Abstract:Academic achievement is a measure of students' learning outcomes, encompassing aspects of knowledge and skills. Academic performance serves as a crucial indicator in evaluating students' learning progress. MAS Al-Wasliyah…
h Petatal is committed to providing quality education but still faces limitations in applying technology to evaluate student learning. The current evaluation process relies on teachers' subjective assessments, which restricts the information about students' progress. Therefore, the implementation of machine learning is proposed as a solution to enhance objectivity in student learning evaluation through more effective data processing. The method used is the K-Means Clustering algorithm, which can group or classify data based on specific patterns. This study aims to evaluate the extent to which machine learning can process student learning evaluation data through the analysis results obtained from the clustering process, which are then used as benchmarks to improve the evaluation system and provide feedback for students needing improvement in their academic performance. The data used comprises students' grades from the odd semester of the 2024/2025 academic year, with a total of 210 data points. The clustering results produced three clusters: the "good" cluster with 60 students, the "average" cluster with 99 students, and the "low" cluster with 51 students.
Abstract:Bread product sales have become an important aspect of the bakery business, influenced by fluctuations in demand that are not easily predictable. Efficient sales management requires a deep understanding of sales patterns.…
. This study aims to optimize bread product sales by using the K-Means Clustering algorithm to analyze daily sales performance at Toko Roti Amin. The data used includes sales volume and transaction frequency for bread products, consisting of 356 data points. The results show that the bread products can be grouped into three clusters: 129 data in the “Good Sales” cluster, 28 data in the “Moderate Sales” cluster, and 199 data in the “Low Sales” cluster. These findings assist bakery owners in managing stock, production planning, and more targeted marketing strategies. Although there are limitations in using K-Means Clustering, such as dependence on the initial centroid selection, this study proves that applying this technique can enhance inventory management and maximize profit in the bakery business.
Abstract:Technological advances support digital transformation in sales data management. Wan Toys & Sport stores face difficulty understanding sales patterns, such as the highest sales months and most popular products. This research…
rch uses the K-Means clustering method with the CRISP-DM approach to group sports products based on their sales level. The analysis results show that this method is able to divide products into three categories: high, medium and low, thus providing strategic insight for stock management and marketing. Products with high sales are prioritized for stock, while products with low sales are targeted for promotion. This method effectively supports operational efficiency and data-based decision making at Wan Toys & Sport stores.
Abstract:Everbright has become one of the distributing companies selling various products, one of which is the Dettol product. In optimizing the stock and marketing of Dettol products, the company faces difficulties in identifying…
g the best-selling products and those less favored by customers. This research data originates from the sales transaction data of Dettol soap in November, comprising 77 sales data. Through the use of data mining, particularly the K-means Clustering method, it becomes a relevant approach to solving this issue. The objective of this research is to avoid excess inventory that remains unsold while meeting the diverse needs of customers. The results of this study show the visualization of the distribution of Dettol soap product clusters and grouping based on sales levels. There were 21 best-selling item data, 31 well-selling item data, and 25 less popular item data, enabling the company to manage inventory based on the best-selling items.
Abstract:The use of smartphones in Indonesia has been steadily increasing each year. In the era of the Fourth Industrial Revolution, smartphones have become a lucrative business sector, leading to intense market competition. Consequently,…
equently, smartphone companies must pay closer attention to the market segmentation desired by consumers. Data mining is the process of discovering significant relationships and patterns by analyzing large datasets using statistical and mathematical techniques. This study aims to identify and analyze the market segments of Android smartphone users among students at STMIK Royal. The data used in this research were collected from 122 student respondents. The study employs clustering using the K-means algorithm. The resulting data modeling will categorize market segments into several clusters. This segmentation yields three clusters: Cluster 1 (features), consisting of 36 respondents who prioritize price, battery, camera, and warranty; Cluster 2 (product), with 49 respondents who value all attributes except warranty; and Cluster 3 (superiority), comprising 37 respondents who prioritize camera, brand, and RAM.
Abstract:Pengangguran terbuka di Indonesia merupakan salah satu masalah sosial dan ekonomi yang signifikan, yang dapat menghambat pertumbuhan ekonomi serta mempengaruhi kesejahteraan masyarakat secara luas. Penelitian ini bertujuan…
an untuk menganalisis Tingkat Pengangguran Terbuka (TPT) Indonesia dari tahun 2020 hingga 2023, dengan fokus pada perubahan klaster data TPT menggunakan metode K-Means Clustering. Data yang digunakan diambil dari Badan Pusat Statistik (BPS), yang memberikan gambaran komprehensif mengenai variasi TPT antarprovinsi. Metodologi yang diterapkan mencakup pengumpulan dan pengolahan data untuk memastikan akurasi serta kelengkapan informasi. Hasil analisis menunjukkan bahwa hanya Provinsi Riau yang berhasil naik ke cluster 1 (TPT rendah), sementara Provinsi Sumatera Barat mengalami penurunan ke cluster 2 (TPT tinggi). Evaluasi menggunakan Davies-Bouldin Index menegaskan pemisahan cluster yang optimal pada jumlah cluster 2, mengindikasikan efektivitas pengelompokan. Temuan dari penelitian ini diharapkan dapat memberikan wawasan berharga bagi pembuat kebijakan untuk merumuskan strategi yang lebih efektif dalam mengatasi masalah pengangguran di Indonesia. Dengan mempertimbangkan karakteristik pengangguran di setiap provinsi, hasil penelitian ini dapat menjadi dasar bagi upaya pengurangan tingkat pengangguran yang lebih terarah dan berdampak.
