Ema Utami’s research while affiliated with Universitas Amikom Yogyakarta and other places

Advanced Encryption Standard (AES) adalah salah satu algoritma yang paling popular digunakan . Penelitian ini bertujuan untuk menganalisis kinerja algoritma Advanced Encryption Standard (AES) yang telah dimodifikasi dalam proses enkripsi dan dekripsi. Fokus utama penelitian ini adalah mengukur nilai Avalanche Effect, throughput, dan kecepatan proses enkripsi serta dekripsi dari AES termodifikasi dengan mengganti fungsi mixcolumn dengan bit permutasi dan melakukan penguarangan putaran terhadap Algortima AES konvensional, twofish, serpent, DES dan 3DES. Hasil menunjukkan bahwa algoritma AES termodifikasi memiliki Avalanche Effect 50.20% , lebih tinggi dibandingkan dengan algoritma AES konvensional yang memiliki Avalanche Effect 49.63%. Pengujian Througput menunjukan bahwa AES termodifikasi tidak mengalami perubahan yang signifikan dibanding algoritma AES standar,Serpent, Twofish, DES dan 3DES saat proses enkripsi dan mengalami perubahan yang signifikan saat proses dekripsi sebesar 467.37% 46 dibanding DES dan 1286% dibanding 3DES

Komoditas jagung di Indonesia menjadi tanaman pangan terbesar kedua setelah padi sebagai sumber karbohidrat. Namun dikarenakan keterbatasan kemampuan petani dan faktor lingkungan menyebabkan upaya penanganan tanaman jagung akibat adanya serangan organisme pengganggu tanaman menjadi terhambat. Penelitian ini mengusulkan upaya deteksi secara dini terhadap jenis penyakit pada daun tanaman jagung menggunakan metode Convolutional Neural Network (CNN) yang dikenal sebagai algoritma pembelajaran mesin berkinerja tinggi dalam mengklasifikasikan jenis penyakit tanaman ke dalam beberapa kelas seperti Blight, Common Rust, Grey Leaf Spot, dan Healthy. Selain itu, transformasi warna citra dari RGB, HSV dan Grayscale, proses segmentasi dengan Region of Interest (ROI) serta dilengkapi dengan penerapan ektraksi fitur tekstur dengan menggunakan GLCM telah mampu menghasilkan tingkat akurasi sebesar 94% dan nilai loss rate yang relatif kecil yaitu 0.1742. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN terbukti secara efisien & efektif dalam melakukan identifikasi jenis penyakit tanaman.

Acute Lymphoblastic Leukemia (ALL) is the most prevalent form of leukemia that occurs in children. Detection of ALL through white blood cell image analysis can assist in prognosis and appropriate treatment. In this study, the author proposes an approach for classifying ALL based on white blood cell images using a Convolutional Neural Network (CNN) model called InceptionV3. The dataset used in this research consists of white blood cell images collected from patients with ALL and healthy individuals. These images were obtained from The Cancer Imaging Archive (TCIA), which is a service for storing large-scale cancer medical images available to the public. During the evaluation phase, the author used training data evaluation metrics such as accuracy and loss to measure the model's performance. The research results show that the InceptionV3 model is capable of classifying white blood cell images with a high level of accuracy. This model achieves an average ALL recognition accuracy of 0.9896 with a loss of 0.031. The use of CNN models like InceptionV3 in medical image analysis has the potential to enhance the efficiency and accuracy of image-based disease diagnosis.

The political year for 2024 has now increasingly entertained all Indonesian people to hold a democratic party. Various political parties have become quite dramatic in expressing their coalitions and declaring their alignment with several presidential candidates that are known to the whole community. The electability of each presidential candidate that is determined is increasingly interesting and hotly discussed, which often makes anyone take action to voice their partisanship between the pros and cons. One of them is Ganjar Pranowo, who is a political figure for the governor of Central Java. Recently, in the middle of 2023, a political party has proposed him to advance to the seat of head of state as a presidential candidate for the upcoming 2024 election. With the existence of various polemics of opinion from various layers of society, this is the right moment to carry out an analysis as a form of polarization unanimity which is presented from various public opinions as a general description and an outline in sentiment in the form of information on the conclusions of public opinion. The stages in this research began with conducting a literature study and exploring studies related to opinions and alignments with public sentiment regarding the electability of Ganjar Pranowo as a presidential candidate, and then collecting opinion data from Twitter on the electability of Ganjar Pranowo. At the experimental stage, the authors divided the data with a percentage of 80% training data and 20% testing data. The modeling used is K-Nearest Neighbor (KNN) and Naïve Bayes to classify text data as well as make comparisons of the two. In the implementation process, the author uses python as a programming language in building the model. Confusion Matrix is used for every performance evaluation related to model accuracy in each algorithm. The results showed that the division of training data and testing data and the value of k in the K-Nearest Neighbor (KNN) model greatly affect the accuracy of the model. From the test results on the comparison of the two models, the K-Nearest Neighbor model has the best accuracy with an accuracy value of 99% of the K-Nearest Neighbor with an accuracy value of 96%. The percentage of sentiment with a comparison of 96.6% positive sentiment and 3.4% negative sentiment concluded that most people still dominate positive sentiment.

