Article Per Year (5 Year)
p-Index From 2021 - 2026
1.235
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika Journal of Intelligent Decision Support System (IDSS) Jurnal Mandiri IT Nomico
Hidayati, Ajeng
Unknown Affiliation
Computer Science & IT Economics, Econometrics & Finance Education Library & Information Science Mathematics

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search
Journal : Jurnal Mandiri IT

 1 
Comparative study of machine learning algorithms for predicting drug induced autoimmunity using molecular descriptors Delfiero, Yusuf Rio; Hidayati, Ajeng; Saputra, Bagus Hendra
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.436

Abstract

Drug induced autoimmunity (DIA) poses significant challenges in pharmaceutical development due to its complex immunological mechanisms and delayed clinical manifestations. This study proposes a comparative evaluation of three ensemble machine learning models CatBoost, XGBoost, and Gradient Boosting for predicting DIA using molecular descriptors. A curated dataset of drug compounds with known autoimmune outcomes was analyzed through a systematic workflow incorporating preprocessing, stratified sampling, and model evaluation using accuracy, F1 score, and ROC AUC. Results indicate that CatBoost achieved the highest ROC AUC, while XGBoost demonstrated superior balance between precision and recall, as reflected by its F1 score. Feature importance analysis using SHAP highlighted key molecular properties such as SlogP_VSA10 and fr_NH2 as major contributors to prediction outcomes. The study provides a reproducible and interpretable framework for early toxicity screening, offering valuable insights for data driven decision making in drug safety assessment.
Implementation of association method using fp-growth algorithm on sales transaction data at Koperasi Primer Pullahta Hankam Pusdatin KEMHAN RI Aulia, Regifia Ningrum Nur; Prabukusumo, M Azhar; Hidayati, Ajeng
Jurnal Mandiri IT Vol. 14 No. 1 (2025): July: Computer Science and Field.
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i1.446

Abstract

The conventional recording of sales transaction data frequently results in inaccuracies and presents significant obstacles to comprehensive data analysis. This study was conducted at Primkop Pullahta Hankam Pusdatin Kemhan RI with the aim of generating a product list based on item categories that are most frequently purchased together. These item combinations are expected to assist the cooperative in optimizing sales performance. The research employed a data mining technique known as association rule mining, which is designed to identify and predict customer purchasing behavior through analysis of transaction patterns. The dataset used comprised sales transaction records collected between September and November 2024. The FP-Growth algorithm was selected for its efficiency in identifying frequent itemsets without candidate generation. This algorithm utilized minimum support and confidence thresholds to generate association rules. The modeling process produced five association rules, each meeting the criteria of a minimum support of 20% and a minimum confidence of 80%, indicating strong co-occurrence among specific product combinations. Functional testing using the blackbox method demonstrated that all implemented features performed in accordance with specified functional requirements. The findings offer valuable insights for cooperative management by enabling data-driven decision-making in inventory planning, promotional bundling, and strategic sales targeting. These implications underscore the practical contribution of the research in enhancing operational efficiency and sales strategy within the cooperative sector.
Design and development of the spacelog web application for inventory management and asset tracking using QR codes at the Cyber Defense Center of the Ministry of Defense Sitanggang, Johan Adrian; Saputra, Bagus Hendra; Hidayati, Ajeng; Saragih, Hondor
Jurnal Mandiri IT Vol. 14 No. 3 (2026): Jan: Computer Science and Field
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mandiri.v14i3.481

Abstract

SpaceLog is a web-based inventory information system developed for the Cyber Defense Center of the Indonesian Ministry of Defense to address the limitations of spreadsheet-based management, which is static, non-real-time, and lacks accountability. This study proposes a novel approach by implementing a unit-centric architecture combined with Role-Based Access Control (RBAC) specifically tailored for the high-security requirements of the defense sector. The system development utilizes the Rapid Application Development (RAD) method, built upon Laravel, MySQL, and Bootstrap frameworks. Key features include unique QR Code tracking for individual assets, hierarchical location mapping, and a comprehensive audit trail. Testing results using the Black-Box method demonstrate that all functional scenarios, including item tracking and tiered access rights (Superadmin, Section Head, Staff), operate with 100% validity. Furthermore, the implementation significantly improves operational success by transforming asset management from a manual, error-prone process into a real-time, fully auditable digital ecosystem, thereby meeting the strict accountability standards of the Ministry of Defense.
Co-Authors Anindito Anjani, Prasashti Alya Aulia, Regifia Ningrum Nur Delfiero, Yusuf Rio Gafur, Hanif Saha Gumilar Rusliwa Somantri Haryono, Didi Hondor Saragih Manurung, Jonson Muhammad Syaroni Rofii Prabukusumo, M Azhar Putra, Kadek Rolavito Andrianto Putra, Muhammad Ridho Alghifari Saputra, Bagus Hendra Sitanggang, Johan Adrian