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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Journal of ICT Research and Applications International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) JOIV : International Journal on Informatics Visualization Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Journal of Information Technology and Computer Science (JOINTECS) JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JURIKOM (Jurnal Riset Komputer) Building of Informatics, Technology and Science Journal of Information Systems and Informatics RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Teknika Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Journal of Dinda : Data Science, Information Technology, and Data Analytics Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Jurnal INFOTEL RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi
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Journal : Jurnal INFOTEL

Opinion mining indonesian presidential election on twitter data based on decision tree method Nur Ghaniaviyanto Ramadhan; Merlinda Wibowo; Nur Fatin Liyana Mohd Rosely; Christoph Quix
JURNAL INFOTEL Vol 14 No 4 (2022): November 2022
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v14i4.832

Abstract

Indonesia is a country led by a president. The term of the leadership of a president will be democratically elected every five years. The current president will end his term of office in 2024. So that in that year, the people will hold a direct general election to determine the president between 2024 and 2029. Before the general election was held in Indonesia itself, it was thick related to the campaign for each presidential candidate carried out by his supporters. The campaign is carried out directly to village locations and on social media Twitter/Facebook/YouTube. His campaign writing on Twitter is exciting to analyze. Even now, many tweets related to the 2024 presidential election contain various opinions from the public. This study will examine the sentiment of someone's tweet to see the public's statement regarding the 2024 presidential election. The resulting sentiment categories are positive, negative, and neutral, and the word tweet related to the sentiment category will be visualized. The results of the sentiment category will then be classified using a tree-based method, namely a decision tree. The accuracy generated by applying the decision tree method is 99.3%. The decision tree method is also superior to the regression-based way by 2.5%.
Improving malaria prediction with ensemble learning and robust scaler: An integrated approach for enhanced accuracy Azka Khoirunnisa; Nur Ghaniaviyanto Ramadhan
JURNAL INFOTEL Vol 15 No 4 (2023): November 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v15i4.1056

Abstract

Mosquito bites are the primary transmission method for malaria, a prevalent and significant health concern worldwide. In the context of malaria incidence, Indonesia is the second most affected country after India. According to the Ministry of Health's report, Papua Province reported 216,380 malaria cases in 2019. Additionally, East Nusa Tenggara and West Papua said 12,909 and 7,029 points, respectively, reflecting the substantial national burden of this disease. Predicting malaria occurrence based on symptomatic presentation is a crucial preventive strategy. Machine learning models offer a promising approach to malaria prediction. This study focused on malaria detection by using patient data from Nigeria. This research proposes a detection system utilizing the Ensemble method, such as Decision Tree, Random Forest, and Bagging. This study also employing Robust Scaler for effective normalization and integrating K-fold cross-validation to enhance model robustness. Various experiments were conducted by systematically varying K values and the number of decision trees to ascertain the most effective hyperparameters yielding the highest accuracy. The findings indicate that the optimal accuracy 82% is achieved at a K value of 20, showing comparable accuracies across different decision tree quantities, underlining the robustness of the employed method. This research significantly advances malaria detection strategies, offering valuable insights into the effective deployment of machine learning in healthcare decision-making.