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All Journal JURNAL MATEMATIKA STATISTIKA DAN KOMPUTASI Transformasi: Jurnal Pengabdian Masyarakat JOIN (Jurnal Online Informatika) Prima: Jurnal Pendidikan Matematika JPPM (Jurnal Pengabdian dan Pemberdayaan Masyarakat) International Journal of Educational Management and Innovation (IJEMI) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Abdimas Sang Buana Jurnal Riset Statistika Bandung Conference Series: Statistics Jurnal Teknik Mesin, Industri, Elektro dan Informatika Indonesian Journal of Statistics and Its Applications
Reny Rian Marliana
Prodi Statistika, Fakultas Farmasi Dan Sains, Universitas Islam Bandung, Indonesia
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Public Health Social Sciences Other

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Reduksi Noise Data Curah Hujan di Kota Bandung Menggunakan Variational Mode Decomposition (VMD) Riska Juniarti Anugrah; Reny Rian Marliana
Bandung Conference Series: Statistics 271-280
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.25669

Abstract

Abstract. Daily rainfall data in Bandung City exhibit nonlinear and nonstationary characteristics caused by seasonal patterns and extreme fluctuations, making accurate forecasting a challenging task. To address this issue, this study applies Variational Mode Decomposition (VMD) as a preprocessing technique to reduce data complexity and noise before forecasting. VMD decomposes the original rainfall signal into several Intrinsic Mode Functions (IMFs) through an adaptive variational optimization process, enabling a more stable decomposition while overcoming the mode-mixing problem commonly found in Empirical Mode Decomposition (EMD). The optimal number of decomposition modes (K) was determined using three quantitative evaluation criteria proposed by Wu et al. (2020), namely energy entropy, minimum gap center frequency, and minimum energy proportion, with K values ranging from 2 to 6. The results indicate that K = 2 is the optimal choice, producing the lowest energy entropy (0.2480), the largest minimum gap center frequency (0.16486), and no redundant IMF, as indicated by the minimum energy proportion of 6.78%, which exceeds the 1% threshold. The decomposition separates the rainfall signal into IMF1, which captures the dominant long-term trend, and IMF2, which represents high-frequency fluctuations associated with noise. These findings demonstrate that VMD effectively simplifies complex rainfall data into more homogeneous signal components, providing a more suitable representation for subsequent forecasting models. Keywords: Variational Mode Decomposition, rainfall, signal decomposition Abstrak. Data curah hujan harian di Kota Bandung memiliki karakteristik nonlinier dan non-stasioner akibat pengaruh variasi musiman serta fluktuasi yang tinggi, sehingga proses prediksi menjadi lebih kompleks. Untuk mengatasi permasalahan tersebut, penelitian ini menerapkan Variational Mode Decomposition (VMD) sebagai tahap preprocessing untuk mereduksi kompleksitas dan noise sebelum proses forecasting. Metode VMD mendekomposisi sinyal curah hujan menjadi beberapa Intrinsic Mode Function (IMF) melalui pendekatan optimasi variasional yang adaptif, sehingga mampu menghasilkan dekomposisi yang lebih stabil sekaligus mengurangi permasalahan mode mixing yang umum terjadi pada Empirical Mode Decomposition (EMD). Penentuan jumlah mode optimal (K) dilakukan menggunakan tiga kriteria evaluasi yang dijelaskan oleh Wu et al. (2020), yaitu energy entropy, minimum gap center frequency, dan proporsi energi minimum, dengan pengujian pada K = 2 hingga 6. Hasil penelitian menunjukkan bahwa K = 2 merupakan jumlah mode yang paling optimal karena menghasilkan energy entropy terkecil (0,2480), minimum gap center frequency terbesar (0,16486), serta tidak ditemukan IMF redundan dengan proporsi energi minimum sebesar 6,78% yang berada di atas ambang batas 1%. Hasil dekomposisi memisahkan sinyal curah hujan menjadi IMF1 yang merepresentasikan tren jangka panjang dan IMF2 yang menggambarkan fluktuasi berfrekuensi tinggi yang menyerupai noise. Temuan ini menunjukkan bahwa VMD mampu menyederhanakan data curah hujan yang kompleks menjadi komponen yang lebih homogen sehingga lebih sesuai digunakan sebagai masukan pada tahap pemodelan prediksi selanjutnya. Kata Kunci: Variational Mode Decomposition, curah hujan, dekomposisi sinyal
Klasifikasi Citra Sampah Daur Ulang Menggunakan CNN dengan Fungsi Aktivasi ReLU untuk Mendukung Sistem RVM Akmal Nufus; Reny Rian Marliana
Bandung Conference Series: Statistics 281-290
Publisher : UNISBA Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/bcss.v6i2.24217

