Articles
<![CDATA[Application of computer vision techniques to detect diseases and pests of chili plants ]]>
<![CDATA[Nurokhman, Akhmad]]>;
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Kurniawan, Rifki Dwi]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/idss.v7i1.201
<![CDATA[This research aims to develop a disease and pest detection system in chili plants using computer vision techniques. In this study, deep learning methods, especially Convolutional Neural Networks (CNN), were applied to identify and classify various types of diseases and pests that often attack chili plants. The data used included images of chili leaves infected with various diseases and pests, which were then trained in CNN models to recognize certain patterns that indicate the presence of infection. The results showed that the developed system was able to detect and classify diseases and pests in chili plants with a very high degree of accuracy. The novelty of this research lies in the use of computer vision techniques combined with sophisticated deep learning algorithms to automatically detect diseases and pests, which were previously done manually by farmers or agricultural experts. These findings make an important contribution to improving efficiency and effectiveness in chili crop health management, offering innovative solutions to support agricultural sustainability through the use of advanced technology.]]>
<![CDATA[Application of fuzzy expert system method for early detection of dengue fever ]]>
<![CDATA[Prayoga, Alan Eka]]>;
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Kurniawan, Rifki Dwi]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/idss.v7i1.217
<![CDATA[The application of the Fuzzy Expert System method in the early detection of dengue fever offers a promising approach to improve diagnostic accuracy. This study aims to develop a system that can overcome the diversity of dengue fever symptoms and uncertainty in the diagnosis process. Using medical record data of patients who have confirmed DHF, the study designed fuzzy rules for symptom evaluation, resulting in more precise diagnostic outputs. The results indicate the system's success in identifying dengue cases with high sensitivity and good positive predictive value. These findings confirm the importance of FES technology in clinical practice, especially for controlling and preventing dengue fever in endemic areas. Continued research will test this system in a broader clinical scenario to ensure its effectiveness and practicality in diverse medical environments.]]>
<![CDATA[Implementation of fuzzy mamdani method in predicting cayenne chili prices in Tegal Regency ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Mutaqin, Ahadan Fauzan]]>;
<![CDATA[Kurniawan, Rifki Dwi]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/idss.v7i2.231
<![CDATA[This study investigates the application of Fuzzy Mamdani's method in predicting the price of cayenne pepper in Tegal Regency, one of the important agricultural commodities that has significant economic implications. This study aims to develop an accurate and reliable cayenne pepper price prediction model in Tegal Regency using the fuzzy Mamdani method. Research methods include collecting historical data on cayenne pepper prices, cayenne pepper production, and rainfall, as well as the implementation of the Mamdani fuzzy method consisting of fuzzification, inference, and defuzzification using Python programming language computing. The results showed that the fuzzy Mamdani method can predict the price of cayenne pepper with a good level of accuracy, with an average prediction error of 16.653285% and a prediction correctness rate of 83.346715%. This finding has implications for improving production planning capabilities and marketing strategies for cayenne pepper farmers in Tegal District, as well as contributing to the scientific literature in the application of fuzzy methods in agriculture]]>
<![CDATA[Application of artificial neural network method for early detection of dengue fever ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Ningrum, Isna Lidia]]>;
<![CDATA[Ananda, Pingky Septiana]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/idss.v7i2.240
