Identification of Batik in Central Java using Transfer Learning Method

Batik was recognized as a human heritage for oral and nonmaterial culture by UNESCO due to its symbolic and philosophical ties to the lives of Indonesians. However, the younger generation is gradually losing its legacy because of technological and sociological changes that have influenced Indonesian batik. Consequently, batik knowledge is disappearing. A convolutional neural network and transfer learning techniques were utilized in deep learning to construct a model recognising batik motifs. The study utilized a dataset of one thousand images, five classes of batik designs (Banji, Kawung, Slope, Parang, and Slobog), and pre-trained architectural models VGG16 and VGG19 on Keras. The best model utilizes the VGG16 architecture, and the number of epochs is 50, with the result of testing accuracy of 0.9200

Analisis Sentimen Review Hotel Menggunakan Metode Deep Learning BERT

Pandemi COVID-19 telah menyebabkan penurunan kunjungan pariwisata dan okupansi hotel. Penting bagi pengusaha hotel untuk memantau gaya hidup pengunjung guna menjaga kelangsungan bisnis. Salah satu cara untuk melakukannya adalah dengan memahami sentimen pengunjung hotel melalui analisis review agar mendapatkan pemahaman yang lebih baik dalam pengambilan keputusan terkait layanan dan aspek bisnis di sektor perhotelan. Penelitian ini menerapkan model deep learning natural language processing BERT untuk menganalisis sentimen positif dan negatif dari review pengunjung hotel di Indonesia. Model BERT yang digunakan telah menjalani proses pretrained dan diterapkan metode fine-tuning untuk menghasilkan analisis sentimen yang akurat. Hasil evaluasi menunjukkan bahwa model fine-tuning SmallBERT yang dilatih menggunakan dataset 515k review hotel selama 5 epoch memberikan performa yang baik. Model SmallBERT mencapai akurasi sebesar 91,40%, presisi 90,51%, recall 90,51%, dan skor f1 90,51% saat dievaluasi dengan dataset yang diberi label secara manual. Visualisasi hasil perbandingan sentimen yang didominasi oleh sentimen positif, dilakukan menggunakan Tablea

Penerapan Algoritma Pathfinding A* dalam Game Dual Legacy berbasis Android

A* Pathfinding Algorithm Implementation in Dual Legacy Game based on Android. Games have 2 characters, the player, and the NPC (Non-Playable Character) which cannot be controlled by the player,so the NPC movements are easy to predict. A Star (A*) algorithm is a pathfinding algorithm or finding a way to a destination, in this case searching for the closest path to the player and avoiding obstacles. The enemy NPC is tasked with chasing the player, and the enemy NPC must reduce the player's health. A* algorithm calculatesthe distance of one of the paths and then calculatesthe distance of the other paths. The algorithm will choose the shortest path when all paths have been completed. Research focuses on the NPC's task of finding the shortest route. The A* in the “Dual Legacy” 2D Side-Scrolling RPG game based on Android is expected with this algorithm NPC can search for and chase players/players via the nearest path. The conclusion is that the A Star Algorithm has been successfully implemented, the NPC approaches the player through the shortest distance by avoiding obstacles.Keywords: A Star (A*) algorithm, NPC, game, Android, 2D side-scrolling RPG Penerapan Algoritma Pathfinding A* dalam Game Dual Legacy berbasis Android. Game biasanya terdapat 2 karakter yaitu player dan NPC (Non-Playable Character) yang tidak bisa dikendalikan oleh player sehingga pergerakan karakter NPC mudah ditebak. Algoritma A Star (A*) merupakan algoritma pathfinding atau mencari jalan ke tujuan, dalam kasus ini mencari jalan terdekat ke player dan menghindari rintangan yang ada. NPC musuh ini bertugas untuk mengejar player dan NPC musuhharus mengurangi darah player. Algoritma A* menghitung jarak satu jalur, menyimpannya, lalu menghitung jarak jalur lainnya. Setelah semua jalur dihitung, algoritma A* memilih jalur terpendek . Penelitian berfokus pada tugas NPC untuk pencarian rute terdekat. Menerapkan algoritma pathfinding A* pada NPC game Dual Legacy 2D Side-Scrolling RPG berbasis Android diharapkan dengan algoritma tersebut NPC dapat mencari dan mengejar pemain / player melalui jalan terdekat. Kesimpulan perancangan ini adalah algoritma A Star berhasil diimplementasikan, NPC mendekati player melalui jarak terdekat dengan menghindari halangan yang ada.Kata Kunci: algoritma A Star (A*), NPC, game, Android, 2D side-scrolling RP

