Article Per Year (5 Year)
p-Index From 2021 - 2026
5.648
P-Index
This Author published in this journals
All Journal JUSIFO : Jurnal Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Teknik Informatika (JUTIF) Malcom: Indonesian Journal of Machine Learning and Computer Science The Indonesian Journal of Computer Science
Rice Novita
Universitas Islam Negeri Sultan Syarif Kasim Ria
Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

Published : 46 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 46 Documents
Search

 1   2   3   4   5 
Analisis Sentimen Ulasan Pengguna Aplikasi Penjualan Pulsa Menggunakan Algoritma Naïve Bayes Classifier Syafi'i, Azis; Afdal, M.; Saputra, Eki; Novita, Rice
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41364

Abstract

Many credit sales applications are commonly used by outlets or counters, such as DigiPOS, Tetra Pulsa, and Orderkuota. However, there are common problems with these applications such as prices that are starting to be less competitive, difficult to use, transactions that often fail, security, service and others. Therefore, this study analyzes the sentiment of user reviews to identify the strengths and weaknesses of these apps, to help developers improve their services, and to guide agents in choosing the right app. NBC algorithm is proposed to be used for sentiment classification. The analysis results show the dominance of positive sentiments on all apps, with Tetra Pulsa having the highest positive sentiment (97.10%), followed by Orderkuota (84.40%) and DigiPOS (64.00%). Then the results of the implementation of the NBC algorithm can perform sentiment classification well. Tetra Pulsa application has an accuracy of 97.10%, Orderkuota 92.39%, and DigiPOS 91.10%. The results of this study can be considered to evaluate and improve the application so that it can provide better service to users of the credit sales application.
Analisis Sentimen Ulasan Pengguna Aplikasi Mobile Banking Menggunakan Algoritma K-Nearest Neighbor Munandar, Darwin; Afdal, M.; Zarnelly, Zarnelly; Novita, Rice
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.41409

Abstract

Mobile banking is evident in the improvement of business processes in the banking industry. Even so, the m-banking application cannot be separated from the problems experienced by its users. Therefore, further analysis is required. This research proposes a sentiment analysis technique using K-Nearest Neigbor (KNN) algorithm to identify user opinions and reviews of m-banking applications. Three popular m-banking apps were selected for further analysis namely BRImo, BSI Mobile, and Livin' by Mandiri. The analysis shows that BRImo is the most popular m-banking application, with a positive sentiment percentage of 58.25%, Livin' by Mandiri with 22.50%, and BSI Mobile with the lowest percentage of 12.70%. Modeling results using the KNN algorithm with K = 3, 5 and 7 test values show K = 3 has better capabilities. Based on the application, the best modeling is produced on BRImo with 82.9% accuracy, then Livin' by Mandiri with 70.3% accuracy, and BSI Mobile with 71.35% accuracy. Analysis and visualization were also conducted using word clouds to see keywords that are often discussed in reviews. As a result, m-banking apps have problems with difficult login, complicated registration or verification, and balance deduction despite failed transfer status.
Prediction Of Andesit Stone Production using Support Vector Regression Algorithmression Azzahra, Aura; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4155

Abstract

PT. Atika Tunggal Mandiri is a company engaged in andesite stone mining located in the fifty municipalities, West Sumatra. The demand for andesite stones in the company continues to increase, necessitating an increase in production to meet it. Therefore, accurate prediction is needed to assist effective operational planning, enabling the estimation of future andesite stone production to meet market demand. This study aims to predict andesite stone production using the Machine Learning method, specifically the Support Vector Regression algorithm. The research utilizes data from January 2022 to November 2023 with an 80%:20% split for training and testing data. The experimental results using the Linear Kernel yielded an RMSE value of 3444.12 and an MAPE of 9.27%, categorized as "Very Good," followed by the RBF kernel and Polynomial kernel. Based on the obtained error results, the Support Vector Regression algorithm is the best algorithm for predicting andesite stone production.
A Comparison of K-Means and Fuzzy C-Means Clustering Algorithms for Clustering the Spread of Tuberculosis (TB) in the Lungs Ramadani, Faradila; Afdal, M.; Mustakim, Mustakim; Novita, Rice
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.4277

Abstract

Tuberculosis (TB) is an airborne infectious disease that affects people of all ages, including infants, children, teenagers and the elderly. This disease is prevalent in different areas of Indragiri Hilir Regency, so it is important to identify and group the areas that are the focus of its spread. The purpose of this study is to help hospitals organize training in areas where tuberculosis is common. This study uses a data mining method with grouping techniques of K-Means and Fuzzy C-Means algorithms based on patient data from Puri Husada Tembilahan Hospital from 2020 to 2023. After several experiments, the results were evaluated with DBI, which showed that K- Means gave the best validity with a value of 0.9146. Which shows that the areas with high risk of TB are Tembilahans aged 55-64 who have been diagnosed with complicated TB. This method was then applied to the TB group information system of Puri Husada Tembilahan District Hospital in the hope that it could help the hospital reduce the spread of the disease in the affected area.Keywords: DBI, fuzzy c-means, clustering, k-means, tuberculosis.
Analisis Sentimen Ulasan Aplikasi PLN Mobile Menggunakan Algoritma Naïve Bayes Classifier dan K-Nearest Neighbor: Sentiment Analysis of PLN Mobile Application Review Using Naïve Bayes Classifier and K-Nearest Neighbor Algorithm Syafrizal, Syafrizal; Afdal, M.; Novita, Rice
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 1 (2024): MALCOM January 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i1.983

