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All Journal Jurnal DISPROTEK EBISMEN Jurnal Ekonomi Bisnis dan Manajemen
Mochamad Sirodjudin
Universitas Negeri Malang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Electrical & Electronics Engineering Industrial & Manufacturing Engineering Other

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Implementasi Digital Marketing Oleh UMKM Di Indonesia: A Scoping Review Mochamad Sirodjudin; Sudarmiatin Sudarmiatin
Jurnal Ekonomi, Bisnis dan Manajemen Vol. 2 No. 2 (2023): Juni : Jurnal Ekonomi, Bisnis dan Manajemen
Publisher : FEB Universitas Maritim Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58192/ebismen.v2i2.783

Abstract

Previous literature review studies have explored the current status of digital marketing implementation in Indonesia by the SME, a literature review that focuses explicitly on digital marketing implementation is scarce. The objective of this scoping review was to map the existing digital marketing knowledge to meet digital marketing practices. The scoping review followed the protocol outlined by Arksey and O'Malley (Arksey & O’Malley, 2005) that has five main stages: (1) identifying research questions; (2) identifying relevant studies; (3) study selection; (4) data charting, and (5) collating, summarizing and reporting the results. The reviewed studies were selected from two electronic databases: Scopus and Google Scholar within six years time span (2017-2023). The results of the scoping review indicated that the majority of the SME already knowing about digital marketing. However, as has been claimed by Aries Kurniawan and Asharudin (Kurniawan & Asharudin, 2018), SME have some obstacles that must be handle so they can survive and grow. Emphasis should be given to how to understanding digital marketing strategic and how to upgrade SME's knowledge about digital marketing. According to Katoyusyi Kano (Kano et al., 2022), digital marketing strategies from the consumer perspective should be given more attention in order to make digital marketing more effective in attracting attention and winning consumer trust.
KLASIFIKASI MUTU BERAS MENGGUNAKAN K-NN BERBASIS BACKWARD ELIMINATION Muhammad Faishol Amrulloh; Muhammad Mahrus Ali; Mochamad Sirodjudin
JURNAL DISPROTEK Vol 14, No 2 (2023)
Publisher : Universitas Islam Nahdlatul Ulama Jepara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34001/jdpt.v14i2.3676

Abstract

CLASSIFICATION RICE QUALITY USING K-NN BACKWARD-BASED ELIMINATIONRice is one of the most important agricultural products. And it is a strategic commodity because almost all Indonesian people need it. Because of the importance of the function of rice as a staple food ingredient, the quality of the rice to be consumed must be ensured to be of high quality. Determination of the quality or quality of rice until now has been done by many previous researchers. However, of the several methods that have been selected, the majority use image processing. Rice quality data processing using data mining is still rarely done. In this study, the dataset that will be used comes from the Probolinggo Regency Agriculture Service database which has 1 special attribute and 9 regular attributes. The attributes are: variety, length, shape, color, taste, rice planting technique, season, pests, PH and quality. The method that will be used in this study is the backward elimination-based K-NN method.From the results of the analysis and computation of several methods, it can be concluded that the backward elimination-based K-NN method used to increase the accuracy of rice classification resulted in several conclusions. Namely the attributes that affect the determination of rice quality are variety, shape, color, technique, season, pests and PH. While the features that are considered to have no effect are taste and length. As for the data as much as 4952, the best accuracy is 83.08% when k = 1, while for the data as much as 2000, then the best accuracy is 87.70% at k = 1, while for the data as much as 1000, the best accuracy is 98.60%, namely when k=1.
Co-Authors Muhammad Faishol Amrulloh Muhammad Mahrus Ali Sudarmiatin Sudarmiatin