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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Teknik Industri ASEAN Journal of Systems Engineering J@TI (TEKNIK INDUSTRI) Jurnal Rekavasi SITEKIN: Jurnal Sains, Teknologi dan Industri JURNAL PASTI (PENELITIAN DAN APLIKASI SISTEM DAN TEKNIK INDUSTRI) Jurnal Optimasi Sistem Industri Jurnal Ilmiah Teknik Industri JOURNAL OF INDUSTRIAL AND MANUFACTURE ENGINEERING Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri International Journal of Advances in Data and Information Systems Jurnal Media Teknik dan Sistem Industri Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Optimasi Teknik Industri (JOTI) Industrika : Jurnal Ilmiah Teknik Industri Go-Integratif : Jurnal Teknik Sistem dan Industri Green Intelligent Systems and Applications Journal of Industrial Engineering and Halal Industries Spektrum Industri IJoICT (International Journal on Information and Communication Technology)
Achmad Pratama Rifai
Department Of Mechanical And Industrial Engineering, Universitas Gadjah Mada
Religion Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Transportation Other

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Simulated Annealing untuk Perancangan Tata Letak Industri Furniture dengan Model Single dan Double Row Layout Kusumaningsih, Devita Ayuni; Azim, Ahmad Fadhil; Albab, Disya Amalia Ikhsani Ulil; Hans, Feishal Rey; Korin, Filbert; Pohan, Rafi Naufal Al Mochtari; Ananta, Vhysnu Satya; Rifai, Achmad Pratama
Jurnal Media Teknik dan Sistem Industri Vol 6, No 1 (2022)
Publisher : Universitas Suryakancana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/jmtsi.v6i1.1773

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are promising contributors to national economic growth. One of the significant components in a business is the layout  aspect of production facilities. This study aims to produce an optimal layout  for an MSME engaged in furniture production, namely UD Bazooka in Yogyakarta. Currently, the layout  type used by UD Bazooka is a process layout  to produce a large variety of products. The use of simple process layout  affects on inefficient material movement, thus causing longer travelled distance and higher material handling cost as relative to the total production cost. Therefore, this research aims to improve the layout  of UD Bazooka with minimizing total travelled distance as the objective. The data collection is performed by observing the production line at UD Bazooka which will be translated into relationship data or REL charts, from-to-charts, and space requirements. In this study, the layout  of the facility is modelled into single and double-row layout  problems. This study developed a Simulated Annealing (SA) algorithm to solve the two models. Furthermore, Modified Spanning Tree (MST) is used as a benchmark method. To evaluate the best layout , we use total travelled distance and adjacency weight as the objective function. The results showed that the best layout  with minimum total travelled distance was the output of the double row layout  model solved using the Simulated Annealing. Usaha Mikro Kecil dan Menengah (UMKM) merupakan kontributor yang cukup signifikan dalam pertumbuhan ekonomi suatu negara. Salah satu komponen penting dalam sebuah bisnis adalah aspek tata letak fasilitas produksi. Penelitian ini bertujuan untuk menghasilkan tata letak optimal sebuah UMKM yang bergerak dalam bidang produksi furniture yaitu UD Bazooka yang berlokasi di Yogyakarta. Jenis tata letak yang digunakan oleh UD Bazooka saat ini adalah tipe process layout . Penggunaan tata letak fasilitas berupa process layout  yang sederhana menyebabkan perpindahan material dan komponen yang tidak efisien, sehingga menyebabkan waktu dan biaya perpindahan yang relatif tinggi dibandingkan dengan total biaya produksi. Dengan demikian, penelitian ini melakukan perancangan ulang tata letak fasilitas dengan tujuan meminimasi total travelled distance. Data diperoleh dengan mengobservasi lini produksi UD Bazooka yang kemudian akan diterjemahkan dalam data relationship atau REL chart, from-to-chart, dan space requirement. Pada penelitian ini, tata letak fasilitas dimodelkan menjadi single dan double row layout  problem. Penelitian ini mengembangkan algoritma Simulated Annealing (SA) untuk menyelesaikan kedua model tersebut. Selanjutnya, Modified Spanning Tree (MST) digunakan sebagai metode pembanding. Untuk mengevaluasi tata letak terbaik, digunakan total traveled distance dan adjacency weight sebagai fungsi tujuan. Hasil eksperimen menunjukkan bahwa layout  terbaik dengan total travelled distance paling rendah adalah output model double row layout  yang diselesaikan dengan Simulated Annealing.
Facility Layout Planning of Sheet Metal Working Industry Using Metaheuristics Ludwika, Adinda Sekar; Shalehah, Mar’atus; Mohamad, Rakan Raihan Ali; Oktavia, Andiny Trie; Normasari, Nur Mayke Eka; Tho, Nguyen Huu; Rifai, Achmad Pratama
Spektrum Industri Vol. 22 No. 2 (2024): Spektrum Industri - October 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/si.v22i2.141

