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All Journal Jurnal Sistem Informasi dan Sistem Komputer Bulletin of Information Technology (BIT)
Susliansyah
Universitas Bina Sarana Informatika
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Other

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Pengelompokan Data Penjualan Produk Cetakan Pada Algoritma K-Means Dengan Bantuan Tool Orange Susliansyah; Muhammad Ridho Caroko; Heny Sumarno; Hendro Priyono; Linda Maulida
Bulletin of Information Technology (BIT) Vol 7 No 2 (2026)
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v7i2.2659

Abstract

The main problem faced is the large amount of unstructured sales data, making it difficult to perform manual analysis. With the application of the K-Means algorithm, sales data can be grouped into clusters representing products with high and low sales. The research process begins with the stages of problem identification, data collection, preprocessing, application of the K-Means algorithm, evaluation of clustering results, and then analysis and interpretation. Iteration results show that cluster C1 consists of a number of high-selling sales data, while cluster C2 encompasses the majority of low-selling sales data. Evaluation using the Davies-Bouldin Index (DBI) yields a value of 0.2818, indicating fairly good cluster quality, while the Silhouette Plot provides values of 0.082 for C1 and 0.276 for C2, indicating that cluster C2 is more stable compared to C1. Scatter Plot visualization shows the data distribution forming a slanted pattern from C1 to C2. The result of this research is that by using the K-Means algorithm, it can effectively cluster sales data of printed products, so it can be used as a basis for business decision-making related to marketing strategies, stock control, and product performance evaluation.
Perbandingan Sentimen Ulasan Aplikasi Gemini Pada Google Play Store Menggunakan Metode SVM dan Naive Bayes Dimas Pungky Eprianza; Mochamad Agung Laksono; Aria Farhan Qur’ani; Susliansyah; Hendro Priyono
Jurnal Sistem Informasi dan Sistem Komputer Vol 11 No 2 (2026): Vol 11 No 2 - 2026
Publisher : STIMIK Bina Bangsa Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51717/simkom.v11i2.1334

Abstract

Teknologi kecerdasan buatan (AI) berkembang pesat dan telah diterapkan di berbagai aspek kehidupan masyarakat, termasuk chatbot seperti Gemini AI dari Google. Meskipun memiliki banyak fitur menarik, beberapa pengguna telah mengungkapkan ketidakpuasan terhadap fungsionalitas aplikasi tersebut. Berdasarkan ulasan dari Google Play Store, penelitian ini menyelidiki persepsi pengguna terhadap aplikasi Gemini. Metode Support Vector Machine dan Naive Bayes digunakan untuk menganalisis 1.000 ulasan yang dikumpulkan melalui proses scraping. Pra-pemrosesan, pembobotan TF-IDF, pemisahan data, implementasi algoritma, dan evaluasi model Google Colab merupakan bagian dari fase penelitian. Berdasarkan temuan penelitian ini, SVM mencapai akurasi 81,4%, sedangkan teknik Naive Bayes mencapai akurasi terbaik sebesar 82,4%. Hasil analisis mengindikasikan bahwa kedua metode memiliki kemampuan yang lebih baik dalam klasifikasi sentimen positif relatif terhadap sentimen negatif.
Co-Authors Aria Farhan Qur’ani Dimas Pungky Eprianza Hendro Priyono Heny Sumarno, Heny Linda Maulida Mochamad Agung Laksono Muhammad Ridho Caroko