Article Per Year (5 Year)
p-Index From 2021 - 2026
0.659
P-Index
This Author published in this journals
All Journal INDONESIAN JOURNAL OF APPLIED PHYSICS BAREKENG: Jurnal Ilmu Matematika dan Terapan Physics Communication
Iis Widya Harmoko, Iis Widya
Jurusan Teknik Informatika, Universitas Budi Luhur Jakarta
Astronomy Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Energy Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Transportation

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Study of a Weather Prediction System Based on Fuzzy Logic Using Mamdani and Sugeno Methods Setyanugraha, Noval; Al Aziz, Sofyan; Harmoko, Iis Widya; Fianti, Fianti
Physics Communication Vol 6, No 2 (2022): August 2022
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/physcomm.v6i2.39703

Abstract

Weather is a very important factor in supporting various human activities. However, weather is a natural event that keeps on changing due to various air conditions that affect it. One way to anticipate weather changes that may occur early is to create a system that can predict weather changes. Fuzzy logic is one of the methods that can be used in system prediction to find out the cause at a certain time and place. In this system, two fuzzy logic methods were used, they are the Mamdani and Sugeno methods, with three supporting criteria, including air temperature, humidity, and air pressure. In this research, data levers were carried out in June 2022 and resulted in a percentage accuracy of 73.34% for the Mamdani method and 70% for the Sugeno method.
COMPARISON OF RANDOM FOREST AND NAÏVE BAYES CLASSIFIER METHODS IN SENTIMENT ANALYSIS ON CLIMATE CHANGE ISSUE Fauzi, Fatkhurokhman; Setiayani, Wiwik; Utami, Tiani Wahyu; Yuliyanto, Eko; Harmoko, Iis Widya
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 3 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss3pp1439-1448

Abstract

The last decade was recorded as a decade with a bad record on the issue of disasters in the world due to climate change. Measuring public opinion is one of the steps to mitigate the impact of climate change. Twitter is a popular social media for channeling opinions. Twitter provides a great source of data for understanding public opinion and the perceived risk of an issue. In recent decades, when discussing climate change, there are those who agree and those who oppose it. Sentiment analysis is a branch of learning in the realm of text mining that is used as a solution to see opinions on a problem, one of which is climate change. In this study, we will try to analyze opinions on climate change issues using the Random Forest and Naïve Bayes classifier methods. Data were obtained from Twitter for the period January 2022-June 2022. The training data used in this research is 80%:20%. There are slightly more positive statements than negative ones. The results obtained with the Naïve Bayes classifier method are an accuracy of 76.25%, an F-1 score of 78%, and a recall of 80%. While the results of the random forest method are 70.6% accuracy, 69% F-1 score, and 63% recall. The Nive Bayes method is better than the Random Forest method for classifying climate change opinions with an accuracy of 76.25%.
Evaluating The Accuracy of Gridded Climate Datasets for Precipitation, Surface Air Temperature, and Sea Surface Temperature in Central Java, Indonesia Harmoko, Iis Widya; Zainuri, Muhammad; Wirasatriya, Anindya; Supari, Supari
INDONESIAN JOURNAL OF APPLIED PHYSICS Vol 15, No 2 (2025): October
Publisher : Department of Physics, Sebelas Maret University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.13057/ijap.v15i2.104276

Abstract

Studies of climate information that rely on accurate and reliable data are essential in hydrometeorological monitoring, early warning, and climate change impacts in areas with varied topography and limited observation data, such as Central Java, Indonesia. This study aims to assess the accuracy of gridded satellite and reanalysis on three main variables. Precipitation was analyzed utilizing CHIRPS, ERA5 Precipitation, and GSMaP products; surface air temperature (SAT) was assessed with ERA5-Land, FLDAS, and AIRS; and sea surface temperature (SST) was evaluated using OSTIA, RAMSSA, and GAMSSA. Observational data from six BMKG stations and iQuam functioned as the reference standard. The datasets were extracted using bilinear interpolation and evaluated using a bias, mean absolute error (MAE), mean absolute percentage error (MAPE), symmetric mean absolute percentage error (SMAPE) for precipitation, and root mean square error (RMSE). The evaluation showed that CHIRPS performed better estimation with the lowest RMSE and SMAPE (17.20 mm/day; 111.42 mm/month; 96.97% daily; 54.09% monthly) compared to ERA5-Precipitation and GSMaP. ERA5-Land in SAT showed better accuracy in MAE and MAPE of 1.2°C and <10% at most locations. For SST evaluation, OSTIA demonstrated the highest agreement with iQuam, showing RMSE of 0.246°C and MAPE of 0.552% in the Southern Sea, while GAMSSA recorded the highest errors across all zones. This study presents a variety of gridded dataset performances based on scale and time to illustrate the importance of validation against observational data. These results can guide researchers in processing the right dataset collection in climate applications in tropical ocean areas.
Co-Authors Al Aziz, Sofyan Anindya Wirasatriya Eko Yuliyanto, Eko Fauzi, Fatkhurokhman Fianti Fianti, Fianti Muhammad Zainuri Setiayani, Wiwik Setyanugraha, Noval Supari Supari, Supari Tiani Wahyu Utami