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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Manajemen Informatika dan Sistem Informasi MATHunesa: Jurnal Ilmiah Matematika Dinamika Sosial: Jurnal Pengabdian Masyarakat Dan Transformasi Kesejahteraan
Agung Teguh Setyadi
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Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Law, Crime, Criminology & Criminal Justice Mathematics

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SISTEM PREDIKSI PRODUKSI PADI DI SUMATERA MENGGUNAKAN REGRESI LINEAR Yudha, Ery Permana; Arif Rohmadi; Agung Teguh Setyadi
Jurnal Manajemen Informatika dan Sistem Informasi Vol. 8 No. 1 (2025): MISI Januari 2025
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/misi.v8i1.1411

Abstract

Pulau Sumatera merupakan salah satu pulau yang menjadi lumbung padi nasional karena sebagai salah satu daerah penghasil padi terbesar di Indonesia. Namun, produktivitas yang tinggi di pulau Sumatera juga terdapat beberapa tantangan seperti perubahan iklim yang tidak menentu, luas lahan, curah hujan, kelembapan, dan suhu rata-rata. Untuk mengatasi permasalahan tersebut perlu strategi yang inovatif dan berbasis data. Salah satu strategi tersebut dengan menerapkan pengolahan data untuk menghasilkan model prediksi produktivitas padi. Teknik ini melibatkan algoritma dan pembelajaran mesin untuk menganalisis pola dan tren dalam pertanian. Model ini mempermudah stakeholder terkait untuk mempersiapkan kebutuhan pangan nasional agar selalu terpenuhi. Pada penelitian ini, diusulkan sebuah metode prediksi produktivitas padi di Sumatera menggunakan metode regresi linear. Penelitian ini menghasilkan model prediksi masing-masing di setiap provinsi di Sumatera. Secara umum, tahapan yang dilakukan yaitu preprocessing, seleksi fitur, training dan testing, dan evaluasi. Uji coba yang dilakukan dengan menghitung nilai Mean Squarred Error (MSE). Beberapa algoritma yaitu Regresi Linear, Support Vector Regression (SVR), Random Forest Regression (RFR) menghasilkan nilai rata-rata MSE sebesar 0,022; 0,075; 0,026. Regresi linear mampu menghasilkan model yang lebih baik dibandingkan metode SVR dan RFR.
Bahasa Inggris Ahmad Saikhu; Agung Teguh Setyadi; Victor Hariadi
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 6 (2023): December 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i6.5296

Abstract

For the optimization of computer networks with high bandwidth requirements, it is necessary to predict the traffic of the wireless network. Its goal is to reduce maintenance costs and improve internet services. Feature selection is a major issue in multivariate time series (MTS) spatio-temporal modeling. Another problem is the dependency between input features, time lags, and spatial factors, so an appropriate model is needed. This study aims to provide solutions to two problems. The first is to improve a feature extraction and selection process in spatio-temporal MTS data for relevant features using Detrended Partial Cross-Correlation Analysis (DPPCA) and nonredundant features associated with linear using Pearson's correlation (PC) filters and non-linear associations using Symmetrical Uncertainty (SU) and a combination of both PCSUF. The second is to develop a spatiotemporal framework model using recurrent neural networks (RNNs) to get better performance than the traditional model. These methods are combined and tested using a data set of cellular networks with one hour intervals during November in three locations. Testing the effectiveness of the feature selection technique showed that 27.6% of the total extracted features were. The forecasting model with the DPCCA-SU-RNN combination method is the best performance by having RMSE = 380.7, R2 = 97% and MAPE = 10%.
Digitalisasi Layanan Kesehatan: Pelatihan IT untuk Kader Posyandu Desa Simogirang dalam Pencatatan Data Kesehatan Rony Kriswibowo; Rusina Widha Febriana; Johan Suryo Prayogo; Purwanto Purwanto; Selfya Ningrum; Agung Teguh Setyadi; Firdaus Kamilullah Suhada; Mohammad Alif Riskyansah
Dinamika Sosial : Jurnal Pengabdian Masyarakat dan Transformasi Kesejahteraan Vol. 2 No. 2 (2025): Juni : Dinamika Sosial : Jurnal Pengabdian Masyarakat dan Transformasi Kesejaht
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/dinsos.v2i2.1910

Abstract

This community service program was implemented to strengthen the digital literacy and competencies of health cadres in Simogirang Village, Prambon Sub-district, Sidoarjo Regency, particularly in the recording and management of Posyandu (integrated health service post) data. The initiative responded to ongoing issues stemming from manual documentation methods, which were prone to human error, inefficiency, and lack of integration with regional health information systems. To address these challenges, the program delivered an IT-based training module using a combination of direct instruction, demonstrations, and hands-on simulations. The training emphasized the use of accessible and cost-effective tools such as Google Forms and Google Sheets, designed in alignment with the principles of adult learning (andragogy) and Kurt Lewin’s change management model to facilitate behavioral adaptation and technology acceptance among the participants. A total of 25 health cadres participated in the program. Pre- and post-training evaluations demonstrated significant improvement in digital skills, with 80% of participants achieving a minimum of 85% accuracy in completing digital forms and managing records. In addition, a prototype digital system based on Google Workspace was successfully piloted in two Posyandu locations. This system enhanced the timeliness, accuracy, and integration of health data reporting to local health centers (Puskesmas), offering a scalable and sustainable solution for grassroots healthcare data management. Despite the positive outcomes, the program also identified key challenges, notably limited internet infrastructure and varying levels of prior digital exposure among cadres. These findings underscore the need for continuous mentoring, support from local government stakeholders, and potential investment in digital infrastructure. Overall, the initiative contributes to Indonesia’s broader goals of digital transformation in public health, particularly at the village level.
PERBANDINGAN TRANSFORMASI WAVELET DISKRIT DAN TRANSFORMASI WAVELET STASIONER UNTUK DENOISING CITRA Ahmad Khairul Umam; Irma Wulandari; Agung Teguh Setyadi; Hasnah Aulia Zahra; Khansa Nadhif Shafa
MATHunesa: Jurnal Ilmiah Matematika Vol. 13 No. 2 (2025)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The wavelet transform is an improvement of the Fourier transform. Discrete Wavelet Transform (DWT) and Stationary Wavelet Transform (SWT) are part of the wavelet transform. DWT and SWT can be used to reduce noise in images. In this research, DWT and SWT methods are compared for image denoising process. The PSNR value and computation time for level 1 and 2 wavelets are compared. A grayscale test image of Lena and the cat measuring 512×512 pixels are used. We use Haar, Daubechies, biorthogonal, symlets, and coiflets wavelets. From this research results, the highest PSNR value is for the SWT method. As for the fastest computation time, it is all for DWT method.
Co-Authors Ahmad Saikhu Arif Rohmadi Firdaus Kamilullah Suhada Hasnah Aulia Zahra Irma Wulandari Johan Suryo Prayogo Khansa Nadhif Shafa Mohammad Alif Riskyansah Purwanto Purwanto Rony Kriswibowo Rusina Widha Febriana Selfya Ningrum Victor Hariadi Yudha, Ery Permana