQ), Cost Optimization, Raw Materials, Transportation Costs
Markov Chains Slash Inventory Costs in Indonesia: Rantai Markov Memangkas Biaya Persediaan di Indonesia Mishani, Adinda Chamilia; Sukmono, Tedjo
Indonesian Journal of Innovation Studies Vol. 25 No. 4 (2024): October
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v25i4.1184

Abstract

PT. Varia Usaha Beton, facing an 8% decline in 2022 targets due to uncontrolled inventory and a 15,150-ton overstock of sand at its BSP Sayung plant, needed an effective solution. This study applied a Markov chain model with perfect enumeration to optimize sand inventory management. Qualitative data from observations, expert interviews, and company records (2021-2022) and quantitative analysis identified Policy 58 as optimal, reducing excess costs to Rp. 32,463,360 monthly. Implementing improvements in specific overstock states (3, 4, 5, and 6) provides a strategic approach to minimize sand overstock and associated costs. Highlight: Inventory Decline: 8% target drop due to uncontrolled inventory in 2022. Optimal Policy: Policy 58 reduced monthly excess costs to Rp. 32,463,360. Strategic Improvements: Implement changes in states 3, 4, 5, and 6 to manage overstock. Keywoard: Inventory Control, Markov Chain, Perfect Enumeration, Overstock Management, Construction Materials
The Role of Traditional Agroforest (Cinnamamon burmanii) to The Bird Conservation In Kerinci Seblat National Park. Sartika, Dani; Subagyo, Agus; Sukmono, Tedjo
Biospecies Vol. 2 No. 2 (2009): Juni 2009
Publisher : Universitas Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22437/biospecies.v2i2.256

Abstract

We undertook a research about the role of cinnamon agroforest in conserving bird inKerinci Seblat National Park. To observe the bird we used method of Indice Poctuale De'Abundance,whereas for the bird identification we employed some methods developed by Mckinnon ( 2001) andKing and Dickison (1993). As a control we also carried out the similar observation in the closed byprimary and secondary forest. The result shows that the bird diversity in cinnamon agroforest is notsignificant compare to that in primary and secondary forest (p>0.05). The index of bird diversity ( H') incinnamon agroforest and secondary forest is in medium class (3.3 and 3.49) while in the primary forestthe index is high (3.50).
Co-Authors Abdul Rohman Abdul Rohman Adistyas Nastiti, Octavia Afreni Hamidah Afrilia, Riska Agus Subagyo Ahmad Fikri Ardianto Alfian Fajar Gunawan Ali Mashar Ali Sadikin Amatullah, Dhiny Angela, Fitrah Cornellya Apriliana Sari Wulandari, Indah Arba, Risqi Mutia Asni Johari Azhari, Asri Bambang Haryadi Benedika Ferdian Hutabarat Boy Isma Putra Cahyana , Atikha Sidhi Cahyaningurm, Rista Dwi Dani Sartika Dawam Suprayogi Diwanti Faradiba, Nabila Dristiana, Fila Dwi Kakung Saputro Dzati Fauziyah Erwin Widiantono Faradiba, Vanisa Reyhan Fikrianto, Muhammad Hafid Fila Dristiana Gusti Nurina Azhariani Hadian, Mohammad Ekki Hafizah, Mutia Hana Catur Wahyuni Harlis Harlis Hartanti, Lusia Permata Sari Hery Murnawan, Hery Ihsan, Mahya Jamaluddin Jamaluddin Khairatinisa, Khairatinisa Krisna Risky Putra Irawan Leksono, Rudy Bowo Lestari, Wiwik Puji Lindyawati, Lely M.Haris Efendi Hsb Mardhotillah, Bunga Marodiyah, Inggit Mauli, Fajar Dwi Mishani, Adinda Chamilia Mochammad Imam Mashuri Mohammad Buchori Much Syafiudin Muhammad Arizki Zainul Ramadhan Muhammad Dio Dwi Septian Mukhammad Rifky Ramadhan Mukhammad Surya Lesmana Muswita Muswita Nafis Khumaidah Natalia, Desfaur Naufalut Tharif Qurniawan Nazifa, Boti Iffa Novi Prastyanda Putra Pratama Nugraha, A. Prima Nugroho, Dizsa Arliansyah Nurma M. Hidayatulloh Octavia Adistyas Nastiti Pangestu, Retno Putra, Tri Syukria Putri, Andini Faizatul Putri, Melinda Aprilia Rachman, Meisya Azzahra Radiana Atika Sari Rany Riyantati, Dena Rasyid, Mohammad Andi Ribangun Bamban Jakaria Rizky Janatul Magwa Rudy Bowo Leksono Salsabila, Nisrina SANDI KURNIAWAN Sanjaya, Muhammad Erick Saputra, Nur Qomaruddin Sari , Indah Apriliana Sari W, Indah Apriliana Sari Wulandari, Indah Apriliana Setiawan, Ardhi Wahyu Setiawan, Ari Rio De Sigit Wahono sisiliani, fitria trisna Sofillauny, Zahara Suhadak Sulistiono Suzanti, Sriliah Syaifullah, Dikril Ilham Tia Wulandari Upik Yelianti Utomo, Pradita Eko Prasetyo Varid Jainuri Wahono, Sigit Wahyu Nugroho Wahyu Setiawan, Ardhi Wardhani, Devira Kusuma Wawan Kurniawan Wijatmiko, Erie Fadma Noer Fitriana Winda Dwi Kartika, Winda Dwi Wiwik Sulistiyowati Wulandari, Indah Apriliana Sari Yoppie Wulanda Yusuf Effri Prastyo Budi Zurweni Zurweni