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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Indonesian Journal on Computing (Indo-JC) Jurnal Kimia Riset JOIN (Jurnal Online Informatika) Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jiko (Jurnal Informatika dan komputer) Jurnal Teknik Informatika (JUTIF) Charity : Jurnal Pengabdian Masyarakat eProceedings of Engineering Jurnal INFOTEL Scientific Journal of Informatics LOGIC: Jurnal Penelitian Informatika Proceeding of Community Service and Engagement
Isman Kurniawan
Universitas Telkom
Arts Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Social Sciences

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Journal : scientific journal of informatics

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Gene Expression-Based Lung Cancer Prediction in Smokers Using SVM and Moth-Flame Optimization Algorithm Ramandha, Salma Safira; Afinda, Angel Metanosa; Kurniawan, Isman
Scientific Journal of Informatics Vol. 13 No. 1: February 2026
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v13i1.38268

Abstract

Purpose: Lung cancer remains one of the leading causes of death worldwide, especially among active smokers, yet early detection is still difficult because traditional imaging methods have limited sensitivity for identifying early-stage abnormalities. This study was conducted to address the need for a more accurate computational approach capable of detecting lung cancer at a molecular level using gene expression data. The goal is to build a model that can reliably distinguish cancerous from non-cancerous samples based on genomic features. Methods: This study uses the GSE4115 gene-expression dataset consisting of 187 bronchial epithelial samples and 22,215 gene features. The Moth-Flame Optimization (MFO) algorithm was implemented to select the most informative subset of genes from this high-dimensional dataset. A Support Vector Machine (SVM) classifier was then trained using multiple kernels, with hyperparameter tuning performed to identify the optimal configuration for each kernel. Results: Experimental results show that the Polynomial kernel achieved the highest performance using 286 MFO-selected features, reaching an accuracy of 0.84 and an F1-score of 0.85. These results confirm that combining MFO with SVM improves classification performance compared to using raw gene data without feature selection. Novelty: This study provides the first application of MFO-based feature selection for lung cancer prediction in smokers using the GSE4115 dataset. The findings demonstrate the value of nature-inspired optimization for handling high-dimensional genomic data and offer a promising direction for developing early computational detection methods.
Optimized LSTM Model Using Simulated Annealing for Autoignition Temperature (AIT) Prediction as a Hazard Indicator Zahra, Nurul Izzah Abdussalam; Afinda, Angel Metanosa; Kurniawan, Isman
Scientific Journal of Informatics Vol. 12 No. 4: November 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i4.38278

Abstract

Purpose: Autoignition Temperature (AIT) is the lowest temperature at which a substance will spontaneously ignite in normal air without any external ignition source. AIT is an important safety parameter in industries that handles flammable materials. Measuring AIT with conventional method is unfortunately slow, costly, and dangerous. As an alternative, an AIT prediction model can be developed using in silico approaches, specifically based on machine learning. Methods: One of the methods that can be used is Long Short-Term Memory (LSTM) since it is good at modeling the complex relationships that is involved, but unfortunately it is difficult to tune manually due to their numerous hyperparameters. Therefore, an automated strategy can be used to find the best hyperparameters for the architecture. This study aims to develop an AIT prediction model as a hazard indicator using an LSTM model optimized with Simulated Annealing (SA). Result: The experiment showed that the SA-LSTM model which uses a cooling schedule of Delta T = 0.7 outperformed the unoptimized baseline model. Novelty: The optimization raised the R2 on test data from 0.5682 to 0.5939 while also lowering the RMSE from 74.35 K to 72.10 K and the MAPE from 9.29% to 8.87%. These results confirmed that optimizing LSTM with SA gave a more robust tool for hazard indicator.
Co-Authors Afinda, Angel Metanosa Ali Assegaf Annisa Aditsania Ardannur Deswanto, Farrel Ardhyka Dewantara Arionmaro Asi Simaremare Arsytania, Ihsani Hawa Azizah, Kholishoh Nur Bambang Hadi Prakoso Brillyando Magathan Achmad Crisanadenta Wintang Kencana Danang Triantoro Murdiansyah Danang Triantoro Murdiansyah Delfina, Haura Adzkia Deni Saepudin Devi Munandar Didit Adytia Diya Namira Purba Ekky Wicaksana Erwin Budi Setiawan Erwin Budi Setiawan Fadhli Rahman Farid, Husnul Khotimah Farid, Mochammad Rafi Fhira Nhita Furqon Hidayat Husnul Khotimah Farid Hutama, Nanda Yonda I Kadek Andrean Pramana Putra Pramana Iljannah, Nissa Indra A Aditya Indra Lukmana Sardi Jayanata, Wisnu Kamil, Nabilla Kemas Muslim Lhaksmana Khoerunnisa, Ghina Komang Triolascarya Lela Lailatul Khumaisah Lela Mukmilah Yuningsih Lusi Sofiana Silaban Maya Rosalinda Mela Mai Anggraini Mohammad Adietya Perdana Nabilla Kamil Nanda Yonda Hutama Nurul Ikhsan Ramandha, Salma Safira Reina Wardhani Reza Fauzi Iskandar Reza Rendian Septiawan Rian Febrian Umbara Rizki Amanullah Hakim Rizki, Alfath Muhammad Salsabil, Adinda Arwa Sapitri, Nazwa Martina Saut Sihol Ritonga Shinta Yulia Puspitasari Siti Sa'adah Wicaksana, Ekky Wisesty , Untari Novia Yusuf, Muhammad Rizky Yusfian Zahra, Nurul Izzah Abdussalam