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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Journal of Intelligent Decision Support System (IDSS) Computer Science and Information Technologies Indonesian Journal of Data and Science Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues The Indonesian Journal of Computer Science Linier: Literatur Informatika dan Komputer
Darwis, Herdianti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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The Effect of Clinical Rule-Based Domain Filtering on the Performance of FP-Growth-Based Drug Recommendation Systems Luluang, Muhammad Zaqly; Irawati; Darwis, Herdianti
Indonesian Journal of Data and Science Vol. 7 No. 1 (2026): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v7i1.398

Abstract

This study analyzes the effect of domain filtering on drug recommendation systems based on association rule mining using the FP-Growth algorithm with Neural Collaborative Filtering (NCF) as a comparison. The dataset used was derived from patient medical records containing attributes such as complaints, diagnoses, and drug therapies, with a total of 1,000 patient transactions. To avoid data leakage, the dataset was randomly divided into 70% training data and 30% test data before the modeling process was carried out. Domain filtering was applied by limiting the rule structure so that complaints and diagnoses acted as antecedents and drugs as consequents. The performance of the recommendation system was evaluated using the Precision@5, Recall@5, and Normalized Discounted Cumulative Gain (NDCG@5) metrics. The results of the experiment show that the FP-Growth approach with domain filtering produces higher Precision@5 and NDCG@5 values than the non-filtering approach. The Wilcoxon Signed-Rank test shows that the difference is statistically significant, while effect size analysis using Cliff's Delta shows a practically meaningful impact. Furthermore, a comparison with Neural Collaborative Filtering shows that the collaborative filtering-based approach is less effective on transactional clinical prescription data with limited historical interactions. These findings indicate that integrating medical domain knowledge into FP-Growth can improve the clinical relevance and quality of drug recommendation rankings
Identifying key patterns of college student’s background through exploratory data analysis Jabir, Sitti Rahmah; Darwis, Herdianti
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.332

Abstract

The declining of student interest had forced universities to examine the characteristics of each student. According to higher education statistics on the number of new students, fluctuating values ​​have been found in recent years. Several research used exploratory data analysis (EDA) approach to analyze new student admissions data. EDA is offered a summary of the dataset analysis and preliminary findings. There are variables decided to be dropped because consisted high number of missing values. On the other hand, some data filled with mean and mode because the number of missing not more than 20%. The missing values in each of attribute might be cleaned using another way. The admission team in university might encourage the registrants to complete and input correct data to the system. Based on the visualization, we found that some college students applied to university from several background of area, demographic and etc. The marketing division might apply another strategy is area had small number of college which is Kalimantan. Public health, computer science and insutry technology are major that have potential to be promoted due to the job prospects.
Co-Authors Adela Regita Azzahra Adnan, Adam Aji Prasetya Wibawa Alfitriana Riska Amaliah, Tazkirah Amelia, Ayu Annisa Febrianti Arfah, Jumrayanti Asfar, A. Muh. Fitrah Astuti, Wistiani Atmajaya, Dedy Atussaliha, Nur Almar' Azwan Triyadi Backar, Sunarti Passura Basri, Haerunnisa Basri, Nur Anisa Dian Fitrah Yusti Randana Harlinda Harlinda, Harlinda Hasanuddin, Tasrif Haviluddin Haviluddin Irawati Irawati Irawati jabir, sitti rahmah Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Luluang, Muhammad Zaqly Magfirah, Magfirah Malik, Muh Aristsyah Mardiyyah Hasnawi Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nia Kurniati Nur wahidah Purnawansyah Purnawansyah Purnawansyah Puspitasari, Rahma Rachman Manga, Abdul Rahma, Reyna Aprilia Rahmasari, Putri Aulia Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Sastra, Ramdan Siska Anraeni Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi Zahirah, Dinna Zulkifli, Muh. Rifqi