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Akbar Iskandar
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Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Published by Universitas Teknologi Akba Makassar
ISSN : 20886705     EISSN : 26215608     DOI : https://doi.org/10.35585/inspir.v12i2.21
Core Subject : Science,
Computer Science & IT
Inspiration: Jurnal Teknologi Informasi dan Komunikasi is a scientific journal that publishes research results in the field of Information and Communication Technology (ICT). The ICT research area that is the focus of this journal can be seen on the Focus and Scope page. Journals are published twice a year, in June and December. Papers submitted for Inspiration: Jurnal Teknologi Informasi dan Komunikasi in volume 12 Number 2 in 2022, using a new template and be written in English for an initial review stage by the editor and a further review process by a minimum of two reviewers. This is a peer-review journal in information technology and communication research fields. Focus and scope of this journal deal with some research topics, including: - Artifical Intelligence - Database - Data Communication - Computer Networks - Software Engineering - Human Computer Interaction - Microcontrollers - Robotics - Data Mining - Image Processing - Information Retrieval - Natural Language Processing - Content Based Image Retrieval - Green Computing - Internet of Things
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 73 Documents
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Analysis of Determining Outpatient Service Time Based on Urgency in a Hospital Using The K-Means Clustering Method Johari; Syafruddin Syarif; Respaty Namruddin
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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Abstract

This study aims to optimize outpatient service time at Maryam Citra Medika Hospital, which has been facing long waiting times due to linear queues without considering the patient's urgency level. This results in patients with urgent conditions having to wait with patients with lower needs, resulting in ineffective service time and increasing health risks. The K-Means Clustering method was applied to group patients based on urgency level and determine efficient service time allocation. The data used were 8,308 outpatient visit records from January to December 2024, covering clinic service times, vital signs, chief complaints, and the patient's general condition. Data was processed using attribute selection and Min-Max Scaling normalization. Determining the optimal number of clusters (k) using the Elbow method identified k=3 as the best value. The implementation of K-Means Clustering with k=3 resulted in three patient clusters with unique clinical characteristics. Model performance evaluation using the Davies-Bouldin Index yielded a value of 0.164, which is excellent and close to ideal, indicating that the clusters formed are internally cohesive and externally well separated. Based on the characteristics of each cluster, recommendations for patient service time allocation are based on clinical condition priority. Cluster 0 (elderly patients with severe hypertension) is recommended to receive the shortest wait time (<30 minutes) and the longest consultation time (15 minutes), while Cluster 2 (healthy young adults) is allocated the maximum wait time (55 minutes) and the minimum consultation time (5 minutes). This implementation is expected to optimize patient flow, improve service safety and quality, and serve as a basis for operational decision-making for hospital management.
Determining The Priorities for Small and Medium Enterprise Development in Candipuro District Using the Analytical Hierarchy Process (AHP) Method Yuli Syafitri; Reni Astika; Irwandi; Deddy Sulaimawan
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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Abstract

This study aims to determine the priority of SME development strategies in Candipuro Subdistrict, South Lampung, by applying the Analytical Hierarchy Process (AHP) method as a multi-criteria decision-making approach. This study fills the gap in previous studies, which generally only assessed single factors and did not comprehensively integrate capital, marketing, human resources, and technology at the subdistrict level. Data were obtained through expert assessment and analysed using a pairwise comparison matrix. The results showed that capital was the most important criterion in SME development, followed by marketing, human resources, and technology. The strategy of increasing access to capital is considered the top priority alternative, followed by strengthening digital marketing and increasing human resource capacity. All calculations meet the consistency ratio value (CR < 0.1), so the results are considered valid. This emphasises the need for policies to strengthen access to funding and digitalisation of MSMEs as the main focus in local economic development.
Lightweight Deep Learning for Mobile Crab Larvae Detection in Aquaculture Environments Furqan Zakiyabarsi; Yabes Dwi Nugroho; Muhammad Muhaimin Nur; Muhammad Ulil Amri; Akbar Hendra; Arizal Arizal
Inspiration: Jurnal Teknologi Informasi dan Komunikasi Vol. 15 No. 2 (2025): Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Publisher : Pusat Penelitian dan Pengabdian Pada Masyarakat Sekolah Tinggi Manajemen Informatika dan Komputer AKBA Makassar

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Abstract

Efficient monitoring of crab larvae remains a critical challenge in aquaculture, as early-stage mortality is high due to the lack of practical and scalable detection systems. Although deep learning-based object detection has demonstrated strong performance for small aquatic organisms, many existing approaches are computationally intensive and unsuitable for mobile or resource-constrained hatchery environments. This study investigates the feasibility of lightweight deep learning models for mobile crab larvae detection in aquaculture environments. Using crab larvae at the zoea stage as a case study, lightweight YOLO-based architectures are evaluated to analyze the trade-off between detection accuracy and computational efficiency. The results indicate that extremely lightweight models offer minimal memory requirements and high deployment feasibility, but with limited detection accuracy. In contrast, more advanced lightweight architectures achieve substantially higher accuracy at the cost of increased model size and computational complexity. Rather than focusing solely on algorithmic comparison, this work emphasizes deployment-oriented insights for selecting appropriate lightweight detection models under practical mobile constraints. The findings demonstrate that lightweight deep learning provides a viable foundation for mobile aquaculture applications and establish a baseline for future optimization toward efficient on-device deployment.

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