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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 JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Teknologi Sistem Informasi dan Aplikasi J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Manajemen Informatika EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Progresif: Jurnal Ilmiah Komputer Jurnal Teknologi Informasi dan Terapan (J-TIT) 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
Winarsih, Nurul Anisa Sri
Universitas Dian Nuswantoro
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities 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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Prediksi Kerusakan Bangunan Pasca Gempa Bumi Menggunakan Metode Deep Neural Network Fakhrurrozi, Fakhrurrozi; Ratmana, Danny Oka; Winarsih, Nurul Anisa Sri; Saraswati, Galuh Wilujeng; Rohman, Muhammad Syaifur; Saputra, Filmada Ocky; Pramunendar, Ricardus Anggi; Shidik, Guruh Fajar
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 1 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i1.37181

Abstract

Addressing the challenge of predicting earthquake-induced building damage, this study proposes the innovative use of Deep Neural Networks (DNN) as a solution. Focusing on optimizing predictive models, the research evaluates the effectiveness of various optimizers - ADAM, SGD, RMSprop, and Adagrad - coupled with adjustments in the learning rate to determine the most efficient configuration. The experiment was conducted to compare the performance of each optimizer in predicting post-earthquake building damage, a critical issue in disaster mitigation. The results demonstrate that ADAM significantly outperforms other optimizers, achieving the highest accuracy of up to 90.50% at a learning rate of 0.001, with RMSprop as its closest competitor. While SGD and Adagrad yielded lower accuracies, SGD showed improvement with higher learning rates. The variance analysis confirmed that the choice of optimizer significantly impacts model performance, with the p-value indicating strong statistical significance for optimizers (1.23E-09), whereas the learning rate had no significant impact (p-value 0.56098964). These findings underline the importance of selecting the appropriate optimizer to enhance the accuracy of DNN models for building damage prediction, a crucial aspect in emergency response planning and earthquake disaster mitigation efforts. This research contributes significantly to the development of more accurate predictive models, which are essential in minimizing the risks of earthquake disasters.
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.
Lightweight Deep Learning Approach for Sugarcane Leaf Disease Classification Using MobileNetV2 Paramita, Cinantya; Pradana, Rifky Bintang; Winarsih, Nurul Anisa Sri; Pramunendar, Ricardus Anggi
Jurnal Teknologi Informasi dan Terapan Vol 12 No 2 (2025): December
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v12i2.456

Abstract

Sugarcane is one of Indonesia’s strategic crops, yet its productivity is frequently disrupted by leaf diseases such as yellow leaf, rust, and red rot. Previous studies have shown that deep learning models are promising for plant disease detection, but many of them rely on heavy architectures that limit deployment in real-world agricultural settings. To address this gap, this study applies MobileNetV2, a lightweight Convolutional Neural Network, for the classification of sugarcane leaf diseases. Using the publicly available Kaggle dataset, the model was trained and evaluated on four classes: healthy, yellow leaf, rust, and red rot. The results demonstrate that MobileNetV2 achieved 97.0% test accuracy, with strong precision, recall, and F1-scores across all categories. These findings highlight that efficient deep learning architectures can deliver reliable disease classification while remaining practical for implementation on mobile or edge devices. Compared with previous approaches, this study contributes by demonstrating that lightweight model like MobileNetV2 can provide a balance of accuracy and efficiency, making them suitable for supporting precision agriculture practices in resource-limited environments
Co-Authors Abu Salam Adhitya Nugraha Ahmad Zainul Fanani Akmaludin Akbar Alvin, Fris Ardytha Luthfiarta Astuti, Yani Parti Azzahra, Tarissa Aura Catur Supriyanto Danny Oka Ratmana Dhani, Iqbal Djaka, Thesa Permatasari Djaka 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 Muhammad Syaifur Rohman Muhammad Syaifur Rohman Mulyanto, Edy Naufal Fawwazi Paramita, Cinantya Pradana, Rifky Bintang Purwanto Purwanto Raihatuzzahra, Farah 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