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All Journal BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Scientific Journal of Informatics
Afinda, Angel Metanosa
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Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Social Sciences Other

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Pengembangan Purwarupa Platform Digital Pelaporan Nilai Siswa Sebagai Pendukung Kurikulum Merdeka Berbasis Aplikasi Web di Lingkungan Sekolah Binekas Septiawan, Reza Rendian; Nasution, Surya Michrandi; Afinda, Angel Metanosa; Ruriawan, Muhammad Faris
Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Vol 3 No 3: BATIK Desember 2025
Publisher : Institut Riset dan Publikasi Indonesia (IRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/batik.v3i3.2181

Abstract

Sekolah Binekas is a private school in South Bandung that has implemented the Merdeka Curriculum in its learning process. However, monitoring students’ learning achievements is still done manually using traditional records or basic digital spreadsheets, making it difficult for teachers and school management to analyze progress systematically. This community service project aimed to develop a prototype of a web-based digital reporting system to enable real-time tracking of student learning outcomes. The development process involved requirement analysis, user interface design, and system implementation using a user-centered design approach. The system enables efficient data entry, assessment recap, and learning achievement visualization, offering better accuracy and usability than manual methods. Furthermore, it supports school administrative tasks, such as preparing reports for accreditation and evaluating the curriculum. Trial use at Sekolah Binekas indicated that the system simplifies assessment data access for teachers and aids school management in making data-driven decisions. The system offers a practical contribution to a more structured, digital, and locally adaptive monitoring process for Merdeka Curriculum implementation.
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 Isman Kurniawan Muhammad Faris Ruriawan Ramandha, Salma Safira Reza Rendian Septiawan Surya Michrandi Nasution Zahra, Nurul Izzah Abdussalam