<![CDATA[Dengue fever is a tropical disease whose diagnosis is often delayed due to limitations of conventional diagnostic methodologies, which have an impact on the effectiveness of medical interventions. This research is designed to develop an Artificial Neural Network (ANN) model aimed at improving accuracy and speed in dengue diagnosis. Through quantitative methods, clinical data from 50 patients during the period 2020-2021 were analyzed using machine learning techniques to train the ANN model, including the process of data normalization and selection of relevant features. The test results of the model showed excellent diagnostic performance with accuracy reaching 87%, precision 92%, and F1-Score 92%, indicating its effective ability to identify positive and negative cases. The conclusion of this study is that the developed ANN model is able to overcome the limitations of conventional diagnostics and shows significant potential in improving medical responses to dengue outbreaks. Further research is recommended to expand the datasets used in order to improve the validation and generalization of the model in the context of broader clinical applications]]>
<![CDATA[Detection of normal chicken meat and tiren chicken using naïve bayes classifier and glcm feature extraction ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Ubaidillah, Muhamad Rizal]]>;
<![CDATA[Syefudin, Syefudin]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Journal of Intelligent Decision Support System (IDSS) Vol 7 No 2 (2024): June: Intelligent Decision Support System (IDSS) ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/idss.v7i2.245
<![CDATA[The chicken farming industry is an important sector in the Indonesian economy, but there are food security issues with the presence of tiren chicken. This research aims to develop a more accurate and efficient method of detection of tiren chickens using Naive Bayes Classifier with Gaussian and Bernoulli kernels and GLCM feature extraction. Data is collected from various internet sources, then pre-processing and feature extraction is carried out. The Naive Bayes Classifier algorithm is implemented with two kernels and evaluated using accuracy, precision, recall, and f1-score metrics. The Gaussian kernel showed an accuracy of 0.75, higher than Bernoulli's kernel which was only 0.50. Models with Gaussian kernels have high performance in detecting tiren chickens and normal chicken precision. The combination of Gaussian and Bernoulli kernels and GLCM feature extraction is effective in improving the detection accuracy of tiren chickens, contributing significantly to food safety and consumer confidence]]>
<![CDATA[Penerapan Metode Dobel Exponential dan Smoothing Analytical Hierarchy Process untuk Prediksi Tingkat Kerawanan Tanah Longsor Di Kabupaten Brebes ]]>
<![CDATA[Putra, Alif Sya’Bani]]>;
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Andriani, Wresti]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Innovative: Journal Of Social Science Research Vol. 4 No. 3 (2024): Innovative: Journal Of Social Science Research ]]>
Publisher : <![CDATA[Universitas Pahlawan Tuanku Tambusai ]]>
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DOI: 10.31004/innovative.v4i3.10505
<![CDATA[Pengembangan metode prediksi tingkat kerawanan tanah longsor di Kabupaten Brebes menggunakan kombinasi double exponential smoothing dan analytical hierarchy process (AHP). Tujuan penelitian ini adalah meningkatkan pemahaman dan prediksi terhadap fenomena tanah longsor dan memanfaatkan data historis dan analisis kriteria multi-faktor. Metodologi penelitian ini melibatkan analisis seri waktu menggunakan double exponential smoothing untuk memprediksi variabel-variabel penting seperti curah hujan, dan pergerakan tanah. Sementara AHP digunakan untuk menilai dan mengintegrasikan berbagai faktor risiko tanah longsor, termasuk kondisi geologi, kemiringan lereng, dan penggunaan lahan. Hasil penelitian ini adalah model yang diusulkan mampu memprediksi tingkat kerawanan tanah longsor dengan akurasi yang lebih tinggi dibandingkan metode yang ada. Penelitian ini memberikan kontribusi penting dalam upaya mitigasi bencana tanah longsor di Kabupaten Brebes, serta membuka peluang untuk aplikasi metode serupa di wilayah lain yang memiliki risiko tanah longsor.]]>
<![CDATA[Application of association rule for prediction of menu ordered at café minapadi ]]>
<![CDATA[Zain Hidayatullah, Fikri]]>;
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Andriani, Wresty]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Jurnal Mandiri IT Vol. 12 No. 4 (2024): April: Computer Science and Field. ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/mandiri.v12i4.279
<![CDATA[This research aims to develop a predictive model that helps prepare menus based on customer preferences at Café Minapadi, hoping to improve operational efficiency and customer satisfaction. Using rule-association data mining techniques, the study uncovered hidden patterns in extensive transaction data, applying a priori algorithms in datasets to explore menu ordering frequencies and trends. Data analysis includes cleansing, transforming, and selecting features to generate relevant insights. The results found that items such as coffee and chocolate cake were often purchased together, providing an opportunity for menu optimization and special promotions. Evaluation of predictive models shows the possibility of increased accuracy in stock preparation and adjustment of menu offerings, providing significant benefits in business decision-making in the culinary sector.]]>
