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All Journal JURNAL MEDIA INFORMATIKA BUDIDARMA Sebatik JURTEKSI JOISIE (Journal Of Information Systems And Informatics Engineering) Progresif: Jurnal Ilmiah Komputer Indonesian Journal of Electrical Engineering and Computer Science Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology Jurnal Komtekinfo Jurnal Teknologi
Dewi Eka Putri
Universitas Putra Indonesia YPTK Padang
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Social Sciences

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Pemanfaatan Teknologi dalam Pengelompokkan Produk pada Minimarket Eka Praja Wiyata Mandala; Dewi Eka Putri
Jurnal Teknologi Vol. 11 No. 2 (2021): Jurnal Teknologi
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (576.336 KB) | DOI: 10.35134/jitekin.v11i1.52

Abstract

The retail industry is currently growing rapidly, especially in Indonesia. One form of the retail industry is modern retail which includes supermarkets, minimarkets and others. This study focuses on the grouping of products sold at minimarkets. This research is caused by seeing the phenomenon of the large number of transactions that occur in one day, the result is the number of products sold. This makes it difficult for minimarket managers to determine the next product procurement. Therefore, This study is conducted to group the products sold so that the products that need to be procured are seen next. This study propose a software to perform the grouping using the K-means algorithm. For the data sample, this study obtained sales transaction data for 3 months from the Sastra Mart minimarket. In this study, manual calculations were carried out on 10 samples of beverage data taken randomly from sales transactions which would be divided into 3 clusters. The results of manual calculations, there are 3 drink data entered into the “Sangat Laris” cluster, 2 drink data entered the “Laris” cluster and 5 drink data entered the “Kurang Laris” cluster. The software produced from the research gives the same results as manual calculations in classifying products. This study has also carried out software testing to test all its functionalities, from the test results, everything runs normally and as expected.
Data mining technique for grouping products using clustering based on association Eka Praja Wiyata Mandala; Dewi Eka Putri
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 2: August 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i2.pp835-844

Abstract

There is high competition between these minimarkets so many products sold in each minimarket are not sold until they expire. The aim of this study is to help retail managers cluster products in minimarkets. The data obtained will be processed using the hybrid data mining approach by combining two methods in data mining. In the first section, association uses the FP-Growth algorithm, and in the second section, clustering uses the K-means algorithm. From the experimental results, it can be seen that the proposed approach can minimize the number of products to be grouped. After the association process is carried out, from 29 products in 12 transactions, 6 products can be obtained that has a frequency above the minimum support and minimum confidence. After the clustering process, 6 products are grouped into 2 clusters, so that 1 product is included in the most interested product cluster and 5 products are included in the interested product cluster. We minimize data processing so that retail managers can process data directly from sales transaction data on the cashier's computer and can quickly get the results of product grouping.
Augmented Reality dengan Model Generate Target dalam Visualisasi Objek Digital pada Media Pembelajaran Randy Permana; Eka Praja Wiyata Mandala; Dewi Eka Putri; Musli Yanto
Majalah Ilmiah UPI YPTK Vol. 30 (2023) No. 1
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v30i1.143

Abstract

Augmented Reality (AR) is a digital technology that allows the creation of a combination between the real world and digital content projections to produce additional valuable information for users. This technology has begun to implement in various fields of human life like industry, health, military, entertainment, and education. AR technology will be applied to the education sector in this community service activity. This activity aims to introduce AR technology and a development model based on the Model Generate Target (MGT) to service partners, namely SMA INS Kayu Tanam. The Generate Target model is an AR application development concept by adopting the concept of Digital Twins, where digital content projections will be made similar to real objects. The projected digital content will serve as a descriptive object from the real world, so users can interact more flexibly with objects from the real world. The activity was carried out by providing an introduction to AR technology, installing AR design software, and practicing making simple AR applications using the MGT concept for partner service to the community. In this activity, the designed AR will use spherical objects in the real world and the resulting digital projection is a virus. Projection of digital content onto spherical objects will provide better information and learning experiences in viral object recognition because 3D objects will appear and be attached to real objects. The expected results of this community service activity are introducing AR technology to partners engaged in education and providing a new solution in implementing new digital content-based teaching media that can be applied to several subjects such as biology, chemistry, and glasses to community service partners.
Penerapan K-Means Clustering dalam Segmentasi Siswa Berdasarkan Status Sosial Ekonomi Dewi Eka Putri; Eka Praja Wiyata Mandala
Progresif: Jurnal Ilmiah Komputer Vol 21, No 2 (2025): Agustus
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i2.2809

