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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) JISKa (Jurnal Informatika Sunan Kalijaga)
Agus Mulyanto
Sunan Kalijaga State Islamic University Yogyakarta
Computer Science & IT Electrical & Electronics Engineering Library & Information Science

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Peningkatan Akurasi Temu Kembali Pengetahuan pada Kasus Sakit Kepala Menggunakan Integrasi Case-Based Reasoning dan Natural Language Processing Agus Mulyanto
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 2 (2026): May 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.6189

Abstract

Headache disorders represent a significant global neurological challenge, yet medical diagnosis is often hindered by subjective patient complaints and limited facilities. This study proposes an integrated model combining Natural Language Processing (NLP) and Case-Based Reasoning (CBR) to enhance the accuracy of medical decision-making for headache cases. The model utilizes the Random Forest algorithm for NLP classification and Cosine Similarity within the CBR framework to identify case relevance. The dataset consists of medical records for two types of headaches: Cluster headache (G44.0) and Tension-type headache (G44.2). Experimental results demonstrate that data augmentation significantly improves model performance, increasing accuracy from 62% to 69%. For the G44.0 label, the model achieved a precision of 0.84, while the G44.2 label reached a recall of 0.87. Furthermore, the CBR system strengthens the diagnosis with a similarity level of up to 0.82 and continuous learning capabilities through the Retain stage. This integration effectively provides faster, more targeted diagnostic recommendations for medical professionals.
Comparison methods in a decision support system for determining JavaScript frameworks Rofif Aghna Fakhri Diya; Agus Mulyanto
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 24, No 1: February 2026
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v24i1.27241

Abstract

The selection of an appropriate JavaScript framework in web-based software development often leads to errors when the chosen framework is incompatible with the design. The ability to make decisions quickly, accurately, and precisely is therefore a key factor in successful software design. Addressing this need, the present study analyzes the accuracy of the analytical hierarchy process-weight product (AHP-WP), analytical hierarchy process-technique for order preference by similarity to ideal solution (AHP TOPSIS), and analytical hierarchy process-simple multi-attribute rating technique (AHP-SMART) methods in determining the most suitable JavaScript framework according to the International Organization for Standardization (ISO) 9126 classification. To evaluate accuracy, the mean absolute percentage error (MAPE) was applied as a cost function to measure the error percentage of each method. The analysis was conducted on ten popular JavaScript frameworks selected based on their popularity and usage trends. The evaluation considered six quality criteria: functionality, reliability, usability, efficiency, maintainability, and portability. The results show the ranking of each alternative for all methods. Accuracy measurement using MAPE revealed that the AHP-WP method produced the smallest error percentage (37.77645%), compared to AHP-TOPSIS (47.12566%) and AHP-SMART (46.4041%). Accordingly, the AHP-WP method is recommended for decision support system (DSS) development.
Co-Authors Rofif Aghna Fakhri Diya