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All Journal Jurnal Teknoin OPSI Seminar Nasional Teknik Kimia Kejuangan Heuristic Jurnal Sistem dan Manajemen Industri Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri INTECOMS: Journal of Information Technology and Computer Science Journal of Appropriate Technology for Community Services Enrichment : Journal of Management International Journal of Industrial Optimization (IJIO) Dinamika Kerajinan dan Batik : MAJALAH ILMIAH International Journal of Computer and Information System (IJCIS) International Journal of Multidisciplinary Research and Literature (IJOMRAL) Journal Science Innovation and Technology (SINTECH)
Muhammad Ridwan Andi Purnomo
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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 Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Transportation Other

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Penggunaan Teknik Analisis Data Deep Learning dalam Pengoptimalan Pemeliharaan Terrencana Berkapasitas Purnomo, Muhammad Ridwan Andi
Jurnal Sistem dan Manajemen Industri Vol. 6 No. 2 (2022): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v6i2.5076

Abstract

Manufacturing systems must be supported by the availability of materials, a streamlined production process and a prepared production line to achieve the production target. In a mass customization manufacturing system, the number of machines required for customization is relatively small. Conse-quently, maintenance on critical machines will impact this manufacturing system the most. Two types of maintenance strategies are implemented: corrective and preventive maintenance. The corrective maintenance requires more resources since the time and cost to repair the breakdown machine will be higher due to fatal failure. For the management to consider preventive maintenance while the binding machines are still operational, it must be equipped with a deep analysis demonstrating that fewer resources will be required. This paper discusses two deep analyses: accurate prediction of the binding machines' breakdown based on Mean Time Between Failure (MTBF) data using a deep learning data analytics technique and optimizing the maintenance total cost in the available capacitated time. The findings and results of this paper show that the proposed deep learning data analytics technique can increase the MTBF prediction accuracy by up to 66.12% and reduce the total maintenance cost by up to 4% compared with the original model.
Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment Purnomo, Muhammad Ridwan Andi
Jurnal Sistem dan Manajemen Industri Vol. 7 No. 2 (2023): December
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v7i2.6495

Abstract

Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.
Intelligent optimisation for multi-objectives flexible manufacturing cells formation Purnomo, Muhammad Ridwan Andi; Widodo, Imam Djati; Zukhri, Zainudin
Jurnal Sistem dan Manajemen Industri Vol. 8 No. 1 (2024): June
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsmi.v8i1.7974

Abstract

The primary objective of conventional manufacturing cell formation typically uses grouping efficiency and efficacy measurement to reduce voids and exceptional parts. This objective frequently leads to extreme solutions, such as the persistently significant workload disparity among the manu­facturing cells. It will have a detrimental psychological impact on operators who work in each formed manufacturing cell. The complexity of the problem increases when there is a requirement to finish all parts before the midday break, at which point the formed manufacturing cells can proceed with the following production batch after the break. This research examines the formation of manufacturing cells using two widely recognized intelligent optimization techniques: genetic algorithm (G.A.) and particle swarm optimisation (PSO). The discussed manufacturing system has flexible machines, allowing each part to have multiple production routing options. The optimisation process involved addressing four simultaneous objectives: enhancing the efficiency and efficacy of the manufacturing cells, minimizing the deviation of manufacturing cells working time with the allocated working hours, which is prior to the midday break, and ensuring a balanced workload for the formed manufacturing cells. The optimisation results demonstrate that the G.A. outperforms the PSO method and is capable of providing manufacturing cell formation solutions with an efficiency level of 0.86, efficacy level as high as 0.64, achieving a minimum lateness of only 24 minutes from the completion target before midday break and a maximum difference in workload as low as 49 minutes.
Peningkatan Produktivitas UMKM Kuliner dengan Pendekatan Sustainable Human Resource Management Berbasis Sustainability Employee Life Cycle: Peningkatan Produktivitas UMKM Kuliner dengan Pendekatan Sustainable Human Resource Management Berbasis Sustainability Employee Life Cycle Utami, Indah Wahyu; Purnomo, Muhammad Ridwan Andi
Journal Science Innovation and Technology (SINTECH) Vol. 6 No. 1 (2025): SINTECH JURNAL BULAN NOVEMBER 2025
Publisher : Universitas Duta Bangsa Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47701/s76aw988

Abstract

Micro, Small, and Medium Enterprises (MSMEs) are a key pillar in supporting regional economic growth, but they often face serious challenges in managing human resources (HR) sustainably to maintain productivity stability. Difficulties in retaining quality HRM have caused culinary MSMEs in Surakarta City to frequently face challenges in employee turnover and retention due to a lack of incentives and limited training. Culinary MSMEs have not been fully able to implement sustainable human resource practices. Many culinary MSMEs still face limitations in the use of environmentally friendly raw materials, waste management, and energy efficiency. The dilemma between needs and profitability causes additional costs. This study examines the application of Sustainable Human Resource Management (SHRM) with a Sustainability Employee Life Cycle (SELC) approach in increasing the productivity of Micro, Small, and Medium Enterprises (MSMEs) in the culinary sector in Surakarta City. Using a mixed methods design, specifically Structural Equation Modeling (SEM) and Data Envelopment Analysis (DEA), on a sample of 60 culinary SME actors in Surakarta City. This study assesses the relationship between green recruitment practices, continuous training, value-based performance evaluation, and retention strategies on operational efficiency and human resource performance. The main dimensions analyzed in this model include green recruitment practices, continuous training, value-based evaluation, and employee retention strategies. The results show that the implementation of SHRM integrated with SELC can improve the performance of culinary MSMEs in line with the targets of each factor that supports sustainable productivity.
Co-Authors Agham Satria Pristiwaji Agus Haerudin Agus Haerudin Agus Agus Mansur, Agus Alhabsyi, Feni Silvani P Ali Parkhan Apriani, Ratna Agil Basuki, Demas Emirbuwono Dhuha Khanif Rizky Dian Janari Digdoyo Oktapriandi Febriani, Atik Imam Djati Widodo Kirana, Nabila Lintang Mei Andrianto Meilinda Fitriani Nur Maghfiroh Nashrullah Setiawan Novrianty Rizky Rifa'i Rifa'i Rosyid, Yaqub Kasuma Saputra, Rafly Galih Saputro, Iswoyo Seno Sholeh Ma’mun Sri Indrawati Taufiq Immawan, Taufiq Utami, Indah Wahyu Wahyu Sidiq Saputra Widodo, Imam Djati Zainudin Zukhri