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JSAI (Journal Scientific and Applied Informatics)
Published by Universitas Muhammadiyah Bengkulu
ISSN : 26143062     EISSN : 26143054     DOI : -
Core Subject : Science,
Computer Science & IT
Jurnal terbitan dibawah fakultas teknik universitas muhammadiyah bengkulu. Pada jurnal ini akan membahas tema tentag Mobile, Animasi, Computer Vision, dan Networking yang merupakan jurnal berbasis science pada informatika, beserta penelitian yang berkaitan dengan implementasi metode dan atau algoritma.
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Articles 494 Documents
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Prediksi Potensi Banjir Menggunakan Machine Learning Dengan Pendekatan XGBoost Dan Logistic Regression Nurita Evitarina; Fitriyanti, Fitriyanti; Utami, Tri Dewi Yuni
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9867

Abstract

Flooding is one of the most frequent natural disasters in Indonesia, causing significant material losses and casualties. This study aims to develop a flood potential prediction model based on weather data using machine learning approaches, namely XGBoost and Logistic Regression. The dataset consists of 1,513,505 weather records with 1,165 flood events (0.077%). The features include temperature, humidity, wind speed and direction, weather codes, and temporal features generated using a sliding window approach for H-1, H-2, and H-3. Data imbalance was addressed using a combination of stratified undersampling and SMOTE, changing the class ratio from 1:1,298 to 1:3.3. Experimental results show that XGBoost outperforms Logistic Regression, achieving an accuracy of 98.40%, precision of 97.93%, recall of 95.07%, and an ROC-AUC of 99.38%, while Logistic Regression achieved an accuracy of 62.77%. Feature importance analysis indicates that weather codes at H-3 and H-1 are the most influential predictors. With a low false negative rate of 4.9%, the proposed XGBoost model is considered reliable for implementation as a flood early warning system.
Model Kausal Pengaruh Fasilitas dan Konten Digital terhadap Kompetensi Teknologi Informasi Siswa dengan Mediasi Minat Belajar Aziz, Ezar; Yulianti; Probonegoro, Wishnu Aribowo
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9869

Abstract

This study aims to analyze the effect of facility availability and digital content on students’ information technology competencies with learning interest as a mediating variable. A quantitative approach with a survey method was employed involving 178 students from Information Technology and Visual Communication Design programs. Data were analyzed using multiple regression, simple regression, and path analysis with SPSS 25. The results indicate that facility availability and digital content have a positive and significant effect on students’ learning interest, accounting for 68.2% of the variance. Learning interest also has a significant positive effect on students’ information technology competencies, contributing 61.5% of the variance. Furthermore, digital content shows a significant indirect effect on students’ competencies through learning interest. These findings highlight the strategic role of learning interest as a mediating variable in the causal model of digital learning and imply that improving students’ competencies requires not only adequate facilities and high-quality digital content but also instructional strategies that foster students’ learning interest.
Evaluasi Keberhasilan Implementasi Sistem Informasi Perdagangan Daerah Berbasis HOT-Fit dan Technology Acceptance Model Dzalfa Tsalsabila Rhamadiyanti; Aditya Ahmad Fauzi; Fithriawan Nugroho
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9896

Abstract

The Pangkalpinang City Trade Information System (SIPGK) was developed as a digital instrument to support trade data management and data-driven public information services. This study aims to evaluate the implementation success of SIPGK using the Human–Organization–Technology Fit (HOT-Fit) model, with the Technology Acceptance Model (TAM) employed as a complementary interpretative lens. A qualitative evaluative approach was applied through observation, interviews, and system documentation. The results indicate that the technology aspect demonstrates a system availability rate of 95%, reflecting good system quality and service stability, while the organizational aspect is supported by formal policies and standard operating procedures. However, the human aspect remains a key limiting factor due to disparities in digital literacy and data input consistency, along with suboptimal cross-unit data integration. These findings reveal a gap between technological and organizational readiness and human resource capacity in achieving strategic system utilization. The novelty of this study lies in applying the HOT-Fit model to a regional trade information system context, which has been rarely examined, and in integrating TAM as an interpretative framework to explain user acceptance.
Evaluasi Metode Retrieval pada Chatbot Domain Khusus Berbasis Retrieval-Augmented Generation Asmaidin, Asmaidin; Budy Santoso, Cahyono
JSAI (Journal Scientific and Applied Informatics) Vol 9 No 1 (2026): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v9i1.9897

Abstract

This study evaluated retrieval methods in the implementation of a domain-specific chatbot based on Retrieval-Augmented Generation to improve information accuracy and relevance while reducing hallucination risks. The primary problem addressed was the incorrect selection and prioritization of contextual documents in chatbot systems built on large language models, particularly in technical domains. An experimental approach was applied by comparing three retrieval strategies: lexical retrieval based on term frequency–inverse document frequency, semantic retrieval using vector representations, and a hybrid retrieval method combining lexical and semantic signals. System performance was measured using Recall at different ranking thresholds and Mean Reciprocal Rank to assess both document discovery and ranking quality. The results demonstrated that lexical retrieval achieved the highest precision at the top-ranked position, while semantic retrieval showed reduced effectiveness due to semantic drift in technical documents. The hybrid approach improved mid-range recall performance but still exhibited ranking ambiguity for top-ranked results. These findings indicated that retrieval quality in Retrieval-Augmented Generation systems depended more on effective ranking and context prioritization than on document availability alone. The study concluded that systematic evaluation of retrieval methods was essential for developing reliable domain-specific chatbots.

Page 50 of 50 | Total Record : 494

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