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All Journal Jurnal Ilmu Komputer JAIS (Journal of Applied Intelligent System) Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi Jurnal Manajemen Informatika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Journal of Robotics and Control (JRC) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Pengabdian kepada Masyarakat Nusantara J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics Inovasi Sosial: Jurnal Pengabdian Masyarakat Advance Sustainable Science, Engineering and Technology (ASSET)
Winarsih, Nurul Anisa Sri
Universitas Dian Nuswantoro
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences

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Implementasi Algoritma Floyd Warshall Pada Aplikasi Dewan Masjid Indonesia (Dmi) Kota Semarang Untuk Menentukan Masjid Terdekat Rohman, Muhammad Syaifur; Saraswati, Galuh Wilujeng; Winarsih, Nurul Anisa Sri
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 3 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i3.4895

Abstract

Location Based Service (LBS) is a service on smartphones that functions as a navigation device based on the user's position to determine the location where the user is. LBS utilizes GPS capabilities in finding geolocation information and sometimes using Google maps to display a complete map of the location. But the results of previous research studies Google Map does not give shortest and accessible routes. Furthermore, to improve work of LBS, Floyd Warshall algorithm is used because the algorithm has the principle of optimality in calculating the total of all routes optimally. According to data recorded by the Ministry of Religion of the Republic of Indonesia there have been 1,304 Mosques in the City of Semarang, but with this much data it should be easier to find places of worship for Muslims. Most mosques that are visited are mosques on the highway because it is more visible even though there are many other mosques that can be accessed. By using the White Box and Black Box tests, finding shortest path to find places of worship in the city of Semarang can be given accurately. The result was the Floyd Warshall algorithm could provide shortest path route and it was more accessible better than Google Map navigation.
Perbandingan Kinerja Model IndoBERT, IndoBERTweet, dan Algoritma Klasik pada Analisis Sentimen Isu Indonesia Gelap Alvin, Fris; Winarsih, Nurul Anisa Sri
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.v7i3.8636

Abstract

This study aims to compare the performance of Transformer-based models, namely IndoBERT and IndoBERTweet, with three classical machine learning algorithms, namely Support Vector Machine (SVM), Logistic Regression, and Random Forest, in analyzing public sentiment regarding the “Indonesia Gelap” issue that has been widely discussed on social media. The dataset was collected using a crawling process on TikTok user comments containing keywords related to the issue, resulting in 5.000 comments. After the preprocessing stage, 4.667 comments were deemed suitable for analysis and were labeled into positive, negative, and neutral sentiment categories using a lexicon-based approach. To address the imbalance in class distribution, three oversampling strategies were applied: without oversampling, oversampling before data splitting, and oversampling after data splitting applied only to the training data. Each model was evaluated using four performance metrics: accuracy, precision, recall, and F1-score. The results show that oversampling before data splitting yielded the best performance across all models, with IndoBERT achieving the highest F1-score of 0.93, followed by IndoBERTweet with 0.91, while the classical algorithms achieved average F1-scores ranging from 0.89 to 0.90. Meanwhile, both the non-oversampling scenario and oversampling after data splitting on the training data resulted in lower performance, with average F1-scores ranging from 0.70 to 0.78. These findings indicate that Transformer-based models are more effective in capturing informal language characteristics commonly found in social media comments. Furthermore, balancing the dataset before model training significantly improves the stability and performance of sentiment classification on imbalanced data.
Stacking of DT, RF, and Gradient Boosting Algorithms for Classification of Building Damage Due to Earthquakes Ilmi, Nur Aqliah; Winarsih, Nurul Anisa Sri
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.v9i6.11272

Abstract

Classification of building damage levels due to earthquakes is an important aspect in disaster mitigation and post-disaster risk assessment. This study aims to improve classification accuracy on imbalanced data using an ensemble stacking method. It combines Decision Tree, Random Forest, and Gradient Boosting algorithms, with Logistic Regression as a meta-learner. The building damage dataset from the 2015 Gorkha Nepal earthquake underwent data cleaning, categorical transformation, normalization, and balancing using ADASYN. Evaluation showed that Random Forest was the best single model. The stacking model achieved the highest accuracy of 91.77% after balancing. These results show that stacking improves generalization and classification accuracy on imbalanced data. This suggests significant potential for integration into disaster decision-support systems that require fast, accurate building-damage assessment.
Pelatihan Pembuatan Website Pembelajaran Berbasis Google Sites Bagi Siswa SMA Mardisiswa Semarang Utomo, Danang Wahyu; Kurniawan, Defri; Luthfiarta, Ardytha; Supriyanto, Catur; Winarsih, Nurul Anisa Sri; Fitriyani, Shelomita; Salam, Abu; Dewi, Ika Novita; Rakasiwi, Sindhu
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 7 No. 1 (2026): Edisi Januari - April
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v7i1.8211

