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All Journal Jurnal Teknologi dan Manajemen Informatika REKAYASA Jurnas Nasional Teknologi dan Sistem Informasi Edu Komputika Journal Sistemasi: Jurnal Sistem Informasi JIKO (Jurnal Informatika dan Komputer) INOVTEK Polbeng - Seri Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Compiler KACANEGARA Jurnal Pengabdian pada Masyarakat MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer INTEK: Informatika dan Teknologi Informasi Jurnal Linguistik Komputasional JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) International Journal of Engineering, Technology and Natural Sciences (IJETS) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JUKI : Jurnal Komputer dan Informatika International Journal of Business, Humanities, Education and Social Sciences (IJBHES) Indonesian Journal of Data and Science TIN: TERAPAN INFORMATIKA NUSANTARA Infotek : Jurnal Informatika dan Teknologi Teknika Science in Information Technology Letters Bulletin of Computer Science Research Journal of Social Research Journal Of World Science Jurnal Informatika Teknologi dan Sains (Jinteks) NERO (Networking Engineering Research Operation) SmartComp Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues
Adityo Permana Wibowo
Universitas Teknologi Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Improving Mental Health Diagnostics through Advanced Algorithmic Models: A Case Study of Bipolar and Depressive Disorders Wibowo, Adityo Permana; Taruk, Medi; Tarigan, ⁠⁠Thomas Edyson; Habibi, Muhammad
Indonesian Journal of Data and Science Vol. 5 No. 1 (2024): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v5i1.122

Abstract

This study explores the efficacy of a voting classifier integrating K-Nearest Neighbors (K-NN), Gaussian Naive Bayes (GNB), and Random Forest algorithms in diagnosing bipolar and depressive disorders. Utilizing a dataset of 120 psychology patients exhibiting 17 essential symptoms, the research employs a 5-fold cross-validation method to assess the model's diagnostic performance. Results indicate variability in accuracy (66.67% to 91.67%), precision (66.46% to 93.75%), recall (identical to accuracy), and F1-Scores (65.96% to 91.43%) across folds, demonstrating the model's robustness and potential to enhance psychiatric diagnostic processes. The findings suggest that the voting classifier significantly outperforms traditional diagnostic methods, offering a promising tool for more accurate and efficient mental health diagnostics. This research contributes to the burgeoning field of machine learning applications in mental health care, highlighting the potential of ensemble methods in addressing the complexities of psychiatric diagnosis. Given the limitations related to data diversity and model sensitivity, future research should focus on employing larger, more varied datasets and exploring the integration of additional algorithms to further refine diagnostic accuracy. This study lays the groundwork for advancing mental health diagnostics through innovative machine learning techniques.
Vision-based chicken meat freshness recognition system using RGB color moment features and support vector machine Sutarman, Sutarman; Avianto, Donny; Wibowo, Adityo Permana
Science in Information Technology Letters Vol 4, No 2 (2023): November 2023
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v4i2.1230

Abstract

Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.Chicken meat is a highly sought-after food product among various segments of the general population, known for its high nutritional value and easy accessibility. Presently, meat identification is primarily conducted manually, relying on visual inspection or tactile assessment of the meat's color and texture. However, this approach presents several limitations, particularly when consumers lack the discernment to differentiate the quality of chicken meat freshness. This research aims to identify the freshness level of chicken meat using the Support Vector Machine method, employing the extraction of RGB color moment features to determine the freshness of the meat. The feature extraction process involves calculating the percentage of intensity values for R (Red), G (Green), and B (Blue) in each chicken meat image. Based on the image processing results, the percentage of intensity values, particularly in the R and B parameters, can be used as determining factors. The study involves software testing using fresh and non-fresh chicken meat. The developed system can identify the freshness level of fresh chicken meat with an accuracy rate of 71.6% using the linear kernel SVM and 60.5% using the RBF kernel SVM.  This research represents a significant step toward the automation of chicken meat freshness assessment, potentially reducing food waste and enhancing food safety in the food industry. Further research and development could improve the system's accuracy and expand its applications in various food quality control settings.
Named Entity Recognition on Tourist Destinations Reviews in the Indonesian Language Hidayatullah, Ahmad Fathan; Putra, Muhammad Fakhri Despawida Aulia; Wibowo, Adityo Permana; Nastiti, Kartika Rizqi
Jurnal Linguistik Komputasional Vol 6 No 1 (2023): Vol. 6, NO. 1
Publisher : Indonesia Association of Computational Linguistics (INACL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jlk.v6i1.89

