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All Journal International Journal of Electrical and Computer Engineering Jurnal Ilmu Komputer dan Informasi Jupiter Berkala Ilmiah Pendidikan Biologi (BioEdu) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JSAI (Journal Scientific and Applied Informatics) Jurnal Teras Fisika : Teori, Modeling, dan Aplikasi Fisika Indonesian Journal of Electrical Engineering and Computer Science Reswara: Jurnal Pengabdian Kepada Masyarakat Law Development Journal
EVI YULIANTI
Faculty Of Computer Science, Universitas Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Social Sciences

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Analisis Tren Penjualan dan Prediksi Produk CV. Sentosa Menggunakan Regresi Linier Dona Marcelina; Indah Pratiwi Putri; Evi Yulianti; Agustina Heryati
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7649

Abstract

This study analyzed sales trends and forecasted the sales of CV Sentosa's products, namely Ater 360 New (X1), Bon Bon (X2), Mini Peanut Crackers (X3), and Marie Susu Int (X4), during the period of January 2019 to August 2023. Monthly sales data were processed using exploratory data analysis (EDA) and linear regression to predict sales trends. The linear regression analysis results indicated that X2 and X3 experienced sales growth with a slope of m=0.01, representing an average increase of 0.01 units per month. Conversely, X4 showed a slight decline with m=−0.01, while X1 remained stable with m=−0.00, indicating minimal changes in sales volume. The accuracy evaluation of the predictions based on MAE, MSE, and RMSE showed that X2 had the best performance with MAE 0.14, MSE 0.03, and RMSE 0.19, followed by X1 and X3, which had similar prediction errors. Although X4 initially showed significant growth, its model exhibited higher prediction errors (MAE 0.17, MSE 0.04, RMSE 0.21). This study provides valuable insights for CV Sentosa's business strategies, highlighting X2 and X3 as promising products due to their consistent growth trends and accurate predictions. This research provides a strong foundation for CV Sentosa in formulating more effective marketing strategies and product development in the future
Expertise Retrieval Using Adjusted TF-IDF and Keyword Mapping to ACM Classification Terms Aini, Lyla Ruslana; Evi Yulianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 3 (2025): June 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i3.6397

Abstract

In an era of collaboration, knowing someone's expertise is becoming increasingly necessary. Recognizing individuals' proficiency can be challenging because it requires considerable manual time. This study explores the expertise of lecturers from the Computer Science Department, Universitas Indonesia (Fasilkom UI), based on scientific publications. The data were obtained from the Sinta journal website’s scrapping process, which includes Scopus, Garuda, and Google Scholar data sources. The approach used was keyword extraction using the adjusted TF-IDF. The resulting keywords were then mapped to the ACM classification class using cosine similarity calculations with various embedding models, including BERT, BERT multilingual, FastText, XLM Roberta, and SBERT. The experimental results highlighted that combining the adjusted TF-IDF with mapping to the ACM classes using SBERT is a promising approach for gaining the best expertise. The use of abstract data has proved to be better than that of full-text data. Using title-abstract-EN data achieved a score of 0.49 for both the P@1 and NDCG@1 metrics, whereas the title-abstract-ENID data attained a score of 0.75 for both metrics P@1 and NDCG@1.
Prediksi Harga Saham Menggunakan Empirical Mode Decomposition dan Feed Forward Neural Networks Saluza, Imelda; Mohammad Taufikurrahman; Lastri Widya Astuti; Hartati; Dhamayanti; Evi Yulianti
JUPITER (Jurnal Penelitian Ilmu dan Teknologi Komputer) Vol 15 No 2 (2023): Jurnal Penelitian Ilmu dan Teknologi Komputer (JUPITER)
Publisher : Teknik Komputer Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.10068884

Abstract

Stocks are a very market and shares are a characteristic of a company and their movements are influenced by the market. So if a company experiences problems, the company's shares may experience a spike. As happened with the Bank Syariah Indonesia (BSI) company which experienced service problems on the 8th to 20th. May 11, 2023, which caused a sharp decline in the company's shares. Volatile spikes can cause a risk of loss for investors and business people in the company. So both need to estimate their portfolio. Therefore, it is necessary to predict the share price, the closing price of BSI shares. This research uses time series data from the closing price of BSI shares, which is followed by decomposition using Empirical Model Decomposition (EMD) to break down the original data into several signals which then select these signals using Correlation Based Feature Selection (CFS) for feature selection and ends with make predictions using the Feed Forward Neural Networks (FFNN) algorithm. Based on the proposed model, the Mean Square Error (MSE) (training: 3.84E-02, testing: 1.73E-05) and Mean Absolute Error (MAE) (training: 1.48E-01, testing: 3.40E-03) values ​​are low for both training and testing data compared to without perform EMD and CFS from original data.
Co-Authors Achmad Sulchan Agung Bambang Setio Utomo Agustina Heryati Aini, Lyla Ruslana Aris Haryadi Dana Indra Sensuse Dhamayanti Dhamayanti Dhamayanti Dona Marcelina Edi Suharyadi Evvy Kartini Hartati Imelda Saluza, Imelda Indah Pratiwi Putri Jatmiko Budi Baskoro Kartika Sari Lastri Widya Astuti Lenggo Geni Meganingrum Arista Jiwanggi Mohammad Taufikurrahman Nicholas Pangestu Nuzulul Khairu Nissa Rahadianti, Laksmita sunardi sunardi Wihantoro Wihantoro