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All Journal Jurnal Ilmiah Edunomika (JIE) JURSIMA (Jurnal Sistem Informasi dan Manajemen) INFOKUM JURSIMA
Septian Simatupang
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Library & Information Science Social Sciences

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Simulasi Monte Carlo dalam Memprediksi Ketersediaan Barang (PT. Terang Abadi Pekanbaru) Septian Simatupang
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 1 (2022): Jursima Vol. 10 No. 1, April Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i1.399

Abstract

Abstract Inventory problems not only occur in companies engaged in manufacturing, but the problem also occurs in companies engaged in retail. Inventory problems also occur at PT. Terang Abadi. The inventory system used at this company mostly still uses intuition, estimation, and habits. As a result, predicting inventory becomes very risky when using the simple inventory model. Without a good inventory management, the company will be faced with the risk of being unable to meet customer demand so that inventory analysis needs to be done so that the company does not experience losses. One method that can be used is the monte carlo method. The data taken is inventory data for the last 3 years, namely 2019 to 2021. This data is simulated by programming PHP as a data implementation system. Simulation results from this study obtained an accuracy rate of 90% for simulations in 2019 and 97% for simulations in 2020. By getting greater accuracy, this method is feasible to use and apply to predict the availability of goods in the future. Keywords: Simulation, Monte Carlo, Prediction, Inventory
Simulasi Monte Carlo dalam Memprediksi Ketersediaan Barang (PT. Terang Abadi Pekanbaru) Septian Simatupang
JURSIMA (Jurnal Sistem Informasi dan Manajemen) Vol 10 No 1 (2022): Jursima Vol. 10 No. 1, April Tahun 2022
Publisher : INSTITUT TEKNOLOGI DAN BISNIS INDOBARU NASIONAL

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47024/js.v10i1.399

Abstract

Abstract Inventory problems not only occur in companies engaged in manufacturing, but the problem also occurs in companies engaged in retail. Inventory problems also occur at PT. Terang Abadi. The inventory system used at this company mostly still uses intuition, estimation, and habits. As a result, predicting inventory becomes very risky when using the simple inventory model. Without a good inventory management, the company will be faced with the risk of being unable to meet customer demand so that inventory analysis needs to be done so that the company does not experience losses. One method that can be used is the monte carlo method. The data taken is inventory data for the last 3 years, namely 2019 to 2021. This data is simulated by programming PHP as a data implementation system. Simulation results from this study obtained an accuracy rate of 90% for simulations in 2019 and 97% for simulations in 2020. By getting greater accuracy, this method is feasible to use and apply to predict the availability of goods in the future. Keywords: Simulation, Monte Carlo, Prediction, Inventory
The Menu Clustering At Doktor Kopi Using K-Means Algorithm To Increase Sales Septian Simatupang; Monsyah Juansen; Rizki Ramadhansyah; Taufiqurrahman; Windi Saputri Simamora
INFOKUM Vol. 12 No. 04 (2024): Engineering, Computer and Communication, November 2024
Publisher : Sean Institute

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Abstract

Coffee Doctoris a coffee shop with a variety of menus and a strategic location in Medan City, this coffee shop is currently developing a strategy to increase sales of their products. Effective menu arrangement is an important factor in increasing product sales in coffee shops or cafes. This study aims to optimize sales by utilizing the K-Means algorithm to group menus based on customer purchasing patterns. The sales data analyzed includes product types, purchase frequency, and revenue contributions from each menu. Through the clustering process, menus can be grouped into several categories, such as the best-selling, medium and less popular menus. The results of this clustering are used to design a more structured menu arrangement strategy, such as arranging menu positions on the list, special promotions, or eliminating less effective menus. The implementation of the K-Means algorithm shows that a data-based menu arrangement strategy can improve customer experience and significantly drive product sales. Thus, this study provides a practical contribution for coffee shop or cafe managers to optimize sales through a technology and data-based approach.
UTILIZATION OF THE INTERNET OF THINGS IN CREATING SMART LIBRARIES AND INCREASING STUDENTS' INTEREST IN READING Herni Ramayanti; Yetnimar; Peni Astuti; Septian Simatupang; Aloysius Eka Wenats
JURNAL ILMIAH EDUNOMIKA Vol. 9 No. 2 (2025): EDUNOMIKA
Publisher : ITB AAS Indonesia Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/jie.v9i2.17033

Abstract

Abstract This study is a qualitative study with a descriptive approach, namely approaching the description of the main parts used in this article. In this case, the main parts in question are the Internet of Things and Smart Library. The data used in this article is secondary data that researchers obtain from sources indirectly obtained or it can be said that researchers obtain from credible quotations such as Books, Journals, and Websites. After the data is collected, the existing data is reduced, selected, and conclusions are drawn. The result in this article show that Internet of Things in creating a smart library that has four concepts and has been proposed by researchers above can be achieved easily. With a good Internet of Things, the internet network will be stronger and wider. The references owned are also increasing and in accordance with the needs and desires of students. This can make students happy and increase their interest in reading. Keywords: Internet of Things, Smart Library, Students
Co-Authors Aloysius Eka Wenats Herni Ramayanti Monsyah Juansen Peni Astuti Rizki Ramadhansyah Taufiqurrahman Windi Saputri Simamora Yetnimar