Claim Missing Document
Check
Articles

PERANCANGAN SISTEM KERJA PADA USAHA PENCUCIAN MOTOR DAN KARPET DENGAN MENGGUNAKAN METODE GREEN ERGONOMI DAN ECO-EFFICIENCY: (Studi Kasus: Cucian Motor Dan Karpet Dua Putri) Harahap, Nurul Fazrin Anni; Nofirza, Nofirza; Anwardi, Anwardi; Nurainun, Tengku
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.21400

Abstract

Saat ini kebutuhan akan sarana transportasi sangat penting untuk mendukung berbagai aktivitas masyarakatnya. Mayoritas penduduk, terutama mahasiswa, cenderung memilih menggunakan sepeda motor sebagai alat transportasi. Fenomena ini menyebabkan peningkatan permintaan akan sepeda motor. Bagi masyarakat, menggunakan sepeda motor dianggap lebih praktis dan ekonomis, sehingga menjadikan kebersihan dan perawatan sepeda motor menjadi hal yang sangat penting. Permasalahan dari usaha pencucian motor dan karpet yang paling utama adalah kerusakan jalan akibat genangan air dan beban kendaraan, pencemaran lingkungan akibat limbah yang dihasilkan dan juga timbulnya bau tidak sedap akibat genangan air limbah. Tujuannya adalah melakukan perancangan perbaikan sistem kerja pada usaha pencucian motor dan karpet yang tidak memberikan efek buruk bagi tubuh manusia melainkan akan membantu pekerja pada usaha pencucian motor dan karpet dalam upaya dengan pendekatan green ergonomic dan eco-efficiency. Perancangan sistem kerja dengan konsep green ergonomi memiliki kemampuan untuk menjamin efisiensi lingkungan dengan menjadikan proses kerja lebih efisien dan ramah lingkungan. Selain itu, penambahan filter air sebagai penyaringan limbah pencucian motor dan karpet mampu meningkatkan eco-efficiency, serta perbaikan desain lantai kerja yang membuat pekerja bekerja dengan nyaman, aman, dan ergonomis
Strategi Pengembangan Usaha Menggunakan Metode Quantitative Strategic Planning Matrix pada UMKM Mokey Sandra, Winnie; Suherman, Suherman; Kusumanto, Ismu; Nofirza, Nofirza; Harpito, Harpito
Jurnal Ilmiah Universitas Batanghari Jambi Vol 25, No 1 (2025): Februari
Publisher : Universitas Batanghari Jambi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33087/jiubj.v25i1.5836

Abstract

The aim of this research is to identify an analysis of the internal and external conditions of Mokey MSMEs and to design the best alternative strategy proposal in order to increase sales and maintain the business being run using the SWOT (Strength, Weakness, Threat, Opportunity) matrix and the QSPM (Quantitative Strategic Planning Matrix) method. The research results showed that the position of MSME Mokey was categorized as high in utilizing internal and external conditions with a total of IFE 3.9 and EFE 3.8 and the results of analysis using the QSPM method showed that there were 7 strategies that MSME Mokey could use to increase sales and maintain the business.
Implementation of Data Mining to Classify Potential Customers Using the C5.0 Algorithm Rizki, Muhammad; Maghfirah, Cintya Nil; Norhiza, Fitra Lestari; Nofirza, Nofirza; Lubis, Fitriani Surayya
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.v11i1.39167

Abstract

PT Pegadaian, as listed on its official website, is a fast-growing financial company. One of its key challenges is late installment payments, which can lead to financial losses. Using pawn customer data from 2013 to 2021, this study found that out of 534 customers, 68 were late in paying installments, and 10 did not pay. To address this issue, this research applies customer classification to identify borrowers who are more likely to pay on time. The classification model is developed using data mining with the C5.0 algorithm to generate decision-tree rules. Prior to modeling, the dataset is processed through the Knowledge Discovery in Databases (KDD) stages, including data selection, cleaning, and transformation. The proposed model produces 26 classification rules and achieves an accuracy of 87.04%. All data processing, modeling, and validation are conducted using RapidMiner Studio. Keywords: Classification, Decision Tree, C5.0 Algorithm, Data Mining
Controlling the Inventory of Boiler Ash Raw Materials in Organic Fertilizer Using the Minmax Method (Case Study: UMTR Belilas Organic Fertilizer) Aditama, Dhimas; Nurainun, Tengku; Lubis, Fitriani Surayya; Nofirza, Nofirza; Anwardi, Anwardi
IJIEM - Indonesian Journal of Industrial Engineering and Management Vol 7, No 1: February 2026
Publisher : Program Pascasarjana Magister Teknik Industri Universitas Mercu Buana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/ijiem.v7i1.31728

Abstract

This study evaluates the inventory management of boiler ash raw materials in an independent organic fertilizer company, UMTR Belilas, using the Min-Max method supported by forecasting. The main problem in this study is that consumer demand is often not met on time due to suboptimal inventory management. The purpose of this research is to maintain the availability of raw materials in order to meet consumer demand optimally while reducing the risk of out-of-stock and overstocking. The results show that the application of the Min-Max method results in a minimum stock limit of 88.5 kg and a maximum stock of 171 kg, with an optimal purchase quantity of 82.5 kg per order. The safety stock level is calculated at 6.1 kg, while the Reorder Point (ROP) is set at 88.5 kg. With an order frequency of 120 times per year, this method has succeeded in optimizing storage costs through faster stock turnover. The combination of the Min-Max method and forecasting has proven to be effective in responding to fluctuations in demand, ensuring the availability of raw materials on time, and supporting the operational sustainability of organic fertilizer production.