Dalam rangka peningkatan peluang dan pengembangan potensi usaha pada UMKM yang selalu bertambah setiap tahun, perlu adanya aksi baik support berupa pembinaan maupun pelatihan dari tenaga lain di luar UMKM. Hal semacam ini juga terjadi di Desa Selojari Kecamatan Grobogan Kabupaten Purwodadi Provinsi Jawa Tengah. Kendala dalam pengembangan usaha kian meningkat seiring munculnya pesaing bisnis dengan bidang usaha yang sama. Usaha kuliner di Desa Selojari saat ini telah menelurkan beberapa macam produk antara lain berbagai kue kering, keripik, dan minuman. Permasalahan yang sering di temui oleh pelaku usaha UMKM umumnya antara lain: minimnya modal, kurangnya inovasi produk, minimnya cara untuk mendistribusikan produk, pemasaran online belum maksimal, belum adanya branding, tidak ada izin usaha resmi disertai label BPOM atau PIRT pada kemasan produk. Untuk memaksimalkan pemasaran online, hal utama yang harus dilakukan pengusaha UMKM adalah memilih saluran pemasaran online yang tepat, lalu fokus memasarkan di saluran tersebut, dan terus mengoptimasinya melalui website yang dapat di update sesuai kebutuhan pemasaran dan dapat dikontrol oleh pemilik usaha “DJOKOWI” di Desa Selojari.
Syntactic analysis is a series of processes in order to validate a string that is received by a language. In this section it is often difficult to be explained properly, especially when making the rules up into a tree decline valid received by language. This study aims to produce an application that can generate information in the form of image files visual and textual representations of trees from the strings of the received language, a decrease in the rules of grammar, as well as the basic code of the image file that can be compiled and customization of its own. The algorithm chosen is CYK algorithm (Cocke Younger Kasami) for parsing the context free grammar (CFG) in the form of Chomsky Normal Form (CNF). CYK algorithm is very easy to understand and implement, therefore, appropriate given the decline in the teaching of automata to understand the rules to produce a valid tree of the input string. In the study of automata, understanding the process of reduction in the rules to be a tree (parse tree) is important to be taught well
Purpose: Javanese script is a legacy of heritage or heritage in Indonesia originating from the island of Java needs to be preserved. Therefore, in this study, the classification and identification process of Javanese script letters will be carried out using the CNN method. The purpose of this research is to be able to build a model which can properly classify Javanese script, it can help in the process of recognizing letters in Javanese script easily.Methods: In this study, the Javanese script classification process has been used the transfer learning process of Convolutional Neural Network, namely GoogleNet, DenseNet, ResNet, VGG16 and VGG19. The purpose of using transfer learning is to improve the sequential CNN model, processing can be better and optimal because it utilizes a previously trained model.Result: The results obtained after testing in this study are using the transfer learning method, the GoogleNet model gets an accuracy of 88.75%, the DenseNet model gets an accuracy of 92%, the ResNet model gets an accuracy of 82.75%, the VGG16 model gets an accuracy of 99.25% and the VGG19 model gets an accuracy of 99.50%.Novelty: In previous studies, it is still very rare to discuss the Javanese script classification process using the CNN transfer learning method and which method is the most optimal for performing the Javanese script classification process. In this study, it had been resulted find an effective method to be able to carry out the Javanese script classification process properly and optimally.
