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All Journal Inersia : Jurnal Teknik Sipil dan Arsitektur MEDIA KOMUNIKASI TEKNIK SIPIL REKA RACANA Harmonia: Journal of Research and Education Dinamika Informatika Jurnal Sains dan Teknologi Farmasi Jurnal Kimia dan Kemasan Teras Jurnal JURNAL MEDIA INFORMATIKA BUDIDARMA Al-Dzikra: Jurnal Studi Ilmu al-Quran dan al-Hadits Rekayasa Teknologi JSFK (Jurnal Sains Farmasi & Klinis) Jurnal Inovator PengabdianMu: Jurnal Ilmiah Pengabdian kepada Masyarakat Bina Teknika International Journal for Educational and Vocational Studies Ijtihad Building of Informatics, Technology and Science Jurnal Pengabdian Dharma Bakti JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Letters in Information Technology Education (LITE) Infotech: Journal of Technology Information Jurnal Teslink : Teknik Sipil dan Lingkungan INVEST : Jurnal Inovasi Bisnis dan Akuntansi Widya Pranata Hukum : Jurnal Kajian dan Penelitian Hukum BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi Al-Mizan: Jurnal Hukum Islam dan Ekonomi Syariah Cantilever: Jurnal Penelitian dan Kajian Bidang Teknik Sipil Jurnal Teknoif Teknik Informatika Institut Teknologi Padang Jurnal Ilmu Komunikasi Dan Media Jurnal Sistem Informasi dan Manajemen Suluah Pasaman (Journal of Environmental Sustainability Management) Singkite Journal J-Sil (Jurnal Teknik Sipil dan Lingkungan)
Erizal Erizal
Pusat Aplikasi Teknologi Isotop dan Radiasi, BATAN Jl. Lebak Bulus Raya No.49, Jakarta 12070
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Journal : Building of Informatics, Technology and Science

 1 
Perbandingan Algoritma SVM dan Decision Tree Dalam Klasifikasi Kepuasan Pengguna Aplikasi Migo E-Bike di Playstore Al Azkiah, Dina Sakinah; Erizal, Erizal; Hikmah, Fitri Nur
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5315

Abstract

Currently, transportation has become an essential need in daily life, and the rapid development of digital technology has had a significant impact on the use of services and interaction with mobile applications, including in the transportation sector. The Migo E-Bike app is the first electric bike rental service application in Indonesia, offering environmentally friendly services to reduce air pollution. This research aims to assess the effectiveness of two data mining algorithms, SVM and Decision Tree, in classifying user satisfaction of the Migo E-Bike app based on reviews and ratings on the Playstore. The research findings indicate that the Decision Tree algorithm performs better than SVM. The Decision Tree achieved an accuracy of 76.39%, with balanced precision and recall for both satisfaction categories. In contrast, SVM exhibited significant imbalance with an overall accuracy of only 51.25%. Therefore, the Decision Tree algorithm is more effective in handling the user rating dataset for the Migo E-Bike app.
Analisis Sentimen Terhadap Ulasan Aplikasi Disney+ Hotstar Pada Google Playstore Menggunakan Metode Naïve Bayes Arsad, Reza Al; Erizal, Erizal
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6641

Abstract

Technology in Indonesia has advanced rapidly, making many changes in all aspects of life, one of which is the online streaming aspect, namely the Disney+ Hotstar application. Now Disney+ Hotstar is available on tablets, smart TVs, computers, and smartphones accessed from various places and times. Disney+ Hotstar has thousands of hours of various Pixar, Marvel films, as well as exclusive Indonesian and various countries' series. Although Disney+ Hotstar has a variety of interesting films and features, it does not guarantee that users are satisfied using the application. Because users have different opinions and assessments, this point can be seen from user reviews available on the Google Playstore. The main purpose of this study was to determine the assessment or sentiment of user reviews of the Disney+ Hotstar application by analyzing it. The technique used uses the Naive Bayes algorithm. A total of 1000 review data were obtained on December 28, 2024 from the Google Playstore via Google Colab, then processed using RapidMiner. The dataset went through the cleaning and preprocessing stages to become 873 review data. There were 128 good reviews and 745 bad reviews. TF-IDF weighting was performed before classification using 873 datasets. The classification stage used a cross-validation system and applied the Naive Bayes approach. Testing from this study revealed the accuracy results of the Naive Bayes algorithm of 76.06%, precision of 34.12%, and recall of 67.97%.
Perbandingan Metode Naïve Bayes Dengan SVM Pada Analisis Sentimen Aplikasi Pemesanan Tiket Kapal Ferizy Sulhan, Muhammad; Erizal, Erizal
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6715

