Claim Missing Document
Check
Articles

Found 2 Documents
Search
Journal : The Indonesian Journal of Computer Science

Pemanfaatan TOPSIS (Technique For Order Preference By Similarity To Ideal Solutions) untuk Rekomendasi Objek Wisata di Provinsi Sulawesi Tengah Ulhak, Muhamad Zia; Pratama, Septiano Anggun; Ardiansyah, Rizka; Angreni, Dwi Shinta
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4404

Abstract

Tourism is one of the key sectors in driving economic growth in Central Sulawesi. To support the enhancement of tourism, this research developed a web-based decision support system using the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) to provide tourism destination recommendations. The system assists users in selecting tourist destinations based on several relevant criteria, such as facilities, accessibility, cost, cleanliness, and safety. By applying the TOPSIS method, the system can rank tourism destinations by comparing the distances between positive and negative ideal solutions. This implementation is expected to help tourists make more informed and accurate decisions regarding the destinations they wish to visit and contribute positively to the development of tourism in Central Sulawesi.
PERBANDINGAN AKURASI LINEAR REGRESSION DAN SUPPORT VECTOR REGRESSION DALAM PREDIKSI SUHU RATA-RATA Lesnusa, Gideon Namlea; Dwi Shinta Angreni; Ardiansyah, Rizka
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.3944

Abstract

The weather in Indonesia varies significantly and is influenced by geographical location, topography, and regional climate. Weather patterns differ between the western and eastern parts of Indonesia. This study explores time series models to predict weather data in Palu City, a region that is complex due to various weather factors. The focus is on the unique weather patterns reflected by the geography and topography of Palu City. Evaluation was conducted on time series models, including Linear Regression and Support Vector Regression (SVR), to estimate weather conditions in Palu City. The evaluation results show that the SVR model has an RMSE of 0.6302, while linear regression has an RMSE of 0.6328. This research has the potential to improve early warning and decision-making regarding extreme weather