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Analisis Sentimen Aplikasi Bank Digital Pada Google Play Store Menggunakan Algoritma Naive Bayes Arkan Hilman Hakim; Hananto, April Lia; Nurapriani, Fitria; Huda, Baenil
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/jict.v7i1.405

Pesatnya perkembangan teknologi perbankan digital telah mendorong munculnya beragam aplikasi perbankan yang tersedia di Google Play Store. Ulasan pengguna terhadap aplikasi-aplikasi ini menjadi sumber informasi yang berharga untuk menilai tingkat kepuasan mereka, yang kemudian dapat dianalisis melalui pendekatan sentiment analysis. Penelitian ini dilakukan untuk mengkaji kecenderungan sentimen pengguna terhadap beberapa aplikasi bank digital dengan memanfaatkan algoritma Naïve Bayes. Data yang dianalisis berasal dari 1.000 ulasan pengguna untuk masing-masing aplikasi, yaitu Seabank, Krom Bank, Bank Jago, Blu by BCA, dan Bank Saqu. Seluruh proses analisis dan pengolahan data dilakukan menggunakan platform Google Colab, dengan menerapkan metode Multinomial Naïve Bayes (MNB) untuk mengklasifikasikan sentimen ke dalam dua kategori, yaitu positif dan negatif. Hasil penelitian mengungkapkan bahwa Seabank menunjukkan performa tertinggi, dengan accuracy sebesar 94%, precision 93%, recall 100%, dan F1-score 97%, serta total 945 ulasan positif dan 55 ulasan negatif. Temuan ini memperlihatkan bahwa analisis sentimen dapat memberikan masukan yang bernilai bagi pengembang untuk meningkatkan kualitas aplikasi, sekaligus menjadi panduan bagi pengguna dalam memilih layanan perbankan digital yang sesuai dengan preferensi kebutuhan.

Analisis Sentimen Aplikasi Bank Digital Pada Google Play Store Menggunakan Algoritma Naive Bayes Arkan Hilman Hakim; Hananto, April Lia; Nurapriani, Fitria; Huda, Baenil
Journal of Informatics and Communication Technology (JICT) Vol. 7 No. 1 (2025)
Publisher : PPM Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52661/jict.v7i1.405

Pesatnya perkembangan teknologi perbankan digital telah mendorong munculnya beragam aplikasi perbankan yang tersedia di Google Play Store. Ulasan pengguna terhadap aplikasi-aplikasi ini menjadi sumber informasi yang berharga untuk menilai tingkat kepuasan mereka, yang kemudian dapat dianalisis melalui pendekatan sentiment analysis. Penelitian ini dilakukan untuk mengkaji kecenderungan sentimen pengguna terhadap beberapa aplikasi bank digital dengan memanfaatkan algoritma Naïve Bayes. Data yang dianalisis berasal dari 1.000 ulasan pengguna untuk masing-masing aplikasi, yaitu Seabank, Krom Bank, Bank Jago, Blu by BCA, dan Bank Saqu. Seluruh proses analisis dan pengolahan data dilakukan menggunakan platform Google Colab, dengan menerapkan metode Multinomial Naïve Bayes (MNB) untuk mengklasifikasikan sentimen ke dalam dua kategori, yaitu positif dan negatif. Hasil penelitian mengungkapkan bahwa Seabank menunjukkan performa tertinggi, dengan accuracy sebesar 94%, precision 93%, recall 100%, dan F1-score 97%, serta total 945 ulasan positif dan 55 ulasan negatif. Temuan ini memperlihatkan bahwa analisis sentimen dapat memberikan masukan yang bernilai bagi pengembang untuk meningkatkan kualitas aplikasi, sekaligus menjadi panduan bagi pengguna dalam memilih layanan perbankan digital yang sesuai dengan preferensi kebutuhan.

