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IMPLEMENTATION OF AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD FOR PT XL AXIATA TBK STOCK PRICE PREDICTION WITH WEBSITE-BASED DASHBOARD VISUALIZATION Alawiyah, Tuti; Permadi, Ipung; Afuan, Lasmedi; Maryanto, Eddy; Rahayu, Swahesti Puspita
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2700

The financial market is a dynamic and uncertain sector, with stocks being one of the most commonly used investment instruments. PT XL Axiata Tbk, a telecommunications company listed on the Indonesia Stock Exchange as a blue chip stock, attracts the attention of many investors due to its financial stability and consistent performance. Technical analysis, particularly the ARIMA (Autoregressive Integrated Moving Average) method is used to predict prices. This research focuses on the use of the ARIMA method in predicting the closing price of PT XL Axiata Tbk shares and the implementation of visualization of prediction results through a web-based dashboard. The results of the analysis obtained the best model for stock prediction is ARIMA (2,1,2) with RMSE and MAPE are 50.743 and 0.01653, respectively. The closing price prediction results for 10 consecutive days are 2,190; 2,194; 2,193; 2,196; 2,194; 2,197; 2,195; 2,197; 2,195; and 2,197. Visualization for the results of this prediction is based on a website with the Streamlit framework that presents the results of stock prediction analysis. The existence of a website-based dashboard visualization can help readers find out the prediction results easily and interactively.

CORRELATION ANALYSIS OF SENTIMENT OF 2024 ELECTION RESULTS AND STOCK MOVEMENTS OF POLITICAL ACTORS IN INDONESIA Sari, Enjelita; Afuan, Lasmedi; Permadi, Ipung; Maryanto, Eddy; Rahayu, Swahesti Puspita
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.2701

General elections (elections) are one of the crucial moments in the political life of a country, where the public democratically elects leaders and their deputies to manage the government. Public sentiment towards the results of elections significantly impacts the political stability and economic conditions of a country. This research aims to analyze the relationship between public sentiment towards the 2024 General Elections in Indonesia and changes in the stock prices of political actors using technological approaches and data analysis. The Long Short-Term Memory (LSTM) method is used to classify sentiment based on Twitter data collected with Harvest Tweet. Evaluation of the LSTM model shows an accuracy rate of 90%, precision of 93.6%, and recall of 92.7%. The correlation analysis using the Spearman coefficient indicates a significant negative relationship with a coefficient of 0.402 and a p-value of 0.046. Implementation of an interactive dashboard using Streamlit facilitates visualization of the data used in this study. Recommendations include increasing the amount of training data for sentiment models, exploring alternative correlation methods for deeper analysis, and refining the interface and data integration on the dashboard to enhance user experience and analysis accuracy. This research is expected to contribute to understanding the dynamics of public sentiment and its impact on the stock market in the context of Indonesian politics.

ANALYSIS OF THE MOVIE DATABASE FILM RATING PREDICTION WITH ENSEMBLE LEARNING USING RANDOM FOREST REGRESSION METHOD Marpid, Nuravifah Novembriana; Kurniawan, Yogiek Indra; Rahayu, Swahesti Puspita
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.2025.6.1.1563

The film industry has become a very profitable industry. However, during COVID-19 the film industry experienced an unfavorable impact with the delay in the screening schedule of new films, many cinemas were prohibited from operating so they were completely closed, and it wasn’t easy to obtain permits to carry out the filmmaking process. To survive in this industry from the impact of the pandemic, it is necessary to consider several factors such as targeted promotion methods by using the right selection of predictive decisions with market and trends. Predicting the success of a film is very helpful in determining the success rating and quality of the film to be released. The Random Forest Regression method is used to conduct predictive analysis on films. This study uses the M-estimate encoding technique to handle categorical data into numerical data, and the result shows that the application of M-estimate encoding increases the correlation value between features. In the Random Forest Regression method with 1000 trees, dividing 80% training data and 20% testing data, the R2 performance score was 86%, the MSE score was 12%, the RMSE score was 35% and the MAE score was 22%. The 10-fold cross-validation score in this study was 85%. This shows that the Random Forest Regression method using 80% training data produces the best performance score.

