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All Journal MATICS : Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Journal of Artificial Intelligence and Digital Business ROUTERS: Jurnal Sistem dan Teknologi Informasi SmartComp
Wulandari, Umi Meganinditya
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Library & Information Science

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Dampak Berita Emas Palsu Terhadap Harga Saham PT Aneka Tambang TBK (ANTM): Analisis dan Prediksi) Ispaniyah, Ispaniyah; Tyas, Putri Cahyaning; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.3969

Abstract

Fake news or hoaxes, have become a major problem around the world in recent years. This phenomenon not only affects public opinion but can also affect various aspects of socio-economic life, including financial markets. Currently, global stock prices continue to rise and have reached their highest level since 2012-2013. One of the leading mining companies in Indonesia, PT Aneka Tambang Tbk (ANTM), is not entirely dependent on its share price. The impact of fake news on stock prices has become a topic of growing interest in the academic literature. Various previous studies have attempted to identify the relationship between the spread of fake news and stock price fluctuations. Using the RapidMiner application, an analysis of PT ANTM's stock price prediction was conducted using Neural Network (NN) and Linear Regression (LR) algorithms. To assess the accuracy of the prediction, the analysis is performed using the Root Mean Square Error (RMSE) results. The comparative analysis conducted shows that the Neural Network algorithm has a lower error rate of 14,806 +/- 0.000 compared to the Linear Regression algorithm which has a value of 22,379 +/- 0.000. This shows that the Neural Network algorithm has higher accuracy in predicting the share price of PT ANTM. A smaller RMSE value indicates a more accurate prediction. In addition, this study also identified that the time span of the data used (December 19, 2023 - June 19, 2024) can affect the prediction results. Based on the conclusions, the researcher suggests that using a dataset with a longer time span and applying other Deep Learning algorithms to improve prediction accuracy can be used for future research.
Pengaruh Aksi Boikot Terhadap Harga Saham Unilever: Pendekatan Prediktif Dengan Neural Network Dan Linear Regression Yani, Ririn Yuli; Nidaa, Syafiqotun; Suseno, Akrim Teguh; Wulandari, Umi Meganinditya
ROUTERS: Jurnal Sistem dan Teknologi Informasi Vol. 3 No. 1, Februari 2025
Publisher : Program Studi Teknologi Rekayasa Internet, Politeknik Negeri Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25181/rt.v3i1.4009

Abstract

PT Unilever Indonesia Tbk is a  multinational company that produces and markets various consumer goods in various countries to fulfill needs ranging from health, nutrition, daily care and so on. PT Unilever Indonesia Tbk is facing a crisis of calls for a boycott of products due to pro-Israel which has an impact on the Company’s reputation and performance. In the face of this situation, stock price prediction analysis is important to help investors in making decisions. To overcome this problem, this research applies Data Mining Techniques in predicting the share price of PT Unilever Tbk. The two algorithms used are Neural Network and Linear Regression, which are then tested using the Root Mean Squared Error (RMSE) evaluation method. Data processing is done using RapidMiner with historical data period from December 2023 to May 2024. Based on the analysis results, the Linear Regression algorithm produces an RMSE value of 22,745, showing a more accurate prediction compared to the Neural Network algorithm which has an RMSE value of 44,830. The test results show that predicting stock prices using Linear Regression has a lower error rate than the Neural Network. Thus, in this study, the Linear Regression algorithm is superior in predicting the stock price of PT Unilever Indonesia Tbk compared to the Neural Networj. The results of this study are also compared with previous research which shows thaht the accuracy of the stock price prediction model depends on the characteristics of the dataset and the method used. Some previous studies concluded that Neural Network is superior in capturing complex patterns in certain stocks, while Linear Regression is more suitable for data with linear relationships. Therefore, although Linear Regression is better in this study, model selection still needs to be tailored to the characteristics and objectives of the analysis.
Market Basket Analysis Using FP-Growth and Apriori on Distro Store Sales Transaction Wulandari, Umi Meganinditya; Suseno, Akrim Teguh; Kholilurrahman, Muhammad
MATICS: Jurnal Ilmu Komputer dan Teknologi Informasi (Journal of Computer Science and Information Technology) Vol 17, No 1 (2025): MATICS
Publisher : Department of Informatics Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/mat.v17i1.28820

