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All Journal Journal of Appropriate Technology for Community Services Journal of Industrial Engineering and Halal Industries
Jundi Nourfateha Elquthb
Universitas Islam Indonesia
Religion Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Pendampingan Planogram untuk Pemilik Ritel Qurtubi Qurtubi; Joko Sulistio; Vembri Noor Helia; Melinska Ayu Febrianti; Jundi Nourfateha Elquthb
Journal of Appropriate Technology for Community Services Vol. 5 No. 1 (2024)
Publisher : Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jattec.vol5.iss1.art8

Abstract

Retail owners need to utilize transaction data for customer buying patterns. By knowing the pattern of customer purchases, retail owners can adjust the layout of the store and the layout of goods on the display rack or a planogram to increase product visibility which refers to increased sales. Not many retail owners are aware of the benefits of using a good planogram and how to design it, so it is essential to provide training related to planograms to the public so they can know how to create planograms properly and correctly. Community service will be conducted from July to October 2022 in Sleman, Bantul, and Temanggung Regency and its surroundings, with interactive mentoring methods with material delivery and questions and answers. Submission of the material contains definitions, benefits, and illustrations of the application of the planogram in the retail industry, as well as the stages of planning the planogram. With this community service, it is hoped that retail business owners who participate in the mentoring can utilize transaction data at each retailer to develop a planogram based on customer purchasing patterns, thereby increasing sales and customer satisfaction.
Sentiment Analysis of PeduliLindungi User Using Naïve Bayes Classifier Algorithm and Support Vector Machine Rizki Rahmatullah; Jundi Nourfateha Elquthb; Fanya Nindha Al-Qurani; Annisa Uswatun Khasanah
Journal of Industrial Engineering and Halal Industries Vol. 5 No. 1 (2024): Vol. 5 No. 1 June (2024): Journal of Industrial Engineering and Halal Industrie
Publisher : Industrial Engineering Department, Faculty of Science and Engineering, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiehis.4672

Abstract

The Indonesian government is attempting to track the spread of the virus by creating an application named “PeduliLindungi” to deal with the coronavirus's exponential increase in cases across the country. Because it has a feature to disclose the user's location immediately, it is envisaged that this program can reduce the transmission of viruses in monitoring. Indonesians have used the PeduliLindungi, and there are user reviews of both positive and negative experiences. Therefore, to enhance these services, an assessment is required. The text mining method can extract information from users' reviews to collect this data. This method's application additionally uses the Naive Bayes Classifier and Support Vector Machine algorithms, which analyze word associations and do a classification evaluation of the data's accuracy. Based on the two methods' calculations, the NBC algorithm's average classification accuracy was 83.81%, and the SVM algorithm was 93.84%. Following that, discoveries on words that frequently exist or are used by people are obtained through word associations in the sentiment analysis of positive or negative reviews.
Implementation of Class-Based Storage for Garment Accessories Warehouse Management Using FSN Analysis at PT. XYZ Jundi Nourfateha Elquthb; Lulu Riesta Nugroho; Annisa Uswatun Khasanah
Journal of Industrial Engineering and Halal Industries Vol. 5 No. 1 (2024): Vol. 5 No. 1 June (2024): Journal of Industrial Engineering and Halal Industrie
Publisher : Industrial Engineering Department, Faculty of Science and Engineering, UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiehis.4677

Abstract

A manufacturing company has a close relationship with warehouse operations, which function as centres for storing and managing raw materials and finished products. Proper operational expertise is essential in warehouse management to ensure that all processes within the warehouse run smoothly. The warehouse is a meeting point for various raw materials with different specifications, making it highly susceptible to mismanagement. In this context, several issues have been identified in warehouse management, such as a lack of space or empty shelves, outdated data recording processes, and improper placement of raw materials. An analysis was conducted to identify accessory items in the warehouse based on their similarities using class-based storage to overcome these issues. In this study, the FSN (Fast, Slow, Non-moving) analysis method was used to determine the grouping of garment accessory items based on their production usage and movement in the warehouse. A total of 37 types of accessory items were identified, with the majority, 23 items, classified as fast-moving, indicated by a high turnover rate above 3. The remaining items were categorized as slow-moving and non-moving, with turnover rates below three due to their lower movement. The turnover rate indicates how often or quickly an item moves and is used (enters and exits the warehouse). Thus, several underlying causes of the issues hindering the distribution process in the garment accessories warehouse at PT XYZ were identified. Subsequently, several recommendations were provided for warehouse management, such as improving the layout according to item grouping and conducting regular evaluations to ensure a balanced availability of space and stock. This strategy is crucial for enhancing workers' productivity in the garment accessories warehouse.
Co-Authors Fanya Nindha Al-Qurani Joko Sulistio, Joko Khasanah, Annisa Uswatun Lulu Riesta Nugroho Melinska Ayu Febrianti Qurtubi Qurtubi Rizki Rahmatullah Vembri Noor Helia