IMPLEMENTATION OF TEXT MINING ON SONG LYRICS FOR SONG CLASSIFICATION BASED ON EMOTION USING WEBSITE-BASED LOGISTIC REGRESSION Rahayu, Swahesti Puspita; Afuan, Lasmedi; Yunindar, Galih Arditiya
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.2025.6.1.4429

Music has become an essential medium for expressing emotions and enriching human social experiences. However, the manual interpretation of emotions in song lyrics is often inaccurate and time-consuming, especially for complex or ambiguous lyrics. This creates a need for an automated system that can improve the accuracy and efficiency of emotion classification in song lyrics. Various algorithms, such as K-Nearest Neighbor (K-NN), Naive Bayes Classifier, and Support Vector Machine (SVM), have been applied for emotion classification in song lyrics. Previous research has shown that SVM combined with Particle Swarm Optimization (PSO) achieves an accuracy of up to 90%, while K-NN with feature selection produces the highest f-measure of 66.93%, and Naive Bayes achieves an accuracy of up to 45%. In this study, the Logistic Regression algorithm, supported by the Term Frequency-Inverse Document Frequency (TF-IDF) method, is applied to enhance the accuracy of emotion classification. Evaluation results indicate that the model with figurative language transformation achieves a higher accuracy (93.52%) compared to the model without figurative language transformation (92.31%), demonstrating that figurative language contributes to the richness of emotional expression recognized by the model. This model shows competitive results and can be compared to SVM using PSO while providing better performance than K-NN and Naive Bayes. The system implementation is web-based using the Streamlit framework, allowing users to input lyrics and obtain interactive emotion predictions. This research contributes to the analysis of music emotions and offers an efficient and more accessible alternative for emotion classification in song lyrics.

From Monoliths to Microservices: Designing a Scalable Super App Architecture for Academic Services at Universitas Jenderal Soedirman Wijayanto, Bangun; Iskandar, Dadang; Rahayu, Swahesti Puspita
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 4 (2025): JUTIF Volume 6, Number 4, Agustus 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.4.5237

Jenderal Soedirman (Unsoed) currently operates more than 30 monolithic information systems built with heterogeneous technology stacks, resulting in duplicate functionality, inconsistent user experience, and high maintenance costs. This study designs a modular, microservices‑based Super App architecture that integrates core academic services (KRS/KHS, transcript, student & lecturer attendance, lecturer activity log) and a parent/guardian monitoring feature. Using the Design Science Research (DSR) method, we (1) identified problems via a technology audit and problem–objective matrix; (2) designed the artifact with Domain‑Driven Design, C4 modelling, and API‑first contracts; (3) demonstrated a working prototype with API Gateway, SSO, and event‑driven notifications; (4) evaluated performance (<300 ms latency for 500–1000 concurrent users) and stakeholder impact; and (5) communicated results through this paper. The proposed architecture reduces integration complexity, supports zero‑downtime deployment, and enhances transparency for parents without violating consent and privacy. The validated blueprint provides a roadmap for transforming legacy campus systems into a scalable, observable, and governable Super App.

MOBILE APPLICATION DESIGN FOR PROCUREMENT ON XYZ COMPANY Sugianto, Victoria Angela; Suyoto, Suyoto; Rahayu, Swahesti Puspita
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 4 (2023): JUTIF Volume 4, Number 4, August 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.4.974

Procurement of goods in Indonesia continues to experience rapid growth. In 2022, the Indonesian government has allocated a budget of Rp. 2,714 trillion to fulfill government spending. Procurement of goods is almost carried out every month, to meet office needs and support operational facilities. Document storage is one of the important things in every company, however, until now there is no mobile application that can help archiving documents online. This is a problem where files are created and stored manually, so it is still difficult to find files and in transparating data. From the existing problems, there needs to be an innovation that can facilitate the process of managing and storing matching files. One of the innovations was carried out by designing a mobile application using the Design Thinking method. The Design Thinking method can be used to solve problems by rearranging problems into user perspectives that produce design ideas. The results of this study are in the form of a procurement mobile application design. It is hoped that the design and development of the mobile application will be able to provide convenience for Company XYZ in storing procurement documents. So that procurement can be done appropriately, effectively, efficiently, and the procurement documentation process can be carried out quickly.

IMPLEMENTATION OF A COMBINATION OF ADVANCED ENCRYPTION STANDARD CRYPTOGRAPHY WITH SUBBYTES MODIFICATION AND STEGANOGRAPHY BASED ON A WEBSITE Kurniawan, Muhammad Ilham; Maryanto, Eddy; Rahayu, Swahesti Puspita
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2665

The Advanced Encryption Standard (AES) is a symmetric encryption algorithm commonly used to protect digital data. However, concerns about potential attacks on cryptographic keys and the development of cryptanalysis methods further reinforce the need for security enhancement. This study aims to combine two technologies: the Advanced Encryption Standard (AES) cryptography with modifications to the SubBytes, and steganography using the Least Significant Bit (LSB) method in images, to enhance the security level of encrypted messages in the context of transmission through websites. In this study, modifications were made to the AES algorithm by replacing the S-box in the SubBytes process with a perfect SAC S-box with an average SAC value of 0.5. This testing is divided into two types: algorithm testing and system testing. Algorithm testing involves performance testing methods that show longer decryption times with an average difference of 80.27 milliseconds, cryptanalysis testing showing increased ciphertext security based on cryptanalysis time estimates using brute force, and randomness testing to demonstrate improvements in Frequency and Poker tests. System testing using the Black Box method shows results that are valid as expected.

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