Abstract

The design of facility layout in a production floor determines the level of effectiveness and efficiency of the production process. Errors in arranging the layout in the production floor can disrupt the continuity of the production process and prevent optimal results. Production activities that occur over a long period of time make any inaccuracies in layout planning result in significant losses. In companies with Job Shop production type, which is characterized by identical products and varied processes, the production flow changes with each product made. Based on these issues, this research aims to optimize the layout in a company engaged in sheet metal working industry using metaheuristic algorithms such as Simulated Annealing (SA), Large Neighborhood Search (LNS), Adaptive Large Neighborhood Search (ALNS), and Ant Colony Optimization (ACO). The best total distance results were obtained by the SA, LNS, and ALNS algorithms, with a total travelled distance of 897,171 meters and a facility arrangement of 7-5-6-4-3-2-1 or 1-2-3-4-6-5-7. Additionally, considering computation time, the SA algorithm is the best choice as it has the fastest computation time compared to other algorithms.
Classification of single origin Indonesian coffee beans using convolutional neural network Rifai, Achmad Pratama; Sari, Wangi Pandan; Rabbani, Haidar; Safitri, Tari Hardiani; Hajad, Makbul; Sutoyo, Edi; Nguyen, Huu-Tho
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5140-5156

Abstract

This research aims to develop a coffee bean type detection model using convolutional neural networks (CNN), leveraging a dataset of 14,525 images from 116 types of Indonesian coffee beans. Pre-processing steps including resizing, rescaling, and augmentation were applied to improve the dataset quality. The dataset was split into training, validation, and testing sets with proportions of 80%, 10%, and 10%, respectively. Two model development approaches were used: transfer learning with Inception V3 in two scenarios and a model built from scratch. The transfer learning Inception V3 model in scenario 1 achieved the best performance, with a test accuracy of 0.87 and optimal evaluation metrics across precision, recall, and F1-score. This model was fine-tuned using pretrained weights, allowing it to adapt effectively to the coffee bean dataset. The results highlight that transfer learning, especially with Inception V3, provides a robust method for classifying coffee beans, offering potential applications in the coffee industry for improving classification efficiency and accuracy. The study demonstrates how deep learning can enhance the objectivity and precision of coffee bean classification, contributing to greater consistency in product sorting and quality assessment.
A Multi-Objective Double Row Layout Problem Considering Safety Distances and Geometrical Constraints Achmad Pratama Rifai; Setyo Tri Windras Mara; Rachmadi Norcahyo; Andri Nasution
Jurnal Optimasi Sistem Industri Vol. 24 No. 2 (2025): Published in December 2025
Publisher : The Industrial Engineering Department of Engineering Faculty at Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (621.938 KB) | DOI: 10.25077/josi.v24.n2.p174-197.2025