Abstract

Market Basket Analysis analyzes consumer buying habits by finding relationships between items in the consumer's shopping basket. This Market Basket Analysis can provide success to the retail industry with the ability to understand consumer behavior and the speed of response to information obtained by retail business owners. This understanding is the result of an analysis that can help business owners improve marketing and sales strategies while utilizing transaction data. Sales transaction data that has been accumulated so far has only become data warehouses, while large amounts of transaction data can bring major changes to the level of competition in business and business actors in order to survive in the business world. In addition, after the COVID-19 outbreak, Indonesia experienced a slowdown in economic growth of 5.31%. This can be overcome by utilizing Market Basket Analysis to increase sales from their businesses. MBA with the methods used are FP-Growth and Apriori to analyze store transaction data in order to obtain association rules that can be used in improving marketing strategies. This analysis was carried out with 3 scenarios for 3 different minimum support values (1%, 2% and 3%) but the same minimum confidence value of 0.6 (60%). The comparison of the two methods is that 2 out of 3 scenarios produce the same association rule, namely 1 final association rule result with a lift value of 1.42. The three scenario results from both methods can be used by business owners as a consideration in determining sales strategies.
Implementasi Metode Rapid Application Development (RAD) pada Sistem Informasi Perpustakaan Berbasis Web di SMK Nusantara 1 Comal Anshori, Arif Iman; Falah, Miladiyah Nur; Aisah, Siti; Wulandari, Umi Meganinditya
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 3 (2025): Agustus - October
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i3.2208

Abstract

Perkembangan teknologi informasi telah membawa perubahan yang signifikan dalam pengelolaan perpustakaan. Di SMK Nusantara 1 Comal, sistem perpustakaan masih dikelola secara manual, yang seringkali menimbulkan kendala dalam pencatatan data peminjaman dan pengembalian buku. Hal ini menjadi alasan dalam penerapan Sistem Informasi Perpustakaan Berbasis Web untuk meningkatkan efisiensi pengelolaan perpustakaan tanpa menghilangkan proses yang sudah berjalan. Metode yang digunakan adalah Rapid Application development (RAD), yang terdiri dari tiga fase utama, yaitu Requirement Planning, Workshop Design, dan Implementation. Metode RAD diterapkan agar sistem yang dikembangkan dapat lebih fleksibel dan sesuai dengan kebutuhan perpustakaan. Penerapan sistem informasi ini menggunakan bahasa pemrograman PHP dengan framework CodeIgniter 4 dan AdminLTE untuk tampilan antarmuka. Model sistem dirancang menggunakan Unified Modeling Language (UML) untuk memvisualisasikan alur proses dan struktur database. Dengan penerapan sistem berbasis web ini, diharapkan aktivitas perpustakaan di SMK Nusantara 1 Comal menjadi lebih efisien serta meningkatkan kecepatan dan akurasi dalam pengelolaan data perpustakaan. 
Sales Performance Analysis untuk Peningkatan Kualitas Produk Menggunakan K-Means dan Hierarchical Clustering Wulandari, Umi Meganinditya; Kholilurrahman, Muhammad; Anshori, Arif Iman
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 15, No 1 (2026): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v15i1.9719

Abstract

Industri fashion, khususnya toko distro, menghadapi persaingan yang semakin ketat sehingga kualitas dan inovasi produk menjadi faktor utama dalam mempertahankan daya saing. Salah satu tantangan yang sering muncul adalah minimnya pemanfaatan data transaksi penjualan sebagai sumber informasi strategis dalam menilai performa produk. Penelitian ini membahas pengelompokan produk berdasarkan performa penjualan menggunakan teknik data mining serta membandingkan efektivitas dua metode clustering, yaitu K-Means dan Hierarchical Agglomerative Clustering. Data yang digunakan berasal dari transaksi penjualan Toko Distro Sextors, dengan tahapan meliputi praproses data, penerapan algoritma clustering, dan evaluasi hasil menggunakan metrik silhouette score. Tujuan penelitian adalah mengidentifikasi kelompok produk dengan performa tinggi, margin keuntungan optimal, serta tingkat penerimaan pasar yang baik, sekaligus menentukan metode clustering yang paling sesuai. Hasil analisis menunjukkan bahwa algoritma K-Means menghasilkan 4 klaster, sedangkan Hierarchical Agglomerative Clustering menghasilkan 3 klaster. Perbedaan jumlah dan karakteristik klaster dari kedua algoritma memberikan gambaran mengenai variasi pola penjualan produk. Temuan ini diharapkan dapat memberikan rekomendasi praktis bagi pelaku UMKM untuk mengambil keputusan berbasis data, sehingga strategi bisnis dapat lebih tepat sasaran dan meningkatkan daya saing produk di pasar.
Co-Authors Anshori, Arif Iman Falah, Miladiyah Nur Ispaniyah, Ispaniyah Kholilurrahman, Muhammad Nidaa, Syafiqotun Siti Aisah Suseno, Akrim Teguh Tyas, Putri Cahyaning Yani, Ririn Yuli