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All Journal JOIV : International Journal on Informatics Visualization JURNAL MEDIA INFORMATIKA BUDIDARMA Journal Automation Computer Information System (JACIS) The Indonesian Journal of Computer Science Research
Verawati, Ike
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Analisis Sentimen Pengguna Twitter Terhadap Bus Listrik Menggunakan Naïve Bayes Verawati, Ike; Jaelani, Syarif Nurwahid
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 2 (2024): April 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i2.7030

Abstract

Twitter sentiment analysis is one method of identifying and classifying opinions into positive or negative sentiment in tweets. One of the topics that is being widely discussed on Twitter and has received various opinions for and against is electric buses. All of these opinions are still random and their sentiments have not been classified so sentiment classification needs to be carried out.Naïve Bayes can be used to classify sentiment and is easy to implement. The aim of this research is to classify whether sentiment regarding electric buses leads to positive sentiment or negative sentiment using Naïve Bayes and calculate the accuracy obtained. Several steps were taken, namely data collection, preprocessing, lexicon labeling, word weighting, naïve Bayes classification, and confusion matrix evaluation. The results of this stage from 4 trials of different data sharing ratios showed that the highest sentiment was positive sentiment which reached 77.31% with 22.69% negative sentiment at a data sharing ratio of 6:4 with the evaluation results using the confusion matrix obtaining an accuracy of 74.4%. After naïve Bayes was optimized with hyperparameter tuning, the accuracy increased to 78%. At a data sharing ratio of 9:1, the accuracy obtained after optimization shows a decrease to 71.5%, whereas initially Naïve Bayes obtained an accuracy of 75.6%, this shows that the data split ratio can influence the accuracy obtained by the classification model.
Klasifikasi Penyakit Daun Padi Menggunakan KNN dengan GLCM dan Canny Edge Detection Verawati, Ike; Aunurrohim, Ridwan Al Akhyar
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.6906

Abstract

Rice plants have an important role in human survival, especially in Indonesia where rice plants are the staple food source for most of the population. The Central Statistics Agency reported that rice consumption in Indonesia reached 28.69 million tons in 2019. In the same year, rice production in Indonesia reached 31.31 million tons. However, production results decreased compared to the previous year, which amounted to 33.94 million tons. One of the factors causing the decline in quality and even death of rice plants is pests and disease. According to the International Rice Research Institute, every year farmers lose an average of 37 percent of their harvest due to pest and disease attacks. The Food and Agriculture Organization also reported a similar thing, where 20 to 40 percent of world food production failures were caused by pests and diseases. Farmers' lack of knowledge and the limited number of experts result in ineffective disease diagnosis. Therefore, a step or method is needed so that the disease detection process in rice plants becomes more effective. This research uses the K-Nearest Neighbor classification algorithm with Gray Level Co-Occurrence Matrix and Canny Edge Detection to classify diseases in rice plants. The result is that Canny Edge Detection has a positive influence on method performance with accuracy reaching 91.67%, precision 87.37% and recall 87.50% at k=7.
Optimasi Performa Game Dugeon Escape Menggunakan Algoritma A-Star Pramono, Sigit; Verawati, Ike
Journal Automation Computer Information System Vol. 4 No. 2 (2024): November
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jacis.v4i2.96

Abstract

Pertumbuhan dunia game semakin cepat seiring dengan perkembangan teknologi saat ini telah membawa perubahan besar dalam dunia game. Salah satu aspek yang membuat game semakin menarik adalah penerapan kecerdasan buatan (AI). Biasanya, AI diterapkan pada elemen dalam game, baik pada karakter pemain maupun NPC (Non-Playable Character). Implementasi AI pada NPC memiliki berbagai tujuan, seperti menciptakan pengalaman bermain yang lebih dinamis dan menarik. Salah satu metode AI yang sering digunakan adalah Pathfinding, yaitu teknik untuk menemukan jalur yang efisien. Dalam game, metode ini memungkinkan NPC untuk mencari jalur, mengejar, atau menghalangi pemain dalam mencapai tujuan tertentu. Untuk menerapkan metode Pathfinding, diperlukan algoritma yang sesuai. Salah satu algoritma yang sering digunakan adalah algoritma A-Star (A*). Algoritma ini dikenal efektif untuk menemukan jalur terpendek dalam berbagai situasi, termasuk dalam lingkungan permainan. Berdasarkan analisis, algoritma A-Star dapat digunakan sebagai solusi optimal untuk navigasi NPC dalam game. Penelitian ini menghasilkan sebuah game bergenre roguelike, yang memiliki ciri khas seperti generasi prosedural, tantangan tinggi, dan gameplay yang berulang. Game ini dikembangkan menggunakan Unity dan dirancang untuk dijalankan pada perangkat desktop. Penelitian ini mengoptimalkan AI pathfinding pada NPC untuk meningkatkan performa game, menjadikannya lebih responsif dan efisien.
Analisis Performa Logistic Regression dan Random Forest dalam Klasifikasi Kelayakan Penerimaan Kredit Adrian, Andreas; Verawati, Ike
The Indonesian Journal of Computer Science Research Vol. 4 No. 2 (2025): Juli
Publisher : Hemispheres Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59095/ijcsr.v4i2.205

