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All Journal IJCIT (Indonesian Journal on Computer and Information Technology) Jurnal Teknik Komputer AMIK BSI KOMPUTEK JIKA (Jurnal Informatika) Jurnal Infortech Akrab Juara : Jurnal Ilmu-ilmu Sosial Computer Science (CO-SCIENCE) INSANtek – Jurnal Inovasi dan Sains Teknik Elektro 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
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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

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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.
Perbandingan Algoritma Dengan Particle Swarm Optimization Untuk Analisis Sentimen Pada Peraturan PSBB di Indonesia Raharjo, Mugi; Putra, Jordy Lasmana; Sandi, Tommi Alfian Armawan; Napiah, Musriatun
Paradigma - Jurnal Komputer dan Informatika Vol. 24 No. 1 (2022): Periode Maret 2022
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/paradigma.v24i1.968

Abstract

The pandemic has given rise to new rules and terms in society. Various countries have their own regulations, including Indonesia with the name PSBB for that, the author tries to conduct research related to the PSBB condition in Indonesia with the intent and purpose of knowing people's sentiments towards it, the authors carry out this modeling positively and negatively. model in a tweet on Twitter. We capture information through Twitter media which then we process the data so that it is ready to be tested on the algorithm used. In data collection and processing, we use a fast miner application. In this study, Naive Bayes,KNN,and SVM were used. We also did a model comparison with Particle Swarm Optimization. model 1 tested three algorithms using a 0.7-0.8 ratio validation and 10-fold cross-validation, In Model 2 the author used a selection feature, namely Particle swarm Optimization where PSO was used as optimization. From the second model, the accuracy is 88.00%. for SVM + PSO, 88.54%% for NB + PSO and 81.58% for K -NN + PSO. And after testing the 2 methods, it turns out that Naive Bayes + PSO has the highest level of accuracy and precision
Pengembangan Aplikasi Web untuk Resize Citra Digital dengan Fitur Batch Processing Menggunakan Next.Js dan Sharp Waeisul Bismi; Muhammad Qomaruddin; Nila Hardi; Musriatun Napiah; Astrid Noviriandini
KOMPUTEK Vol. 10 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Ponorogo

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Abstract

The exponential growth of digital content has increased the demand for efficient and accessible image processing tools. This research aims to develop a web-based image resize application with batch processing features using Next.js and Sharp. The research method employs Research and Development (R&D) with a Software Development Life Cycle (SDLC) approach using the Waterfall model, encompassing requirements analysis, system design, implementation, testing, deployment, and maintenance phases. The application was developed by integrating Next.js 16 framework for full-stack development, Sharp library for high-performance image processing, and JSZip for archive handling. Implemented features include flexible upload (file, folder, ZIP), downsampling and upsampling options, pixel dimension input, JPEG/JPG/PNG format conversion, and batch processing with progress monitoring. Testing results demonstrated that 100% of features were successfully implemented with a functional testing success rate of 100%. The average response time achieved 1.76 seconds per image, 41% faster than the 3-second target. The quality of the test results shows that the quality of the resized images meets very good quality standards with high structural similarity to the original images for both downsampling and upsampling. This research has produced a web application for image resizing that is accessible without installation, efficient for batch processing, and produces optimal output quality by utilizing the Mitchell interpolation kernel for downsampling and Lanczos for upsampling
Pengembangan Aplikasi Check Font Versi 2 Deteksi Font Pada Dokumen PDF Dengan Algoritma Distribusi Font Mugi Raharjo; Musriatun Napiah; Sujiliani Heristian; Rachmat Adi Purnama
INSANtek Vol. 7 No. 1 (2026): Mei 2026
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/insantek.v7i1.12575

Abstract

Konsistensi format dokumen merupakan salah satu aspek penting dalam penulisan karya ilmiah seperti skripsi, tesis, maupun laporan penelitian. Namun, proses pemeriksaan format dokumen seperti jenis font, ukuran font, dan spasi antar baris masih sering dilakukan secara manual sehingga membutuhkan waktu yang cukup lama serta berpotensi menimbulkan kesalahan dalam proses validasi dokumen. Penelitian ini bertujuan untuk mengembangkan aplikasi Check Font versi 2 berbasis Python yang mampu mendeteksi dan menganalisis format dokumen secara otomatis pada file berformat PDF. Metode yang digunakan dalam penelitian ini adalah pendekatan analisis dokumen dengan memanfaatkan pustaka PyMuPDF untuk mengekstraksi informasi teks, termasuk jenis font, ukuran font, serta posisi teks pada setiap halaman dokumen. Sistem kemudian melakukan proses normalisasi keluarga font (font family normalization) untuk mengelompokkan berbagai variasi nama font yang masih berasal dari jenis font yang sama. Kebaruan pada penelitian ini terletak pada pengembangan fitur analisis yang tidak hanya mendeteksi jenis font, tetapi juga mampu mengidentifikasi distribusi ukuran font, estimasi spasi antar baris, serta visualisasi distribusi font dalam bentuk grafik. Selain itu, sistem juga memberikan penandaan warna pada teks untuk menunjukkan kesesuaian atau ketidaksesuaian font terhadap standar yang dipilih pengguna. Hasil penelitian menunjukkan bahwa aplikasi Check Font versi 2 mampu membantu proses pemeriksaan format dokumen secara otomatis, lebih cepat, dan lebih sistematis dibandingkan pemeriksaan manual maupun versi sebelumnya.
Co-Authors Ade Setiawan Ade Setiawan Aminuyati Arina Selawati Astrid Noviriandini 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 muhammad qomaruddin Mustofa Mustofa Mustofa Mustofa Nila Hardi 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