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All Journal Jurnal Edukasi dan Penelitian Informatika (JEPIN) JOIN (Jurnal Online Informatika)
Fadil, Nurdana Ahmad
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Computer Science & IT Education

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Artificial Neural Network for Classification Task in Tabular Datasets and Image Processing: A Systematic Literature Review Mustaqim, Adi Zaenul; Fadil, Nurdana Ahmad; Tyas, Dyah Aruming
JOIN (Jurnal Online Informatika) Vol 8 No 2 (2023)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v8i2.1002

Abstract

Artificial Neural Network (ANN) is one of the machine learning algorithms that is widely used for classification cases. Some examples of classification cases that can be handled with ANN include classifications in the health sector, banking, and classification in image processing. This study presents a systematic literature review (SLR) of the ANN algorithm to find a research gap that can be used in future research. There are 3 phases used in preparing the SLR. Those are planning, conducting, and reporting. Formulation of research questions and establishing a review protocol is carried out in the planning phase. The second phase is conducted. In this phase, searching for relevant articles is carried out, determining the quality of the literature found and selecting particles according to what has been formulated in the planning phase. The selected literature is then carried out by the process of extracting data and information and then synthesizing the data. Writing SLR articles based on existing findings is carried out in the last phase, namely reporting. The results of data and information extraction from the 13 reviewed articles show that the ANN algorithm is powerful enough with satisfactory results to handle classification cases that use tabular datasets or image datasets. The challenges faced are the need for extensive training data so that ANN performance can be better, the use of appropriate evaluation measures based on the cases studied does not only rely on accuracy scores, and the determination of the correct hyperparameters to get better performance in the case of image processing.
Hybrid TF-IDF and Sentence-BERT for Academic Advisor Recommendation Enhancing Topic Matching in Undergraduate Theses Sajid, Syahmi; Fadil, Nurdana Ahmad; Mtd, Aidizzacky Harizulfadly; Ajiwinata, Habib Gili
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol. 12 No. 2 (2026): Volume 12 No 2
Publisher : Program Studi Informatika

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Abstract

The selection of thesis supervisors is a crucial stage that affects the smooth completion of students’ research. The manual process traditionally used often creates difficulties in finding supervisors whose expertise aligns with the research topic, leading to academic inefficiencies. This study aims to design and develop a thesis supervisor recommendation system based on a hybrid TF-IDF and Sentence-BERT (SBERT) approach to improve the accuracy of matching students’ thesis titles with supervisors’ areas of expertise. The dataset used consists of 60 publications from four areas of expertise and 36 thesis titles from DIKE UGM students. The research stages include data collection, aggregation of supervisor publications, text preprocessing, feature extraction, and evaluation using Accuracy@K and Mean Reciprocal Rank (MRR). Experimental results indicate that the combination of stemming and stopword removal provides the best performance in placing relevant supervisors within the top-3 recommendations. The hybrid TF-IDF + SBERT method demonstrates superior performance compared to single methods, achieving Acc@3 of 0.8056, Acc@5 of 0.8611, and MRR of 0.6607, due to its ability to combine lexical information with semantic context. This study shows that a text-based recommendation system can speed up supervisor assignment and improve the match between research topics and supervisors’ expertise.
Co-Authors Ajiwinata, Habib Gili Dyah Aruming Tyas Mtd, Aidizzacky Harizulfadly Mustaqim, Adi Zaenul Sajid, Syahmi