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INDONESIA
Scientific Journal of Informatics
Published by Universitas Ibrahimy
ISSN : 25497480     EISSN : 25496301     DOI : https://doi.org/10.35316/jimi
Core Subject : Science,
Computer Science & IT
Topics cover the following areas (but are not limited to): 1. Information Technology (IT) a. Software engineering b. Game c. Information Retrieval d. Computer network e. Telecommunication f. Internet g. Wireless technology h. Network security i. Multimedia technology j. Mobile Computing k. Parallel/Distributed Computing 2. Information Systems Engineering a. Development, management and utilization of Information Systems b. Organizational Governance c. Enterprise Resource Planning d. Enterprise Architecture Planning e. e-Bbusinnes f. e-Commerce 3. Business Intelligence a. Data mining b. Text mining c. Data warehouse d. Online Analytical Processing e. Artificial Intelligence f. Decision Support System g. Machine Learning
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 146 Documents
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Meningkatkan Performa dalam Pengelolaan Data Ketidaktidakan dengan Menggunakan B-Tree Indexing Aryka Anisa Pertiwi; Nisa Hanum Harani
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.15-23

Abstract

Irregularity data management in the logistics industry plays an important role in ensuring smooth operations and maintaining service quality. However, the Excel-based manual system still used by many companies often faces various obstacles, such as unstructured data, redundancy, and slow and inefficient access. This study aims to improve the efficiency of irregularity data management by developing a web-based management application that integrates data normalization and indexing using the B-Tree structure. The novelty of this study lies in the application of a combination of data normalization methods and B-Tree indexing structures in the context of irregularity data management in the logistics industry, which has not been widely applied in an integrated manner in previous studies. The normalization process is designed to organize data, reduce redundancy, and improve data integrity. Meanwhile, B-Tree indexing is applied to accelerate the process of searching and processing data, allowing for faster and more accurate access. Testing was conducted using historical data from logistics companies to evaluate the performance of the developed system. The results showed a significant increase in the speed of reporting, searching, and data analysis compared to the manual system. This application also provides real-time access, which supports more efficient and data-driven strategic decision making. Thus, this study provides an effective technology-based solution to address the challenges of irregular data management, as well as contributing to improving operational efficiency and service quality in the logistics industry.
Pengembangan Aplikasi Monitoring dan Kontrol untuk Greenhouse Hidroponik Muhammad Aji; Husni Thamrin
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.1-14

Abstract

This research aims to develop an Internet of Things (IoT) based monitoring and control application for hydroponic greenhouses using the Nutrient Film Technique (NFT) method for lettuce (Lactuca sativa L.). Hydroponics has emerged as an efficient solution for agricultural production in limited spaces, but environmental management challenges often remain a primary obstacle. The application is designed to automatically monitor and control environmental conditions, such as temperature, solution pH, and nutrient concentration, to reduce the risk of bolting that can diminish harvest quality. Software development was conducted using the Scrum methodology, which supports iterative and adaptive processes, employing Model-View-ViewModel (MVVM) architecture and clean architecture to create a modular and easily maintainable code structure. Jetpack Compose was utilized to generate a responsive and efficient user interface. Research findings demonstrate that the application offers key features including real-time monitoring through MQTT protocol, manual and automatic control, data history visualization in graphs, weather prediction, and hydroponic guidance. Application validation using the Aiken index showed a very high validity level (> 0.80), while black box testing ensured that all application functions operated by user requirements. This research is expected to improve hydroponic greenhouse management efficiency and significantly contribute to IoT technology development in the agricultural sector.
Sistem Pendukung Keputusan Untuk Pemilihan Program Studi Bagi Calon Mahasiswa Baru di Universitas Negeri Medan Menggunakan Metode Entropy Dan SAW Silvi Pratiwi; Debi Yandra Niska; Risna Tutiarna Simorangkir; Mufida Azza Iskandar Lubis
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.24-36

