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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnas Nasional Teknologi dan Sistem Informasi JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT
Erwin Yudi Hidayat
Faculty of Computer Science, Universitas Dian Nuswantoro
Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Social Sciences

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Penerapan 1D-CNN untuk Analisis Sentimen Ulasan Produk Kosmetik Berdasar Female Daily Review Hidayat, Erwin Yudi; Handayani, Devioletta
Jurnal Nasional Teknologi dan Sistem Informasi Vol 8 No 3 (2022): Desember 2022
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v8i3.2022.153-163

Abstract

Pada tahun 2020 tercatat sekitar 797 industri kosmetik berskala besar maupun kecil yang terdapat di Indonesia. Berdasarkan tahun sebelumnya, angka ini naik 4.87%. Kondisi ini menyebabkan munculnya persaingan perusahaan kosmetik, salah satunya adalah Emina. Berbagai media digunakan sebagai sarana untuk menyampaikan sentimen atau opini masyarakat. Pihak perusahaan dapat memanfaatkan sentimen untuk mengetahui umpan balik masyarakat terhadap brand mereka. Website Female Daily Review menjadi salah satu platform yang digunakan untuk menampung segala bentuk opini mengenai produk kecantikan. Proses pengambilan data dari website pada penelitian ini menggunakan web scraping. Dari 11119 data ulasan yang didapatkan diperlukan analisis opini, emosi, dan sentimennya dengan memanfaatkan text mining untuk identifikasi serta mengekstrak suatu topik. Analisis sentimen dapat membantu mengetahui tingkat kepuasan pengguna terhadap suatu brand kosmetik. Algoritma yang digunakan adalah 1D-Convolutional Neural Network (1D-CNN). Sebelum dilakukan klasifikasi data, perlu diterapkan text preprocessing agar dataset mentah menjadi lebih terstruktur. Hasil dari klasifikasi sentimen  dibagi ke dalam 3 kategori yaitu positif, negatif, dan netral. Berdasarkan eksperimen dalam membangun model analisis sentimen menggunakan 1D-CNN sebanyak 30 percobaan, didapatkan model terbaik dalam menganalisis sentimen dengan akurasi sebesar 80.22%.
Comparison of Gradient Boosting and Random Forest Models in the Detection System of Rakaat during Prayer Darmawan, Raihan Aris; Hidayat, Erwin Yudi
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17886

Abstract

Abstract. Errors in the execution of prayer among Muslims can occur due to a lack of profound understanding of the prayer procedure. This research aims to compare two machine learning models, Random Forest and Gradient Boosting, in classifying prayer movements, subsequently extending to calculate the number of prayer cycles (rakaat). A total of 7220 manually gathered data based on 33 landmark coordinates using Mediapipe Pose Detection were employed. The research findings reveal that the Random Forest model with a 70:30 ratio achieves 99.9% accuracy, precision, and recall, with the fastest training time being 3.8 seconds. Both models exhibit testing results close to 100%, but the Gradient Boosting model faces challenges in classifying specific movements. On the other hand, Random Forest successfully overcomes thesechallenges, enabling accurate prayer cycle calculations. The findings can contribute to the development of tools supporting Muslims in correct prayer execution, positively impacting religious and well-being aspects.
A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction putra, Permana langgeng wicaksono ellwid; Naufal, Muhammad; Hidayat, Erwin Yudi
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.5703

Abstract

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.
Co-Authors Abdul Syukur Abu Salam Adhitya Nugraha Affandy Affandy Ardytha Luthfiarta Astuti, Yani Parti Ayu Pertiwi Darmawan, Raihan Aris Dzaky, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Hafidhoh, Nisa'ul Handayani, Devioletta Handayani, Ni Made Kirei Kharisma Hardiansyah, Raindy Wicaksana Haresta, Alif Agsakli Junta Zeniarja Kurniawan, Defri Muhammad Naufal, Muhammad Nugroho, Nur Cahyo Tio Octaviani, Dhita Aulia Paramita, Cinantya Putra, Permana Langgeng Wicaksono Ellwid Rakajati, Bima Rizky, Aditya Saiful Sindhu Rakasiwi Sukamto, Titien S.