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All Journal JOIN (Jurnal Online Informatika) JOIV : International Journal on Informatics Visualization Jurnal Inovasi Teknologi dan Edukasi Teknik
Putra Utama, Agung Bella
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Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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PSO based Hyperparameter tuning of CNN Multivariate Time- Series Analysis Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Muladi, Muladi; Nafalski, Andrew
JOIN (Jurnal Online Informatika) Vol 7 No 2 (2022)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v7i2.858

Abstract

Convolutional Neural Network (CNN) is an effective Deep Learning (DL) algorithm that solves various image identification problems. The use of CNN for time-series data analysis is emerging. CNN learns filters, representations of repeated patterns in the series, and uses them to forecast future values. The network performance may depend on hyperparameter settings. This study optimizes the CNN architecture based on hyperparameter tuning using Particle Swarm Optimization (PSO), PSO-CNN. The proposed method was evaluated using multivariate time-series data of electronic journal visitor datasets. The CNN equation in image and time-series problems is the input given to the model for processing numbers. The proposed method generated the lowest RMSE (1.386) with 178 neurons in the fully connected and 2 hidden layers. The experimental results show that the PSO-CNN generates an architecture with better performance than ordinary CNN.
FUZZY LOGIC ALGORITHM: REVIEW AND IMPLEMENTATION Sofyan Maulana, Aditya; Putra Utama, Agung Bella; Kasanah, Anis Nikmatul; Fauziah, Aprilia; Prawidya Murti, Della Murbarani
Jurnal Inovasi Teknologi dan Edukasi Teknik Vol. 4 No. 9 (2024)
Publisher : Universitas Ngeri Malang

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Abstract

SPK atau yang lebih dikenal sebagai sistem pendukung keputusan merupakan sebuah sistem yang dpaat membantu manusia pengambilan keputusan dengan memanfaatkan data dan suatu model untuk memberikan keputusan. Sistem Pendukung Keputusan (SPK) mempunyai beberapa kelebihan yaitu Sistem Pendukung Keputusan (SPK) mampu mendukung dalam mecari solusi dari masalah yang kompleks. Sistem Pendukung Keputusan (SPK) juga dapat merespon secara cepat dalam situasi yang tidak diharapkan atau dalam kondidsi yang dapat beribah-ubah. Sistem Pendukung Keputusan (SPK) dapat menghasilkan sebuah keputusan yang lebih tepat dan dapat meningkatkan produktivtas dalam sisiem analisis. Karena logika fuzzy sesuai dengan proses penalaran pada pemikiran manusi maka logika fuzzy sering digunakan sebagai sisitem pendukung keputusa(SPK). Penggunaan dari Fuzzy logic salah satunya adalah untuk menyampaikan informasi data yang bersifat ambiguous. Dalam Logika fuzzy terdapat beberapa metode yaitu metode Sugeno, metode mamdani dan metode tsukamoto. Pemanfaatan logika fuzzy terdapatt di berbagai bidang, seperti pada bidang kesehatan yang diterapkan pada pendiagnosisan penyakit, pada bidang bencana dapat digunakan untuk memprediksi adanya gempa bumi, dan dalam bidang pendidikan dapat diterapkan pada peminataan jurusan. Beberapa tahapan dalam logika fuzzy antar lain adalah fuzzification, Inference rules atau pemberian aturan pada logika fuzzy, fungsi keanggotaan atau membership function yaitu mempresentasikan masalah dan menghasilkan keputusan yang pasti dan yang terakhir adalah metode de-fuzzification.
Neural Machine Translation of Spanish-English Food Recipes Using LSTM Dedes, Khen; Putra Utama, Agung Bella; Wibawa, Aji Prasetya; Afandi, Arif Nur; Handayani, Anik Nur; Hernandez, Leonel
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.6.2.804

Abstract

Nowadays, food is one of the things that has been globalized, and everyone from different parts of the world has been able to cook food from other countries through existing online recipes. Based on that, this study developed a translation formula using a neural machine translation (NMT). NMT is a recently proposed approach to machine translation. Unlike the traditional statistical machine translation, the neural machine translation aims at building a single neural network that can be jointly tuned to maximize the translation performance. The models proposed recently for neural machine translation often belong to a family of encoder–decoders. Our experiment led to novel insights and practical advice for building and extending NMT with the applied long short-term memory (LSTM) method to 47 bilingual food recipes between Spanish-English and English-Spanish. LSTM is one of the best machine learning methods for translating languages because it can retain memories for an extended period concurrently, grasp complicated connections between data, and provides highly useful information in deciding translation outcomes. The evaluation for this neural machine translation is to use BLEU. The comparing results show that the translation of recipes from Spanish-English has a better BLEU value of 0.998426 than English-Spanish with a data-sharing of 70%:30% during epoch 1000. Researchers can convert the country's popular cuisine recipes into another language for further research, allowing it to become more widely recognized abroad.
Co-Authors A.N. Afandi Aji Prasetya Wibawa Anik Nur Handayani Dedes, Khen Fauziah, Aprilia Kasanah, Anis Nikmatul Leonel Hernandez, Leonel Muladi Nafalski, Andrew Prawidya Murti, Della Murbarani Sofyan Maulana, Aditya