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All Journal Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Tekinkom (Teknik Informasi dan Komputer) Takuana: Jurnal Pendidikan, Sains, dan Humaniora
Dewantoro, Mahendra
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Arts Humanities Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences

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DIAGNOSIS CEREBROVASCULAR ACCIDENTS MENGGUNAKAN TEKNIK SMOTEEN DENGAN MEMBANDINGKAN METODE KLASIFIKASI DECISION TREE DAN XGBOOST Fadli, Muhammad; Purwanti, Dian Sri; Surono, Muhammad; Dewantoro, Mahendra; Suryono, Ryan Randy
Jurnal Teknik Informasi dan Komputer (Tekinkom) Vol 8 No 1 (2025)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v8i1.2025

Abstract

Cerebrovascular Accident (stroke) is a critical health issue in Indonesia, often leading to high mortality and long-term disability. Early detection through machine learning has emerged as a promising approach to improve diagnosis and treatment outcomes. This study aims to compare the performance of two classification algorithms, Decision Tree and Extreme Gradient Boosting (XGBoost), in diagnosing stroke using the SMOTEENN (Synthetic Minority Over-sampling Technique and Edited Nearest Neighbor) technique to address data imbalance. The dataset used contains 5110 samples with 11 independent variables and one dependent variable (stroke status), obtained from a public repository. After preprocessing and data balancing, both models were trained and evaluated based on accuracy, precision, recall, and F1-score. The results show that XGBoost outperforms Decision Tree in all evaluation metrics, achieving an accuracy of 96.48%, precision of 94.75%, recall of 99.03%, and F1-score of 96.85%, compared to Decision Tree’s accuracy of 91.55%, precision of 89.82%, recall of 95.32%, and F1-score of 92.49%. These findings confirm that the combination of XGBoost and SMOTEENN provides a more effective and reliable classification model for early stroke diagnosis. Future research is encouraged to explore deep learning techniques to further enhance diagnostic accuracy.
Integrasi Konteks Semantik dan Waktu Akses dalam Algoritma Caching Adaptif untuk Optimalisasi Kinerja Sistem Dewantoro, Mahendra Dewantoro; Dewantoro, Mahendra; Santosa, Budi; Prasetio, Mugi; Amarudin
Jurnal Ilmu Komputer dan Desain Komunikasi Visual Vol 10 No 2 (2025): Jurnal Ilmu Komputer dan Desain Komunikasi Visual
Publisher : Fakultas Ilmu Komputer Universitas Nahdlatul Ulama Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55732/a66z4p43

Abstract

 In modern computing systems, caching is an essential technique for improving data access efficiency. However, traditional caching algorithms such as LRU and LFU have limitations in handling complex data dynamics. This research proposes an adaptive caching algorithm that integrates semantic context and access time profiles to enhance system performance. The approach utilizes a semantic embedding model based on Sentence-BERT and temporal analysis of user access patterns. Testing was conducted through simulations using real and synthetic datasets, and compared with conventional caching methods such as KVShare and LRU. Evaluation results show that the proposed algorithm is capable of increasing the cache hit rate by more than 83%, reducing average latency to around 61 ms, and maintaining resource usage efficiency. In addition, the algorithm demonstrates strong adaptability to dynamic access pattern changes and responsiveness to semantic parameter adjustments. Thus, the integration of semantic context and temporal information provides significant contributions to optimizing cache management. This algorithm has potential applications in edge computing systems, LLM services, and cloud-based platforms. Suggestions for future research include implementation in real-world environments, application of predictive machine learning models, and dynamic exploration of adaptive parameters.  
Perancangan Sistem Inventarisasi Aset Terpadu Berbasis Web di Lingkungan Perguruan Tinggi Faridaashri, Fakihah Farhah; Dewantoro, Mahendra; Damayanti, Damayanti
Takuana: Jurnal Pendidikan, Sains, dan Humaniora Vol. 4 No. 4 (2026): Takuana (January-March)
Publisher : MAN 4 Kota Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56113/takuana.v4i4.379

Abstract

Asset management in higher education institutions is essential to ensure efficiency, transparency, and accountability in the use of resources. However, many institutions still rely on manual or semi-digital systems, leading to data duplication, information inconsistency, and difficulties in reporting. This study aims to develop an Integrated Web-Based Asset Inventory System to address these issues. The research methodology follows a Research and Development (R&D) approach, combining Prototype and Waterfall methods. The system is designed through stages including needs analysis, UML design, implementation using technologies such as PHP/CodeIgniter, MySQL, and Bootstrap, followed by functional and usability testing. Key features of the system include asset data input and updating, categorization by type and location, periodic notifications, automated reporting, role-based access control, and activity logs. The results show that the system provides an accurate, real-time, and integrated solution for asset management. With the appropriate use of information technology, this system is expected to improve operational efficiency and serve as a reference for other educational institutions in implementing digital-based asset management.
Co-Authors Amarudin Budi Santosa Budi Santosa Damayanti Damayanti Dewantoro, Mahendra Dewantoro Fadli, Muhammad Faridaashri, Fakihah Farhah Prasetio, Mugi Purwanti, Dian Sri Ryan Randy Suryono Surono, Muhammad