Abstract

To find information about tourist destinations, tourists usually search the reviews about the destinations they want to visit. However, many studies made it hard for them to see the desired information. Named Entity Recognition (NER) is one of the techniques to detect entities in a text. The objective of this research was to make a NER model using BiLSTM to detect and evaluate entities on tourism destination reviews. This research used 2010 reviews of several tourism destinations in Indonesia and chunked them into 116.564 tokens of words. Those tokens were labeled according to their categories: the name of the tourism destination, locations, and facilities. If the tokens could not be classified according to the existing categories, the tokens would be labeled as O (outside). The model has been tested and gives 94,3% as the maximum average of F1-Score.
Performance Analysis of the Decision Tree Classification Algorithm on the Pneumonia Dataset Ahmad Naswin; Adityo Permana Wibowo
International Journal of Artificial Intelligence in Medical Issues Vol. 1 No. 1 (2023): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v1i1.83

Abstract

The rapid advancements in machine learning have paved the way for innovative approaches in medical imaging diagnostics. In this context, this study explored the efficacy of the Decision Tree Classification Algorithm for distinguishing between normal and pneumonia-diagnosed X-ray images. We sourced our dataset from pediatric X-rays obtained from the Guangzhou Women and Children’s Medical Center. To enhance the classifier's performance, a methodical pre-processing strategy was adopted. This encompassed the application of the Canny segmentation technique, followed by feature extraction using humoments. The evaluation phase involved a 5-fold cross-validation, revealing a commendable average accuracy of 82.72%. These findings highlight not only the utility of Decision Trees in such specialized diagnostic tasks but also accentuate the pivotal role of systematic pre-processing in achieving optimal results. As medical diagnostics steadily move towards automation, this research provides valuable insights and benchmarks for future endeavors aiming to harness the power of machine learning in healthcare.
IMPLEMENTASI AUGMENTED REALITY PADA APLIKASI PEMBELAJARAN PENGENALAN PLANET TATA SURYA Raihan Anmar Widyasmoro; Adityo Permana Wibowo
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1051

Abstract

The lack of knowledge about solar system information for elementary school students is still a problem that cannot be overcome until now. Developing technology can make a very influential contribution to these problems, therefore this research proposes the implementation of Augmented reality (AR) technology in the context of solar system learning applications. This application is designed with features that can provide interactive and visual information in real-time. Development is carried out using the SDLC method which is a structured method for software development, in the development of the testing phase using the beta testing method with the type of Blackbox testing, with this application students can do more interesting and effective learning so that students' understanding of solar system material is increasing and students are more eager to find out the next materials because of the very interesting presentation. The positive impact when the application is implemented to students is very significant and the learning process does not feel boring for students when the learning process takes place, after being evaluated with the quiz feature student understanding increases. With the results of the tests carried out stating that in the learning system there are no features that fail when run in the sense that the system is feasible to use.
PEMBENTUKAN POHON KEPUTUSAN UNTUK PENERIMA BANTUAN BERAS MISKIN MENGGUNAKAN ALGORITMA DECISION TREE C4.5 Avianto, Donny; Wibowo, Adityo Permana
NERO (Networking Engineering Research Operation) Vol 9, No 1 (2024): Nero - 2024
Publisher : Jurusan Teknik Informatika Fakultas Teknik Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/nero.v9i1.28020

