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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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Monitoring Infus Dan Detak Jantung Berbasis Internet Of Things (IoT) M Syafiih; Nadiyah Nadiyah; Sri Astutik Andayani; Nur Hatima Inda Arifin
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): 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.v8i2.124-131

Abstract

An alternative medical device called an IV is used to replenish lost body fluids and maintain the body's electrolyte balance. Infusion fluid administration is a very useful way to help and speed up the recovery of the patient's condition during the treatment period. In hospitals, clinics and health centers, nurses generally still control and monitor the use of IV fluids manually. Nurses must periodically check the condition of each patient's infusion. Thus, delays in infusion replacement often occur. This has a negative impact on the patient, such as blood being sucked into the infusion hose and the possibility of clotting in the hose. So that patients will experience losses to these conditions, nurses are required to always be on time in replacing infusions. There are still many people who do not understand the normal human heart rate, because health workers do not socialize to the public about heart rate. Lack of understanding of the human heartbeat that is often found in our environment. Based on the above problems, the research to be carried out will provide several solutions, it is necessary to make a sensor to detect infusion filling for patients who are under treatment so that it will help nurses in controlling infusion filling. The existence of monitor results related to human heart rate as a tool so that it can measure human heart rate. The method used is Rapid Application Development (RAD), this research produces a sensor tool to detect infusion and human heart rate.
Klasifikasi Naïve Bayes dan Confusion Matrix pada Pengguna Aplikasi E-Commerce di Play Store Mohamad Rizki Humaidi; Alif Maulani
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): 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.v8i2.132-139

Abstract

Shopee is an e-commerce application that is very popular among Indonesians. Shopee is an online shopping service center that is in great demand by Indonesians and offers many types of products. In addition, the shopee application has features, one of which is shopee food and shopee pay, which distinguishes the shopee application from other e-commerce applications. Although there are many Shopee application users, of course not all reviews and ratings given by users are good as a reference for improving the Shopee application features. In conducting research, a method is needed that can classify review data into positive and negative reviews. One that provides a review and rating feature is the Play Store application. In this study, using 1528 review data, Positive and negative class labels are the categories used.. The machine learning classification method used is Naïve Bayes classification. The outcomes of the method's classification accuracy testing are measured using a confusion matrix. So that the accuracy result using Naïve Bayes classification is 0.87 or 87%. Based on these results, using the Naïve Bayes classification gets high accuracy results in the process of classifying review data on Shopee application user research in the Play Store.
Analisis Perbandingan Prediksi Harapan Hidup Hepatitis Menggunakan Algoritma K-Nearest Neighbor dan C4.5 Karina; Herlina Hanum; Anita Desiani
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): 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.v8i2.98-111

Abstract

Hepatitis is an inflammatory disease of the liver caused by a virus that causes damage to the cells and function of the liver. This study compares the accuracy, precision, and recall results of the K-Nearest Neighbor (K-NN) and C4.5 algorithms using the Percentage Split and K-fold Cross Validation methods. Of the two algorithms, the best level of accuracy is obtained using the K-fold Cross Validation method. Based on the accuracy and error rate, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-NN algorithm. Based on the special Precision and Recall values ​​on the Recall value to predict class zero the best algorithm is obtained using the C4.5 algorithm. To assess Precision and Recall, the other best algorithm in predicting the fixed response variable is obtained by using the K-NN algorithm. Overall, the best algorithm for predicting life expectancy for hepatitis sufferers is the K-Nearest Neighbor (K-NN) algorithm.
Sistem Pendukung Keputusan Kawasan Kumuh Kota Semarang Menggunakan Metode Copras Dengan Data Vitalitas Non Ekonomi Sebagai Data Kriteria Saifur Rohman Cholil; Yudi Prayoga; Yudi Cahyono; Susanto Susanto
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): 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.v8i2.112-123

