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All Journal Jurnal Online Mahasiswa (JOM) Bidang Perikanan dan Ilmu Kelautan Jurnal Sistem Informasi Prosiding University Research Colloquium Jurnal Riset Rumpun Ilmu Teknik (JURRITEK) LEGALITAS : Jurnal Ilmiah Ilmu Hukum Saturnus: Jurnal Teknologi dan Sistem Informasi
Imron Imron
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Earth & Planetary Sciences Environmental Science Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology

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Analisis Sentimen Aplikasi Liputan6.Com pada Ulasan Pengguna di Google Playstore dengan Menggunakan Algoritma Support Vector Machine (Svm) dan Naïve Bayes Yayang Tika Robiatush Sholiha; Lubna Asjad Muhda Nabilah; Imron Imron
Saturnus : Jurnal Teknologi dan Sistem Informasi Vol. 3 No. 3 (2025): Juli : Saturnus : Jurnal Teknologi dan Sistem Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/saturnus.v3i3.867

Abstract

This study aims to evaluate user sentiment toward the Liputan6.com application available on the Google Play Store. In the digital era, user reviews serve as a significant indicator in assessing the quality of an application. However, the inconsistency between rating scores and review content renders manual analysis less objective. To address this issue, a machine learning approach was adopted by comparing two algorithms, namely Support Vector Machine (SVM) and Naïve Bayes (NB). A total of 2,500 reviews were collected through a web scraping process and automatically labeled based on the rating (positive if ≥ 3, negative if < 3). The data preprocessing stages included cleaning, case folding, tokenizing, stopword removal, and token filtering. Subsequently, word weighting was carried out using the TF-IDF method, followed by classification using 10-Fold Cross Validation in RapidMiner. The evaluation results indicate that, in the positive class, NB demonstrated superior precision (89.47%), whereas SVM achieved higher recall (98.94%) and F1-score (90.96%). In the negative class, SVM performed better in terms of precision (66.15%), while NB attained higher recall (65.65%) and F1-score (36.34%). Further evaluation based on AUC and accuracy positioned SVM in the good category (AUC 0.842; accuracy 83.82%), while NB was categorized as fail (AUC 0.505; accuracy 60.87%). Overall, SVM is considered to be more effective than NB.
Penerapan Bulding Information Modelling (BIM) Autodesk Revit dalam Pembuatan Bar Bending Schedule (BBS) Pondasi Pile Cap Proyek Apartemen Jkt Living Star - Jakarta Timur Muhamad Alimin; Imron Imron; Muhammad Taulani
JURNAL RISET RUMPUN ILMU TEKNIK Vol. 2 No. 2 (2023): Oktober : Jurnal Riset Rumpun Ilmu Teknik
Publisher : Pusat riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jurritek.v2i2.1599

Abstract

Bar bending schedule is a systeem of bending patterns of reinforcement that includes data on diameter, shape, length and number of reinforcement. The purpose of this study is to obtain images of bar bending schedule documents, pile cap foundation iron volumes and find out whether Autodesk Revit software is more efficient and effective than conventional methods. This research uses quantitative methods and the manufacturing process uses the concept of Building Information Modeling (BIM) with autodesk revit software. The results showed that the use of Autodesk Revit software makes it more effective, especially in making bar bending schedule documents, because everything is made automatically. The volume of pile cap iron produced by Autodesk Revit software is 46,878 kg has a difference with the existing (conventional) volume. The volume result of autodesk revit software is less with a difference of 1.29%. This shows that Autodesk Revit software is more efficient in terms of calculating the volume of concrete iron.
Co-Authors Abdul Ghopur Ahmad Jaelani Joko Samiaji Lailatul Mufida Laily Mardiyati Lubna Asjad Muhda Nabilah Maryam Jamilah Yanayirtika Miftakhuddin Miftakhuddin Muhamad Alimin Muhammad Taulani Rifardi Untaji Affan Yayang Tika Robiatush Sholiha