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All Journal JUSIM (Jurnal Sistem Informasi Musirawas) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC)
Ahmad Sobri
Universitas Bina Insan, Lubuklinggau
Computer Science & IT Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering

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Implementasi Sistem Pakar Diagnosis Penyakit Pada Ibu Hamil Menggunakan Metode Naïve Bayes Ahmad Sobri; Satrianansyah Satrianansyah; Bagus Ahmad Noverendi
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3836

Abstract

Pregnant women need special attention to maintain their health and that of the fetus they are carrying. To support this process, an expert system has been developed that is capable of diagnosing diseases in pregnant women. The Naïve Bayes method is one of the approaches used in this expert system to classify diseases based on the symptoms experienced by pregnant women. The purpose of this research is to implement an expert system based on the Naïve Bayes method to support the diagnosis of diseases in pregnant women. The Naïve Bayes method was chosen because of its ability to deal with classification problems with incomplete or unbalanced data. After the Naïve Bayes model is a solution, testing and evaluation is carried out using test data. The accuracy of the expert system is measured by comparing the diagnosis given by the system with the actual diagnosis. The results showed that the expert system with the Naïve Bayes method was able to provide an accurate diagnosis for pregnant women. This proves the effectiveness of the Naïve Bayes method in supporting the diagnosis process in pregnant women.
Model Hybrid dalam Penentuan Stok Barang Bangunan Melalui Pendekatan Machine Learning Intan Bintang Adinda; Davit Irawan; Joni Karman; Ahmad Sobri
Journal of Computer System and Informatics (JoSYC) Vol 7 No 1 (2025): November 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v7i1.8065

Abstract

This study aims to develop a machine learning-based construction material stock prediction model using a hybrid approach that combines K-Means Clustering as a sales pattern grouping method and Support Vector Machine (SVM) as a classification method to predict material sales levels. This research was motivated by the problem of stock management at Toko Usaha Jaya in Lubuklinggau City, which is still done manually, thus potentially causing excess stock that increases storage costs and stock shortages that can lead to lost sales opportunities and decreased customer satisfaction. The data used includes material names, initial stock quantities, quantities sold, remaining stock, and selling prices collected during the period from January to December 2023. The results show that the hybrid model is capable of grouping materials into three categories, namely very popular, fairly popular, and less popular, with a Silhouette Score of 0.42, indicating fairly good clustering quality. Furthermore, the SVM model produced a classification accuracy rate of 99%, reflecting an increase in stock prediction accuracy compared to manual management methods. These findings indicate that the application of the K-Means and SVM hybrid model can improve inventory management efficiency and support more accurate and effective data-driven decision making.
IMPLEMENTASI TRANSFER LEARNING DALAM KLASIFIKASI KEMATANGAN PEPAYA MENGGUNAKAN METODE DESNET 1.2.1 Muhammad Syariffudin; Joni Karman; Ahmad sobri
JUSIM (Jurnal Sistem Informasi Musirawas) Vol. 11 No. 3 (2026): September
Publisher : LPPM UNIVERSITAS BINA INSAN

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32767/jusim.v11i3.3004

Abstract

Manual determination of papaya fruit ripeness is still widely practiced by farmers and traders, relying solely on visual observation. This approach is prone to errors caused by subjectivity and the limitations of human perception. This study aims to implement the Transfer Learning method using the DenseNet121 architecture to automatically classify papaya fruit ripeness levels based on digital images. The dataset used is divided into three ripeness classes: unripe, semi-ripe, and ripe. The research stages include dataset collection, image preprocessing using ImageDataGenerator, data division into training, validation, and testing sets, and model training using the Adam optimizer with a learning rate of 0.001, batch size of 32, and 50 epochs. The model was built using the Python programming language with the TensorFlow and Keras libraries. Furthermore, model performance was evaluated using a confusion matrix, classification report, and ROC curve. The results show that the DenseNet121 model was able to learn the visual characteristics of papaya ripeness levels very well. Based on the evaluation, the model achieved an accuracy of 97.96%, with high precision, recall, and F1-score values for each ripeness category. In addition, the ROC curve yielded an Area Under Curve (AUC) value close to 1.00, indicating excellent classification performance. These findings demonstrate that the application of Transfer Learning with the DenseNet121 architecture is effective in classifying papaya fruit ripeness levels. This method has the potential to be developed into a decision support system for farmers and agricultural businesses to improve the efficiency and accuracy of the fruit sorting process.
Co-Authors Bagus Ahmad Noverendi Davit Irawan Intan Bintang Adinda Joni Karman Muhammad Syariffudin Satrianansyah Satrianansyah