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All Journal Semantik Jurnal Simetris Telematika Jurnal Transformatika CESS (Journal of Computer Engineering, System and Science) KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Jurnal Informatika Upgris E-Dimas: Jurnal Pengabdian kepada Masyarakat Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Teknologi Terpadu Jurnal Review Pendidikan dan Pengajaran (JRPP) Building of Informatics, Technology and Science Journal of Information Systems and Informatics IJAIT (International Journal of Applied Information Technology) E-Link: Jurnal Teknik Elektro dan Informatika Jurnal Teknik Informatika C.I.T. Medicom JPKMI (Jurnal Pengabdian Kepada Masyarakat Indonesia) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Jurnal Program Kemitraan dan Pengabdian Kepada Masyarakat Transformasi Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Teknologi Informasi Cyberku JURNAL ABDIKARYASAKTI Jurnal Pengabdian Kolaborasi dan Inovasi IPTEKS Jurnal Kabar Masyarakat NERO (Networking Engineering Research Operation) Tematik EduInovasi: Journal of Basic Educational Studies Jurnal Pengabdian Masyarakat Teknologi dan Pendidikan (MANTAP) Dinamis: Jurnal Pengabdian Kepada Masyarakat Jurnal DIMASTIK Komputasi : Jurnal Ilmiah Ilmu Komputer dan Matematika
Astrid Novita Putri, Astrid
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Perbandingan Naïve Bayes dan Support Vector Machine Dalam Analisis Sentimen Google Maps Pusat Perbelanjaan Eliza Cahyaningrum; Astrid Novita Putri
Building of Informatics, Technology and Science (BITS) Vol 8 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i1.9558

Abstract

The rapid growth of user reviews on Google Maps is not always accompanied by ease in understanding the sentiment contained within them, causing tourists and the general public to face difficulties in determining shopping centers with good reputation and service quality. The lack of information regarding visitor satisfaction levels, along with various facility-related issues such as crowd density, limited parking space, and the comfort of public facilities, combined with the large number of subjective and unstructured reviews, makes manual sentiment analysis ineffective and potentially leads to less accurate conclusions. This investigation aims to analyze sentiment from Google Maps reviews of shopping centers in the city of Semarang utilizing the Support Vector Machine (SVM) and Naïve Bayes methods. The data were collected from five shopping centers with the highest number of reviews in Semarang, namely Paragon Mall, Mall Ciputra, Java Mall, DP Mall, and Queen City Mall. The investigation method includes text preprocessing, TF-IDF weighting, and sentiment classification into three classes: negative, neutral, and positive. The dataset was divided into training and testing data with a ratio of 80:20. The outcomes reveal that the Naïve Bayes method achieved an accuracy of 85.56%, while the Support Vector Machine (SVM) method achieved an accuracy of 89.20%. Considering the outcomes, the SVM method performs better in classifying sentiment from Google Maps reviews of shopping centers in Semarang.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN KARYAWAN TERBAIK MENGGUNAKAN METODE SAW DAN TOPSIS PADA KPPN SEMARANG II Muhamad Reski Mufiani; Astrid Novita Putri
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 8 No 2 (2026): EDISI 28
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v8i2.7307

Abstract

Penelitian ini bertujuan untuk merancang dan mengimplementasikan Sistem Pendukung Keputusan (SPK) dalam pemilihan Pegawai Terbaik di KPPN Semarang II menggunakan metode Simple Additive Weighting (SAW) dan Technique for Order Preference by Kemiripan ke Solusi Ideal (TOPSIS). Penilaian dilakukan berdasarkan lima kriteria, yaitu Ketertiban/Disiplin, Nilai Kinerja Pegawai (NKP), Sikap & Pelayanan, Tanggung Jawab, serta Inisiatif & Kompetensi. Metode SAW digunakan untuk menghitung nilai preferensi melalui penjumlahan terbobot, sedangkan TOPSIS menentukan alternatif terbaik berdasarkan kedekatan terhadap solusi ideal. Hasil perhitungan menunjukkan bahwa sistem mampu menghasilkan kinerja pegawai secara objektif dan terukur. Pengujian menggunakan User Acceptance Test (UAT) memperoleh tingkat penerimaan sebesar 81,25%, yang menunjukkan bahwa sistem berada pada kategori baik dan layak digunakan sebagai alat bantu pengambilan keputusan.
Implementasi User-Based Collaborative Filtering dengan Cosine Similarity untuk Sistem Rekomendasi Produk pada Marketplace Botol Plastik Berbasis Web Ridho Alvin Saputra; Astrid Novita Putri
EduInovasi:  Journal of Basic Educational Studies Vol. 6 No. 1 (2026): EduInovasi:  Journal of Basic Educational Studies
Publisher : Intitut Agama Islam Nasional Laa Roiba Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of web-based marketplaces has increased the number of available products, making it difficult for users to find products that match their needs and preferences. This condition creates information overload, which can reduce the effectiveness of product searching in marketplaces. This study aims to implement the User-Based Collaborative Filtering method with Cosine Similarity in a product recommendation system for a web-based plastic bottle marketplace. The research used a quantitative method with an implementation approach using real rating data from the marketplace consisting of 108 ratings, 36 users, and 15 products with a sparsity level of 80%. The research stages included building a user-product matrix, calculating cosine similarity, selecting K=5 neighbors, and predicting ratings using weighted average. The results showed that the system was able to generate product recommendations based on user preference similarities with the highest similarity value of 0.9939 and the highest predicted rating of 5.0000. The study also found that high sparsity caused many user pairs to have only a few co-rated items, resulting in trivial similarity values of 1.0000. Therefore, the User-Based Collaborative Filtering method with Cosine Similarity can be implemented in a web-based plastic bottle marketplace to support personalized product recommendations.
Co-Authors Adinugroho, Suryo Agusta Praba Ristadi Pinem Ahmad Rifa’i April Firman Daru Arya Jonathan Trijadi Ashour, Salim Fathi Salim Basworo Ardi Pramono Caesar, Meisya Eliza Cahyaningrum Febrian Wahyu Christanto Fitrianti, Ayang Hadi, Soiful Handayani, Sri Hanif, Mohammad Burhan Joko Suntoro Khoirudin Khoirudin Kusumaningrum, Yulinda Lakhmudien, Lakhmudien Latius Hermawan M. Zakki Abdillah Mochamad Hariadi Muhamad Reski Mufiani Nur Latifah Dwi Mutiara Sari Nur Wakhidah paulus harsadi Perkasa, Attila Elang Pratama Angga Buana Rastri Prathivi Rastri Prathivi Ridho Alvin Saputra Ruri Suko Basuki Sari, Yuhana Indah Setiaji, Galet Guntoro Setiarso, Galih Shona Chayy Bilqisth Siti Asmiatun, Siti Soiful Hadi Susanto Susanto Titin Winarti Vensy Vydia Victor Gayuh Utomo Wardana, Hafiq Ibnu Wardhani, Anindya Khrisna Wardhani, Anindya Khrisna Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Wiratama Adi Wibawa ZAMAN, BADROE