Abstract

Abstract. The total amount of waste generated nationwide in Indonesia reaches 144,839 metric tons per day, while only about 25% is managed optimally. This situation underscores the need for technologies capable of supporting automated waste management and sorting processes. One such technology is the Reverse Vending Machine (RVM), a machine that can accept and sort recyclable waste while providing incentives to users. To perform these functions optimally, RVM must be able to automatically and accurately recognize various types of trash. Therefore, this study aims to classify images of recyclable waste using a Convolutional Neural Network (CNN) with a Rectified Linear Unit (ReLU) activation function. The dataset used consists of five waste classes: paper/cardboard packaging, beverage cans, dented beverage cans, plastic bottles, and glass bottles. The CNN architecture used consists of three convolutional layers with a 3×3 kernel size and 32, 64, and 128 filters, respectively, and is supplemented by max pooling, flatten, and fully connected layers. The model was trained using the Stochastic Gradient Descent (SGD) algorithm with a mini-batch approach. The research results show that the model is capable of classifying images of recyclable waste with good performance, achieving an accuracy of 84.52%, a precision of 86.18%, a recall of 84.52%, and an F1-score of 84.46%. Abstrak. Jumlah timbulan sampah nasional di Indonesia mencapai 144.839 ton per hari, sementara hanya sekitar 25% yang terkelola secara optimal. Kondisi tersebut mendorong perlunya penerapan teknologi yang mampu mendukung proses pengelolaan dan pemilahan sampah secara otomatis. Salah satu teknologi yang dapat diterapkan adalah Reverse Vending Machine (RVM), yaitu mesin yang mampu menerima dan memilah sampah daur ulang sekaligus memberikan insentif kepada penggunanya. Untuk menjalankan fungsi tersebut secara optimal, RVM memerlukan kemampuan untuk mengenali berbagai jenis sampah secara otomatis dan akurat. Oleh karena itu, penelitian ini bertujuan untuk mengklasifikasikan citra sampah daur ulang menggunakan metode Convolutional Neural Network (CNN) dengan fungsi aktivasi Rectified Linear Unit (ReLU). Dataset yang digunakan terdiri atas lima kelas sampah, yaitu kertas/karton kemasan, kaleng minuman, kaleng minuman penyok, botol plastik, dan botol kaca. Arsitektur CNN yang digunakan terdiri dari tiga lapisan konvolusi dengan ukuran kernel 3×3 dan jumlah filter bertingkat 32, 64, dan 128, serta dilengkapi lapisan max pooling, flatten, dan fully connected. Proses pelatihan model dilakukan menggunakan algoritma Stochastic Gradient Descent (SGD) dengan pendekatan mini-batch. Hasil penelitian menunjukkan bahwa model mampu mengklasifikasikan citra sampah daur ulang dengan performa yang baik, mencapai accuracy sebesar 84,52%, precision sebesar 86,18%, recall sebesar 84,52%, dan F1-score sebesar 84,46%.
Faktor Penentu Keberhasilan Manajemen E-learning dan Minat Belajar Mahasiswa : (Studi kasus mahasiswa FTI Universitas Sebelas April Sumedang) Leni Nurhayati; Reny Rian Marliana; Sri Bekti Handayani Ningsih; Iyat Ratna K
Jurnal Teknik Mesin, Industri, Elektro dan Informatika Vol. 3 No. 3 (2024): September : JURNAL TEKNIK MESIN, INDUSTRI, ELEKTRO DAN INFORMATIKA
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jtmei.v3i3.4152

Abstract

Along with the development of the digital era, the use of e-Learning has become inevitable. Almost all countries in the world, including Indonesia, are implementing e-learning in the world of education. However, to be able to implement e-learning successfully, there are several determining factors for success that must be met. The determining factors for the success of implementing e-learning cannot be separated from how management is carried out by an institution in implementing learning using e-learning. The success of e-learning implementation is also influenced by the level of user satisfaction. The successful implementation of e-learning can also influence students' interest in learning. The aim of this research is to determine the factors determining the success of e-learning implementation and their influence on students' interest in learning. The research method used is quantitative research. The conclusion obtained from the results of this research is that there are 2 factors that determine success in implementing e-learning, management of the content development process and management of the e-learning environment. E-learning environment management has a significant influence on four constructs of student learning interest, namely involvement, interest, acceptance and feelings of enjoyment. And management of the content development process has a significant effect on acceptance and feelings of enjoyment. So the research results show that E-Learning Environment management has a significant effect on interest in learning, while Content Development Process management for buying gas only has a significant effect on aspects of acceptance and feelings of pleasure.
Co-Authors Aang Subiyakto A’ang Subiyakto Abd. Rohman, Aedah Binti Aedah Binti Abd. Rohman Akmal Nufus Atep Ruhiat David Setiadi Dithia Permana, Riyaldi Dithia Permana Dwi Yuniarto Fauziah Roshafara Hendriawan, Muhammad Rifqi Iyat Ratna K Leni Nurhayati Leni Nurhayati Leni Nurhayati Maya Suhayati Muhammad Wildan Ramadhan Muthia Nadhira Faladiba Permana, Riyaldi Dithia Riska Juniarti Anugrah Rizki Sopian Salma Nur Hamidah Septiadi Padmadisastra Shafira Agnia Latfalia Sri Bekti Handayani N. Sri Bekti Handayani Ningsih Sri Bekti Handayani Ningsih Suliadi Suliadi Tiara Ambarwati Rahman Tutik Maya Saroh Uun Pujayanti Wagus Sejati Wahyu Noergana Wilda Ainun Nisa Zakiyah Romadhoni