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All Journal JRIIN :Jurnal Riset Informatika dan Inovasi Jurnal Publikasi Teknik Informatika
Ahmad Apip
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management

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APLIKASI PENCARIAN DATA WARGA DI LINGKUNGAN RW 09 BERBASIS ANDROID Citha Ahmad Fauzi; Ahmad Apip; Nadia Rahmawati; Randy Tri mullya; Aries Saifudin
Jurnal Riset Informatika dan Inovasi Vol 1 No 7 (2023): JRIIN : Jurnal Riset Informatika
Publisher : shofanah Media Berkah

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Abstract

Sistem pencarian atau pencatatan dokumen data warga membutuhkan waktu yang lama, dan dokumen kertas manual rusak jika hilang atau terkena air. Karena data tersebut berisi nik, nama, alamat, umur, dan informasi lainnya, dokumen warga sangat penting. Smartphone adalah teknologi terbaik saat ini karena praktis dan dapat digunakan di mana saja. Sekarang siapa yang tidak mengenal SmartPhone? Selain dapat digunakan untuk menghubungi dan mengirim pesan, SmartPhone juga dapat digunakan untuk mencatat, menyimpan, dan mengelola data. Diharapkan bahwa ponsel berbasis Android saat ini, dengan spesifikasi yang sebanding dengan laptop dan komputer, akan mampu menyelesaikan masalah pengelolahan data masyarakat. Diharapkan telepon berbasis Android ini dapat memotoring data warga di wilayah RT09 dan membantu kinerja perangkat RT dan RW.
Perbandingan Kinerja Algoritma Naive Bayes dan K-Nearest Neighbors untuk Analisis Sentimen Ulasan Aplikasi Lazada Menggunakan Python Ahmad Apip; Kurniawan, Aa
Jurnal Publikasi Teknik Informatika Vol. 5 No. 1 (2026): Januari: Jurnal Publikasi Teknik Informatika
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jupti.v5i1.6437

Abstract

The rapid growth of e-commerce in Indonesia has led to an increasing number of user reviews shared across various digital platforms, including the Lazada application. These textual reviews contain valuable insights into user satisfaction and experiences but have not been fully utilized for automated sentiment analysis. This study aims to compare the performance of the Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying user sentiment from Lazada reviews collected from the Google Play Store.The data preprocessing stages include text cleansing and case folding, tokenization, stopword removal, and stemming using the Sastrawi library. The cleaned text data were then transformed into numerical representations using the Term Frequency–Inverse Document Frequency (TF-IDF) method before classification. Model performance was evaluated using 10-Fold Cross Validation based on four key metrics: accuracy, precision, recall, and F1-score.The experimental results indicate that the Naïve Bayes algorithm achieved superior performance with an accuracy of 89.56%, precision of 89.53%, recall of 89.56%, and an F1-score of 89.53%. In contrast, the K-Nearest Neighbor (KNN) algorithm obtained an accuracy of 73.22%, precision of 75.21%, recall of 73.22%, and an F1-score of 65.64%. These findings suggest that Naïve Bayes demonstrates higher effectiveness and stability in classifying user sentiment on Lazada reviews compared to the KNN algorithm.
Co-Authors Aries Saifudin Citha Ahmad Fauzi Kurniawan, AA Nadia Rahmawati Randy Tri mullya