Article Per Year (5 Year)
p-Index From 2021 - 2026
0.444
P-Index
This Author published in this journals
All Journal JMM (Jurnal Masyarakat Mandiri) Technema: Journal of Intelligent Engineering and Computing
Alfred Yulius Arthadi Putra
Universitas Widya Dharma Pontianak
Other

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
MENINGKATKAN KEMAMPUAN SISWA SMA DALAM PEMBUATAN JARINGAN LOCAL AREA NETWORK Fredrikus Suarezsaga; Alfred Yulius Arthadi Putra; Amok Darmianto; Kristina Kristina
JMM (Jurnal Masyarakat Mandiri) Vol 7, No 2 (2023): April
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jmm.v7i2.13462

Abstract

Abstrak: Jaringan komputer merupakan suatu cabang ilmu pengetahuan di bidang komputer dan dianggap sebagai pengimplementasian yang sangat penting mengingat jaringan komputer banyak diterapkan di berbagai bidang. Jaringan komputer memiliki beberapa klasifikasi berdasarkan lokasi, salah satunya adalah jaringan local area network (LAN). Pembuatan jaringan LAN sangat sederhana. Pelatihan pembuatan jaringan LAN dilaksanakan di SMA Widya Pratama Kubu Raya pada tanggal 12 November 2022. Tujuan dari kegiatan ini adalah mengenalkan kepada siswa bagaimana membuat sebuah kabel jaringan komputer lokal dan berbagi berkas di area lokal. Metode yang dilaksanakan berupa penyuluhan dan praktikum. Kegiatan ini diikuti oleh 27 siswa kelas XII. Hasil dari kegiatan ini dievaluasi dengan dua instrumen, yaitu tes dan survey kualitas PKM. Dari pelatihan yang sudah dilaksanakan, berdasarkan hasil pre-test dan post-test siswa mendapatkan peningkatan pengetahuan mengenai jaringan komputer sebesar 70,1 % dan untuk tingkat kualitas kegiatan ini sebanyak 91,36 % menyatakan setuju bahwa kegiatan ini bermanfaat. Abstract: Computer network is a dicipline of science in the computers science and is considered a very important implementation considering that computer networks are widely applied in various fields. Computer networks have several classifications based on location/geography, one of which is a local area network (LAN). In order to make a LAN network is very simple. LAN network creation training was held at SMA Widya Pratama Kubu Raya on November 12, 2022. The purpose of this activity is to introduce students to how to make a local computer network cable and share files in the local area. The method implemented is in the form of tutorial and practicum. This activity was attended by 27 students of class XII. The results were evaluated with two instruments, the test and the survey to measure of quality. From the test that has been carried out, based on the results of the pre-test and post-test students get an increase in knowledge about computer networks by 70.1% and for the quality level of this activity as much as 91.36% agree that this activity is useful.  
Short-Term Electricity Load Forecasting Using Hybrid CNN–LSTM Models on the UCI Electricity Load Dataset Ricky Imanuel Ndaumanu; Alfred Yulius Arthadi Putra
Technema: Journal of Intelligent Engineering and Computing Vol. 1 No. 1 (2026): March: Technema: Journal of Intelligent Engineering and Computing
Publisher : CV SCRIPTA INTELEKTUAL MANDIRI

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

Abstract

Accurate short-term electricity load forecasting (STLF) is critical for efficient energy management, demand response, and grid stability in modern residential environments. This study presents an empirical investigation of a hybrid convolutional neural network–long short-term memory (CNN–LSTM) model applied to the UCI Electricity Load dataset, integrating convolutional layers to extract localized temporal features and stacked LSTM layers to model long-term dependencies across households. The model is trained using Python and TensorFlow on a GPU-enabled workstation, with preprocessing including normalization, sliding-window sequence generation, and train-validation-test splitting. Performance is evaluated through mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and k-fold cross-validation. Comparative benchmarking against LSTM, CNN-GRU, and multi-scale CNN-LSTM architectures demonstrates superior accuracy, stability, and generalizability of the proposed hybrid model. Sensitivity and interpretability analyses further reveal critical temporal patterns, feature contributions, and operational insights, facilitating actionable energy management decisions. These results substantiate the hybrid CNN–LSTM approach as a robust, interpretable, and operationally relevant solution for STLF applications.
Co-Authors Amok Darmianto Fredrikus Suarezsaga, Fredrikus Kristina Ricky Imanuel Ndaumanu