Cryptography is a solution or method of securing the data precisely to preserve confidentiality and authenticity of data. It is also able to enhance the security aspects of a data or information. The aim of the method is the confidential information which is sent via a network, cannot be known or used by someone else.Cryptography provides two aspects of information security, first is the protection of the confidential data/ information, counterfeiting and undesired alteration of information. RSA algorithm is an asymmetric encryption algorithm, in other word this algorithm uses the same key for both encryption and decryption process. Transposition Cipher algorithm encrypts the plain text by removing smallpieces of fhe message around. By using a combination of the RSA algorithm and Transposition Cipher algorithms, it will be able to improve the security of data file storage in a database
http://lppm.dinus.ac.id/majalah/view_abstraksi/799/Kombinasi-Algoritma-Rsa-Dan-Algoritma-GipherTransposisi-Untuk-Keamanan-Database
The maximum value obtained to test the encrypt hill cipher uses the avalanche effect with modification of one, two, three, and four key matrices 35.71%, therefore an additional security technique is needed. The Least Significant Bit (LSB) method is used to insert the ciphertext that has been generated from the hill cipher algorithm, has been testing using RGB, CMY, CMYK and YUV shapes with 6142 characters in 128 x 128 character images producing the highest PSNR value of 51.2826 dB in CMYK images. Steganography technique is applied because it has advantages in terms of imperceptibility, for example the results of a stego image are so similar to the original image that it is difficult to be distinguished by the human senses. Tests were carried out with 10 images, five images measuring 512 x 512 and five images measuring 16 x 16. While the messages to be inserted were 240, 480, and 960 characters for images measuring 512 x 512 and 24, 48 and 88 characters for images measuring 16 x16. Test results that I have done are calculated using Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR) with a minimum PSNR of 51.2907 dB which means the resulting image is quite good. Computation time is calculated using tic toc on Matlab by encrypting 2000 and 6000 characters, and also computation time.
Aksara jawa merupakan salah satu warisan budaya di Indonesia yang umumnya berasal dari Jawa Tengah dan Yogyakarta. Pemahaman akan aksara jawa terutama pada siswa di sekolah dirasa masih kurang karena sulitnya membedakan bentuk aksara jawa dengan huruf alfabet. Maka dari itu pada makalah ini mengusulkan metode pengenalan aksara jawa menggunakan Local Binary Pattern (LBP) dan K-Nearest Neighbors (KNN). LBP digunakan untuk mengekstrak ciri unik dari citra tulisan aksara jawa. Sementara KNN digunakan untuk menentukan kelas dari citra aksara jawa berdasarkan hasil ekstraksi ciri LBP. Pengujian dilakukan dengan menggunakan 160 dataset citra yang dibagi menjadi 40 citra uji dan 120 citra latih. Evaluasi hasil pengujian menggunakan perhitungan confusion matrix untuk menentukan akurasi dari kombinasi tersebut. Dari hasil pengujian didapatkan akurasi tertinggi adalah 82,5% dimana parameter yang digunakan adalah cell size berukuran 64x64 dan nilai k = 3.
Motif batik biasanya dipengaruhi oleh kehidupan masyarakat setempat. Batik kudus mempunyai ciri khusus dengan motif senada dengan warna menara kudus yaitu soga tembelekan dengan warna coklat kehijauan. Fungsi batik yang pada zaman dahulu menjadi salah satu media penyebaran agama islam sehingga motif batik kudus banyak bercerita mengenai sejarah islam dengan corak kaligrafi. Batik kudus juga dipengaruhi oleh industri kretek sehingga batik kudus juga dikenal dengan batik kretek. Keberakagaman motif, corak, warna batik kudus menjadi salah satu point yang dapat digunakan untuk mengimplementasikan teknologi, dimana sistem mampu mengenali motif batik kudus secara akurat. Klasifikasi menggunakan K-NN dinilai efektif karena jumlah yang cukup besar, sedangkan untuk mendeteksi pola dari batik digunakan fitur ekstraksi ciri GLCM (Gray Level Co-occurrence Matrix). Dalam makalah ini, dipilih 21 jenis motif dengan masing-masing motif terdiri dari 100 citra. Motif batik antara lain : rumah adat, sekar jagad, kapal kandas, merak buketan, tiga negri, beras kecer, tembakau cengkeh, menara kudus, lentog tanjung, tari kretek, bunga setaman, kupu parijotho, buketan lily, pagi sore, gendoro gendiri, giling rokok, ukir kudus, ukir bunga, rokok kretek, romo kembang dan gulo tumbu. Menggunakan 700 citra training dan 560 citra testing pada GLCM dihasilkan akurasi tertinggi pada K=1 yaitu 97% dan terendah pada K=7 yaitu 91%.
