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All Journal Equilibria Pendidikan : Jurnal Ilmiah Pendidikan Ekonomi Majalah Ilmiah UNIKOM Planta Simbiosa : Jurnal Tanaman Pangan dan Hortikultura SELL (Scope of English Language Teaching, Linguistics, and Literature) Journal Jurnal Teknik Informatika (JUTIF) Eruditio : Indonesia Journal of Food and Drug Safety
Eko Aprianto
universitas tanjungpura
Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Economics, Econometrics & Finance Education Engineering Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Public Health Social Sciences

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Journal : Jurnal Teknik Informatika (JUTIF)

 1 
COMPARISON OF INDEX, PARTITION, AND MATERIALIZED VIEW METHODS ON THE ORACLE DATABASE STUDY ON CENTRAL GOVERNMENT FINANCIAL REPORTS (LKPP) Rachman, M Harviandi; Samidi, Samidi; Aprianto, Eko
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 4 (2024): JUTIF Volume 5, Number 4, August 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.4.1962

Abstract

The Indonesian Central Government Financial Report (LKPP) is a financial document prepared to increase transparency and accountability in the implementation of the State Revenue and Expenditure Budget (APBN). It is prepared within a tight schedule, hence changes made by each entity must be updated promptly. Therefore, this research focuses on the optimal table design for presenting financial reports. Query optimization is a major concern in database design, with the use of indexing concepts to increase data search speed. Table partitioning is also a strategy to consider, namely dividing a table into parts that form separate data ranges. The use of a Materialized View (MV) is another alternative, providing increased performance with the space-for-time trade-off principle. Experiments were carried out by comparing the response time of applying index, partition, and materialized views to produce financial report data. Experimental results indicate that materialized views can provide significant advantages when faced with large volumetric data. The decision to choose a materialized view can be considered contextually, depending on the specific needs and characteristics of the data encountered in a database system.
Optimizing Bag of Words and Word2Vec with Vocabulary Pruning and TF-IDF Weighted Embeddings for Accurate Chatbot Responses in Indonesian Treasury Services Aprianto, Eko; Mahdiana, Deni; Wibowo, Arief
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.5370

Abstract

The high volume of support tickets submitted to the HAI DJPb Service Desk has caused delays and inconsistent response quality in payroll-related inquiries across Indonesian treasury work units (Satker). To improve the accuracy and efficiency of public service responses, this research proposes an optimized text-vectorization framework for chatbot development using a hybrid combination of Bag of Words (BoW), Word2Vec, vocabulary pruning, and TF-IDF weighted embeddings. The dataset consists of 2024 ticket logs, curated FAQs, and questionnaire data related to the Satker Web Payroll Application. The method includes preprocessing (snippet removal, normalization, tokenization, stopword removal, stemming), vocabulary pruning based on empirical frequency thresholds (<5 and >80) while preserving domain-specific technical terms, and semantic weighting through TF-IDF. Four vectorization models—BoW, BoW with pruning, Word2Vec, and Word2Vec + TF-IDF—were evaluated using cosine similarity, response time, and accuracy. Results show that BoW achieved the highest accuracy of 88.32%, while Word2Vec produced the most stable response time with an average of 47.32 ms and a cosine similarity of 0.99. The findings demonstrate that frequency-based representations remain highly effective for structured administrative datasets, while weighted embeddings improve semantic relevance. This study contributes to the field of Informatics by providing an efficient hybrid vectorization framework tailored for Indonesian administrative language, enabling more accurate and scalable chatbot solutions for e-government services.
Co-Authors Adler, John Arief Wibowo Cahyaningsih, Eni Deni Mahdiana Dwi Fita Heriyawati Herawati, Desty Miftahurrohmah, Nur Netti Nurlenawati Rachman, M Harviandi Samidi Samidi, Samidi Triadinda, Dexi Wahyuni, Reza Zaqyah, Irene