Abstract

This study addresses an enhanced version of the Double Row Layout Problem (DRLP) by incorporating two critical constraints: minimum safety distances between machines and geometric limitations on row lengths. A bi-objective mixed-integer non-linear programming (MINLP) model is formulated to simultaneously minimize material handling costs and penalties for violating safety distance requirements. To solve the problem efficiently, a novel metaheuristic called Improved Multi-Objective Variable Neighborhood Search (IMOVNS) is proposed. IMOVNS extends the standard MOVNS by integrating an adaptive archive update strategy and a probabilistic acceptance mechanism inspired by AMOSA, thereby improving both convergence and diversity in Pareto front generation. This study contributes to the layout optimization literature by proposing a tailored MOVNS variant explicitly designed for safety-aware and geometry-constrained DRLP, a challenging problem variant that has received limited attention in prior research. Extensive experiments on 27 DRLP instances show that IMOVNS demonstrates strong performance, significantly outperforming NSGA-II and showing competitive or superior results compared to AMOSA and MOVNS in terms of convergence and solution diversity. Statistical tests further confirm the significant superiority of IMOVNS, particularly over NSGA-II. Additionally, a key managerial insight reveals that layouts with unbalanced row lengths favour safety compliance, while balanced layouts minimize material handling costs. The Pareto-optimal solutions generated by IMOVNS enable decision-makers to select layout configurations that align with specific operational priorities. These findings highlight the practical relevance and robustness of IMOVNS in solving real-world multi-objective facility layout problems under complex spatial and safety constraints.
Optimizing Vehicle Routing for Perishable Products with Time Window Constraints: : A Case Study on Bread Distribution Fauzi, Rifqi; Priansyah, Adi; Puspadewa, Paskalis Krisna; Awal, Syifa Maulvi Zainun; Nguyen , Huu Tho; Rifai, Achmad Pratama
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 27 No. 1 (2025): June 2025
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.27.1.1-20

Abstract

This research investigates the application of optimization methods to the Capacitated Vehicle Routing Problem with Time Windows in the context of bread distribution for the efficiency of different approach for managing large-scale goods delivery. Managing this distribution requires considering complexities such as travel distance, vehicle capacity, and time windows. Specifically, it compares the performance of ALNS, SA, and GA in minimizing total travel distance while adhering to strict delivery windows. The research is conducted across different cases, each distinguished by varying levels of demand, nodes, and time windows for each case. Based on four cases, ALNS is the most effective method among the three methods in optimizing bread distribution. It was averagely 33.02% more efficient than SA and 57.21% than GA for minimizing travel distance and offering a robust solution, improving delivery efficiency across different case scenarios.
A Genetic Algorithm for the Double Row Layout Problem Rifai, Achmad Pratama; Windras Mara, Setyo Tri; Kusumastuti, Putri Adriani; Wiraningrum, Rakyan Galuh
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 22 No. 2 (2020): December 2020
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.22.2.85-92

Abstract

The double row layout problem (DRLP) is an NP-hard and has many applications in the industry. The problem concerns on arranging the position of  machines on the two rows so that the material handling cost is minimized. Although several mathematical programming models and local heuristics have been previously proposed, there is still a requirement to develop an approach that can solve the problem efficiently. Here, a genetic algorithm is proposed, which is aimed to solve the DRLP in a reasonable and applicable time. The performances of the proposed method, both its obtained objective values and computational time, are evaluated by comparing it with the existing mathematical programming model. The results demonstrate that the proposed GA can find relatively high-quality solutions in much shorter time than the mathematical programming model, especially in the problem with large number of machines.
Tool Condition Monitoring with Convolutional Neural Network for Milling Tools and Turning Inserts Rifai, Achmad Pratama; Briliananda, Silvyaniza; Aoyama, Hideki
Jurnal Teknik Industri: Jurnal Keilmuan dan Aplikasi Teknik Industri Vol. 25 No. 1 (2023): June 2023
Publisher : Institute of Research and Community Outreach - Petra Christian University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9744/jti.25.1.1-16

Abstract

Tool wear is one of the cost drivers in the manufacturing industry because it directly affects the quality of the manufactured workpiece and production efficiency. Identifying the right time to replace the cutting tool is a challenge. If the tool is replaced too soon, the production time can be disrupted, causing unscheduled downtime. Conversely, if it is replaced too late, there will be an additional cost to replace raw materials damaged by broken tools. Therefore, researchers continue to develop tool condition monitoring (TCM) methods to analyze tool wear. A recent popular method is machine vision with convolutional neural networks (CNN). The present research aims to develop classification models that can categorize the image data of milling and turning inserts into GO (suitable for use) and NO GO (not suitable for use). Two approaches are selected for the modeling process, custom learning and transfer learning, with image data input from smartphones and microscope cameras. The experimental results show that the best model is the transfer learning approach using Inception-V3 architecture with a smartphone image. The model reaches 92.2% accuracy, hence demonstrating a relatively good performance in determining whether the tool is suitable for use or not.
MULTI-OBJECTIVE OPTIMIZATION OF NON-IDENTICAL PARALLEL MACHINE SCHEDULING FOR MINIMIZING MAKESPAN, CARBON EMISSIONS, AND TARDINESS Ramadhan, Fairuzzaky; Rifai, Achmad Pratama
ASEAN Journal of Systems Engineering Vol 10, No 1 (2026): ASEAN Journal of System Engineering (in progress)
Publisher : Master in Systems Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ajse.v10i1.115408

