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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 Sistem Cerdas Building of Informatics, Technology and Science 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

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Penerapan Machine Learning Pada Analisis Sentimen Twitter Sebelum dan Sesudah Debat Calon Presiden dan Wakil Presiden Tahun 2024 Dwinnie, Zairy Cindy; Novita, Rice
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7504

Abstract

The 2024 Presidential Election has become the hottest topic in the past two years. The KPU has confirmed that there are 3 candidates for President and Vice President. For this reason, as a momentum for voters to assess the 2024 Presidential and Vice Presidential candidates, the KPU is holding the 2024 Presidential Choice Debate which is based on Law Number 7 of 2017 concerning General Elections. Based on the information presented on the kpu.go.id page, the debate will be held 5 times with 3 presidential candidate debates and 2 vice presidential candidate debates. For this reason, it is necessary to carry out an analysis to find out how public sentiment is positive, negative, and neutral on Twitter towards the three candidates for President and Vice President in 2024 before and after the debate was held. The aim is to estimate public support or disapproval of the three candidate pairs. This research uses three algorithms as a comparison of classification accuracy, namely the Support Vector Machine algorithm, Random Forest, and Logistic Regression. Where the data used is tweet data on Twitter related to before and after the debate as many as 30 datasets with a total of 9000 data. From the classification results, the average accuracy obtained for the three algorithms, namely SVM and Random Forest, was 78%, and Logistic Regression was 79%. The highest polarity obtained from the classification of the three algorithms is in the positive class. This indicates that the Logistic Regression algorithm provides better performance in classifying Twitter sentiment regarding the 2024 presidential and vice presidential candidate pairs.
Penerapan Algoritma K-Means Menggunakan Model LRFM Dalam Klasterisasi Nilai Hidup Pelanggan Afifah, Tiara Afrah; Novita, Rice; Ahsyar, Tengku Khairil; Zarnelly, Zarnelly
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7605

Abstract

In implementing customer relationship management, there are still many companies that have not utilized CRM optimally as part of their business strategy. As is the case with UD Sandeni. UD Sandeni still has problems in managing its relationships with customers because UD Sandeni does not fully understand the difference between customer information that is profitable and unprofitable for the company's sustainability. UD Sandeni has used a system to manage customer transaction data. However, this system is only used to calculate profits and create bookkeeping for registered agents so that UD Sandeni does not have an in-depth understanding of the characteristics of its customers. To overcome this problem, the solution that can be applied is to use customer grouping techniques, such as clustering. Customer transaction data is processed using a clustering process with K-Means and LRFM. Test the validity of cluster results using DBI and calculate CLV values using AHP weights to produce cluster rankings. The results of this research obtained customer clustering which consists of 2 segments, namely cluster 1 which has the highest CLV value of 0.3171156 with a total of 298 customers and includes the High Value Loyal Customers segmentation, and cluster 2 with a CLV value of 0.1434054 with a total of 72 customers. which is included in the segmentation of uncertain new customers (uncertain lost customers).
Implementasi Algoritma Support Vector Machine Untuk Analisa Sentimen Data Ulasan Aplikasi Pinjaman Online di Google Play Store: Implementation of Support Vector Machine Algorithm for Sentiment Analysis of Online Loan Application Review Data on Google Play Store Iqbal, Muhammad; Afdal, M; Novita, Rice
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1435

Abstract

Pinjaman online (pinjol) banyak menuai pro dan kontra karena aksesnya yang mudah dan iklannya tersebar di media sosial. Penyelenggara pinjaman daring juga seringkali menggunakan metode penagihan yang mengganggu, memberlakukan bunga yang tinggi, dan menetapkan jangka waktu pembayaran yang pendek, terutama pada pinjaman daring ilegal. Karenanya, penelitian ini melakukan analisis sentimen pada lima aplikasi pinjol, yaitu Kredivo, Easycash, Rupiah Cepat, Kredit Pintar, dan Ada Pundi. Data ulasan aplikasi diambil dari Google Play Store menggunakan teknik scraping. Kemudian, pelabelan sentimen dilakukan secara otomatis menggunakan kamus sentimen Bahasa Indonesia (Inset). Hasil pelabelan menunjukkan bahwa semua aplikasi pinjol mayoritas memiliki sentimen negatif. Kredivo menjadi aplikasi dengan jumlah sentimen positif terbanyak (46%), sementara itu Easycash memiliki sentimen negatif terbanyak (65%). Data yang di labeli kemudian digunakan untuk pemodelan klasifikasi dengan algoritma Support Vector Machine (SVM). Hasil evaluasi menghasilkan algoritma SVM mempunyai kinerja yang cukup baik dengan rata-rata akurasi sebesar 72%, presisi 76%, dan recall 85%. Namun secara khusus, SVM sangat baik melakukan klasifikasi sentimen pada aplikasi Kredit Pintar dengan akurasi sebesar 83%. Analisis visualisasi menggunakan word cloud juga dilakukan untuk memahami konteks ulasan pengguna aplikasi pinjol. Hasil pengamatan menunjukkan bahwa pengguna hampir selalu membahas tentang limit pinjaman disetiap sentimen pada kelima aplikasi.
Implementation of Density-Based Spatial Clustering of Applications with Noise and Fuzzy C – Means for Clustering Car Sales Auliani, Sephia Nazwa; Mustakim; Novita, Rice; Afdal, M
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4135

Abstract

This study compares the performance of two clustering algorithms, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Fuzzy C-Means (FCM), in clustering car sales data at PT. XYZ. The dataset, comprising sales transactions from 2020 to 2023, includes information about vehicles, customers, and transactions. Preprocessing methods such as data transformation and normalization were applied to prepare the data. The results indicate that DBSCAN produces clusters with better validity, measured using the Silhouette Score, compared to FCM. Specifically, DBSCAN achieves the highest Silhouette Score of 0.7874 in cluster 2, while FCM reaches a maximum score of 0.3666 in cluster 3. Thus, DBSCAN proves to be more optimal for clustering car sales data at PT. XYZ, highlighting its superior performance in terms of cluster validity.
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.
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.
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 Andriawan, Ahmad Rizky Anisa Putri Auliani, Sephia Nazwa Azzahra, Aura Dwinnie, Zairy Cindy F. Safiesza, Qhairani Frilla Hamzah, Muhammad Luthfi Khairil Ahsyar, Tengku Kurniawan, Viki M Afdal M. Afdal Muhammad Iqbal Munzir, Medyantiwi Rahmawati Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustofa, Asdar Novansyah, Rizky nursalisah, febi Pangestu, Prayugo Pertiwi, Tata Ayunita Pulungan, Joliando Putri, Suci Maharani Rahayu, Dwi Sri Ramadani, Faradila Rozanda, Nesdi Evrilayan Sasmita, Indah Vamilina, Vina Wibisono, Yudistira Arya Yuda, Afi Ghufran Zarnelly Zarnelly Zarnelly