<![CDATA[Prediction of Bank Central Asia stock prices after dividend distribution using ARIMA method ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Sulthon, Muhammad]]>;
<![CDATA[Anandianskha, Sawaviyya]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/mandiri.v13i1.294
<![CDATA[This study explores the prediction of Bank Central Asia (BBCA) stock prices following the annual dividend distribution using the Autoregressive Integrated Moving Average (ARIMA) method. The primary goal is to provide a robust forecasting tool to aid investors and financial analysts in making informed decisions. The research employs a quantitative approach with a quasi-experimental design, analyzing weekly BBCA stock price data from January 2019 to February 2024. The findings demonstrate that the ARIMA (2, 1, 2) model provides stable and reliable predictions of BBCA stock prices, showing slight weekly variations but overall stability. Practically, these predictive models can be integrated into a web-based platform, allowing real-time stock price forecasting and broader accessibility for users. This study contributes to the financial literature by validating the ARIMA model's applicability in the Indonesian stock market and suggesting the exploration of hybrid models and external economic factors for future research.]]>
<![CDATA[Application of weighted aggregated sum product assessment method in determining the best flour to produce vermicelli ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Rivaldiansyah, Rafik]]>;
<![CDATA[Dwi Kurniawan, Rifki]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/mandiri.v13i1.296
<![CDATA[This study explores the application of the Weighted Aggregated Sum Product Assessment (WASPAS) method's selection of the best wheat flour for vermicelli production, which aims to improve product quality and production efficiency. The study aimed to integrate experimental data with sophisticated decision-making models to identify the most suitable type of flour based on a comprehensive set of criteria. Using a quantitative approach, this study combines experimental methods, quantitative analysis, and model validation, using the WASPAS method to evaluate and rank various flours. The results showed significant differences among flour types, with selected flours showing superior performance across multiple parameters, including chemical composition and functional properties. The study's findings underscore the potential of advanced decision-making tools such as WASPAS in improving food production processes, demonstrating broader applicability across the food industry to optimise raw material selection.]]>
<![CDATA[Comparison of dijkstra and genetic algorithms for shortest path guci ]]>
<![CDATA[Surorejo, Sarif]]>;
<![CDATA[Al Fattah, Muhammad Raikhan]]>;
<![CDATA[Andriani, Wresti]]>;
<![CDATA[Gunawan, Gunawan]]>
<![CDATA[ Jurnal Mandiri IT Vol. 13 No. 1 (2024): July: Computer Science and Field ]]>
Publisher : <![CDATA[Institute of Computer Science (IOCS) ]]>
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DOI: 10.35335/mandiri.v13i1.298
<![CDATA[This study aims to compare the performance of the Dijkstra algorithm and the Genetics algorithm in determining the shortest path to the Guci tourist destination. The research design combines experimental methods, quantitative analysis, and model validation. The data used is the distance between points on two alternative routes to Guci. Data pre-processing is done to ensure quality and consistency. The relevant variables are selected, and model optimization is performed to obtain the best parameter configuration for both algorithms. Dijkstra and Genetics algorithms are implemented using Python, taking into account computational efficiency and ease of integration. Model evaluation is done through a series of tests with time execution and convergence metrics. The results showed that Dijkstra's algorithm was superior in finding the shortest path with a distance of 43.0 km and an execution time of 0.0017 seconds, compared to the Genetics algorithm which found a path with a distance of 44.7 km and an execution time of 0.0048 seconds. It can be concluded that Dijkstra's algorithm is more effective and efficient in this case, but Genetics algorithms have the potential for more complex optimization problems.]]>