Abstract

Production scheduling in modern manufacturing must simultaneously address operational efficiency, environmental sustainability, and customer satisfaction. As industries face increasing pressure to reduce carbon emissions while maintaining competitiveness, traditional single-objective scheduling approaches prove insufficient. Non-identical parallel machine scheduling problem (NIPMSP), where machines possess heterogeneous processing capabilities and environmental impacts, represents a prevalent configuration in manufacturing facilities. However, existing research has not simultaneously optimized makespan, carbon emissions, and tardiness—three critical objectives reflecting productivity, sustainability, and service reliability.In this study, we developed a comprehensive mathematical model with 18 constraints and proposed Multi-Objective Adaptive Large Neighborhood Search (MOALNS) incorporating five destroy operators, four repair operators, and adaptive weight mechanisms. Performance evaluation against NSGA-II across eight problem instances using five metrics (Hypervolume, Inverted Generational Distance, Diversification Metric, Spacing Metric, CPU Time) with rigorous statistical testing (Wilcoxon signed-rank test, Cohen's d) revealed that MOALNS significantly outperforms NSGA-II in solution diversity (p < 0.05 in 50% of instances, d = 6.31) and spacing uniformity (75% win rate, d = 0.70), with comparable Pareto front coverage. This superiority comes at computational cost (192% slower), acceptable for offline planning but not real-time applications. This research provides evidence-based algorithm selection guidelines for sustainable manufacturing, demonstrating that environmental objectives can be integrated without sacrificing operational efficiency or delivery reliability.
Real-Time Coffee Bean Defect Detection Based on SNI 01-2907-2008 Standards Using Lightweight YOLOv5s Architecture Nanda Aptana Irsyadul Bahy; Achmad Pratama Rifai
IJoICT (International Journal on Information and Communication Technology) Vol. 12 No. 1 (2026): Vol.12 No.1 Jun 2026
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v12i1.10584

Abstract

Physical quality control of coffee beans in Indonesia, which relies heavily on manual sorting methods, has limitations in terms of consistency, time efficiency, and subjective observation. This study aims to develop an automated system for detecting physical defects in coffee beans using a Deep Learning-based Object Detection algorithm with the YOLOv5s architecture, aligned with SNI 01-2907-2008 regulations. Through a training mechanism that combined hyperparameter optimization and data augmentation across 20 defect classification classes, the developed model achieved robust generalization performance, with a global mAP@0.5 of 0.867 and a mAP@0.5-95 of 0.601. A prediction time evaluation of average 14 ms confirmed the model's capability for real-time sorting system implementation. However, a per-class metric decomposition analysis revealed performance heterogeneity, where the model showed superior accuracy on distinctive morphological features but struggled with texture bias and contrast ambiguity in minor defect classes and foreign materials. Crucially, the model's low computational demand presents a cost-effective solution for Small and Medium-sized Enterprises (SMEs), enabling the adoption of automated quality control without the need for expensive high-end hardware infrastructure. Overall, this study validates the effectiveness of YOLOv5 as an objective and standardized digital inspection instrument to support the modernization of the national coffee industry's post-harvest process.
Overcoming Data Imbalance in Risk Management: A Comparative Study of Sampling Methods Arya Wijna Astungkara; Achmad Pratama Rifai
JTI: Jurnal Teknik Industri Vol 11 No 1 (2025): JUNI 2025
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/jti.v11i1.37368