Emotion Classification in Indonesian Language: A CNN Approach with Hyperband Tuning

In today's world, there is a high demand for accurate techniques to classify emotions in various fields. This study proposed utilizing a Convolutional Neural Network (CNN) optimized with a Hyperband Tuner (HT) to perform the Emotion Classification task in the Indonesian language effectively. Various feature extraction techniques experiments were conducted to explore the best combinations of feature extraction and CNN for the data set, including CountVectorizer (CV), TF-IDF, and Keras Tokenizer (KT). Last, the proposed methodology was evaluated and compared to the stateof-the-art techniques, including K-Nearest Neighbors (KNN), Decision Tree (DT), Naive Bayes (NB), and Boosting SVM. The experimental results revealed that the proposed method in this research outperforms the existing technique as evidenced by the accuracy, precision, recall, and F1-score metrics, which respectively reached 71.5655%, 71.5483%, 71.5655%, and 71.0041%

Analisis Sentimen Ulasan Aplikasi Jamsostek Mobile Menggunakan Metode Support Vector Machine

Sentiment Analysis of Jamsostek Mobile Application Reviews Using the Support Vector Machine Method. Today's technology is evolving quickly, leading to new developments that have helped produce JMO and other mobile applications that can be useful to Indonesians. The reviews or comments in the JMO can be used as a gauge for quality and user satisfaction. This study aims to analyze the quality of JMO applications and classify reviews or opinions into positive, negative, and neutral categories through sentiment analysis. The Support Vector Machine method is used in this analysis process with a linear kernel approach to determine the level of accuracy of classifying JMO application reviews. Research shows that classifying the SVM method against sentiment analysis of reviews or JMO application reviews produces the best accuracy scores, obtaining results with accuracy of 96%, precision of 92%, recall of 96%, and f1-score of 94%, while for the results of most reviews are positive category reviews with a total of 17.571.Keywords: sentiment analysis, JMO, SVM, linear kernel   Perkembangan pesat teknologi saat ini memunculkan inovasi baru untuk menciptakan berbagai aplikasi mobile yang dapat memberi kemudahan bagi masyarakat Indonesia, salah satunya yaitu JMO. Penelitian ini bertujuan untuk menganalisis kualitas aplikasi JMO dan mengklasifikasikan ulasan atau opini kedalam kategori positif, negatif dan netral melalui analisis sentimen. Metode Support Vector Machine digunakan pada proses analisis ini dengan pendekatan kernel linear untuk mengetahui tingkat akurasi dari pengklasifikasian ulasan aplikasi JMO tersebut. Penelitian menunjukkan bahwa pengklasifikasian metode SVM terhadap analisis sentimen ulasan atau review aplikasi JMO menghasilkan nilai akurasi terbaik, didapatkan hasil dengan accuracy 96%, precision 92%, recall 96%, dan f1-score 94%, sedangkan untuk hasil ulasan terbanyak adalah ulasan berkategori positif dengan jumlah 17.571.Kata Kunci: analisis sentimen, JMO, SVM, kernel linea

Analisis Sentimen AicoGPT (Generative Pre-trained Transformer) Menggunakan TF-IDF

Peran artificial intelligence memudahkan mencari informasi yang tepat dan akurat bahkan penyelesaian masalah dengan model yang kompleks. Salah satu terobosan berbasis AI adalah ChatGPT oleh OpenAI pada tahun 2020, dilanjutkan dengan versi terbaru pada tahun 2023 yaitu GPT–3. Sejak saat itu, beberapa teknologi AI serupa versi mobile mulai bermunculan, salah satunya AicoGPT. Namun, kinerja dari aplikasi serupa ini belum dapat diandalkan sehingga masih perlu menganalisis tanggapan para penggunanya, apakah akan sama menakjubkannya atau tidak. Dari permasalahan tersebut, penelitian ini dibuat dengan tujuan untuk menganalisis 1443 data ulasan para pengguna aplikasi AicoGPT di Google Playstore dengan teknik analisis sentimen menggunakan TFIDF dan perbandingan klasifikasi LR dan SVM. Dari kedua ujicoba tersebut, menghasilkan akurasi terbaik dengan Algoritma SVM, yaitu sebesar 92%. Sedangkan LR menghasilkan akurasi sebesar 89%. Dari penelitian ini, dapat disimpulkan secara singkat bahwa metode TF-IDF dengan klasifikasi SVM, cocok digunakan untuk melakukan analisis sentimen dari dataset yang diteliti

