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All Journal MATEMATIKA Jurnal Teknik Informatika (JUTIF) Jurnal Pendidikan dan Teknologi Indonesia
Eddy Maryanto
Program Studi Teknik Informatika-Jurusan MIPA-Fakultas Sains dan Teknik-UNSOED
Computer Science & IT Education Engineering Mathematics

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KAJIAN MATEMATIS DISTRIBUSI TEKANAN PADA AIRFOIL JOUKOWSKY MARYANTO, EDDY
MATEMATIKA Vol 10, No 1 (2007): JURNAL MATEMATIKA
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (43.38 KB)

Abstract

The objectives of this research are to investigate the effect of the translation of the coordinate system to the shape of the Joukowsky airfoils formed and to compute lift coefficient of the Joukowsky airfoils. The result of the research is the higher the absisca and ordinate of the translation the higher the lift coefficient (with absisca must be greater than ordinate and  both are greater to zero).  If the circle is translated along positive x-axis, the airfoil formed is symmetrical with respect to x-axis.  While if it is  translated along positive y-axis, the airfoil formed is curve shaped.   From the research we also have found the fact the higher the distance of the translation from the center of the coordinate system the higher the thickness of the airfoil formed.  
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.
An Integrated Pipeline with Hierarchical Segmentation and CNN for Automated KTP-el Data Extraction on the e-Magang Platform Syafrie Rahardian, Nuansa; Maryanto, Eddy; Nawangnugraeni, Devi Astri
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5279

Abstract

In alignment with Indonesia's digital transformation agenda, this research addresses the inefficiencies and error-prone nature of manual data entry on the Foreign Policy Strategy Agency's (BSKLN) e-magang platform. This study introduces a comprehensive, end-to-end Optical Character Recognition (OCR) pipeline, specifically designed for structured identity documents and real-world government platform integration. The proposed methodology features a robust workflow, including image preprocessing with histogram matching, hierarchical segmentation using vertical projection, and intelligent postprocessing to structure the output. To overcome the limitations of a small dataset, three specialized Convolutional Neural Network (CNN) models were rigorously trained and validated using a stratified 5-fold cross-validation technique. The final system was successfully integrated, connecting a Flask-based model engine with the existing Laravel and React platform. End-to-end testing demonstrated strong performance, achieving an average character-reading accuracy of 93.31% with a mean processing time of 14.48 seconds per image. The primary contribution of this research to the field of informatics is the development of a complete and deployable system architecture that ensures data interoperability and reliability, providing a practical blueprint for integrating intelligent automation into digital public services.
Prediksi Nilai Pasar Pemain Sepak Bola Menggunakan Algoritma Random Forest Berdasarkan Atribut Permainan Dari Game Football Manager 2023 Pada Lima Liga Top Eropa (Berdasarkan Koefisien UEFA) Affandi, Syihabuddin; Maryanto, Eddy; Kurniawan, Yogiek Indra
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 10 (2024): JPTI - Oktober 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.672

Abstract

Sepak bola bukan hanya sekadar olahraga, tetapi juga industri bernilai miliaran dolar, khususnya di Eropa. Salah satu aspek krusial dalam industri ini adalah penentuan nilai pasar pemain, yang menjadi dasar bagi transaksi transfer pemain. Nilai pasar pemain dipengaruhi oleh berbagai faktor, seperti performa, usia, posisi, serta aspek fisik dan mental. Namun, terdapat kesenjangan dalam penilaian nilai pasar, di mana pemain dengan statistik performa tinggi terkadang memiliki nilai pasar yang lebih rendah dibandingkan pemain dengan performa yang kurang optimal. Oleh karena itu, prediksi nilai pasar pemain secara objektif menjadi tantangan penting bagi klub sepak bola dalam pengambilan keputusan strategis. Penelitian ini mengusulkan model prediksi berbasis Random Forest untuk mengestimasi nilai pasar pemain secara objektif dengan memanfaatkan data atribut permainan dari Football Manager 2023. Dataset mencakup 1.405 pemain dari lima liga top Eropa (berdasarkan koefisien UEFA 2023) dengan 66 variabel. Metodologi penelitian meliputi tahap preprocessing data (handling missing values,label encoding), Exploratory Data Analysis (EDA), pembangunan model Random Forest, dan implementasi sistem berbasis web. Pembagian data menggunakan rasio 80:20 (training-testing), sementara evaluasi kinerja model dilakukan melalui metrik RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), dan R² (Koefisien Determinasi). Hasil eksperimen menunjukkan bahwa model baseline dengan parameter default memperoleh nilai Root Mean Squared Error (RMSE) sebesar 0.63, Mean Absolute Error (MAE) sebesar 0.517, dan koefisien determinasi (R²) sebesar 0.75. Setelah dilakukan optimasi hyperparameter menggunakan Grid Search, kinerja model mengalami peningkatan yang signifikan dengan RMSE sebesar 0.62, MAE sebesar 0.513, dan R² sebesar 0.76. Model optimal diimplementasikan ke dalam sebuah situs web untuk mempermudah melakukan prediksi nilai pasar pemain. Hasil penelitian menunjukkan bahwa model Random Forest Regression mampu memberikan prediksi nilai pasar dengan tingkat akurasi yang lebih baik dibandingkan metode lain yang telah diuji dalam penelitian terdahulu.
THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE Adyatma, Adrian Dwinanda; Afuan, Lasmedi; Maryanto, Eddy
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1310

Abstract

In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) from Gojek users on the Google Play Store, and performing performance validation with K-Fold at K=10, the system that uses the combined TF-IDF feature extraction of unigrams and bigrams achieves the best performance among the three systems with an accuracy of 0.84, however the accuracy of the system that uses unigrams solely has accuracy of 0.83, and 0.7 for the system that uses bigram. From the results of the research, it can be concluded that the use of the combination of unigram and bigram can increase the accuracy of the classification result.
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

Abstract

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.
Co-Authors Adyatma, Adrian Dwinanda Affandi, Syihabuddin Ipung Permadi Kurniawan, Muhammad Ilham Kurniawan, Yogiek Indra Lasmedi Afuan NAWANGNUGRAENI, DEVI ASTRI Nur Chasanah Rahayu, Swahesti Puspita Sari, Enjelita Syafrie Rahardian, Nuansa Teguh Cahyono Tuti Alawiyah