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All Journal Jurnal Tekno Kompak Journal of Students‘ Research in Computer Science (JSRCS)
Arrasyid, Rizky Maulana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management

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Pengelompokan Penerima Bantuan Kerusakan Bangunan Akibat Bencana Alam di Jawa Barat Menggunakan Algoritma K-Means Arrasyid, Rizky Maulana; Herlawati, Herlawati
Journal of Students‘ Research in Computer Science Vol. 5 No. 1 (2024): Mei 2024
Publisher : Program Studi Informatika Fakultas Ilmu Komputer Universitas Bhayangkara Jakarta Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31599/c8btc203

Abstract

Disasters have a tremendous impact on society, one of the impacts of natural disasters is building damage, a province that is very prone to natural disasters is West Java province and results in a lot of building damage due to disasters, the solution to this disaster needs building assistance caused by natural disasters. In this study discusses the application of the K-Means Clustering algorithm for recipients of aid due to natural disasters, this study took data from West Java open data with a data set of house damage this data consists of 2012-2022 covering 27 districts / cities in West Java Province. This research uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) method which has six stages. The results of the data processed using K-Means clustering are divided into 4 clusters, namely, the level of highly prioritized clusters (C0), the level of prioritized clusters (C1), the level of less prioritized clusters (C2), and the level of non-prioritized clusters (C3), In this study, clusters that are highly prioritized in receiving assistance are Bogor Regency, Bandung Regency, Cianjur Regency, Garut Regency, Sukabumi Regency, and Tasikmalaya Regency.
Analisis Sentimen Review Pembelian Produk di Marketplace Shopee Menggunakan Pendekatan Natural Language Processing Arrasyid, Rizky Maulana; Putera, Diaz Enggar; Yusuf, Ajif Yunizar Pratama
Jurnal Tekno Kompak Vol 18, No 2 (2024): AGUSTUS
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jtk.v18i2.3813

Abstract

Penelitian ini menganalisis ulasan produk yang ada di pasaran, dengan fokus pada produk "Kaos Oversize", untuk mengklasifikasikannya ke dalam ulasan positif dan negatif. Penelitian ini bertujuan untuk menunjukkan keefektifan penggunaan algoritma K-Nearest Neighbors (KNN) dan Term Frequency-Inverse Document Frequency (TF-IDF) dengan pendekatan Natural Language Processing (NLP) dalam mengklasifikasikan ulasan produk. Penelitian ini menemukan bahwa metode NLP mencapai tingkat akurasi, presisi, dan recall yang lebih tinggi dibandingkan dengan tidak menggunakan NLP. Hasil penelitian menunjukkan bahwa menganalisis kata kunci dalam ulasan dapat mewakili opini keseluruhan pembeli terhadap produk, yang dapat menjadi informasi yang berguna bagi pengecer untuk mengevaluasi produk dan layanan mereka.
Co-Authors Herlawati Herlawati Putera, Diaz Enggar Yusuf, Ajif Yunizar Pratama