Abstract

Perkembangan teknologi informasi memberikan dampak positif pada literasi digital, yaitu semakin berkembang. Adanya literasi digital menjadikan proses pembelajaran interaktif. Kompetensi TIK penting bagi siswa dalam mengembangkan media pembelajaran secara digital. Namun, SMA Mardisiswa menghadapi permasalahan rendahnya kompetensi TIK siswa, yang berdampak pada kurang optimalnya pemanfaatan media pembelajaran digital. Solusi yang diusulkan adalah pelatihan berbasis learning by doing dengan menerapkan siklus Kolb’s experiential learning yang menekankan praktik langsung dalam pembelajaran. Pelatihan dilaksanakan melalui tahapan pemberian materi, praktik pembuatan website menggunakan Google Sites, serta pendampingan. Peserta kegiatan berjumlah 30 siswa kelas XII. Hasil evaluasi menunjukkan adanya peningkatan kompetensi dasar pengembangan web pembelajaran. Rata-rata nilai post-test sebesar 84 meningkat dari nilai pre-test sebesar 64, atau mengalami peningkatan 31,25%. Selain itu, siswa mampu mengembangkan media pembelajaran berbasis web secara mandiri. Metode yang diterapkan terbukti dapat meningkatkan kompetensi TIK siswa dalam pengembangan web dasar.
OPTIMASI MODEL U-NET BACKBONE RESNET50 PADA SEGMENTASI CITRA BANJIR MENGGUNAKAN SEQUENTIAL HYPERPARAMETER TUNING Moh Adzka Fawaid; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7377

Abstract

Flooding is a natural disaster that has become increasingly frequent and causes significant impacts on urban environments, highlighting the need for rapid and accurate mapping of affected areas. Deep learning–based image segmentation, particularly using the U-Net architecture, has been widely applied for this purpose. However, model performance is not determined solely by network architecture, but is also strongly influenced by the selection of training hyperparameters. This study aims to optimize the performance of a U-Net model with a ResNet50 backbone for flood image segmentation using a Sequential Hyperparameter Tuning approach based on a one-factor-at-a-time scheme. The dataset consists of approximately 3,400 RGB flood images with corresponding binary ground truth masks at an original resolution of 512 × 512 pixels, which are resized to 256 × 256 pixels and preprocessed using CLAHE, gamma correction, and unsharp masking to enhance contrast and boundary clarity of inundated areas. The optimization focuses on optimizer selection, batch size, learning rate, and number of training epochs, as these parameters directly affect convergence stability and segmentation accuracy. Hyperparameter tuning is performed sequentially by evaluating model performance on the validation set using Intersection over Union (IoU) and Dice Similarity Coefficient. Based on this process, the optimal configuration employs the AdamW optimizer, a batch size of 8, a learning rate of 0.00015, and 100 training epochs. Final evaluation is conducted on the test set through retraining with three different random seeds, and performance is reported using mean values. The optimized model achieves a mean IoU of 0.7664 and a mean Dice score of 0.8499, with low standard deviation, indicating stable performance and good generalization capability. These findings demonstrate that systematic hyperparameter optimization plays a crucial role in improving the performance of U-Net ResNet50 for flood image segmentation and provides practical insights for remote sensing–based flood mapping systems.
Analisis Kesehatan Vegetasi Multi-Temporal Berbasis WebGIS Menggunakan NDVI Sentinel-2 dan Penilaian Multi-Kriteria SAW Akner Yosha Ade Saputra; Nurul Anisa Sri Winarsih; Muhammad Syaifur Rohman; Danny Oka Ratmana
Infotekmesin Vol 17 No 2 (2026): Infotekmesin: Juli 2026
Publisher : P3M Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/

Abstract

This study integrates Sentinel-2 NDVI, the Simple Additive Weighting (SAW) method, and WebGIS to analyze vegetation health dynamics across 19 sub-districts in Grobogan Regency (±1,975.87 km²). Data were sourced from four Sentinel-2 Level-2A images (September 14–October 9, 2025) and supporting meteorological parameters. Results indicate NDVI values ranging from 0.2424 to 0.6297. On September 14, 42.1% of the areas were classified as High. However, by September 19, 12 sub-districts experienced a significant decline anomaly (), leaving only 21.1% in the High category. The SAW assessment yielded scores between 0.2793 and 0.4918. Spearman's validation demonstrated a perfect correlation () between NDVI and SAW rankings. The observed temporal fluctuations correlated with rainfall and humidity. In conclusion, this integrated approach effectively evaluates vegetation anomalies to support precise land management decisions.
Spatiotemporal Analysis of Peatland Fire Hotspots and Fire Intensity in Riau Province Using MODIS–VIIRS Multisensor Satellite Data Najwa Ratu Afi; Ramadhan Rakhmat Sani; Ricardus Anggi Pramunendar; Nurul Anisa Sri Winarsih; Ika Novita Dewi
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12686