Abstract

Data imbalance is a significant challenge in risk management, especially in classification tasks where critical events—such as loan defaults, employee attrition, or company bankruptcy—occur less frequently than normal cases. This paper presents a comparative study of eight sampling methods—Random Undersampling (RUS), Random Oversampling (ROS), Edited Nearest Neighbor (ENN), One-Sided Selection (OSS), SMOTE, ADASYN, SMOTEENN, and SMOTETomek—across three imbalanced datasets: Taiwanese Bankruptcy Prediction, IBM HR Analytics Employee Attrition, and Loan Prediction. Using eight machine learning classifiers, the study evaluates performance using F1 Score and Negative Predictive Value (NPV), two metrics suited for imbalanced data. The results reveal that ENN achieves the highest F1 scores in high-dimensional and severely imbalanced datasets, while SMOTE-based methods perform best in large-scale datasets with moderate imbalance. Notably, RUS consistently delivers the highest NPV, highlighting its effectiveness in minimizing false negatives and supporting conservative decision-making. The findings underscore the importance of aligning sampling strategies with dataset characteristics and specific risk management objectives.
Co-Authors Afrido Ainayyah Bintang Agista Akbar, Hafizh Naufaly Al Kautsar, M. Nurudduja Albab, Disya Amalia Ikhsani Ulil Aldyno, Achmad Farhan Alfarasyied Syahrizad Amirah Meutia Noorfadila Ananta, Vhysnu Satya Andiny Trie Oktavia Andri Nasution Anom , Mauli Ardyaksa Diptya Pramudita Arista Adriani Armaisya, Dimas Dwi Arulloh Sonja Arya Wijna Astungkara Asa Pragasel Natuna Asfandima, Ilhan Alim Astungkatara, Arya Wijna Awal, Syifa Maulvi Zainun Azim, Ahmad Fadhil Basirun, Arif Reza Briliananda, Silvyaniza Buchari, Muhammad Achirudin Dawi Karomati Baroroh Devita Ayuni Kusumaningsih Evan Alvaro Radeva Fadilah, Andara Fahreza Baskara Hediandra Fath, Hamzah Fatiha Widyanti Fauzi, Rifqi Fransisca Astri Dianswari Gopal Sakarkar Hajad, Makbul Hans Bastian Wangsa Hans, Feishal Rey Haposan Vincentius Manalu Hartanti, Sri Hasibuan, Narsico Rafael Hideki Aoyama Hikam , Azka Huu Tho, Nguyen Ihsan Ramadhana Jordiva Fernanda Junaidi, Faiza Ulinnuha Kafi, Mochamad Egidio Pramudya Khania O.P.P. Nugraha Korin, Filbert Kusumaningsih, Devita Ayuni Kusumastuti, Putri Adriani Ludwika, Adinda Sekar Mohamad, Rakan Raihan Ali Muchammad Ismail Muhammad, Audi Ziyad Afkar Muhtar , Dini Nanda Aptana Irsyadul Bahy Nandila, Alisyafira Sayyidina Naufal Nur Akmal Nguyen , Huu Tho Nguyen, Huu Tho Nguyen, Huu-Tho Nur Mayke Eka Normasari Nurraudah, Restu Oda, Ahlam Nauf Oktavia, Andiny Trie Pamungkasari, Panca Dewi Penchala, Sathish Kumar Permana, Ari Pohan, Rafi Naufal Al Mochtari Pratama, Dhika Wahyu Priansyah, Adi Prihatmaja, Dhonadio Aurell Azhar Puspadewa, Paskalis Krisna Puspitasari, Afifa Putri, Oktaviana Rabbani, Haidar Rachmadi Norcahyo Radhitya Virya Paramasuri Sunarso Rahmawatie, Noor Athiea Ramadhan, Fairuzzaky Safira, Aretha Safitri, Tari Hardiani Saifurrahman, Anas Saptomo, Amanat Bintang Sari Ningsih Sari, Dwi Kumala Sarudi As., L. M. Setiawan, Kevin Stephen Setyo Tri Windras Mara Shalehah, Mar’atus Sholihati, Ira Diana Susilo, Nazhifa Rahmi Sutoyo, Edi Syahdan Haris Abdilah Tama, Mradipta Nindya Tanaji, Irvantara Pradmaputra Thawafani, Lathiifah Tho, Nguyen Huu Valencia, Bella Renata Violita Anggraini Wangi Pandan Sari Wibisono, Ragil Aditya Windras Mara, Setyo Tri Wiraningrum, Rakyan Galuh Yana, Anak Agung Istri Anindita Nanda