Abstract

Bukti nyata PLN terus meningkatkan pelayanannya adalah dengan meluncurkan sebuah aplikasi yaitu PLN Mobile. Banyak pelanggan yang merasakan kemudahan dengan adanya aplikasi tersebut. Namun kini beberapa pelanggan mulai menjumpai permasalahan seperti gagal memuat lokasi saat melakukan pengaduan dan saat pembelian token dengan virtual account, saldo telah terpotong namun kode token tidak muncul. Penelitian ini melakukan analisis sentimen terhadap ulasan pengguna aplikasi PLN Mobile menggunakan pendekatan text mining. Pendekatan ini dapat melakukan klasifikasi sentimen pada ulasan pengguna dengan cepat. Data dikumpulkan menggunakan teknik scrapping pada Google Play Store dan mendapatkan 3000 baris data. Data tersebut kemudian diberi label oleh seorang pakar sehingga menghasilkan 2099 sentimen positif (69,97%), 368 netral (12,27%) dan 533 negatif (17,77%). Selanjutnya dilakukan pemodelan menggunakan algoritma NBC dan KNN dengan K-Fold Cross Validation sebagai teknik validasi. Hasilnya menunjukkan model NBC lebih baik dibandingkan KNN dengan akurasi sebesar 77,69%, recall 53,14%, precision 59,84% dan F1-Score 54,09%. Selanjutnya proses analisis dilakukan dengan visualisasi data menggunakan word cloud. Hasilnya yaitu dengan adanya aplikasi PLN Mobile memberikan kemudahan kepada pelanggan dalam menggunakan layanan PLN seperti pembelian token, pengaduan, dan berbagai fitur lainnya. Namun aplikasi PLN Mobile masih memiliki beberapa permasalahan yang sering menjadi ulasan penggunanya salah satunya adalah saat melakukan pembayaran token.
Analisis Sentimen Masyarakat Terhadap Liga Indonesia Menggunakan Algoritma Naïve Bayes Classifier dan Support Vertor Machine Pada Platform X dan YouTube Irwanda, Mahyuda; Afdal, M; Novita, Rice; Zarnelly, Zarnelly
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7294

Abstract

The Indonesian League is a national football competition that attracts a lot of public attention. However, various problems such as controversial referee decisions, fan riots, and match-fixing issues are often in the spotlight. This study aims to analyze public sentiment towards the Indonesian League using the Naïve Bayes Classifier (NBC) and Support Vector Machine (SVM) algorithms. Data were collected from social media platform X (Twitter) as many as 2000 tweets and YouTube as many as 2000 comments in the period from January 2023 to December 2024. After going through preprocessing stages such as cleaning, case folding, tokenizing, stopword removal, and stemming, the data was classified into positive, negative, and neutral sentiments. The results showed that SVM had a higher accuracy (99%) than NBC (85%) in sentiment analysis.
Co-Authors Abror, Naufal Afandi, Rival Afdal, M.Afdal Afifah, Tiara Afrah Ahsyar, Tengku Khairil Aini, Delvi Nur Akhiril Anwar Harahap Al-Qadr, Nola Ardelia Alfakhri, Rezky Alfian, Zhevin Andriawan, Ahmad Rizky Angraini Angraini Anisa Halifa Anisa Putri Arif Marsal Arifin, Abdullah Arrazak, Fadlan Auliani, Sephia Nazwa Azzahra, Aura Butar Butar, Febiola Siska Dwinnie, Zairy Cindy Eki Saputra F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Inggih Permana Irwanda, Mahyuda Jazman, Muhammad Khairil Ahsyar, Tengku Kurniawan, Viki Loka, Septi Kenia Pita M. Afdal M. Afdal Megawati - Mochammad Imron Awalludin Mubarak, Haykal Alya Mulya, Anggi Munandar, Darwin Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Nabiilah, Nabiilah Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Celine Mutiara Putri, Suci Maharani Rahayu, Dwi Sri Rahmawita M, Medyantiwi Ramadani, Faradila Rozanda, Nesdi Evrilayan Safiq Tama, Naufal Sasmita, Indah Siti Rohimah Syafi'i, Azis Syafrizal Syafrizal Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly Zarqani, Zarqani Zulkarnain Zulkarnain