Abstract

Penentuan kelayakan penerimaan kredit merupakan proses yang sangat penting dalam industri perbankan dan keuangan. Hal ini sangat berpengaruh bagi badan keuangan tersebut, bahkan dapat menyebabkan kondisi finansial badan keuangan tersebut tidak sehat karena kesalahan dalam keputusan kelayakan kredit. Machine learning hadir untuk meminimalisir kesalahan tersebut. Untuk meningkatkan akurasi dan efisiensi dalam klasifikasi kelayakan kredit, penelitian ini berfokus pada penerapan dua model machine learning, yaitu Logistic Regression dan Random Forest Classifier. Logistic Regression dipilih karena kemampuannya dalam mengidentifikasi hubungan linear antara variabel input dan output, sedangkan Random Forest Classifier memiliki keunggulan dalam menangani dataset yang kompleks dan non-linear. Tujuan utama dari penelitian ini adalah untuk membandingkan performa kedua model tersebut dalam tugas klasifikasi kelayakan kredit. Perbandingan dilakukan dengan tahapan Studi Literatur, Akuisisi Data (Pengumpulan data) yang mengambil dataset perbankan public di kaggle, EDA, Pre-Processing, Modelling, Evaluasi, dan Analisis Evaluasi Model. Dataset yang akan digunakan mencakup informasi data finansial dari nasabah. Perbandingan performa pada penelitian ini menggunakan matrix akurasi, precision, recall, F1-Score dan AUC-ROC untuk mengevaluasi kinerja masing-masing model. Penelitian ini menghasilkan bahwa model random forest lebih unggul dengan skor Akurasi 0.95, Presisi 0.93, Recall 0.98 dan F1 Score 0.96. Skor AUC yang digunakan untuk melihat seberapa baik model dalam membedakan class 1 dan 0 mencapai 0.98. Hasil penelitian ini diharapkan mampu memberikan rekomendasi yang bermanfaat bagi industri perbankan dalam memilih model yang paling tepat untuk penilaian kelayakan kredit
An Intrusion Detection System Using SDAE to Enhance Dimensional Reduction in Machine Learning Hanafi, Hanafi; Muhammad, Alva Hendi; Verawati, Ike; Hardi, Richki
JOIV : International Journal on Informatics Visualization Vol 6, No 2 (2022)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2.990

Abstract

In the last decade, the number of attacks on the internet has grown significantly, and the types of attacks vary widely. This causes huge financial losses in various institutions such as the private and government sectors. One of the efforts to deal with this problem is by early detection of attacks, often called IDS (instruction detection system). The intrusion detection system was deactivated. An Intrusion Detection System (IDS) is a hardware or software mechanism that monitors the Internet for malicious attacks. It can scan the internetwork for potentially dangerous behavior or security threats. IDS is responsible for maintaining network activity under the Network-Based Intrusion Detection System (NIDS) or Host-Based Intrusion Detection System (HIDS). IDS works by comparing known normal network activity signatures with attack activity signatures. In this research, a dimensional reduction and feature selection mechanism called Stack Denoising Auto Encoder (SDAE) succeeded in increasing the effectiveness of Naive Bayes, KNN, Decision Tree, and SVM. The researchers evaluated the performance using evaluation metrics with a confusion matrix, accuracy, recall, and F1-score. Compared with the results of previous works in the IDS field, our model increased the effectiveness to more than 2% in NSL-KDD Dataset, including in binary class and multi-class evaluation methods. Moreover, using SDAE also improved traditional machine learning with modern deep learning such as Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). In the future, it is possible to integrate SDAE with a deep learning model to enhance the effectiveness of IDS detection
Co-Authors Adrian, Andreas Alva Hendi Muhammad Aunurrohim, Ridwan Al Akhyar Hanafi Hanafi Jaelani, Syarif Nurwahid Richki Hardi Sigit Pramono