Article Per Year (5 Year)
p-Index From 2020 - 2025
3.058
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Transmisi: Jurnal Ilmiah Teknik Elektro FIKI 2013 Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Pseudocode International Journal of Advances in Intelligent Informatics Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) JUSTIN (Jurnal Sistem dan Teknologi Informasi) Moneter : Jurnal Keuangan dan Perbankan Community: Jurnal Pengabdian Pada Masyarakat
Ika Novita Dewi
Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
Fuzzy tahani Implementation for Food Nutritional Status in Achieving Balanced Nutritional Dietary Dewi, Ika Novita; Priyo Utomo, Rino Agung; Sani, Ramadhan Rakhmat
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3644

Abstract

Fulfilling nutritional needs with the Recommended Dietary Allowances (RDA) shows the average value of the number of vitamins, protein and other nutrients the body needs to function properly. Each person's nutritional needs are different, so the RDA can be calculated based on different age groups and gender. In general, there are still many people who do not know the nutritional value of food and consume food without considering whether it meets the body's needs. This usually happens because calculating the RDA value is not yet familiar to do. Efforts are needed to increase awareness about the importance of a balanced diet through RDA calculations. Calculation and determination of nutritional status using the RDA number serves as a measuring tool to monitor whether a person's nutritional intake is in accordance with daily needs. This research developed a web-based application to calculate RDA numbers and group RDA numbers into nutritional status of less, enough, or more. Determination of nutritional status is carried out using the fuzzy tahani method, by displaying the results in percentage form, so that users can easily see the proportion or percentage of nutritional status obtained. This application not only calculates nutritional values, but provides a food record feature to help users manage healthy eating patterns.
Enhancing Interpretable Multiclass Lung Cancer Severity Classification using TabNet Norman, Maria Bernadette Chayeenee; Dewi, Ika Novita; Salam, Abu; Utomo, Danang Wahyu; Rakasiwi, Sindhu
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11417

Abstract

Lung cancer poses a significant global mortality challenge, with early clinical detection hindered by non-specific symptoms making accurate diagnosis dependent on extracting subtle patterns from often complex medical tabular data. Traditional machine learning approaches often fall short in capturing intricate patterns within such heterogeneous datasets, hindering effective clinical decision support. This research introduces TabNet, an interpretable deep learning architecture, for multiclass lung cancer severity prediction (low, medium, high). Utilizing the Kaggle Lung Cancer dataset, our methodology leverages TabNet's unique attention-based feature selection for end-to-end processing of tabular data, enabling adaptive identification of key predictors and crucial model interpretability. To effectively assess its predictive capabilities and ensure robust performance, the model was trained with default configurations and validated through stratified 5-fold cross-validation, achieving outstanding performance on the test set: 98.50% accuracy, a 0.98 F1-score, and a 0.9996 macro-AUC-ROC. Beyond its robustness, confirmed by stable learning curves, interpretability analysis highlighted 'Genetic Risk' and 'Shortness of Breath' as dominant factors. Our results underscore TabNet's efficacy as a reliable, robust, and inherently interpretable solution, offering significant potential to improve the precision and transparency of lung cancer severity assessment in clinical practice.
Optimasi Algoritma Decision Tree Menggunakan GridSearchCV untuk Klasifikasi Tipe Obesitas Laurent, Feby; Winarno, Sri; Dewi, Ika Novita
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8638

Abstract

The rise in obesity cases in various countries, including Indonesia, has become a serious public health problem because it increases the risk of chronic diseases and affects individuals' psychological aspects. One of the main challenges in obesity management is the differences in obesity types in each individual, which are influenced by various factors. Therefore, accurate classification methods are needed to ensure more targeted treatment. In this context, machine learning-based technology is a potential solution for classifying obesity types. However, variations in individual characteristics make the classification process complex, as models often struggle to accurately distinguish obesity types. To overcome this problem, the Decision Tree algorithm was chosen because of its easy-to-interpret results. However, using Decision Tree with default parameters on datasets with many attributes and high variation tends to cause overfitting and decrease accuracy. Furthermore, Decision Tree performance is highly dependent on hyperparameter settings, requiring optimization techniques to achieve optimal results. Based on this, this study aims to optimize the Decision Tree algorithm using GridSearchCV to obtain the most optimal parameters to improve model performance in obesity type classification. The dataset used is from the UCI Machine Learning Repository, consisting of 2,111 rows of data and 17 attributes. Based on the initial test results, the default model achieved 92.58% accuracy, 92.58% recall, 92.66% precision, and 92.56% F1-score. After optimization, the accuracy increased to 95.69%, 95.69% recall, 95.72% precision, and 95.67% F1-score. The 3.1% increase in accuracy demonstrates the effectiveness of GridSearchCV in improving Decision Tree performance, resulting in a more accurate and stable prediction model. This research is expected to contribute as a basis for decision-making in early detection and prevention and treatment of obesity more efficiently and effectively.
Co-Authors Abas Setiawan Abdul Syukur Abdul Syukur Abu Salam Adhitya Nugraha Adriani, Mira Riezky Agung Priyo Utomo, Rino Alzami, Farrikh Ardytha Luthfiarta Arifin, Muhammad Farhan Arry Maulana Syarif, Arry Maulana Asih Rohmani, Asih Atha Rohmatullah, Fawwaz Ayuningsih, Dewi Putri Azhari Azhari Bramantyo, Satrio Bisma Candra Irawan Catur Supriyanto Diana Aqmala Dwi Puji Prabowo, Dwi Puji Dzaki, Azmi Abiyyu Egia Rosi Subhiyakto, Egia Rosi Erwin Yudi Hidayat Erwin Yudi Hidayat Fahri Firdausillah Fajar Agung Nugroho Hafiizhudin, Lutfi Azis Handayani, Sri Haresta, Alif Agsakli Hasan Asari Heribertus Himawan Ifan Rizqa Indrayani, Heni Irawan, Enrico Irvan Muzakkir Irvan Muzakkir Isworo, Slamet Junta Zeniarja Khafiizh Hastuti Khariroh, Shofiyatul Kurniawan, Defri Laurent, Feby Lisa Mardiana Marjuni, Aris Megantara, Rama Aria Muljono Muljono Mumtaz, Najma Amira Norman, Maria Bernadette Chayeenee Octaviani, Dhita Aulia Priyo Utomo, Rino Agung Puri Sulistiyawati Pusung, Elvanro Marthen Ramadhan Rakhmat Sani Reza, Ivan Muhammad Ricardus Anggi Pramunendar Rifamuthia, Titis Ritzkal, Ritzkal Salsabila, Shafa Saputra, Filmada Ocky Sholikun, Sholikun Sindhu Rakasiwi Sri Winarno Subowo, Moh Hadi Sulistyono, Teguh Syarifah, Ulima Muna Utomo, Danang Wahyu Wellia Shinta Sari Wibowo, Isro' Rizky Yanuaresta, Dianna Zainal Arifin Hasibuan