Article Per Year (5 Year)
p-Index From 2021 - 2026
3.138
P-Index
This Author published in this journals
All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI JIKA (Jurnal Informatika) Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Computer Science (CO-SCIENCE) Sci-Tech Journal Paradigma Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Informatics and Software Engineering (ISE) VISA: Journal of Vision and Ideas
Musriatun Napiah
Universitas Bina Sarana Informatika
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Medicine & Pharmacology Public Health Social Sciences Other

Published : 24 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 24 Documents
Search

 1   2   3 
Analisis Sentimen Ulasan Pelanggan Menggunakan Algoritma Naive Bayes pada Aplikasi Gojek Heristian, Sujiliani; Napiah, Musriatun; Erawati, Wati
Computer Science (CO-SCIENCE) Vol. 5 No. 1 (2025): Januari 2025
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/coscience.v5i1.7775

Abstract

Transportation is a means that a person uses to move from one place to another. One mode of transportation that is popular among the public is online motorcycle taxis, such as Gojek. Gojek continues to innovate to meet customer needs more effectively, as well as expand the scope of its services. This research aims to identify the number of positive and negative sentiments in the user review dataset, evaluate the performance of the algorithm used, and measure the level of customer satisfaction with Gojek services. Analysis was carried out on 6,485 customer reviews, which resulted in 4,387 positive sentiments and 2,098 negative sentiments. The classification model used, namely Naive Bayes, shows an accuracy of 88.5%, precision of 88.1%, and recall of 89.0%. The results of this research indicate that the Naive Bayes method provides good performance in analyzing the sentiment of user reviews of Gojek services
Analisis Pengelolaan Kearsipan Menggunakan Algoritma Machine Learning pada Fakultas Arsitektur Lansekap dan Teknologi Lingkungan, Universitas Trisakti Endrawan, Afif; Napiah, Musriatun
Sci-tech Journal Vol. 3 No. 2 (2024): Sci-Tech Journal (STJ) In Press
Publisher : MES Bogor

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56709/stj.v3i2.549

Abstract

This research discusses the analysis of archive management using machine learning algorithms, especially the Decision Tree method, at the Faculty of Landscape Architecture and Environmental Technology, Trisakti University. This research aims to improve the efficiency and quality of incoming and outgoing letter archive management. The results showed that the Decision Tree algorithm is effective in managing archive data, facilitating searches, and improving the systematization and efficiency of the archive storage and maintenance process. The implementation of this algorithm is proven to have a positive and significant effect, so the working hypothesis stating the positive effect of this algorithm on archive management is accepted, while the null hypothesis is rejected.
Analyzing Public Sentiment Toward Makanan Bergizi Gratis Program Using Machine Learning Napiah, Musriatun; Heristian, Sujiliani; Raharjo, Mugi; Purnama, Rachmat Adi
Computer Science (CO-SCIENCE) Vol. 6 No. 1 (2026): January 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/co-science.v6i1.10445

Abstract

Makanan Bergizi Gratis (MBG) program is a strategic initiative of the Indonesian government to improve the nutritional quality of schoolchildren. This research seeks to examine public sentiment regarding the MBG program by leveraging 10,000 tweets obtained from Kaggle. The method used combines Natural Language Processing (NLP) and Machine Learning approaches, several algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, Naive Bayes, XGBoost, and LightGBM were tested to compare classification performance. The dataset contains a collection of public reviews categorized into three sentiment classes: positive, negative, and neutral. The analysis process includes text cleaning, tokenization, stopword removal, and stemming to obtain a cleaner text representation. Text features were then extracted using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The results showed that the Logistic Regression 97% with an F1-score of 0.9552 models showed the most optimal performance. Sentiment analysis revealed 65% positive responses, 25% neutral, and 10% negative, with the dominant keywords being “nutrisi,” “sehat,” “anak sekolah,” and “gratis.” The results visualization, in the form of a Word Cloud and a bar chart, indicate that public opinion tends to be positive towards the implementation of the MBG program, particularly regarding improving the nutrition of schoolchildren. This research is expected to provide input for policymakers in evaluating public perceptions of the implementation of food-based social programs.
Perbandingan Algoritma K-Nearest Neighbor (K-NN) dan Naive Bayes dalam Klasifikasi Tingkat Kemiskinan di Indonesia Juanuari, Juanuari; Ilyas, Maulana; Widodo, Rahmat Tri; Manzis, Ilham; Budiarti, Yusnia; Napiah, Musriatun
VISA: Journal of Vision and Ideas Vol. 6 No. 1 (2026): Journal of Vision and Ideas (VISA)
Publisher : IAI Nasional Laa Roiba Bogor

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

Abstract

Poverty is a major issue in sustainable development in Indonesia that requires a data-driven analysis approach to produce more accurate identification. This study aims to compare the performance of the K-Nearest Neighbor (K-NN) and Naive Bayes algorithms in classifying poverty levels in Indonesia based on social and economic data. The dataset was obtained from the Kaggle platform with the title "Classification of Poverty Levels in Indonesia", which contains 514 district/city data with various poverty indicators. The data was divided with a ratio of 80% for training and 20% for testing, then classification was carried out using the K-NN algorithm with a value of K = 5 and Naive Bayes. Evaluation was carried out using a confusion matrix with metrics of accuracy, precision, recall, and F1-score. The results showed that K-NN provided the best results with an accuracy of 97.09%, precision of 100%, recall of 75.00%, and F1-score of 85.71%, while Naive Bayes achieved an accuracy of 95.15%, precision of 73.33%, recall of 91.67%, and F1-score of 81.48%. This study resulted in better performance of this model compared to the results of previous studies. Therefore, the K-NN algorithm with the right parameters can be used as an effective method to support the data-based poverty level classification process and assist the government in poverty alleviation management and planning policies.
Co-Authors Ade Setiawan Ade Setiawan Aminuyati Arina Selawati Dwiza Riana Eka Kusuma Pratama Endrawan, Afif Eni Heni Hermaliani Erawati, Wati Fajar Akbar Fajar Shidiq Ilyas, Maulana Jordy Lasmana Putra Juanuari, Juanuari Leksono, Ilham Nur Manzis, Ilham Mila Desi Anasanti Mugi Raharjo Muhammad Haris Muhammad Muharrom Muhammad Muharrom Mustofa Mustofa Mustofa Mustofa Rachma Darma Astuti Rachmat Adi Purnama Rachmawati Darma Astuti Rachmawati Darma Astuti Raharjo, Mugi Rahmat Tri widodo Rian Septian Anwar Rifki Sadikin Riziq Shihab, Muhammad Alfata Sujiliani Heristian Susafa'ati Susafa'ati Tika Adilah M Tommi Alfian Armawan Sandi Waeisul Bismi Waesul Bismi Yusnia Budiarti