Abstract

Beras Miskin (Raskin) merupakan salah satu program pemerintah yang bertujuan untuk memberikan bantuan pangan pokok kepada masyarakat kurang mampu. Namun, tantangan besar dalam implementasi program ini adalah ketidaktepatan sasaran, di mana terdapat kasus di mana warga yang seharusnya menerima bantuan malah tidak mendapatkannya, sementara sebagian yang tidak memenuhi syarat justru menerima bantuan. Penelitian ini bertujuan menghasilkan model pohon keputusan yang dapat membantu proses klasifikasi penerima bantuan beras miskin secara lebih mudah dan akurat, sehingga penyaluran program Raskin menjadi lebih tepat sasaran. Pembuatan model dilakukan menggunakan aplikasi RapidMiner Studio versi 10.3 dengan menerapkan algoritma pembentuk Decision Tree C4.5. Dalam menentukan kelayakan penerima, aplikasi menggunakan tujuh kriteria utama: tingkat kesejahteraan, jumlah tanggungan, jenis pekerjaan, sarana sanitasi, sumber air, jenis atap, dan jenis lantai. Algoritma C4.5 pada penelitian ini dilatih menggunakan 100 data pelatihan dan diuji dengan 20 data uji, menghasilkan akurasi sebesar 79,17% dengan faktor yang paling menentukan dalam prediksi adalah jenis lantai. Penelitian ini juga memvisualisasikan pohon keputusan yang terbentuk secara utuh untuk memudahkan interpretasi hasil prediksi dan peluang peningkatan di masa depan.
Implementation of the Naive Bayes Classifier Algorithm for Classifying Toddler Nutritional Status Kamil, Muhammad Insan; Wibowo, Adityo Permana
Journal of Applied Informatics and Computing Vol. 8 No. 2 (2024): December 2024
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v8i2.8669

Abstract

This research addresses the pressing issue of malnutrition among toddlers in Indonesia, aiming to classify their nutritional status using the Naive Bayes Classifier (NBC). The study utilizes a dataset comprising 958 records from Puskesmas Cilandak and categorizes nutritional status into six class labels: good nutrition, at risk of excess nutrition, excess nutrition, obesity, undernutrition, and severe malnutrition. The methodology includes data preprocessing techniques such as class weighting to tackle class imbalance and Principal Component Analysis (PCA) for effective feature extraction. The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1 score, achieving an impressive accuracy of 85.76% when class weighting is applied, which significantly enhances the recall and F1 scores for minority classes. The findings highlight the critical importance of robust preprocessing and evaluation metrics in improving machine learning models for public health applications. Furthermore, they suggest that further exploration of alternative algorithms and dataset expansion could yield more comprehensive insights into the classification of toddler nutritional status.
Penerapan Metode Waterfall pada Sistem E-Order Makanan dan Minuman Berbasis Web dan Mobile Nugroho, Bagus Candra; Wibowo, Adityo Permana
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6052

Abstract

Warung Sate Ojolali is a restaurant that sells various food menus, especially satay, located in Kebasen District, Banyumas Regency, Central Java. In the era of increasingly fierce culinary business competition, many restaurants and cafes have sprung up which requires companies to implement effective strategies to attract more visitors. In this study, a problem was found in the management of a culinary business where a business that has many transactions with customers, but these activities are hampered because the details of the food and beverage menu offered by the restaurant are less informative such as offers that are less detailed regarding the available menus and the delivery of menu books as ordering media that are less complete or updated. Therefore, researchers designed the development of a Web and Mobile Based Food and Beverage Ordering System. This system is designed using the Waterfall method which aims to simplify the ordering process for customers. This Android-based ordering application allows customers to order the desired menu in detail through an intuitive interface. Integration with the internet network makes the ordering experience more efficient, without the need to come directly to the location. The results of the research obtained while in the form of an online food and beverage ordering system that can run according to its function where the admin can add, edit and can delete the menu data provided. In addition, the process of recording orders that were previously done manually can be done automatically by the system so that an admin only needs to recap order data to carry out the transaction process. The implementation of this system not only improves operational efficiency, but also simplifies management, expands market reach, and improves user experience. Thus, this innovation is expected to support business growth and significantly increase customer satisfaction.
Klasifikasi Jenis Madu Akasia dan Madu Hutan Berdasarkan Warna RGB Menggunakan Metode Multilayer Perceptron Halim, Ridwan; Wibowo, Adityo Permana
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6366

