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All Journal Jurnal Ilmiah Informatika Komputer Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Mantik Jurnal Sistem informasi dan informatika (SIMIKA) Buana Information Technology and Computer Sciences (BIT and CS) Journal of Intelligent Decision Support System (IDSS) Sustainable: Jurnal Kajian Mutu Pendidikan Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Mandiri IT Brilliance: Research of Artificial Intelligence Jurnal Minfo Polgan (JMP) Journal of Practical Computer Science (JPCS) Biner : Jurnal Ilmiah Informatika dan Komputer
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Journal : Journal of Intelligent Decision Support System (IDSS)

 1 
Comparison of three fuzzy logic algorithm methods for cellular selection Gunawan Gunawan; Wresti Andriani; Sawaviyya Anandianskha
Journal of Intelligent Decision Support System (IDSS) Vol 6 No 3 (2023): September : Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v6i3.154

Abstract

Many cell phone types are on the market today, increasingly making users feel confused and confused about choosing the cell that suits their needs. As one of the most essential needs at this time, users must be able to match their cellular needs with their income. Many smartphone products are offered. To help users in this study using three methods from the Fuzzy Logic algorithm for Decision Support Systems in choosing cellular according to their needs and desires; from the research that has been done, it is found that using the Fuzzy Tsukamoto method the accuracy is better than Mamdani which is equal to 0.02135, Mamdani is as large as 0.0643, while Sugeno is 0.1007. The cellular chosen is the Samsung A73 brand.
Application of computer vision techniques to detect diseases and pests of chili plants Nurokhman, Akhmad; Surorejo, Sarif; Kurniawan, Rifki Dwi; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.201

Abstract

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.
Application of computer vision for face recognition using viola jones algorithm method Riyadi, Fajar Sugeng; Gunawan, Gunawan; Arif, Zaenul
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.204

Abstract

This research aims to develop a facial recognition system using computer vision technology by applying the Viola-Jones algorithm method. The main focus of this research is to improve accuracy and efficiency in face identification under various lighting conditions and face orientations. The Viola-Jones algorithm, known for its real-time object detection, was chosen for its efficiency in quickly identifying critical facial features. Through testing of various face datasets, the results showed that the system developed was able to recognize faces with a high level of accuracy, even in conditions of non-optimal lighting and various facial poses. The novelty of this research lies in the optimization of the parameters of the Viola-Jones algorithm to improve facial recognition performance, as well as its application in challenging dynamic environments. These findings make a significant contribution to the field of computer vision and facial recognition, offering more effective and efficient solutions for security and surveillance applications, as well as interactive applications that require fast and accurate facial identification.
Classification of fresh chicken meat and tainted chicken meat using naive bayes classifier algorithm Zain, Ahmad Muzakky; Ali Murtopo, Aang; Fadila, Nurul; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.212

Abstract

This research discusses the classification of fresh and tainted chicken meat using the Naive Bayes Classifier (NBC) algorithm based on Gray Level Co-occurrence Matrix (GLCM) feature extraction, with the aim of developing an efficient and accurate classification method. This research aims to utilize image processing and machine learning technologies to distinguish fresh chicken meat from tainted ones, which is crucial for the food industry. The research methodology involved the use of GLCM for texture feature extraction from chicken meat images, with the implementation of the NBC model through RapidMiner, offering an intuitive and efficient approach. The results showed the success of the model in achieving 80% accuracy, with an average precision of 81.25%, recall of 80%, and F1-score of 80.62%, confirming its ability in chicken meat classification. The integration of GLCM and RapidMiner in the application of NBC not only improves accuracy and objectivity in chicken meat classification but also provides a foundation for the wider application of machine learning techniques in ensuring food safety and consumer satisfaction
Application of nearest neighbor interpolation method and naïve bayes classifier for identification of bespectacled faces Setiawan, Dodi; Gunawan, Gunawan; Zaenul Arif
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.213

Abstract

Facial identification has become necessary in the era of advanced technology, especially in security and human-computer interaction. However, accessories such as glasses often complicate the identification process. This research aims to develop a facial identification system that can recognize bespectacled individuals with high accuracy, overcoming the limitations of conventional facial recognition technology. The method combines nearest neighbor interpolation to improve image quality and Naïve Bayes classification to distinguish between bespectacled and non-spectacled faces. The results showed that the developed model effectively identified bespectacled subjects with a high recall rate, although accuracy and precision still needed improvement. The implications of this research are significant for the field of biometric security and facial recognition, offering new solutions for more inclusive and adaptive facial recognition systems and opening up opportunities for further research in method optimization and dataset quality improvement.
Application of the viola-jones algorithm method to recognize faces of Stmik Tegal students Azmi, Muchamad Nauval; Nugroho, Bangkit Indarmawan; Septiana, Pingky; Gunawan, Gunawan
Journal of Intelligent Decision Support System (IDSS) Vol 7 No 1 (2024): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v7i1.214

Abstract

This study examines the application of the modified Viola-Jones algorithm for student facial recognition at STMIK YMI Tegal, aiming to improve the efficiency and safety of the student attendance system. By adapting the algorithm to address the challenge of facial recognition accuracy from different angles and lighting conditions, a quasi-experimental quantitative design involved collecting data through photographic sessions with student subjects, followed by preprocessing to improve the quality of the analysis. The modification was evaluated for its ability to handle variations in facial and lighting conditions, showing significant improvements with 60% accuracy and precision, recall, and an F1-score of 71.43%. These findings demonstrate the effectiveness of the modification in improving facial recognition, potentially contributing significantly to attendance management and safety practices in educational settings. This research not only strengthens the existing literature.
Co-Authors Aang Alim Murtopo Ahmad Zulfikri Aimar Akbar, Aminnur Aisyach Aminarti Santoso Alan Eka Prayoga Ali Murtopo, Aang Anandianshka, Sawaviyya Anandianskha, Sawaviyya Andriani, Wresti Arif, Zaenul Azmi, Muchamad Nauval Bangkit Indarmawan Nugroho Catur Supriyanto Dodi Setiawan Dodi Setiawan Dwi Fina Fahirah Fadila, Nurul Firmansyah, Akhmad Lutfi Hafid Subechi, Fadlan Handayani, Sri Intan Mayla Faiza Intan Mayla Faiza Khadziqul Humam Munfi Kurniawan, Rifki Dwi Miftakhudin, Muhammad Moonap, Dinar Auranisa Muchamad Nauval Azmi Muh Ridwan Nugroho Adhi Santoso Nur Aisyah Nurokhman, Akhmad Nurul Fadhilah Purwanto Purwanto Rifki Dwi Kurniawan Riyadi, Fajar Sugeng Santoso, Bayu Aji Santoso, Nugroho Adhi Sarif Surorejo Sawaviyya Anandianskha Sawaviyya Anandianskha Sawavyya Anandianskha Septian Ari Wibowo Septiana, Pingky Sya’bani, Adhita Zulfa Syefudin, Syefudin Wilda Shabrina Wresti Andriani Wresti Andriani Wresti Andriani Yan Kurniawan Zaenul Arif Zain, Ahmad Muzakky