Abstract

Pemilihan program studi yang tepat menjadi salah satu faktor krusial dalam menentukan arah pendidikan dan karier calon mahasiswa baru. Universitas Negeri Medan (UNIMED), dengan banyaknya pilihan program studi, menuntut adanya sistem yang mampu membantu calon mahasiswa dalam menentukan pilihan secara objektif dan berdasarkan data. Penelitian ini bertujuan untuk mengembangkan Sistem Pendukung Keputusan (SPK) berbasis web yang dapat memberikan rekomendasi program studi yang sesuai dengan minat, nilai akademik, dan akreditasi program. Metode Entropy digunakan untuk menentukan bobot kriteria secara objektif berdasarkan variasi data, sedangkan metode Simple Additive Weighting (SAW) digunakan untuk melakukan perankingan terhadap alternatif program studi. Penelitian ini melibatkan calon mahasiswa FMIPA UNIMED sebagai objek penelitian dan menggunakan data primer berupa nilai rapor dan minat, serta data sekunder seperti akreditasi program studi. Hasil implementasi sistem menunjukkan bahwa pendekatan kombinasi Entropy dan SAW mampu memberikan rekomendasi program studi yang akurat dan relevan. Sistem ini diharapkan dapat menjadi alat bantu yang efektif dalam pengambilan keputusan bagi calon mahasiswa serta meminimalisir ketidakpastian dalam proses pemilihan program studi.
Deteksi Dini Terhadap Penyakit Tumor Otak Menggunakan Citra Magnetik Resonance Imaging (MRI) dengan Pendekatan Deep Convolutional Neural Network Muhamad Salman; Rudi Kurniawan; Bunga Intan; Budi Santoso
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.37-41

Abstract

This study aims to develop an early detection system for brain tumors using MRI images with a Deep Convolutional Neural Network (DCNN) based on the ResNet152V2 architecture. Rapid detection of brain tumors is crucial for improving recovery chances; however, manual processes often face challenges due to limitations in technology and medical expertise. Therefore, this research offers an automated solution for analyzing MRI images.The methods used include data collection from public datasets, image preprocessing, and training the DCNN model. The ResNet152V2 model was chosen for its ability to address the vanishing gradient problem and its effectiveness in feature extraction. The results show that the model achieved an accuracy of 92.38% in classifying four types of brain tumors: Meningioma, Glioma, Pituitary, and No Tumor. Evaluation using a confusion matrix and classification report indicates good performance. This research is expected to contribute to the early diagnosis of brain tumors and serve as a reference for future studies in the application of artificial intelligence in the medical field.
Klasifikasi Penyakit pada Buah Jeruk Berdasarkan Citra dengan Pendekatan Transfer Learning Menggunakan Arsitektur Densenet-121 Sheli Agustina; Asep Toyib Hidayat; Satrianansyah; Rudi Kurniawan
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.42-47

Abstract

This study aims to develop a classification system for citrus fruit diseases based on digital images using a machine learning approach. The primary challenge in citrus cultivation is disease attacks that affect both the quality and quantity of production. In this research, image processing techniques were applied to extract color, shape, and texture features from citrus fruit images, which were then used as input for classification algorithms. This study uses the DenseNet-121 architecture for orange fruit image classification. The dataset used consisted of images of healthy citrus fruits and those affected by various diseases, such as blackspot, canker, and greening. The testing results showed that the DenseNet-121 architecture achieved the highest accuracy in classifying citrus diseases, with an accuracy rate of up to 99%. This system is expected to assist farmers and relevant stakeholders in early disease detection and in taking appropriate control measures.
Evaluasi Akurasi dan Presisi Large Language Model (LLM) dalam Generasi User Story untuk Perangkat Lunak Maulana Nur Rokhim; Muhammad Akmaluddin Az Zamrudi; Muhammad Ainul Yaqin
Jurnal Ilmiah Informatika Vol. 10 No. 1 (2025): Jurnal Ilmiah Informatika
Publisher : Department of Science and Technology Ibrahimy University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35316/jimi.v10i1.48-60

Abstract

Generating effective user stories is essential yet time-consuming in software development, especially in large scale Agile projects. This study evaluates the performance of three Large Language Models (LLMs): ChatGPT-4.0, DeepSeek, and Gemini 2.5 in generating user stories automatically. The objective is to compare their accuracy and precision to determine the most suitable model for automating requirements documentation. Using seven test prompts from various industry domains, each model generated user stories evaluated with BLEU-4, ROUGE-L F1, and METEOR metrics. Results show that while all models produced structurally valid outputs, Gemini 2.5 achieved the highest average scores (0.386), surpassing DeepSeek (0.355) and ChatGPT (0.348). Gemini 2.5 demonstrated superior consistency, clarity, and semantic completeness. This research contributes a performance benchmark for LLMs in software requirement generation and highlights the practical benefits of LLM-based automation over manual methods, including speed, consistency, and adaptability. Gemini 2.5 is recommended as the optimal model for generating user stories in software engineering contexts.

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