Abstract

Semarang City is the capital of Central Java Province which has a location in the middle of the province. Its strategic location is on the route across the North Coast of Java Island which connects major cities on Java Island. The high population in the city center requires the fulfillment of the need for livable housing, especially to accommodate urbanists whose jobs are concentrated in the trade and service sectors in the commercial area in the city center. The availability of complete facilities and infrastructure in the city center is also an attraction for people to live in the area. With the income level and economy of the community less so high, without realizing it, the need for livable settlements is difficult to accommodate. This study aims to build a decision support system (DSS) for the determination of Slum Areas in Semarang City using the COPRAS method. The COPRAS method has a good degree of selectivity because it can determine the purpose of conflicting criteria. System implementation is done using PHP language and MySQL database. The SPK that was built was able to produce recommendations by providing the ranking of slum areas to users.
Implementasi Metodologi Extreme Programming Pada Sistem Informasi Sekolah Dan Penerimaan Siswa Baru Berbasis Web Saffana Assani'; Hermanto Hermanto; Muhammad Daviq Romadlon; Rizkiyatul Hurriyah; Muhammad Rizqi Hildani; Ahmad Ryan Al Baihaqy
Jurnal Ilmiah Informatika Vol. 8 No. 2 (2023): 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.v8i2.89-97

Abstract

Madrasah Ibtidaiyah Miftahul Ulum III is a basic-level educational institution that requires a school information system and a new student admission system to be used in managing its school. In developing this system, a simple and fast system development methodology was chosen, namely extreme programming. The analysis used is functional and non-functional system analysis. For design, there are several types of design, namely database design using conceptual data models and physical data models, as well as system design using a unified modeling language. The research results at this stage are in the form of analysis documents and design documents that will be used for the implementation and testing stages
ANALISIS KINERJA METODE GLCM DAN LS-SVM DALAM KLASIFIKASI CITRA SAMPAH ORGANIK DAN ANORGANIK Michelyn Angela Sabatini Rajagukguk; Ahmad Fauzi; Bambang Wijonarko
Jurnal Ilmiah Informatika Vol. 10 No. 2 (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.v10i2.105-111

Abstract

Waste management, particularly in distinguishing organic and inorganic types, remains a major environmental challenge. Manual sorting processes are inefficient and prone to errors. This study aims to develop an automated waste image classification system using a combination of Gray Level Co-occurrence Matrix (GLCM) and Least Squares Support Vector Machine (LS-SVM). A total of 1,060 images were used, divided equally between organic and inorganic categories. Texture features such as contrast, correlation, energy, and homogeneity were extracted using GLCM and combined with mean RGB color features. The LS-SVM model with the Radial Basis Function (RBF) kernel achieved an accuracy of 87 percent, outperforming conventional SVM. The model’s effectiveness aligns with previous studies that used SVM-based waste classification and texture feature enhancement with GLCM descriptors. The model was implemented using a Flask web application for real-time predictions.
IMPLEMENTASI MODEL CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK DETEKSI KESEGARAN BUAH PISANG BERDASARKAN CITRA KULIT Alvina Putri Damayani; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (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.v10i2.73-79

Abstract

The assessment of banana freshness is currently still done manually through visual observation, touch, and smell. This method is subjective and prone to errors in perception between individuals, which can cause losses for farmers, traders, and costumers. Inaccuracies in assessing freshness levels can result in the distribution of substandard fruit, reduced market competitiveness, and waste of resources. To address these issues, this study designed and implemented a banana freshness classification system using a Convolutional Neural Network (CNN) algorithm. The system was develoved in the form of a Python and Flask-based website. Equipped with a Text-to-Speech (TTS) feature to improve accessibility for users with visual impairments. The research stages included problem identification, banana image data collection, image preprocessing (resize, normalization, augmentation), CNN architecture design, model training, implementation, and testing. The dataset consist of 1,664 images classified into two categories: fresh and not fresh. The implementation result show that the system can classify banana freshness in real-time through visual and audio displays. This system has the potentional to improve the efficiency and objectivity of classification, as well as support the digitization of the agricultural sector.
ANALISIS PERBANDINGAN KINERJA MODEL LONG SHORT-TERM MEMORY DAN RECURRENT NEURAL NETWORK DALAM PREDIKSI CUACA BERBASIS DATA CUACA REAL-TIME Abdurrahman; Suhirman Suhirman
Jurnal Ilmiah Informatika Vol. 10 No. 2 (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.v10i2.95-104