Abstract

In the digital era, user reviews on application platforms play a crucial role in evaluating service quality and customer satisfaction. This study aims to compare two sentiment analysis methods, namely Naive Bayes and Support Vector Machine (SVM), in classifying the sentiment of Ferizy app reviews on PlayStore into positive, negative, and neutral categories. Naive Bayes, known for its simplicity, efficiency on small datasets, and fast training, is compared to SVM, which is recognized for its high performance on complex data with non-linear distributions and its flexibility in kernel usage. This study also evaluates the performance of both methods based on accuracy, precision, recall, and F1-score metrics, particularly in handling class imbalance and noise in the data. The dataset consists of user reviews of the Ferizy application, which are analyzed to identify sentiment patterns and trends. The implementation results show that Naive Bayes achieves an accuracy of 79.27%, while SVM reaches an accuracy of 82.62%. This difference indicates that SVM is superior in handling more complex patterns in review data, although the margin is relatively small. The findings also reveal significant differences between the two methods, particularly in sentiment classification accuracy. Factors such as language complexity, class imbalance, and algorithm parameter selection are found to influence the performance of each method. This study provides valuable insights for application developers to improve service quality based on user sentiment analysis. Additionally, the results are expected to contribute to the development of more advanced and targeted sentiment analysis strategies, particularly in the digital transportation domain.Keyword: Analisis Sentimen; Naïve Bayes; Support Vector Machine; Ferizy; Ulasan
Analisis Perbandingan Algoritma Naïve Bayes dan Random Forest Dalam Klasifikasi Penyakit Stroke Pada Puskesmas Virgiawan, Iwan; Erizal, Erizal
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6771

Abstract

One of the main reasons people become disabled or die is because of a stroke. The key to swift and effective therapy is an early diagnosis. This research examines the relative performance of the Naïve Bayes and Random Forest algorithms in identifying stroke cases using data collected from patients at the Cipayung Health Center. Age, gender, BMI, smoking status, hypertension, and other physical and mental health issues are some of the characteristics represented in the 644 samples used in the study. Collecting data, cleaning it up, and then evaluating the model using metrics like recall, precision, and accuracy are all part of the research process. With a 92% accuracy rate, the Random Forest algorithm outperformed Naïve Bayes (87% accuracy rate), according to the data. Medical professionals may use these results as a guide to improve stroke detection, which in turn accelerates treatment and lessens the likelihood of consequences. The findings of this study also pave the way for future research into machine learning algorithms.
Co-Authors A.A. Ketut Agung Cahyawan W Ade Davy Wiranata Al Ayyubi, Reza Al Azkiah, Dina Sakinah Alfaridzi, Eko Alminson, Pego Ananda, Fauzan Raflinur Andini, Andini Angga Prayoga Arrafi, Naufal Rifqi Arsad, Reza Al Aulia Shilvi Auzal Halim Azizah, Khusnita Basril A. Basril Abbas Basuki, Umar Ben, Elfi Sahlan Bimantoro, Prabu Budiarto, Djoko Burhan Djamaluddin Burhanuddin Burhanuddin Deswita Deswita Diqi, Muhammad Dwi, Gebi Sintia Emil Budianto Fauzan Azima Firman Noor Hasan Hamimuddin, Moch Hendri, Yon Hikmah, Fitri Nur I Nyoman Winata IBNUL QAYIM Irwan Irwan Julianto, Baskoro Tri Kamaruddin Kamaruddin Khairuddin - Khoirunnisa, Hana Kristian, Tadem Vergi Lamuse, Maulina Listiawan, Indra Maklas, Fikali Marselina Endah Hiswati Mentari, Sekar Metriadi, Metriadi Mohammad Diqi Muhammad Fairuzabadi Muhammad fauzan Muhammad Irfan Muhammad Sulhan Mulawarman Mulawarman Oktafiani, Pradyta Galuh Purwo Mahardi Putra, Heriansyah Rahmawan, Rama Zaky Ramadhanis, Zainab Dwi Rezza Anugrah Mutiarawan Rifqi Amrullah, Muhammad Robidi Robidi Satyanto Krido Saptomo Selvi Kasman Sembiring, Rinawati Seminar, Kudong Boro Sujarweni, V Wiratna Sujarweni, Veronika Wiratna Sultono, Sultono Susilo Nugroho, Nadhya Sutoyo Sutoyo Suwarto Suwarto Thamrin W., Thamrin Thantawi, Firdaus Tonadi, Een Tri Sudibyo Umam, Faqih Nadiya Utami, Anisa Dwi Virgiawan, Iwan Wilma Sriwulan Yulianto, Adhithya Yudo Yunus Yakub Zaidir