Abstract

The advancement of technology has driven the agricultural industry to become more advanced and modern. Distinguishing honey with nearly identical colors is a challenging task. However, The ability to differentiate the color of acacia and forest honey is the simplest approach to ensuring the authenticity and quality of honey products. This study aims to develop a honey color classification model using Multilayer Perceptron (MLP). Image data were collected from various angles under natural lighting, followed by ninety experiments using parameter combinations, including data imbalance handling methods, dense layer structures, and training settings. The results showed that the MLP model with an optimal configuration, utilizing the Adaptive Synthetic Sampling (ADASYN) method for data imbalance, achieved a validation accuracy of 90.63%. This accuracy highlights the potential of the model to support industrial automation processes in reliably distinguishing honey colors.
Implementasi Model Waterfall dalam Aplikasi Manajemen Keuangan Berbasis Android Fernanda, Niko; Wibowo, Adityo Permana
Journal of Information System Research (JOSH) Vol 6 No 2 (2025): January 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i2.6370

Abstract

One of the main components in maintaining the stability and financial well-being of people and families is effective financial management. The myth that expenses will always increase along with income is one of the common misconceptions in society. This study aims to address this by creating financial management software for Android that facilitates effective and efficient financial management for users. The research methodology uses the waterfall model, which means that every step from needs analysis to implementation is completed systematically. The development process of this application utilizes Android Studio with the Kotlin programming language which is known to be efficient, while MySQL is used as a database for secure financial information management. The application system is designed using the Unified Modeling Language (UML) to define workflows and processes in a structured manner. The results of the application test use the blackbox testing method to test this application to ensure that all features such as recording financial information, income and expense transactions, and creating financial reports function as they should. In addition, this application also provides additional features in the form of investments to help users monitor their investment assets. From the tests carried out, all application features showed a success rate of 100%, indicating that the application functions according to the designed specifications. This application allows users to optimize financial management, so they can improve their standard of living in a more planned and systematic way.
Co-Authors AA Sudharmawan, AA Achmad Safruddin Ahmad Fathan Hidayatullah, Ahmad Fathan Ahmad Naswin Akmal Setiawan Wijaya Albertus Carloson Fallo Allam, Sayyidan Fahd Arief Hermawan Arif Amrulloh Barus, M Akmal Shievo Bella Primin Donny Avianto Edi Faizal Fernanda, Niko Haikal Fawwaz Karim Hamzah Fachrurozi Ifannur, Muhammad Taufik Imantoko Imantoko Jullev Atmadji, Ery Setiyawan Kuncorojati, Fariz Wahyu Kustrianugraha, Afri Fajar Kusumastuti, Asriana Dyah Luh Putu Ratna Sundari M. Raihan Alghifari Muhammad Fachrie Muhammad Habibi Muhammad Insan Kamil, Muhammad Insan Nanda, Dwita Dhara Nararya Budiyono Nastiti, Kartika Rizqi Nugroho, Bagus Candra Nur Hidayati Pangesty, Shandika Sayyid Ammar Pratama, Khrisna Yudha Primin, Bella Putra, Elvanda Adi Putra, Muhammad Fakhri Despawida Aulia Qowiim, Asyhar Raihan Anmar Widyasmoro Rianto Ridwan Halim Setyadi, Dian Febry Sri Hartati Sri Wulandari Suryo Sudiro Sutarman Sutarman Tamam Huda, Muhammad Tarigan, ⁠⁠Thomas Edyson Taruk, Medi Tri Waluyo Wiwik Handayani