Abstract

Unpredictable weather changes pose a major challenge in various sectors, including agriculture, transportation, and construction. Inaccurate rainfall predictions, especially on a local scale, often hamper community activities and decision-making that depend on weather conditions. This study aims to compare the performance of two artificial neural network models, namely Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN), in predicting rainfall based on hourly weather data collected in real-time using an ESP32 microcontroller equipped with BME280 and BH1750 sensors. The variables used include air temperature, humidity, rainfall, and light intensity. Both models were trained to predict weather conditions for the next few hours based on observation data that had been processed and normalized numerically. The evaluation was using three main metrics, namely Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²). The results shows that the LSTM model performed better with an MAE of 0.684, MSE of 0.7343, and R² of 0.2421, while the RNN model obtained an MAE of 0.2187, MSE of 0.3422, and R² of 0.8213. These findings prove that LSTM is more stable, efficient, and accurate in capturing the temporal patterns of weather data. This system has the potential to become the basis for developing local weather forecasts based on real-time data that are more adaptive to environmental changes
KLASIFIKASI KESEGARAN IKAN TONGKOL BERDASARKAN CITRA MATA BERBASIS CONVOLUTIONAL NEURAL NETWORK (CNN) Fitria Ningsih; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (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.v10i2.88-94

Abstract

It Fish freshness is a crucial factor in ensuring food quality and safety. However, the conventional assessment process still relies on human observation, which is subjective supporting system, the risk of distributing non-fresh fish to consumers remains high, potentially affecting public health and consumer trust in fishery products. To address this issue, a fish freshness classification system based on eye image analysis using the Convolutional Neural Network (CNN) method was developed. The system development stages include collecting fihs eye image data, labeling, image preprocessing, CNN model training, and implementing the system in an convolution and pooling layers to extract visual features from the images. The initial testing results show that the system can classify fish freshness into two categories, Fresh and Not Fresh, with a high level of accuracy. This system is expected ti assist the public and fishery industry practitioners in evaluating fish quality more accurately ang efficiencly.
DETEKSI PENYAKIT CITRUS VEIN PHLOEM DEGENERATION (CVPD) PADA DAUN JERUK MENGGUNAKAN METODE SEGMENTASI K-MEANS DAN ARSITEKTUR EFFICIENNET Mawar Pratama sari; Agus Suhendar
Jurnal Ilmiah Informatika Vol. 10 No. 2 (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.v10i2.80-87

Abstract

Citrus vein phloem degeneration (CVD) is a devastating disease of citrus plants and seriously impacts crop quality. Although manual detection is feasible, this method faces many challenges, such as the similarity of early symptoms between healthy and infected leaves. Therefore, manual detection is time-consuming and inefficient. Therefore, an accurate and efficient automatic detection method is needed. This study aims to combine two methods: the K-Means segmentation method and the EfficientNet architecture to build an automatic detection model for CVD in citrus leaves. This method aims to improve the classification accuracy of citrus leaf images. This study is divided into two stages: the first stage uses the K-Means algorithm for image segmentation, and the second stage uses the EfficientNet model for classification. The K-Means segmentation method is used to separate the leaf surface from the background, focusing only on the parts of the leaf that show disease symptoms. The segmentation results are then processed in the second stage using the EfficientNet model. The EfficientNet model is known for its efficient feature extraction and excellent performance in recognizing complex visual patterns. The results showed that combining the K-Means segmentation method with the EfficientNet architecture significantly improved the accuracy of CVPD detection compared to a traditional CNN model without segmentation. This system is expected to assist farmers in detecting CVPD and support the implementation of smart agriculture technology in automated plant health monitoring.

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