Analisis Sentimen, Grab Indonesi Analisis Sentimen Grab Indonesia Pada Ulasan Google Play Store Menggunakan Algoritma Naïve Bayes Dan SVM Nurfauzi, Oka Muhamad; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
SMARTICS Journal Vol 11 No 1 (2025): SMARTICS Journal (April 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i1.11789

This study uses the Naïve Bayes and Support Vector Machine (SVM) algorithms to analyze the sentiment of user reviews on the Grab Indonesia app on the Google Play Store. Web scraping was used to gather the review data, which was then processed through a number of stages, such as tokenization, letter modification, the elimination of unnecessary words, and weighting using the TF-IDF approach. The findings of the investigation demonstrate that SVM performs better in classifying positive and negative sentiments and has a greater accuracy (93%) than Naïve Bayes (92%). But in terms of computational efficiency, Naïve Bayes continues to lead the field. This study sheds light on how well both algorithms do sentiment analysis on Indonesian mobile apps

Classification of Reject Patterns Based on Production Stages Using the K-Means Clustering Method Lestari, Renita; Novalia, Elfina; Tukino; Nurapriani, Fitria
Golden Ratio of Data in Summary Vol. 5 No. 4 (2025): August - October
Publisher : Manunggal Halim Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52970/grdis.v5i4.1301

This study aims to classify reject patterns in the production process using the K-Means Clustering method. The dataset consists of 870 records collected from the production line, containing information such as product name, reject type, process stage, and production quantity. Through a data mining approach, data preprocessing steps such as cleaning, encoding, and normalization were performed prior to the clustering process. The Elbow Method indicated that the optimal number of clusters is three. Each cluster exhibits distinct characteristics: light rejects with small quantities in early stages, heavy rejects with large quantities, and moderate rejects with random distribution. These findings are expected to assist management in formulating more targeted strategies for process improvement and quality control. By identifying common reject patterns within each cluster, companies can adopt a more proactive approach to minimizing production defects and enhancing overall operational efficiency.

Integrasi Etnomatematika dalam Pembelajaran Bangun Datar Segi Empat Berbasis Kearifan Lokal untuk Meningkatkan Pemahaman Matematika Lestari, Santi Arum Puspita; Kusumaningrum, Dwi Sulistya; Nurapriani, Fitria
Jurnal Inovasi Penelitian dan Pengabdian Masyarakat Vol. 4 No. 2 (2024): Desember
Publisher : Indonesia Emerging Literacy Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53621/jippmas.v4i2.369

Matematika dianggap sebagai pelajaran wajib dari tingkat pendidikan dasar hingga tinggi karena menjadi dasar dan penghubung bagi mata pelajaran lainnya. Namun, masih ada siswa yang mengalami kesulitan dan memandang matematika hanya sebagai perhitungan dasar. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan pemahaman matematika siswa dengan menghubungkan matematika dan kebudayaan lokal melalui etnomatematika yang berfokus pada bangun datar segi empat. Kegiatan PkM menggunakan metode sosial konstruktivisme yang dibagi menjadi 5 tahap yakni identifikasi masalah, kolaborasi, eskplorasi, implementasi, dan evaluasi. Melalui kegiatan pengabdian kepada masyarakat, dilakukan penyuluhan di SMPN 2 Cilebar, memperkenalkan etnomatematika pada bidang segi empat kepada siswa. Hasilnya menunjukkan bahwa 85% siswa (22 dari 25 siswa) mampu mengenali bentuk segi empat pada rumah adat Sunda. Selain itu, kegiatan ini berhasil meningkatkan minat siswa terhadap matematika dengan mengaitkannya secara nyata dengan kebudayaan lokal. Meskipun berhasil, masih ada faktor penghambat, seperti persepsi sulitnya matematika dan pandangan bahwa matematika bersifat abstrak. Dengan demikian, kesimpulan yang diperoleh dari kegiatan ini adalah memberikan kontribusi positif dalam memahamkan siswa mengenai penerapan matematika pada kehidupan sehari-hari melalui pendekatan etnomatematika.

Analisis Sentimen, Text Mining Penerapan Analisis Sentimen Dan Naive Bayes Terhadap Opini Penggunaan Kendaraan Listrik Di Twitter Agustian, Adittia; Tukino; Nurapriani, Fitria
Jurnal Tika Vol 7 No 3 (2022): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v7i3.1550