Abstract

Peatland fires in Riau Province frequently occur during the dry season and contribute significantly to regional haze, environmental degradation and carbon emissions. Effective monitoring of these fires remains challenging due to their widespread distribution and varying intensity across peatland areas. This research aims to analyze the spatiotemporal characteristics of peatland fire hotspots in Riau Province using multisensor satellite observations from the NASA Fire Information for Resource Management System (FIRMS). The dataset integrates Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) data from the Suomi-NPP, NOAA-20 and NOAA-21 satellites. After applying filtering criteria of confidence ≥70% and Fire Radiative Power (FRP) ≥5 megawatts (MW), a total of 7,297 significant hotspots were identified during the July–October 2025 dry season. The results show that fire activity peaked in July with a maximum daily FRP of 25,611 MW and a monthly total of 65,120 MW, followed by a decline in September and a slight increase in October. The FRP distribution was highly right-skewed, with an average value of13.2 MW, while the most intense hotspots reached 189.4 MW. Estimated carbon dioxide (CO₂) emissions reached approximately 122,472 tons, indicating substantial environmental impacts. Spatial clustering and persistence analysis revealed several high-risk peatland zones with repeated fire occurrences. These findings demonstrate the importance of multisensor satellite monitoring for improving early fire detection, emission assessment and disaster mitigation strategies in peatland regions.
LDWFOX Optimization for Hyperparameter Tuning of Inception CNN in UAV-Based Vegetation Density Mapping Ricardus Anggi Pramunendar; Ashraf Alomoush; Dwi Puji Prabowo; Rama Aria Megantara; Farrikh Alzami; Nurul Anisa Sri Winarsih; Dewi Pergiwati; Guruh Fajar Shidik
Advance Sustainable Science Engineering and Technology Vol. 8 No. 4 (2026): August-October
Publisher : Science and Technology Research Centre Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v8i4.3433

Abstract

Vegetation density classification from UAV imagery is a practical necessity in fire-prone landscapes, since fuel load on the ground directly informs risk management decisions. Convolutional neural networks handle this classification reasonably well, but good performance requires careful hyperparameter tuning, and manual trial and error produces results that are unstable and hard to reproduce. This study proposes LDW-FOX, a modified FOX metaheuristic using a Linearly Decreasing Weight mechanism to automate hyperparameter tuning for an Inception-based CNN. The original FOX algorithm applies fixed movement weights throughout optimization, causing search to stagnate early. LDW-FOX gradually reduces exploration intensity across iterations, pushing search toward exploitation as it converges. Five hyperparameters, namely learning rate, dropout rate, hidden layer size, activation function, and optimizer, were tuned on a balanced 3,000 image UAV dataset spanning three vegetation density classes. Manual tuning peaked at 61.00 percent test accuracy but varied considerably across epoch settings. LDW-FOX reached a peak test accuracy of 82.48 percent and a mean of 58.47 percent, outperforming the original FOX, whose mean was 55.30 percent. LDW-FOX showed a more consistent training-test gap than other swarm-based methods, with LDW variants beating unmodified counterparts under equal budgets. High variance across configurations indicates broader generalization needs testing.
Co-Authors Abu Salam Adhitya Nugraha Ahmad Zainul Fanani Akmaludin Akbar Akner Yosha Ade Saputra Alvin, Fris Alzami, Farrikh Ardytha Luthfiarta Ashraf Alomoush Astuti, Yani Parti Azzahra, Tarissa Aura Catur Supriyanto Danny Oka Ratmana Danny Oka Ratmana Dewi Pergiwati Dhani, Iqbal Djaka, Thesa Permatasari Djaka Dwi Puji Prabowo, Dwi Puji Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Epiphany Shavna Gracia Erlin Dolphina Etika Kartikadarma Fafaza, Safira Alya Fakhrurrozi Fakhrurrozi, Fakhrurrozi Filmada Ocky Saputra Filmada Ocky Saputra Fitriyani, Shelomita Guruh Fajar Shidik Hamid, Maulana As’an Ika Novita Dewi Ilmi, Nur Aqliah Imam Prayogo Pujiono Kartika, Gita Kurniawan, Defri Kusumawati, Yupie Mardiantara, Naya Alifiah az Azar Putri Miranti Alysha Zulia Larasati Moh Adzka Fawaid Muhammad Syaifur Rohman Muhammad Syaifur Rohman Mulyanto, Edy Najwa Ratu Afi Naufal Fawwazi Purwanto Purwanto Raihatuzzahra, Farah Rama Aria Megantara Ramadhan Rakhmat Sani Ramadhani, Irfan Wahyu Ratmana, Danny Oka Ricardus Anggi Pramunendar Rohman, Muhammad Syaifur Romadhoni, Mellynda Noor Saputra, Filmada Ocky Saraswati, Galuh Wilujeng Satria Andromeda, Rayhan Sekar Wangi, Sari Asih Septian Enggar Sukmana Sindhu Rakasiwi Sutrisno, Hendra Utomo, Danang Wahyu Wibowo, Alrico Rizki Yani Parti Astuti Yani Parti Astuti Zainal Arifin Hasibuan