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All Journal Jurnal Teknologi Industri Pertanian Jurnal Energi Dan Manufaktur Jurnal Rekayasa Sistem Industri Industrial and Systems Engineering Assessment Journal (INASEA) Agrointek Sinergi JISI UMJ (Jurnal Integrasi Sistem Industri UMJ) Jurnal Teknologi Pertanian Jurnal Ilmiah Teknik Industri Jurnal Industri Hasil Perkebunan OPERATION EXCELLENCE: Journal of Applied Industrial Engineering JRSI (Jurnal Rekayasa Sistem dan Industri) Jurnal Teknik Industri : Jurnal Hasil Penelitian dan Karya Ilmiah dalam Bidang Teknik Industri Jurnal Pengabdian Masyarakat AbdiMas Jurnal Ilmiah Teknik Industri Jurnal Sistem Teknik Industri JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Advances in Food Science, Sustainable Agriculture and Agroindustrial Engineering (AFSSAAE) Al-Kharaj: Journal of Islamic Economic and Business Jurnal Pengabdian Masyarakat Teknik Jurnal Kepariwisataan Indonesia: Jurnal Penelitian dan Pengembangan Kepariwisataan Indonesia Jurnal Pengabdian Masyarakat Indonesia Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Eduvest - Journal of Universal Studies Metris: Jurnal Sains dan Teknologi Aerospace Engineering Journal of Technology and System Information
Iphov Kumala Sriwana
Universitas Telkom
Aerospace Engineering 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 Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Transportation

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Journal : Sinergi

 1 
HANDLING OF COAL DUST AT COAL HANDLING FACILITY IN COAL POWER PLANT USING SOFT SYSTEM METHODOLOGY (SSM) APPROACH Akhyar Zuniawan; Iphov Kumala Sriwana
SINERGI Vol 23, No 3 (2019)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (471.04 KB) | DOI: 10.22441/sinergi.2019.3.006

Abstract

Currently, many coal-fired powers plants are built to supply electrical energy needs in Indonesia due to relatively inexpensive raw materials and abundant in Indonesia. Handling of coal is mostly done at the power plant using coal handling facilities consisting of ship unloaders, conveyor belts, stock piles, silos or bunkers. The problem that arises in the coal handling facility is dust from coal that fells or hovers in the air so that it can interfere with the environment and health both for workers in the Coal Power and residents around the Coal Power. The purpose of writing this paper is to eliminate the spread of coal dust that arises due to coal handling equipment that is not precise and imperfect. The method used is the Soft System Methodology (SSM), which is a systematic approach used to analyze and solve problems in complex and messy situations. This paper examines the benefits of applying SSM to knowledge management issues in handling coal dust at a power plant. Improvement is done by upgrading coal handling equipment (ship unloader, conveyor belt, stock pile) with the addition of dust suppression, proper sealing system, dust bag, and training to operators on the impact and handling of coal dust and coal handling equipment maintenance, so resulting in a significant decrease in the spread of coal dust, creating a working environment and the environment becomes clean, healthy and safe.
Design of supply chain risk mitigation system using house of risk and Fuzzy AHP methods in precast concrete Made Arya Teguh Dvaipayana; Iphov Kumala Sriwana; Yudha Prambudia
SINERGI Vol 28, No 1 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.1.010

Abstract

Today's global supply chain has many risk factors. These risks include supply disruptions, supply delays, demand fluctuations, price fluctuations, and exchange rate fluctuations. Risks that arise and cannot be mitigated properly in the supply chain can disrupt the company's business processes in various sectors. Companies in the construction sector when working on construction projects face many risks during the project cycle, especially risks in the supply chain process. Partial risk management, namely only on construction projects and not specifically on the supply chain process, causes potential risks in the supply chain process not to be identified in detail, and mitigation strategies cannot be determined effectively for risks in the supply chain. This research was conducted to identify risks and determine appropriate mitigation strategies using the house of risk as a framework and a fuzzy analytical hierarchy process weighting method to select the best mitigation strategy. The research results showed that there were 26 risk events and 21 risk agents identified, and the 5 best mitigation strategies were chosen from the 10 formulated strategies for a mitigation monitoring system. Based on research results, the best risk mitigation strategy can be used as a reference for risk mitigation actions in the company's supply chain as outlined in the form of a dashboard monitoring system.
Drug forecasting and supply model design using Artificial Neural Network (ANN) and Continuous Review (r, q) to minimize total supply cost Izzati, Inaya; Sriwana, Iphov Kumala; Martini, Sri
SINERGI Vol 28, No 2 (2024)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2024.2.002

Abstract

The Mentawai Islands Regency Regional General Hospital faces a significant challenge with an 83% overstock of Medical Consumables, leading to increased inventory costs and potential damage and expiration of items. This exceeds the 1% pharmaceutical drug storage standards the Ministry of Health set. This study aims to optimize demand forecasting and minimize total inventory costs through a two-stage process. Firstly, demand forecasting is conducted using Artificial Neural Network (ANN), predicting a future demand of 10,036 units of Medical Consumables. Subsequently, the optimal order quantity and reorder points are calculated using the continuous review (r, Q) approach. The results reveal the optimal order quantities and reorder points for four types of Medical Consumables. This research introduces a novel approach by employing ANN for demand forecasting, then calculating optimal order quantities and reorder points using continuous review (r, Q). The cost components considered in the inventory cost calculation include purchasing cost, holding cost, shortage cost, order cost, outdating cost, and inspection cost. The designed forecasting models aim to enhance inventory management efficiency, optimize cost control, and improve patient services. The limitation of this research is that it only used five types of consumable medical materials to carry out this research due to limited data access. It is hoped that future research can use other types of drugs as well as a periodic review and forecasting approach using GA.
Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) Setyaningrum, Hanny; Sriwana, Iphov Kumala; Mufidah, Ilma
SINERGI Vol 29, No 1 (2025)
Publisher : Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/sinergi.2025.1.013