Abstract

The accuracy of educational aid distribution remains a challenge, especially when it is not based on structured socioeconomic data. This study aims to group students at SMP Negeri 1 Lunang based on socioeconomic status using the K-Means Clustering algorithm as a segmentation approach. The data used includes parents' income and occupation, number of dependents, social assistance, certificates of poverty, and distance from home to school. After data normalization, clustering and visualization were performed using Principal Component Analysis (PCA). The clustering results yielded three main groups representing different socioeconomic levels: low, medium, and high. Validation using the Silhouette Score yielded a value of 0.2592, indicating that the cluster separation was adequate. These findings suggest that K-Means can serve as a decision-making tool for data-driven aid distribution. This study offers a new approach to student segmentation that simultaneously considers geographical and socioeconomic indicators.Keywords: K-Means; Socioeconomic status; Student segmentation; PCA; Silhouette score  AbstrakKetepatan penyaluran bantuan pendidikan masih menjadi tantangan, terutama ketika tidak berbasis pada data sosial ekonomi yang terstruktur. Penelitian ini bertujuan untuk mengelompokkan siswa SMP Negeri 1 Lunang berdasarkan status sosial ekonomi menggunakan algoritma K-Means Clustering sebagai pendekatan segmentasi. Data yang digunakan mencakup penghasilan dan pekerjaan orang tua, jumlah tanggungan, bantuan sosial, surat keterangan tidak mampu, dan jarak rumah ke sekolah. Setelah data dinormalisasi, dilakukan klasterisasi dan visualisasi menggunakan Principal Component Analysis (PCA). Hasil clustering menghasilkan tiga kelompok utama yang merepresentasikan tingkatan sosial ekonomi berbeda yaitu rendah, menengah dan tinggi. Validasi menggunakan Silhouette Score menunjukkan nilai sebesar 0,2592, menandakan bahwa pemisahan klaster cukup baik. Temuan ini menunjukkan bahwa        K-Means dapat menjadi alat bantu pengambilan keputusan untuk penyaluran bantuan berbasis data. Penelitian ini menawarkan pendekatan baru dalam segmentasi siswa yang mempertimbangkan indikator geografis dan sosial secara bersamaan.Kata kunci: K-Means; Status sosial ekonomi; Segmentasi siswa; PCA; Silhouette score  
Implementasi Algoritma K-Means dalam Klasterisasi Penjualan Lauk Masakan Padang Dewi Eka Putri; Dede Wira Trise Putra; Eka Praja Wiyata Mandala
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1 (2025): Februari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2459

Abstract

Culinary development in Indonesia is very rapid, increasing competition among culinary entrepreneurs. Side dish sales at RM Takana Juo are problematic in stock management due to lack of understanding of sales patterns. This research proposes the use of K-Means algorithm to cluster side dish sales based on parameters that focus on initial stock and number sold. This research begins with the collection of side dish sales datasets, determination of the optimal number of clusters, and application of the K-Means algorithm to group side dishes into two clusters: side dish clusters with higher initial stock and sales and side dish clusters with relatively low initial stock and sales. The results showed that the sales of 20 Padang cuisine side dishes at RM Takana Juo were successfully grouped into the two clusters. The validation of the method's performance showed an average Silhouette Score value of 0.57 which indicates that the K-Means algorithm successfully clustered the data quite well. This research contributes specifically in planning the procurement of Padang cuisine side dishes in organizing daily stock to reduce waste.Keywords: Clusterization; K-Means; Side Dish Sales; Padang Cuisine; Sales Pattern AbstrakPerkembangan kuliner di Indonesia sangat pesat sehingga meningkatkan persaingan antar pengusaha kuliner. Penjualan lauk di RM Takana Juo bermasalah dalam pengelolaan stok karena kurangnya pemahaman pola penjualan. Penelitian ini mengusulkan penggunaan algoritma K-Means untuk mengelompokkan penjualan lauk berdasarkan parameter yang fokus pada stok awal dan jumlah terjual. Penelitian ini dimulai dengan pengumpulan dataset penjualan lauk, penentuan jumlah klaster optimal, dan penerapan algoritma K-Means untuk mengelompokkan lauk ke dalam dua klaster yaitu klaster lauk dengan stok awal dan penjualan yang lebih tinggi dan klaster lauk dengan stok awal dan penjualan yang relatif rendah. Hasil penelitian menunjukkan bahwa penjualan 20 lauk masakan Padang di RM Takana Juo berhasil dikelompokkan ke dalam dua klaster tersebut. Validasi kinerja metode menunjukkan nilai rata-rata Silhouette Score sebesar 0.57 yang mengindikasikan bahwa algoritma K-Means berhasil mengelompokkan data dengan cukup baik. Penelitian ini berkontribusi spesifik dalam merencanakan pengadaan lauk masakan Padang dalam mengatur stok harian untuk mengurangi pemborosan.Kata kunci: Klasterisasi; K-Means; Penjualan Lauk; Masakan Padang; Pola Penjualan
Co-Authors Eka Praja Wiyata Mandala Muhammad Ridwan Musli Yanto Putra, Dede Wira Trise Randy Permana Yuhandri Yuhandri, Yuhandri