Purpose: Hiragana image classification poses a significant challenge within the realms of image processing and machine learning. Despite advances, achieving high accuracy in Hiragana character recognition remains elusive. In response, this research attempts to enhance recognition precision through the utilization of a Convolutional Neural Network (CNN). Specifically, the study explores the efficacy of three distinct optimizers like Adam, Stochastic Gradient Descent with Momentum (SGDM), and RMSProp in improving Hiragana character recognition accuracy. Methods: This research adopts a systematic approach to evaluate the performance of a Convolutional Neural Network (CNN) in the context of Hiragana character recognition. A meticulously prepared dataset is utilized for in-depth testing, ensuring robustness and reliability in the analysis. The study focuses on assessing the effectiveness of three prominent optimization methods: Stochastic Gradient Descent (SGD), RMSProp, and Adam. Result: The results of the model performance evaluation show that the highest accuracy was obtained from the RMSP optimizer with an F1-Score reaching 99.70%, while the highest overall accuracy was 99.87% with the Adam optimizer. The analysis is carried out by considering important metrics such as precision, recall, and F1-Score for each optimizer. Novelty: The performance results of the developed model are compared with previous studies, confirming the effectiveness of the proposed approach. Overall, this research makes an important contribution to Hiragana character recognition, by emphasizing the importance of choosing the right optimizer in improving the performance of image classification models.
Aquascape merupakan seni dalam mengatur tanaman air, batu, dan kayu didalam aquarium sehingga memberikan efek seperti kebun dibawah air. Tanaman aquascape membutuhkan pupuk, cahaya, dan suhu yang cukup untuk kelangsungan hidup dari tanaman. Dalam hal tersebut perlu ada pengawasan agar tanaman dapat tumbuh sempurna dan tidak terjadi masalah. Berdasarkan hal ini menjadi pertimbangan untuk mengembangkan perangkat IoT berbasis aplikasi Android guna untuk memonitor dan mengkontrol cahaya, suhu, dan pemberian pupuk cair kedalam aquascape. Dalam pengembangan digunakan metode Research & Development yaitu metode untuk membuat suatu produk atau mengembangkan produk yang sudah jadi dan menguji keefektifannya. Alat yang digunakan yaitu NodeMcu ESP8266 sebagai mikrokontroler, sensor DS18B20 untuk mengukur suhu yang ada didalam aquascape, sensor TDS Meter untuk mengukur kadar TDS pada aquarium, serta digunakan Firebase Realtime Database untuk menyimpan dan penghubung data antara alat dan aplikasi android yang dikembangkan. Pengujian menggunakan metode blackbox dengan melakukan perbandingan dengan alat yang umum digunakan. Berdasarkan pengujian pada alat yang dikembangkan hasilnya tidak jauh berbeda dengan alat yang umum digunakan
Facial expression recognition is important for many applications, including sentiment analysis, human-computer interaction, and interactive systems in areas such as security, healthcare, and entertainment. However, this task is fraught with challenges, mainly due to large differences in lighting conditions, viewing angles, and differences in individual eye structures. These factors can drastically affect the appearance of facial expressions, making it difficult for traditional recognition systems to consistently and accurately identify emotions. Variations in lighting can alter the visibility of facial features, while different angles can obscure critical details necessary for accurate expression detection. This study addresses these issues by employing transfer learning with ResNet-50 and effective pre-processing techniques. The dataset consists of grayscale images with a 48 x 48 pixels resolution. It includes a total of 680 samples categorized into seven classes: anger, contempt, disgust, fear, happy, sadness, and surprise. The dataset was divided so that 80% was allocated for training and 20% for testing to ensure robust model evaluation. The results demonstrate that the model utilizing transfer learning achieved an exceptional performance level, with accuracy at 99.49%, precision at 99.49%, recall at 99.71%, and an F1-score of 99.60%, significantly outperforming the model without transfer learning. Future research will focus on implementing real-time facial recognition systems and exploring other advanced transfer learning models to further enhance accuracy and operational efficiency.
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