Abstract:Penyakit hewan merupakan salah satu tantangan terbesar dalam divisi hewan yang dapat mengakibatkan kerugian finansial yang signifikan. Bukti yang dapat dikenali dari pola penyebaran penyakit sangat penting untuk mendukung…
g pengambilan keputusan dalam upaya mengantisipasi dan menangani wabah . Pertimbangan ini bertujuan untuk menganalisis dan mengidentifikasi pola penyebaran penyakit hewan menggunakan strategi pengelompokan spasial . Strategi ini memungkinkan pengelompokan wilayah berdasarkan tingkat penyebaran penyakit , sehingga zona yang berpotensi menjadi pusat penyebaran dapat diidentifikasi . Informasi yang digunakan dalam pertimbangan ini mencakup data spasial dan non - spasial yang terkait dengan kasus penyakit hewan dari berbagai wilayah , yang kemudian dianalisis di sana.
Hasil dari penelitian ini menunjukkan bahwa strategi pengelompokan spasial dapat diterapkan di daerah -daerah dengan pola penyebaran penyakit yang sama . Identifikasi zona berisiko tinggi dapat membantu peternak dan pihak terkait dalam mengambil tindakan penanggulangan yang lebih tepat dan efisien . Dengan pengelompokan spasial ini , prosedur penanganan dapat difokuskan pada daerah-daerah yang lebih rentan terhadap kejadian , yang diharapkan dapat mengurangi dampak ekonomi dan meningkatkan kesejahteraan hewan secara keseluruhan . Penelitian ini memberikan kontribusi penting bagi pengembangan kerangka kerja deteksi dini berbasis teknologi untuk mitigasi penyakit ternak
Abstract:Abstract: In a competitive digital era, business owners such as Alya Collection face challenges in determining the right fashion products to offer to consumers. Relying on intuition without data support often results in…
inaccurate and potentially detrimental decisions. This study aims to assist the sales decision-making process by applying a data mining method using the K-Means Clustering algorithm. This method was used to group sales data from February 2024 to January 2025 into three categories: best-selling, least-selling, and non-selling products. The study was conducted using a quantitative approach, through sales data collection and direct interviews with store owners. The system was built using the PHP programming language and MySQL database, and designed with tools such as UML, DFD, and ERD. The results of this clustering system are expected to provide strategic information useful for Alya Collection in planning stock, promotions, and product development, thereby increasing sales effectively and efficiently.
Keywords: data mining; k-means; sales; alya collection; information system
Abstrak: Dalam era digital yang kompetitif, pemilik usaha seperti Alya Collection menghadapi tantangan dalam menentukan produk fashion yang tepat untuk ditawarkan kepada konsumen. Ketergantungan pada intuisi tanpa dukungan data seringkali mengakibatkan keputusan yang kurang akurat dan berisiko merugikan. Penelitian ini bertujuan untuk membantu proses pengambilan keputusan penjualan dengan menerapkan metode data mining menggunakan algoritma K-Means Clustering. Metode ini digunakan untuk mengelompokkan data penjualan dari Februari 2024 hingga Januari 2025 ke dalam tiga kategori: produk terlaris, kurang laris, dan tidak laris. Penelitian dilakukan dengan pendekatan kuantitatif, melalui pengumpulan data penjualan serta wawancara langsung dengan pemilik toko. Sistem yang dibangun menggunakan bahasa pemrograman PHP dan database MySQL, serta dirancang dengan alat bantu seperti UML, DFD, dan ERD. Hasil dari sistem klasterisasi ini diharapkan dapat memberikan informasi strategis yang berguna bagi Alya Collection dalam merencanakan stok, promosi, serta pengembangan produk, sehingga mampu meningkatkan penjualan secara efektif dan efisien.
Kata kunci: data mining; k-means clustering; penjualan; alya collection; sistem informasi
Abstract:Abstract: This study aims to analyze the influence of gender on user responses to social media strategies on the Instagram platform by utilizing data science techniques. The methodology includes collecting user interaction…
on data based on gender, statistical analysis, and applying machine learning algorithms to identify response patterns. The results reveal significant differences in how male and female users respond to social media content and campaigns, affecting marketing strategy effectiveness. Data science analysis showed that the K-Means Clustering method segmented users into three groups based on interaction patterns, with female users showing the highest engagement rate (18%) compared to male users (12%). The Decision Tree model identified gender as the most dominant predictor of engagement (40%), followed by user growth (25%) and content type (20%). The Random Forest model validated that gender-targeted strategies increased marketing effectiveness by up to 22%. Multivariate regression revealed a positive effect of female user proportion (+0.45) and a negative effect of male user proportion (−0.12) on engagement. In conclusion, a deeper understanding of gender-based response differences can assist companies in designing more targeted social media strategies and enhancing engagement.