Sentiment analysis on public opinion regarding the shopee food application is an interesting topic in the context of evaluating service quality in the shopee food application. In this digital era, user opinion has a very important role in shaping public perception of the application. Therefore, sentiment analysis is needed to understand user opinion about the shopee food application. This study uses Decision Tree C4.5 to analyze public sentiment on the use of the Shopee Food application on Twitter users. However, beforehand it is necessary to overcome the problem of data imbalance which is common in datasets, where the number of positive, negative, and neutral sentiments is not balanced. To overcome this problem, three different techniques are used, namely SMOTE, undersampling, and a combination of oversampling and undersampling. The results of this study indicate that the SMOTE technique provides better results in overcoming data imbalances and increasing prediction accuracy. With an accuracy of 0.88. the SMOTE technique can provide more accurate sentiment predictions than the undersampling technique and the combination of oversampling and undersampling. This is because SMOTE can synthetically expand the number of minority samples, thereby preventing the loss of information and maintaining variation in the dataset. In conclusion, sentiment analysis on the Shopee Food application on Google Play using the Decision Tree C4.5 algorithm and the SMOTE technique can overcome data imbalances with a prediction accuracy of 0.88. This technique is more efficient than the undersampling technique and the combination of oversampling and undersampling. These results can provide developers with valuable insights to improve app quality and user satisfaction.

Rice is a staple food source for most people around the world, including Indonesia, which is an agrarian country where most of its population grows and consumes rice. However, rice plants also suffer from various diseases, especially on the leaves, such as bacterial leaf blight, rice blast, dan rice tungro. If the infection or disease in rice plants is not identified early on, it will decrease production and harm farmers. To address this problem, information technology can be utilized in identifying diseases using image processing and image classification. The dataset was taken from public repositories, and data augmentation was also used in this research to increase the dataset's training accuracy. With this background, a disease detection system approach is proposed using Deep Learning method using Convolutional Neural Network (CNN) and several transfer learning architectures, namely VGG16, NASNetMobile, and Xception, for rice leaf disease detection. The best experimental results were obtained using the Xception architecture, where the training accuracy value is 99.13%, validation accuracy is 97.22%, and the testing accuracy is 97.22%.

With the rapid development of e-commerce, which makes it possible to buy and sell products and services online, customers are increasingly using these online shop sites to fulfill their needs. After purchase, customers write reviews about their personal experiences, feelings and emotions. Reviews of a product are the main source of information for customers to make decisions to buy or not a product. However, reviews that should be one piece of information that can be trusted by customers can actually be manipulated by the owner of the seller. Where sellers can spam reviews to increase their product ratings or bring down their competitors. Therefore this study discusses detecting fake reviews on productreviews on Tokopedia. Where the method used is the distribution post tagging feature to perform detection. By using the post tagging feature method the distribution got 856 fake reviews and 4478 genuine reviews. In the fake reviews, there were 628 reviews written with the aim of increasing product sales or brand names from store owners, while there were 228 reviews aimed at dropping their competitors or competitors. Furthermore, the classification is carried out using the random forest algorithm model and the support vector machine. By dividing the dataset for training data by 80% while 20% for data testing. Here it is known that the support vector machine gets much higher accuracy than the random forest. The support vector machine gets an accuracy of 98% while the random forest gets an accuracy of 60%

Di antara berbagai jenis serangan di bidang Teknologi Informasi, serangan DDOS adalah salah satu ancamanterbesar bagi situs internet dan menimbulkan risiko yang menghancurkan keamanan sistem komputer, terutamakarena potensi dampaknya. Oleh karena itu mengapa penelitian di bidang ini berkembang pesat, dengan parapeneliti yang berfokus pada cara-cara baru untuk mengatasi deteksi dan pencegahan intrusi. Machine learningdan Artificial Intelligent adalah beberapa tambahan terbaru dalam daftar teknologi yang diteliti untukmelakukan klasifikasi deteksi intrusi. Studi ini mengeksplorasi perilaku dan penerapan dataset DDoS untukpembelajaran mesin dalam konteks deteksi intrusi. Alur dalam penelitian ini, pertama adalah mengumpulkandataset DDoS mentah dari sumber yang memiliki reputasi baik. Setelah data diperoleh, kumpulan data akhirdibuat untuk pemodelan. Manajemen data melibatkan pembersihan data, transformasi tipe data dan pertukarandata pada pengumpulan data. Proses seleksi disertai dengan model. Dua algoritma terpisah, random danadaboost, digunakan untuk melatih model dengan dataset. Model divalidasi dan dilatih ulang dengan k-foldcross. Model tersebut akhirnya dievaluasi menggunakan data yang tidak terlihat. Hasilnya ditentukan olehberbagai ukuran keluaran. Dalam percobaan, dataset DDoS digunakan: CICDDoS_2019 Performa deteksiintrusi set data ini dianalisis menggunakan dua model pembelajaran mesin. Dataset dibagi dalam rasio 80:20untuk pelatihan model, validasi dan pengujian. Model pembelajaran mesin dipilih secara sistematis dan hatihatiuntuk memastikan bahwa eksperimen dilakukan dengan cara yang tepat. Hasilnya dianalisis menggunakansekumpulan metrik performa, termasuk akurasi, presisi, recall, f-measure, dan waktu komputasi.

E-Journal or Electronic Journal can be interpreted as online storage of information and communication. The characteristics of the E-Journal are that it utilizes electronic technology where publishers, writers, and readers can communicate with each other and use ICT, or written work data is stored so that it can be accessed anywhere and anytime when needed. One of the efforts to increase the effectiveness and efficiency of the scientific publication process requires using various ICTs. Design can use the electronic journal information system to solve problems in compiling a journal integration system based on software requirements analysis. In addition, the system development method used is a waterfall. The design uses UML, ERD, Wireframe, and database implementation using DBMS. The research results are realized in the form of a conceptual design of the system so that it can accommodate the needs to make the system. The development also can choose journals that are following their fields so that they can estimate open access journals based on the estimated submission time.