Abstract

The medical device industry company experienced the problem of prolonged accumulation of finished goods in the warehouse, causing one of the safety box items to be defective and damaged. Therefore, this study aims to plan demand forecasting and design inventory policies that consider repair items caused during the buildup of finished goods in the warehouse to minimize total inventory costs using ANN and Continuous Review (s,Q) methods. Demand forecasting is carried out for the next 20 months, from May 2023 to December 2024, using the ANN model with a total forecasting of 17936 units of inner items and 3370 units of outer items. After that, the inventory policy calculation uses the continuous review (s,Q) method. The calculation results show a decrease in the total inventory cost on inner items by 83% and outer items by 79%. After demand forecasting, there was also a decrease in the total initial inventory cost of inner items by 81% and outer items by 80%. This research develops an inventory optimization model that considers repair items due to the accumulation of goods in the warehouse by integrating holding cost, ordering cost, and repair cost variables to develop inventory policies to be more effective and efficient and to utilize damaged products for repair and resale. The limitation of this research is that it only gets demand forecasting results for the next 20 months because the company only started operating in September 2021 and limited data access. It is hoped that future researchers can plan and design an inventory policy strategy with demand forecasting for the next 10 years, focusing on repair items caused by the accumulation of finished goods in the warehouse.
Co-Authors Adinda, Fhirra Adnan, Septian Rahmat Afiif, Rahmad Fajar Ahmad . Ahmad Chirzun Akhyar Zuniawan Alisodikin, Mochamad Aldi Annisa A. Lestari Anwar Ilmar Ramadhan Ardiansyah, M. Nashir Ari Yanuar Ridwan Arief Suwandi Asma Assa Audi Fisbert Rewa Augustina Asih Rumanti Barus, Wan Habibi Rahman Budi Santosa Budi Santosa Budi Santosa Budiman, Muhammad Fahreza Fajar Budinam, Faisal Debby Karisa Dewi Maya Sari Dewi Maya Sari Dheanty Galuh Saputri Edwin Suryajaya El Hadi, Rosad Ma’ali Endang Chumaidiyah Erlangga Bayu Setyawan Erwin Saputra Erwin Saputra Fadil Abdullah Fadil Abdullah Faizin, Agus FANDI ACHMAD Feby Z Octafani Frans Jusuf Daywin Ginang Natilla Adlina Hamada , Hilda Auliya Hardian Kokoh Pambudi Hardyanto, Muhammad Rafif Haryasena Panduwiyasa Hasari , Fadhil Ahmad I Wayan Sukania I Wayan Sukania Ilma Mufidah Isna Ibnah Mudrikah Istianto B Rahardja Istianto Budhi Rahardja Iveline Anne Marie Izzati, Inaya Jatikusumo, Amarilis Jilan Amarla Diwani Josi , Najwa Faroh Kasim, Nurhijriani Lithrone Laricha Salomon Lithrone Laricha Salomon Luciana Andrawina M. Agung Saryatmo M. Agung Saryatmo M. Derajat Amperajaya Made Arya Teguh Dvaipayana Maharani , Luh Putri Kirana Marimin , Maulanisa, Nida Fariza Maya Dewi Dyah Maharani Ma’ali El Hadi, Rosad Merliana, Karlina Muhammad Almaududi Pulungan Muttaqin , Prafajar Suksessano Nia Novitasari Nida F Maulanisa Nofi . Erni Nofi Erni Nofi Erni Nofi Erni Nofi Erni Nova Indah Saragih Nunung Nurhasanah Nurlia Delila Nurul Hijrah S Olga, Fidiana Tri Pambudi, Hardian K. Prafajar Suksessanno Muttaqin Prambudia, Yudha Pulungan, Muhammad Alamaududi Putri, Sinta Mulyani Dwi Rafi S , Muhammad Rico Adhesi Rizaldi, Artamevia Salsabila Roesfiansjah Rasjidin Rulan Dinary Runik Machfiroh Rusydiana Abdullah Salsabila, Kharisa Santo Christianto Saputri, Dheanty Galuh Septiana , Wengku Ahmad Setiawan, Erlangga Bayu Setyaningrum, Hanny Siwi Lintang Pertiwi Sri Martini Sriwana, Nur Mutia Sukarlina, Nina Suluh Widya Yakti Sylvia Madusari Taufiqur Rachman Ulfa Eka Khapso Usman , Muhammad Fajrin Fauzan Vera Veliria Wan Habibi Rahman Barus Watunglawar, Darmiolla Natasia Wawan Tripiawan Wilson Kosasih Winda Jeania Purnama Winda Jeania Purnama Wira Tri Yolanda Wira Tri Yolanda Yakti, Suluh W. Yandra Arkeman Yennie Salim Yodi Nurdiansyah Yulianti, Femi Yulius . Zuniawan, Akhyar