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DIRJA NUR ILHAM
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PERFECT: Journal of Smart Algorithms
Published by Politeknik Aceh Selatan
ISSN : 30640377     EISSN : 30640377     DOI : https://doi.org/10.62671/perfect.v1i1.1
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Library & Information Science
PERFECT: Journal of Smart Algorithms is an international, Computer Technology, peer-reviewed and open-access journal that provides a platform to produce high-quality original research, Reviews, Letters, and case reports in natural, social, applied, formal sciences, arts, and all other related fields. Our aim is to ameliorate the speedy distribution of new research ideas and results and allow the researchers to create new knowledge, studies, and innovations that will aid as a reference tool for the future. PERFECT is published twice in one year, namely in January and July.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 5 Documents
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Search results for , issue "Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025" : 5 Documents clear
Real-Time Classification of Local Orange Fruit Quality Using YOLO (You Only Look Once) and SVM (Support Vector Machine) Methods Harahap, Muhammad Khoiruddin; Candra, Rudi Arif; Budiansyah, Arie; Aritonang, Romulo P.; Zulfan, Zulfan; Saputra, Devi Satria
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.55

Abstract

Oranges are a fruit that we often encounter and are even consumed by people because of their various benefits. Oranges have commercial value in Indonesia and have a fairly wide reach. In order to increase competitiveness, oranges must also meet market standards, both domestic and foreign, so that they can be accepted by consumers. Of course, in this case, orange selection is very important. increasing sales and market competition by sellers, important indicators in selecting citrus fruit are in terms of size and color. In general, the selection of citrus fruit is done manually and based on human thinking, which causes several weaknesses that must be corrected, including requiring a long time, human visual limitations, and being influenced by human psychology itself. This is what causes inconsistencies in selection. oranges and does not comply with existing market requirements. So a research was carried out regarding the quality classification of local citrus fruit using the YOLO (You Only Look Once) and SVM (Support Vector Machine) methods in real time. In the comparison made between the two methods used, Yolo was found to be the best method for classifying citrus fruit.
From Static to Contextual: A Survey of Embedding Advances in NLP Alkaabi, Hussein; Jasim, Ali Kadhim; Darroudi, Ali
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.77

Abstract

Embedding techniques have been a cornerstone of Natural Language Processing (NLP), enabling machines to represent textual data in a form that captures semantic and syntactic relationships. Over the years, the field has witnessed a significant evolution—from static word embeddings, such as Word2Vec and GloVe, which represent words as fixed vectors, to dynamic, contextualized embeddings like BERT and GPT, which generate word representations based on their surrounding context. This survey provides a comprehensive overview of embedding techniques, tracing their development from early methods to state-of-the-art approaches. We discuss the strengths and limitations of each paradigm, their applications across various NLP tasks, and the challenges they address, such as polysemy and out-of-vocabulary words. Furthermore, we highlight emerging trends, including multimodal embeddings, domain-specific representations, and efforts to mitigate embedding bias. By synthesizing the advancements in this rapidly evolving field, this paper aims to serve as a valuable resource for researchers and practitioners while identifying open challenges and future directions for embedding research in NLP.
An Analysis of User Satisfaction on the Official Website of Politeknik Aceh Selatan Using the EUCS Method Fardiansyah, Fardiansyah; Ihsan, M Arinal; Kurniadi, Sepri
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.97

Abstract

This study aims to assess user satisfaction with the evaluated information system by employing a quantitative research approach, specifically through the use of a five-point Likert scale questionnaire. Respondents were asked to rate their level of agreement with 16 structured statements designed to reflect five key dimensions of user satisfaction: content, accuracy, format, ease of use, and timeliness. The Likert scale ranged from 1 (strongly disagree) to 5 (strongly agree), allowing for the collection of measurable and standardized responses. The collected data were analyzed by calculating the Actual Satisfaction Score (SKN), which represents the total cumulative score based on all respondents’ answers. This score was then compared to the Ideal Satisfaction Score (SKI), which was determined by multiplying the total number of respondents by the total number of questions and the highest possible Likert score. In this case, with 112 valid respondents, the SKN was calculated at 7,841, while the SKI reached 8,960, resulting in a Satisfaction Percentage (PK) of 87.52%. This high level of satisfaction suggests that the system effectively meets user expectations, particularly in delivering high-quality information, ensuring timely services, and providing a user-friendly interface. These findings highlight the system’s strong performance in key usability areas and underscore its potential for further development. Furthermore, the results serve as an evidence-based foundation for future enhancements, especially in fostering user-centered, accessible, and sustainable digital services.
Explainable AI for Medical Imaging: A Taxonomy Based on Clinical Task Requirements Kamber, Ali Nadhim; Alkaabi, Hussein; Al-Rekabi, Mohammed; Jasim, Ali Kadhim
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.115

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical enabler for deploying AI-driven medical imaging systems where transparency, trust, and accountability are paramount. However, most current taxonomies of XAI methods categorize techniques based on algorithmic families (e.g., saliency maps, attribution methods), which often fail to reflect the practical requirements of clinical tasks. This paper proposes a novel task-centric taxonomy of XAI in medical imaging that aligns explanation techniques with four key clinical tasks: classification, detection, segmentation, and prognostic assessment. For each task, we analyze how different XAI methods enhance model interpretability, their suitability for clinical decision-making, and their limitations in real-world applications. Our taxonomy aims to provide a practical framework for researchers and practitioners to select appropriate XAI strategies tailored to the specific demands of medical imaging workflows. Furthermore, we highlight the current gaps in task-specific explainability and propose future research directions towards clinically meaningful, task-driven XAI solutions. This work serves as a step towards bridging the gap between technical XAI developments and the functional needs of clinical practice.
Harnessing Artificial Intelligence for Education Reform in Libya: Opportunities and Challenges Aboseta , Abdarahmah Kamees A Aboseta; Salem, Asma Al Mokhtar Miftah Alhaj; Alnagrat, Ahmed Jamah Ahmed
PERFECT: Journal of Smart Algorithms Vol. 2 No. 2 (2025): PERFECT: Journal of Smart Algorithms, Article Research July 2025
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v2i2.117

Abstract

Many education systems are seeking ways to improve teaching quality, personalize learning, and increase administrative efficiency. Libya faces recovery challenges and regional disparities that make scalable solutions especially important. Objective: This study examines how artificial intelligence could contribute to education reform in Libya while safeguarding equity, privacy, and academic integrity. Methods: We conducted a scoping review of recent empirical studies and policy documents, compared implementation frameworks, and completed a Libya‑focused desk review on governance, infrastructure, human capacity, and curriculum‑aligned content. Results: The synthesis indicates that intelligent tutoring, adaptive practice, automated feedback for low‑stakes writing, and responsible data use can support gains in achievement and teacher efficiency when aligned with curriculum and accompanied by sustained professional development. Constraints include uneven connectivity, capacity gaps, and limited high‑quality Arabic content; these factors can widen disparities if not addressed. Conclusion: A staged roadmap is proposed that prioritizes national guidance and safeguards, teacher capacity building, targeted pilots in foundational literacy, mathematics, and writing support, and careful scale‑up based on evidence and inclusiveness across regions.

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