Article Per Year (5 Year)
p-Index From 2021 - 2026
1.168
P-Index
This Author published in this journals
All Journal Jurnal Riset Informatika Indonesian Community Journal BATIK: Jurnal Pengembangan dan Pengabdian Masyarakat Multikultural Scientific Journal of Informatics
Afinda, Angel Metanosa
Unknown Affiliation
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Public Health Social Sciences Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
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.
Pelatihan Pemanfaatan Aplikasi Kecerdasan Buatan untuk Meningkatkan Kompetensi Guru dalam Penyusunan Bahan Ajar di SD Negeri Cihanjaro, Kabupaten Bandung Novianty, Astri; Afinda, Angel Metanosa; Trisnawan, I Kadek Nuary; Syahrin, Muhammad Alfi; Tama, Muhammad Farrel Ahadi; Aljabbar, Raditya Ghifari; Zanuba, Salma; Wibowo, Darryl Satria
I-Com: Indonesian Community Journal Vol 6 No 1 (2026): I-Com: Indonesian Community Journal (Maret 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i1.9004

Abstract

Integrasi kecerdasan buatan (AI) dalam pendidikan dasar masih terkendala oleh rendahnya literasi digital dan keterampilan guru dalam memanfaatkan teknologi AI secara pedagogis. Program pengabdian kepada masyarakat ini bertujuan meningkatkan pemahaman dan keterampilan guru SD Negeri Cihanjaro dalam memanfaatkan ChatGPT untuk pengembangan materi pembelajaran yang kontekstual dan sesuai karakteristik peserta didik. Metode yang digunakan meliputi observasi dan kuesioner pra-pelaksanaan, pelatihan berbasis modul dengan praktik langsung, serta evaluasi pasca-pelaksanaan melalui kuesioner dan analisis produk pembelajaran. Hasil evaluasi menunjukkan peningkatan signifikan, dengan pemahaman dasar AI meningkat dari 2,73 menjadi 3,91 dan kemampuan pemanfaatan ChatGPT dari 2,36 menjadi 3,91. Seluruh peserta berhasil menghasilkan bahan ajar berbasis ChatGPT yang relevan dengan pembelajaran sekolah dasar, menunjukkan bahwa pendekatan pelatihan berbasis modul dan praktik langsung efektif serta sesuai dengan kebutuhan guru.
Pengembangan Platform Perpustakaan Digital Berbasis Web Sebagai Upaya Peningkatan Akses Informasi di Sekolah Binekas Nasution, Surya Michrandi; Septiawan, Reza Rendian; Afinda, Angel Metanosa; Darmadi, Diedrick Darrell; Yonathan, Kenneth Matthew; Radinka, Kevin
I-Com: Indonesian Community Journal Vol 6 No 1 (2026): I-Com: Indonesian Community Journal (Maret 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i1.9069

Abstract

Pengelolaan perpustakaan Sekolah Binekas masih dilakukan secara manual dan belum terintegrasi, sehingga rentan terhadap kesalahan pencatatan, keterlambatan layanan, serta keterbatasan pemantauan aktivitas literasi siswa. Kondisi ini menghambat optimalisasi fungsi perpustakaan sebagai pusat penguatan budaya literasi di sekolah dasar. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk mengembangkan dan mengimplementasikan sistem perpustakaan digital berbasis web sebagai solusi strategis atas permasalahan tersebut. Metode pelaksanaan meliputi analisis kebutuhan, perancangan dan implementasi sistem, pelatihan pengguna, serta evaluasi melalui observasi pemanfaatan sistem dan kuesioner skala Likert. Hasil menunjukkan bahwa sistem dapat digunakan secara efektif dalam pengelolaan koleksi dan layanan sirkulasi dengan tingkat kepuasan mitra pada kategori sangat baik. Implementasi sistem ini tidak hanya meningkatkan efisiensi layanan dan kerapian administrasi, tetapi juga menyediakan basis data yang mendukung pengambilan keputusan berbasis data dalam perencanaan literasi sekolah. Ke depan, pengabdian lanjutan diarahkan pada pengembangan fitur analitik literasi dan integrasi dengan sistem pembelajaran untuk memperkuat ekosistem literasi digital secara berkelanjutan.
Pengembangan Sistem Informasi Manajemen Pemakaman Berbasis Web untuk Pengelolaan Situs Pemakaman Bersejarah di Yayasan Sajarah Timbanganten Bandung Selviandro, Nungki; Afinda, Angel Metanosa; Firdaus, Fauzan; Febriyani, Widia; Richasdy, Donni; Yusuf, Muhammad Maulana; Saputro, Indah Novitasari Dwi
I-Com: Indonesian Community Journal Vol 6 No 1 (2026): I-Com: Indonesian Community Journal (Maret 2026)
Publisher : Fakultas Sains Dan Teknologi, Universitas Raden Rahmat Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70609/i-com.v6i1.9087

