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All Journal BERKALA FISIKA Proceedings of KNASTIK Jurnal Simetris Bulletin of Electrical Engineering and Informatics Scientific Journal of Informatics JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia
Ipung Permadi
Program Studi Teknik Informatika, Fakultas Sains dan Teknik, Unsoed
Computer Science & IT Education Electrical & Electronics Engineering Engineering Physics

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
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

Abstract

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

Abstract

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.
Monkeypox Classification Using Convolutional Neural Networks (CNN) Pruned Residual Network-50 (ResNet-50) Architecture on Flutter Framework Priatna, Irfan; Permadi, Ipung; Nofiyati, Nofiyati
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.2025.6.4.5232

Abstract

The monkeypox outbreak, which was previously only found in Africa, has now spread to other continents, including Asia, causing public concern as it occurred shortly after the COVID-19 pandemic was declared over. This disease has symptoms similar to cowpox, chickenpox, and measles, making early detection based on visual observation difficult. To address this issue, various studies have developed Deep Learning (DL)-based classification models using datasets such as WSI, MSID, MCSI, and MSLD v2, which are also utilized in this research. This study proposes a pruned ResNet-50 model using the Global MP method for pruning and QAT for quantization. These modifications not only maintain the model's performance with an accuracy of 94.44%, precision of 94.12%, recall of 94.71%, and F1-score of 94.16%, but also significantly reduce the model size to just 20.993 MB. As a result, the model can be implemented on Android devices with limited resources, enabling rapid and practical early detection of monkeypox in the field without requiring large-scale servers. Blackbox testing results show that the Flutter-based application utilizing this model performs well, potentially providing tangible support for medical personnel and the public in monitoring the spread of monkeypox in a more efficient and accessible manner.
Co-Authors Alfarez Marchelian, Reyno Alfarizi, Rizki Zakaria Arief Kelik Nugroho, Arief Kelik Bintang Pradana Yosua, Panky Didit Suprihanto, Didit Eddy Maryanto Faturrahim, Muhammad Hanifa, Aini Hartono Hartono Iqbal Iqbal Kurniawan, Yogiek Indra Lasmedi Afuan Nofiyati Nofiyati, Nofiyati Nurul Hidayat Priatna, Irfan Rahayu, Swahesti Puspita Sari, Enjelita Sugito - Tuti Alawiyah Yasifa, Ana Romadhona