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INDONESIA
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
Published by Universitas Pendidikan Ganesha
ISSN : 20898673     EISSN : 25484265     DOI : -
Core Subject : Science, Education, Engineering,
Computer Science & IT Education Engineering
Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) is a collection of scientific articles in the field of Informatics / ICT Education widely and the field of Information Technology, published and managed by Jurusan Pendidikan Teknik Informatika, Fakultas Teknik dan Kejuruan, Universitas Pendidikan Ganesha. JANAPATI first published in 2012 and will be published three times a year in March, July, and December. This journal is expected to bridge the gap between understanding the latest research Informatika. In addition, this journal can be a place to communicate and enhance cooperation among researchers and practitioners.
Arjuna Subject : -
Articles 16 Documents
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Search results for , issue "Vol. 13 No. 1 (2024)" : 16 Documents clear
Bank Customer Segmentation Model Using Machine Learning Bunga Tiara, Vira; Siregar, Amril Mutoi; Kusumaningrum, Dwi Sulistya Kusumaningrum; Rohana, Tatang
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.75233

Abstract

Banks generally carry out marketing strategies by offering deposit products directly to customers. However, this method is less effective because it requires individualized communication without considering the customer's interest in the product offered. Therefore, this research aims to categorize the classification of bank customers into Yes and No. This research uses a dataset of bank deposits taken from KTM. This research uses a bank deposit dataset taken from Kaggle, the data consists of 11162 rows with 17 attributes.  PCA technique was used for feature selection which was optimized by reducing the dimensionality of the dataset before modeling. It was found that the best model accuracy was SVM RBF kernel with C parameters achieving 80.51% accuracy and ANN 80.78%, but ANN showed a higher ROC graph than SVM because ANN performance results were faster than SVM. Thus, the overall performance measurement of ANN is much better.
Forest Fire Detection Using Transfer Learning Model with Contrast Enhancement and Data Augmentation Ayumi, Vina; Noprisson, Handrie; Ani, Nur
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.75692

Abstract

Forest damage due to fire is unique of the catastrophes that can disrupt and damage the existing ecosystem. There needs to be a quick response to fires because disaster management takes longer, and the impact of the damage will be more severe. To process images to detect fire in the forest, we need to build a suitable deep-learning model. This study proposed research on forest fire detection using an Xception and MobileNet model. Moreover, this research optimizes the accuracy of the model by applying Contrast-Limited-Adaptive-Histogram-Equalization (CLAHE) and data augmentation to tackle the problem of the forest fire image dataset. Based on the experiment, MobileNet with CLAHE obtained 99,66% accuracy in the test phase. In the same phase, MobileNet with CLAHE obtained a value F1-score of 1.00, a value of precision of 0.99, and a value of recall of 1.00. If compared to other model performances, MobileNet with CLAHE obtained the best result.
The Significance of Dynamic COVID-19 Dashboard in Formulating School Reopening Strategies Muqtadiroh, Feby Artwodini; Yuniarno, Eko Mulyanto; Nugroho, Supeno Mardi Susiki; Pahlawan, Muhammad Reza; Rachmayanti, Riris Diana; Usagawa, Tsuyoshi; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76017

Abstract

Experiments conducted with the COVID-19 dataset have predominantly concentrated on predicting cases fluctuating and classifying lung-related diseases. Nevertheless, the consequences of the COVID-19 pandemic have also spread to the education sector. To safeguard educational stability in response to the remote learning policy, we leverage authentic COVID-19 datasets alongside school information across 154 sub-areas in Surabaya City, Indonesia. Our focus is predicting the dynamic within these sub-areas where schools are located. The outcomes of this study, by incorporating the recurrent neural network of long- and short-term memory (RNN-LSTM) architecture and refined hyperparameters, effectively enhanced the predictive model's performance. The findings are showcased on a dashboard, visually representing the transmission of COVID-19 in schools across each sub-area. This information serves as a basis for informed decisions on the safe reopening of schools, aiming to mitigate the decline in education quality during the challenging pandemic.
Adaptive Threshold Filtering to Reduce Noise in Elderly Activity Classification Using Bi-LSTM Rahayu, Endang Sri; Yuniarno, Eko Mulyanto; Purnama, I Ketut Eddy; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76064

Abstract

As the global population ages, there is an increasing need to provide better care and support for older individuals. Deep learning support to accurately predict elderly activities is very important to develop. This research discusses a new model integrating filtering techniques using adaptive thresholds with Bidirectional - Long Short-Term Memory (Bi-LSTM) networks. The problem of activity prediction accuracy, mainly due to noise or irrational measurements in the dataset, is solved with adaptive thresholds. Adaptive characteristics at the threshold are needed because each individual has different activity patterns. Experiments using the HAR70+ dataset describe the activity patterns of 15 elderly subjects and the gesture patterns of 7 activities. Based on body movement patterns, the elderly can be classified as using walking aids. The proposed model design obtains an accuracy of 94.71% with a loss of 0.1984.
Comparison of K-NN, SVM, and Random Forest Algorithm for Detecting Hoax on Indonesian Election 2024 Indra; Agus Umar Hamdani; Suci Setiawati; Zena Dwi Mentari; Mauridhy Hery Purnomo
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76079

Abstract

During the year 2022, The Indonesian National Police (POLRI) received 113 reports related to the spread of hoax news related to 2024 Indonesian Election (PEMILU). There are still relatively few hoax detection tools that already exist in Indonesia. This research creates a system that can detect hoax news in Indonesian tweets about the Indonesian Election (PEMILU) 2024 by comparing three methods, namely K-NN, SVM, and Random Forest. The process of labeling (create model) using validation on ground truth data, namely cekfakta.tempo, cekfakta.kompas, and turnbackhoax.id. In this research, we also check the differences between different types of distance measurements in applying the K-NN algorithm. The method used for feature extraction in this research is TF-IDF. The results of experiments show that the highest accuracy results are obtained using the SVM and K-NN algorithms with distance measurements using Euclidean Distance, which is 86.36%. The best precision value is obtained using the K-NN algorithm with distance measurements using Manhattan Distance, which is 86.95%.
Parallel Hybrid Particle Swarm-Grey Wolf Algorithms for Optimal Load-Shedding in An Isolated Network Sujono; Priyadi, Ardyono; Pujiantara, Margo; Purnomo, Mauridhi Hery
Jurnal Nasional Pendidikan Teknik Informatika : JANAPATI Vol. 13 No. 1 (2024)
Publisher : Prodi Pendidikan Teknik Informatika Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/janapati.v13i1.76093

Abstract

In distribution networks integrated with distributed generation (DG), disconnection from the main grid reduces the power supply significantly. The power imbalance between DG generation and load degrades network stability. This paper proposes a hybrid parallel Particle Swarm Optimization - Grey Wolf Optimizer (PSGWO) algorithm for load shedding optimization. This optimization aims to reduce the DG power not absorbed by the remaining loads and maintain the voltage within the specified limits. The performance of PSGWO is tested on an IEEE 33 bus radial distribution system, considering loading levels of 80% to 140% of the baseload. At a 100% loading level, PSGWO showed the best performance, with a load shedding of 2.2297 MW and a voltage deviation of 0.0049. These values are the smallest compared to the results of the standard PSO and GWO algorithms. The PSGWO algorithm remains superior and converges faster than standard PSO and GWO at all loading levels.

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