Abstract

Pengelolaan pemakaman bersejarah di Yayasan Sajarah Timbanganten Bandung masih menghadapi permasalahan administrasi, inkonsistensi data, dan keterbatasan pengelolaan lahan makam. Kegiatan pengabdian kepada masyarakat ini bertujuan mengembangkan dan mengimplementasikan Sistem Informasi Manajemen Pemakaman TIMGRAVID sebagai solusi digital terintegrasi. Metode yang digunakan adalah Participatory Action Research (PAR) dan User-Centered Design (UCD) dengan keterlibatan aktif mitra. Sistem berbasis web dikembangkan secara modular dengan basis data terpusat untuk mendukung layanan publik, pengelolaan operasional, keuangan, serta pengawasan. Hasil evaluasi menunjukkan peningkatan akurasi dan transparansi pengelolaan serta tingkat kepuasan mitra yang sangat baik dengan nilai rerata di atas 4,5 (skala 1–5). Temuan ini menunjukkan bahwa sistem TIMGRAVID efektif mendukung efisiensi pengelolaan pemakaman sekaligus pelestarian warisan budaya melalui dokumentasi digital.
A SEASONAL IMPUTATION METHOD FOR ADDRESSING MISSING DATA IN ENVIRONMENTAL IOT SENSOR TIME SERIES Ramadhan, Ardiansyah; Nasution, Surya Micrandi; Septiawan, Reza Rendian; Trisnawan, I Kadek Nuary; Afinda, Angel Metanosa
Jurnal Riset Informatika Vol. 8 No. 2 (2026): Maret 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i2.475

Abstract

Missing and incomplete observations in Environmental IoT sensor networks reduce data reliability and disrupt analyses, especially for temperature and humidity time series exhibiting strong diurnal seasonality. This study develops and evaluates a seasonal imputation method to address missing data in IoT-based environmental monitoring, using a workflow of anomaly detection, outlier removal, time-of-day-aware imputation, and performance evaluation under varying missing-rate scenarios. Key challenges include sensor noise, connectivity issues, and intermittent hardware failures, which degrade data integrity and affect trend analysis, forecasting, and anomaly detection. To mitigate these, the method uses hourly and minute-level seasonal patterns after filtering out physically unrealistic values. Experimental results show high accuracy and robustness in reconstructing temperature and humidity data: temperature imputation achieves MAE values of approximately 0.86–0.87°C, and humidity yields MAE values of 3.92–4.01%RH, with no performance drop even at 50% data loss. The imputed series preserves natural diurnal dynamics without introducing distortions, effectively restoring continuity and structural consistency in environmental IoT time series for reliable modeling, feature extraction, and decision support.
Co-Authors Aljabbar, Raditya Ghifari Darmadi, Diedrick Darrell Donni Richasdy Febriyani, Widia Firdaus, Fauzan Isman Kurniawan muhammad alfi syahrin Muhammad Faris Ruriawan Nasution, Surya Micrandi Novianty, Astri Nungki Selviandro Radinka, Kevin Ramadhan, Ardiansyah Ramandha, Salma Safira Reza Rendian Septiawan Saputro, Indah Novitasari Dwi Surya Michrandi Nasution Tama, Muhammad Farrel Ahadi Trisnawan, I Kadek Nuary Wibowo, Darryl Satria Yonathan, Kenneth Matthew yusuf, muhammad maulana Zahra, Nurul Izzah Abdussalam Zanuba, Salma