Twitter is the most popular social media today. Can find out various Twitter responses that fall into the positive, neutral, or negative categories. Technological advances at this time are so rapid that vehicles will provide fuel for electric power or are called electric vehicles. Indonesia has become a country that encourages acceleration in the use of electric vehicles, according to the Minister of State-Owned Enterprises circular letter. The advancement of electric-powered vehicles is an innovation and technology that will continue to develop and transform. With the presence of the electric vehicle, the Indonesian government will serve as an important guest vehicle at the G20 Summit activities in Bali, Indonesia. The purpose of this study is to determine the public's response to electric vehicles which are currently widely used among the people of Indonesia. To find out the public response, sentiment analysis is needed through the responses of Twitter users. By generating positive, neutral, or negative categories. Based on the results of the classification of sentiment analysis on the support of electric vehicles. Data collection uses the Twitter API as an open source that can retrieve Twitter user responses, then the data cleaning process is carried out, converting Indonesian to English, then tested using the Naïve Bayes algorithm, and visualizing twitter data using python. Based on the classification results, public response to electric vehicles is more positive with 82% precision and 44% recall. By having 80% data accuracy through the Naive Bayes confusion matrix through the text mining process, python text blob, and word cloud as the relationship between words and twitter text

Clustering User Sentiment Transportasi Online Gojek Dan Grab Dengan Metode K-Means Annam, Dyno Syaiful; Hananto, Agustia; Nurapriani, Fitria; Tukino, Tukino
Jurnal Tika Vol 8 No 2 (2023): Jurnal Teknik Informatika Aceh
Publisher : Fakultas Ilmu Komputer Universitas Almuslim Bireuen - Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51179/tika.v8i2.2165

As an online transportation service, people often discuss it by sharing their opinions through various social media platforms, one of which is Google Play reviews. The opinions given by the public regarding online transportation services are diverse. Users provide reviews about the application, and naturally, users will choose an application with good reviews. However, monitoring the opinions of the general public is not easy, given the large volume of data to be processed. Therefore, the researcher aims to obtain accurate and precise information from user reviews of Gojek and Grab using clustering techniques, specifically the K-means method, using the RapidMiner application. The results of the testing of both applications can be summarized as follows: Gojek and Grab receive reviews that are not significantly different, although Grab's reviews are slightly better. The classification using the K-Means method offers a solution to the issue of sentiment analysis in user reviews of online transportation applications.

Analisis Sentimen Aplikasi Layanan Streaming Pada Google Play Store Menggunakan Algoritma Naïve Bayes Alparizi, Muhamad Iqbal; Hananto, April Lia; Nurapriani, Fitria; Huda, Baenil
Innovative: Journal Of Social Science Research Vol. 5 No. 3 (2025): Innovative: Journal Of Social Science Research
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/innovative.v5i3.19093

Aplikasi streaming seperti Amazon Prime Video, Disney+ Hotstar, Catchplay+, Netflix, dan Viu semakin populer seiring dengan berkembangnya industri streaming. Ulasan pengguna di Google Play Store menyediakan informasi penting mengenai persepsi dan kepuasan terhadap aplikasi-aplikasi tersebut. Penelitian ini bertujuan untuk menganalisis sentimen dari lima aplikasi streaming dengan memanfaatkan algoritma Naive Bayes. Ulasan pengguna dikategorikan menjadi sentimen positif dan negatif. Proses klasifikasi dievaluasi dengan algoritma Naive Bayes, diukur menggunakan metrik akurasi, presisi, recall, dan skor F1. Hasil penelitian menunjukkan bahwa Amazon Prime Video mencapai akurasi tertinggi sebesar 86% dan skor F1 0,88, disusul oleh Catchplay+ dengan akurasi 83% dan skor F1 0,87. Catchplay+ memiliki ulasan positif tertinggi sebanyak 730 ulasan, sementara Disney+ Hotstar memperoleh ulasan negatif terbanyak dengan 776 ulasan. Temuan ini mengindikasikan bahwa algoritma Naive Bayes efektif dalam menganalisis sentimen aplikasi streaming serta dapat dimanfaatkan untuk meningkatkan kualitas layanan berdasarkan masukan pengguna.