Classification of Cumulonimbus Cloud Formation based on Himawari Images using Convolutional Neural Network model Googlenet

Cumulonimbus clouds (Cb) are dangerous for many human activities. To reduce this effect, a system to classify formations is needed. The formation of Cb clouds can be seen in the Himawari-8 IR image. This research aimed to create a Cb cloud classification system with Himawari-8 IR Enhanced imagery using the GoogleNet model CNN method. The total data used was 2026 image data. Parameter testing was carried out on the CNN GoogleNet model in this study, namely a data distribution ratio of 90:10 and 80:20. The probability of dropout is 0.6, 0.7, and 0.8. and batch sizes of 8, 16, 32, and 64. The trials conducted in this study yielded a sensitivity value of 100.00%, an accuracy of 99.00%, and a specificity of 99.60% obtained from the experimental data distribution of 90:10, probability 0.8, and batch size 8

Klasterisasi Puskesmas dengan K-Means Berdasarkan Data Kualitas Kesehatan Keluarga dan Gizi Masyarakat

One of the fundamental principles followed by the Jember Health Office for decision-making is data. Data plays a crucial role in the decision-making process. Raw data is more difficult to interpret and needs to be analyzed. Clustering is one of the techniques used for analysis. This study discusses using K-Means to cluster Public Health Center data based on AKI, AKB, and stunting prevalence. The data is processed by reducing dimensions and normalizing them. The clustering process is performed using the K-Means method, where the maximum k-value is obtained by calculating WCSS. The clustering process results in three clusters of Public Health Centers in the Jember Regency. These clusters can serve as a reference for the Jember Health Office to formulate family health and community nutrition quality policies.Keywords: data mining, K-Means, clustering, Maternal Mortality Rate, Infant Mortality Rate, the prevalence of stunting Salah satu dasar pengambilan kebijakan oleh Dinas Kesehatan Jember adalah data. Data memiliki peran dalam proses pengambilan keputusan. Data mentah yang didapatkan lebih sulit untuk diinterpretasikan sehingga diperlukan analisis terhadap data tesebut. Salah satu analisis yang dapat digunakan adalah teknik klasterisasi. Padapenelitian ini akan dibahas penggunaan K-Means untuk klasterisasi data puskesmas berdasarkan AKI, AKB, dan prevalensi stunting. Data diproses dengan melakukan reduksi dimensi dan normalisasi. Proses klasterisasi dilakukan dengan metode K-Means dimana nilai k maksimal diperoleh dengan menghitung WCSS. Adapun hasil proses klasterisasi didapatkan tiga kelompok klaster puskesmas yang terdapat di Kabupaten Jember. Hasil klasterisasi dapat digunakan sebagai referensi Dinas Kesehatan Jember dalam mengambil kebijakan terkait kualitas kesehatan keluarga dan gizi masyarakatKata Kunci: data mining, K-Means, klasterisasi, Angka Kematian Ibu, Angka Kematian Bayi, prevalensi stuntin

Blackbox Testing on Virtual Reality Gamelan Saron Using Equivalence Partition Method

Testing is essential in application development because it helps identify and eliminate defects. One of the most used testing methods is Black Box testing, which involves deeply examining the application’s functionality without knowing its internal workings. The Equivalence Partition method is frequently used in Black Box testing to divide input values into groups and select test cases from each group. Potential errors can be identified by testing the available features with appropriate test cases, and future improvements can be made to ensure seamless application performance. In addition, testing results also serve as documentation and research for future development. By using this method, the developers of the VR Gamelan Saron application can ensure that its quality meets user expectations to improve its quality to provide an optimal user experience. In summary, proper testing is crucial in application development, and the Equivalence Partition method is an effective tool foridentifying and eliminating potential issues.Keywords: black box, equivalence partition, test case, error function, use

Comparative Analysis of Sound Response from Simple and Fuzzy Algorithm in Saron Virtual Reality

Virtual reality games with musical instruments require a dynamic sound response because playing the instrument requires real human feelings. A good sound in a game depends on its suitability for the game situation. Time and place limitations are a problem in recording variations in sound sample recording. If the sound samples taken are limited and a simple algorithm is applied, it may sound repetitive and not match the dynamics of music according to real human life. Therefore, in this study, a comparison of a simple algorithm with the fuzzy algorithm was carried out in the Gamelan Saron game. The data processing method used is a comparative analysis obtained from the experimental results of the respondents. On the agreement scale of one to five, most respondents agree that there is a better significant change after being given a fuzzy algorithm described by a mean value of 4.1. Keywords: sound, gamelan, Saron, dynamics, fuzz