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All Journal Infotech: Journal of Technology Information
Alifandra, Dhafa
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Computer Science & IT

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RANCANG BANGUN CHATBOT ZENO SEBAGAI MEDIA AJAR TATA SURYA DAN BAHASA INGGRIS Alam, Indra Nurul; Alifandra, Dhafa; Wijirahayu, Suciana; Yuliani, Mentari
Infotech: Journal of Technology Information Vol 9, No 2 (2023): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v9i2.187

Abstract

Technology continues to evolve alongside the progress in education, as higher levels lead to higher-quality technological advancements. Education, especially elementary school, is of paramount importance. They typically educate children aged 6 to 13 years. Research findings show that media such as images, videos, and texts can enhance comprehension abilities by up to 90% for children in this age group. Therefore, digital technology can play a crucial role in assisting elementary school children in achieving this level of understanding. Through chatbot digital tool, educational materials comprising images, videos, and texts hoping to motivate students to review the lesson. We developed the chatbot using the Rapid Application Development (RAD) to the application quickly and iteratively. The results of the chatbot testing received positive responses from teachers and students. Based on the evaluation, 90% of students reported feeling motivated and assisted in their learning while using the chatbot Zeno. Thus, the conclusion is that the chatbot can effectively aid the learning processof English and Solar System.
ANALISIS KINERJA ALGORITMA MACHINE LEARNING DALAM MENDETEKSI ANOMALI KETINGGIAN AIR LAUT: STUDI PERBANDINGAN ONE-CLASS SVM DAN ISOLATION FOREST Alifandra, Dhafa; Pratiwi, Nunik
Infotech: Journal of Technology Information Vol 11, No 2 (2025): NOVEMBER
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i2.405

Abstract

This study aims to compare the performance of two machine learning algorithms for anomaly detection One-Class SVM and Isolation Forest in identifying anomalies in sea level data in Indonesia, a region with high tsunami risk. The data were obtained from an official Indonesian government source over a one-year period and underwent preprocessing, including data cleaning and standardization. The models were evaluated using statistical analysis (Mann-Whitney U test), clustering metrics (Davies-Bouldin Index and Silhouette Score), and visual inspection. The results indicate that Isolation Forest outperformed the other algorithm with a Davies-Bouldin Index of 0.8124, while One-Class SVM achieved the highest Silhouette Score at 0.4381, although its Davies-Bouldin Index was higher at 0.9163. This study contributes to the selection of effective algorithms for ocean monitoring systems as part of disaster mitigation strategies in Indonesia.
Co-Authors Alam, Indra Nurul Pratiwi, Nunik Suciana Wijirahayu Yuliani, Mentari