PERANCANGAN SISTEM INFORMASI INVENTORI STOK BARANG GUDANG KAIN PADA PT.KARY INDOMAS ELOK Haikal, Fikri; Hananto, Agustia; Nurapriani, Fitria; Tukino, Tukino
Jurnal Cahaya Mandalika ISSN 2721-4796 (online) Vol. 4 No. 3 (2023)
Publisher : Institut Penelitian Dan Pengambangan Mandalika Indonesia (IP2MI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/jcm.v4i3.2014

Abstrak : PT. kary indomas elok yang beralamat di jl. Mitra selatan 2, kav 89,Kawasan industry mitra karawang, karawang, jawa barat 41361, sebuah p[erusahaan textile yang bergerak dibidang pembuatan kain syang berperan dalam memenuhi kebutuhan dalam negeri dan menjadi salah satu perusahaan yang tetap bertahan dalam padatnya persaingan. Dalam pengelolaan data di Gudang kain PT.kary indomas elok textile yaitu depatement yang menyediakan kain putih polos untuk di proses menjadi kain selimut berwarna. Masih dilakukan secara manual dengan alat tulis sehingga informasi yang diperoleh sangat kurang akurat dan membutuhkan waktu yang cukup lama yang menhambat proses bisnis yang berjalan. Penelitian ini bertujuan menganalisis proses bisnis perusahaan yang sedang berjalan, memberikan rekomendasi serta membangun sistem informasi inventori Gudang kain yang dapat mendukung operasional perusahaan. Untuk makai itu dibuatlah sistem informasi inventori Gudang kain untuk mencatat data transaksi pembelian,penjualan, dan stok barang yang dapat mendukung operasiona perusahaan. Penelitian ini menghasilkan sebuah sistem informasi inventori yang dapat memudahkan pencatatan pembelian,penjualan, dan data barang, hasil akhur dari penelitian ini dibuatlah sistem informasi inventori Gudang kain PT. Kary indomas Elok. Kata kunci : Membangun,sistem informasi, Inventori, Gudang kain, PIECES. Abstract : PT. kary indomas elok which is located at jl. Mitra Selatan 2, kav 89, Mitra Karawang industrial area, Karawang, West Java 41361, a textile company engaged in the manufacture of blankets that plays a role in meeting domestic and foreign needs and is one of the companies that survives in dense competition . In data management at PT. Kary Indomas Elok Textile's fabric warehouse, namely Gepatement, which provides plain white cloth to be processed into colored quilt fabric caddies. It is still done manually with stationery so that the information obtained is very inaccurate and takes quite a long time which hampers ongoing business processes. This study aims to analyze the company's ongoing business processes, provide recommendations and build a fabric warehouse inventory information system that can support the company's operations. For this reason, a fabric warehouse inventory information system was created to record purchase, sales and stock transaction data that can support the company's operations. This research produces an inventory information system that can facilitate the recording of purchases, sales, and goods data, the final result of this research is the fabric warehouse inventory information system of PT. Indomas Elok Keyword : Building, Information System, Inventory, Warehouse of Fabrics, PIECES

Analisis Sentimen Ulasan Pengguna Alikasi Traveloka Pada Google Play Store Menggunakan Algoritma Naive Bayes Ikhsan, Muhammad Daffa; Huda, Baenil; Hananto, Agustia; Nurapriani, Fitria
Infotek: Jurnal Informatika dan Teknologi Vol. 8 No. 2 (2025): Infotek : Jurnal Informatika dan Teknologi
Publisher : Fakultas Teknik Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/jit.v8i2.30444

The advancement of the digital era has driven increased usage of online reservation applications, including Traveloka. The abundance of user feedback available on the Google Play Store platform has the potential to become a valuable database for development teams in improving service quality. However, the characteristics of unstructured and spontaneous reviews pose challenges in conventional data processing.This research aims to explore sentiment in Traveloka application user comments using the Multinomial Naïve Bayes algorithm. The dataset used consists of 1,500 review samples obtained through web scraping techniques from the Google Play Store. The research methodology includes several data preprocessing stages, including data cleaning, case normalization, word tokenization (tokenizing), stopword removal, and word stemming to their base forms (stemming). Subsequent processes include data categorization, feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) approach, and building a classification model with the Multinomial Naïve Bayes algorithm.Test results show that the model is capable of classifying sentiment with an accuracy rate of 79%. The model demonstrates high recall values in identifying negative reviews (0.97), but the recall value for positive reviews remains limited (0.64). This indicates that the model has higher sensitivity to negative expressions. From a total of 1,500 review data, there were 461 positive reviews and 543 negative reviews that were successfully categorized clearly.The findings in this study prove that the implementation of the Multinomial Naïve Bayes algorithm is quite efficient in sentiment classification of user reviews, and is capable of providing strategic insights that can be utilized by development teams to improve application service quality

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