Keywords: gender; social media response; instagram; data science; marketing strategy
Abstrak: Penelitian ini bertujuan untuk menganalisis bagaimana pengaruh gender memengaruhi respons pengguna terhadap strategi media sosial di platform Instagram dengan memanfaatkan teknik data sains. Metode yang digunakan meliputi pengumpulan data interaksi pengguna berdasarkan gender, analisis statistik, dan penerapan algoritma machine learning untuk mengidentifikasi pola respons. Hasil penelitian menunjukkan adanya perbedaan signifikan dalam cara pengguna laki-laki dan perempuan merespons konten dan kampanye media sosial, yang berdampak pada efektivitas strategi pemasaran. Analisis data sains menunjukkan bahwa metode K-Means Clustering berhasil mengelompokkan pengguna ke dalam tiga segmen berdasarkan pola interaksi, dengan segmen perempuan menunjukkan tingkat engagement tertinggi (18%) dibandingkan laki-laki (12%). Model Decision Tree mengidentifikasi gender sebagai faktor paling dominan terhadap engagement dengan kontribusi sebesar 40%, disusul oleh pertumbuhan pengguna (25%) dan jenis konten (20%). Random Forest memvalidasi bahwa strategi yang disesuaikan berdasarkan gender meningkatkan efektivitas pemasaran hingga 22%. Regresi multivariat menunjukkan bahwa proporsi pengguna perempuan berkontribusi positif sebesar 0,45 poin terhadap engagement, sedangkan pengguna laki-laki berkontribusi negatif sebesar -0,12. Kesimpulannya, pemahaman mendalam tentang perbedaan respons berdasarkan gender dapat membantu perusahaan dalam merancang strategi media sosial yang lebih tepat sasaran dan meningkatkan engagement.
Kata kunci: gender; respons media sosial; instagram; data sains; strategi pemasaran
Abstract:Abstrak: A pharmacy is a health service facility to help improve the health of the community, a pharmacy is also a place for professional pharmacists to practice their work. To determine the amount of stock inventory, Jaka…
ka Wijaya Pharmacy requires a clusterization of sales stock data. The method that can be used is the K-Means algorithm. This algorithm is based on a simple idea. K-Means is a distance-based clustering method that divides data into a number of clusters and this algorithm only works on numeric attributes. The data processed in this research is a sample taken from the Jaka Wijaya Pharmacy data in 2022. The Jaka Wijaya Pharmacy dataset consists of the attributes No, Drug Item, Type, Packaging, Initial Stock, Cost Price, Unit Conversion, Selling Price, Number of Transactions, Ending Stock, Shelf, Warehouse-Office Codes. With the K-Means Clustering method, it is possible to group drug sales data with stock that is not selling well as cluster 0, stock that is selling well as cluster 1, and stock that is selling very well as cluster 2. The sample data to be tested consists of 170 data from the Jaka Wijaya Pharmacy. Where the cluster results show that there are several results, namely cluster 0 totaling 102, cluster 1 totaling 34, and cluster 2 totaling 34 decisions, where the decisions include very in demand, in demand, not in demand.
Keywords: Data Mining; Jaka Wijaya Pharmacy; K-means Cluster
Abstrak: Apotek merupakan sarana pelayanan kesehatan untuk membantu meningkatkan kesehatan bagi masyarakat, apotek juga sebagai tempat praktik tenaga profesi apoteker dalam melakukan pekerjaan. Untuk menentukan jumlah persediaan stok, Apotek Jaka Wijaya membutuhkan suatu clusterisasi data stok penjualan. Metode yang dapat digunakan yaitu algoritma K-Means. Algoritma ini didasarkan pada ide sederhana. K-Means adalah metode Clustering berbasis jarak yang membagi data ke dalam sejumlah cluster dan algoritma ini hanya bekerja pada atribut numeric. Data yang diolah dalam penelitian ini merupakan sampel yang diambil dari data Apotek Jaka Wijaya pada tahun 2022. Dataset Apotek Jaka Wijaya terdiri dari atribut No, Item Obat, Jenis, Kemasan, Stok Awal, Harga Pokok, Konversi Satuan, Harga Jual, Jumlah Transaksi, Stok Akhir, Rak, Kode Gudang-Kantor. Dengan metode K-Means Clustering maka dapat mengelompokkan data penjualan obat dengan stok kurang laris sebagai cluster 0, stok laris sebagai cluster 1, dan stok sangat laris sebagai cluster 2. Data sampel yang akan diuji terdiri dari 170 data dari Apotek Jaka Wijaya. Yang dimana hasil cluster menunjukkan terdapat beberapa hasil yaitu cluster 0 berjumlah 102, cluster 1 berjumlah 34, dan cluster 2 berjumlah 34 keputusan yang dimana keputusan itu meliputi sangat laris, laris, kurang laris.
Kata kunci: Apotek Jaka Wijaya; Data Mining; K-means Cluster