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All Journal Jurnal Ilmiah Mikrotek Minda Baharu Amalee: Indonesian Journal of Community Research & Engagement Indonesian Journal of Electrical Engineering and Computer Science Jurnal Teknik Informatika (JUTIF) Prosiding Konferensi Nasional PKM-CSR
Hasan Dwi Cahyono
Universitas Sebelas Maret
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Nursing Public Health Social Sciences Other

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Journal : Jurnal Teknik Informatika (JUTIF)

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SENTIMENT ANALYSIS CLASSIFICATION IN WOMEN'S E-COMMERCE REVIEWS WITH MACHINE LEARNING APPROACH Afan Firdaus, Alfiki Diastama; Rahmawan, Rizki Dwi; Mahendra, Yuzzar Rizky; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2392

Abstract

User reviews on e-commerce are one of the important elements in e-commerce. User reviews can help potential buyers make decisions based on the experiences and opinions of other people, for example women's e-commerce reviews. In providing positive, neutral or negative sentiment reviews, understanding customer perceptions is challenging. Classifying sentiment reviews will solve this problem, several classification techniques have been carried out, but there is still room for development in the use of simple machine learning techniques and sampling to overcome data class imbalance. Classification techniques used in this paper include Naive Bayes, SVM, and KNN. These algorithms will be compared to determine the most accurate model. Several preprocessing techniques are also carried out such to balance the dataset using ROS and SMOTE. It was obtained that the SVM method with ROS had the highest accuracy of around 0.94 for accuracy value, 0.93 for precision value, 0.94 for recall, and 0.92 for F1-score value. This research shows that the use of sampling techniques such as ROS and SMOTE can be effective in balancing imbalanced datasets, thereby improving model classification performance. These findings can be a reference for developing more efficient and accurate sentiment classification models, especially in the case of imbalanced data.
STOCK PREDICTION PERFORMANCE OPTIMIZATION: ENHANCING COVARIANCE MATRIX WITH KNN Saputra , Iskandar Abdul Azis; Sidiq, Muhammad Rais; Guritno, Sangaji Suryo; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2399

Abstract

Stock price prediction is a fundamental yet complex challenge in quantitative finance. With the increasing availability of data and advancements in machine learning techniques, various models have been developed to capture intricate patterns in stock price movements. While complex neural network models such as Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Transformers have shown potential in handling stock market data, they often face optimization difficulties and performance limitations, especially when data is scarce. This paper explores the use of simpler and more accessible prediction methods, specifically Linear Regression (LR) and K-Nearest Neighbors (KNN), alongside more advanced models like Temporal Spatial Transformer (TST) and a Multi-Layer Perceptron (MLP) model called Stockmixer. The NASDAQ dataset is utilized in this study, providing a comprehensive view of stock market dynamics with high variability. Results indicate that KNN, among the evaluated models, exhibits superior and more stable performance in predicting validation data compared to MLP. KNN achieved a low Mean Squared Error (MSE) at 100 epochs, and demonstrated positive Information Coefficient (IC) and Return Information Coefficient (RIC) values. Additionally, it showed high Precision at 10 (P@10) and Sharpe Ratio (SR), making it a robust choice for stock price prediction tasks. In contrast, MLP, despite its sophistication, revealed some weaknesses, particularly in the alignment between predictions and actual values. These findings offer valuable insights into the effectiveness of various models for stock price prediction and suggest that simpler models like KNN can provide competitive results compared to more complex models.
OPTIMIZATION OF STOCK PRICE PREDICTION WITH RIDGE REGRESSION AND HYPERPARAMETER SELECTIONS Marwa, Adeline Fellita; Setiyawan, Sitti Ayuningrum; Cahyani, Yonaka Titin Nur; Cahyono, Hasan Dwi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.2384

Abstract

Stock price prediction is a topic that has garnered significant attention in the investment world and has been the subject of various studies. Despite the massive attention, predicting stock price movements using algorithms remains challenging as the algorithms must be agile and highly adaptive to movement trends. Recent studies using deep learning methods for stock price prediction show that deep learning methods have high reliability. However, their computational complexity limits widespread implementation. This study aims to predict Netflix stock prices using a linear regression model with ridge and hyperparameter optimisation. The research consists of three stages: data preprocessing, building a linear regression model with ridge, and predicting and visualizing results. The dataset used is historical Netflix stock price data from 2017 to 2022. In the preprocessing stage, the data was normalized using MinMaxScaler and split into training and test sets. A ridge regression model was built with hyperparameter alpha optimization using GridSearch. Predictions were compared to stock prices and evaluated using Root Mean Squared Error (RMSE). The ridge regression model with hyperparameter optimization performed best with an RMSE of 13.8082. Although the linear regression model demonstrated the fastest execution time of 0.7717 seconds, the ridge regression model with hyperparameter optimization provided an optimal balance between prediction accuracy and time efficiency.
Co-Authors Adiar, Shalsabila Aura Afan Firdaus, Alfiki Diastama Afrizal Doewes, Afrizal Agustinus Bimo Gumelar Amalia Utami, Sekar Gesti Angrainingsih, Rini Ardhi Wijayanto Cahyani, Yonaka Titin Nur Cahyo Darujati Dewi Wisnu Wardani Dewi Wisnu Wardani Dewi Wisnu Wardani Dewi, Nabilla Nurulita Doewoes, Afrizal Guritno, Sangaji Suryo Haryono Setiadi Hendrasuryawan, Brilyan Mahendra, Yuzzar Rizky Marwa, Adeline Fellita Rahmawan, Rizki Dwi Rini Anggrainingsih Saputra , Iskandar Abdul Azis Setiadi, Haryono Setiyawan, Sitti Ayuningrum Sidiq, Muhammad Rais Syamsul Anam Wardhani, Dewi Wisnu Wijayanto, Ardhi