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All Journal Jurnal Simetris Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JTERA (Jurnal Teknologi Rekayasa) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Astuti, Yani Parti
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture 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 Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Performance Improvement of Random Forest Algorithm for Malware Detection on Imbalanced Dataset using Random Under-Sampling Method Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti; Ahmed, Foez
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5207

Abstract

Handling imbalanced dataset has their own challenge. Inappropriate step during the pre-processing phase with imbalanced data could bring the negative effect on prediction result. The accuracy score seems high, but actually there are many problems on recall and specificity side, considering that the produced predictions will be dominated by the majority class. In the case of malware detection, false negative value is very crucial since it can be fatal. Therefore, prediction errors, especially related to false negative, must be minimized. The first step that can be done to handle imbalanced dataset in this crucial condition is by balancing the data class. One of the popular methods to balance the data, called Random Under-Sampling (RUS). Random Forest is implemented to classify the file, whether it is considered as goodware or malware. Next, 3 evaluation metrics are used to evaluate the model by measuring the classification accuracy, recall and specificity. Lastly, the performance of Random Forest is compared with 3 other methods, namely kNN, Naïve Bayes and Logistic Regression. The result shows that Random Forest achieved the best performance among evaluated methods with the score of 98.1% for accuracy, 98.0% for recall, and 98.2% for specificity.
A Comparative Analysis of LSTM and GRU Models for AQI Forecasting in Tourist Destinations Ardianto, Luluk; Astuti, Yani Parti
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.6633

Abstract

The Air Quality Index (AQI) is a critical metric for assessing air quality and its impact on human health, particularly in densely populated and tourist-heavy areas such as Malioboro, Yogyakarta. As one of Indonesia's most popular tourist destinations, the region experiences significant air quality fluctuations influenced by human activities, including transportation and tourism. This study evaluates the performance of two advanced deep learning models, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting AQI and key pollutant parameters, PM10 and PM2.5, using two years of air quality data collected between January 2022 and December 2023. The results demonstrate that the LSTM model consistently outperforms GRU in predicting AQI (MSE: 163.757, RMSE: 12.797, MAE: 7.432, MAPE: 0.133) and PM2.5 (MSE: 32.001, RMSE: 5.657, MAE: 3.005, MAPE: 0.139), indicating its capability to model complex temporal patterns effectively. Conversely, the GRU model achieves better accuracy for PM10 predictions (MSE: 58.592, RMSE: 7.655, MAE: 4.168, MAPE: 0.180), showcasing its computational efficiency with competitive performance. These findings underscore the suitability of LSTM for applications prioritizing accuracy, while GRU provides a viable option for scenarios requiring faster computations. This research highlights the potential of leveraging deep learning models to tackle air quality challenges in urban and tourist areas, paving the way for informed decision-making and sustainable development initiatives
Perbandingan Kinerja Metode Naïve Bayes dan Random Forest untuk Klasifikasi Penyakit Diabetes Berdasarkan Data Medis Pradana, Rendy Risqi; Astuti, Yani Parti
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7446

Abstract

Diabetes mellitus merupakan penyakit tidak menular yang prevalensinya terus meningkat di Indonesia. Proses diagnosis secara konvensional sering menghadapi berbagai tantangan, seperti keterlambatan dan biaya yang tinggi. Penelitian ini bertujuan untuk membandingkan kinerja algoritma Naive Bayes dan Random Forest dalam klasifikasi diabetes dengan menggunakan dataset Pima Indians Diabetes. Untuk mengatasi ketidakseimbangan kelas, dataset diproses menggunakan teknik Synthetic Minority Over-sampling Technique (SMOTE). Evaluasi kinerja dilakukan menggunakan metrik akurasi, presisi, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Random Forest memperoleh akurasi sebesar 79,5%, presisi 79,6%, recall 79,5%, dan F1-score 79,5%. Sementara itu, algoritma Naive Bayes memperoleh akurasi 76,5%, presisi 76,5%, recall 76,5%, dan F1-score 76,5%. Temuan ini menunjukkan bahwa Random Forest unggul dalam menangani data yang kompleks dengan akurasi prediksi yang lebih tinggi, sedangkan Naive Bayes tetap efektif untuk implementasi yang lebih sederhana karena efisiensi komputasinya. Studi ini berkontribusi dalam pengembangan sistem pendukung keputusan cerdas untuk deteksi dini diabetes yang lebih cepat dan akurat, sehingga dapat membantu mengurangi beban pada sistem layanan kesehatan.
Pengembangan Aplikasi Pencatatan Absensi dan Kegiatan Pegawai Aru PT Jasa Raharja Jawa Tengah Iskandar, Marcelino; Kartikadarma, Etika; Astuti, Yani Parti; Subhiyakto, Egia Rosi
Poltanesa Vol 23 No 1 (2022): Juni 2022
Publisher : P3KM Politeknik Pertanian Negeri Samarinda

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51967/tanesa.v23i1.1167

Abstract

PT Jasa Raharja (Pesero) merupakan perusahaan asuransi BUMN di Indonesia yang bertugas untuk dapat memberikan layanan santunan dan perlindungan sosial bagi masyarakat khususnya untuk korban kecelakaan lalu lintas yang terjadi di Indonesia. Berdampingan dengan PT Jasa Raharja bekerja, PT ARU Raharja didirikan dengan tujuan untuk dapat membantu pekerjaan keseharian non-formal pegawai Jasa Raharja. Pegawai ARU Raharja terdiri dari satpam, sopir, dan juru layan. Pada kantor cabang PT Jasa Raharja Jawa Tengah, pegawai ARU tidak memiliki sistem pencatatan absensi dan kegiatan yang berbasiskan teknologi. Penilaian kinerja pegawai ARU masih sulit dikarenakan pencatatan masih mengandalkan pencatatan secara manual. Melalui penelitian ini dikembangkan aplikasi yang akan digunakan oleh pegawai ARU di kantor cabang PT Jasa Raharja Jawa Tengah untuk dapat melakukan pencatatan absensi dan kegiatan saat bekerja. Aplikasi akan mengimplementasikan QR Code dan GeoFencing sebagai teknik pembatasan wilayah akses aplikasi. Diajukan metode pengembangan aplikasi berupa Rapid Application Development (RAD) yang dapat membantu pengembangan aplikasi dengan perencanaan awal yang minim dan waktu pengerjaan yang singkat. Analisis dan perancangan menggunakan metode berorientasi objek dengan menggunakan diagram use case dan diagram aktivitas. Berdasarkan hasil pengujian black box didapatkan bahwa fungsionalitas aplikasi sudah sesuai. Sedangkan dari hasil pengujian white box menggunakan basis path testing sudah berjalan dengan baik dan sesuai.
Perbandingan Kinerja Algoritma CatBoost, XGBoost, LightGBM dan Random Forest Dalam Memprediksi Risiko Infeksi Aids Dalam Dataset Kesehatan Yulianto, Pramudya Ridwan; Astuti, Yani Parti
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.8975

Abstract

This study investigates the prediction of AIDS infection risk using tree-based algorithms CatBoost, XGBoost, LightGBM, and Random Forest applied to a medical and demographic dataset consisting of 2,139 observations and 23 variables. The research process includes data exploration, cleaning, handling extreme values using the interquartile range (IQR) method, normalization with RobustScaler, and class balancing using the Synthetic Minority Over-sampling Technique (SMOTE). Due to the imbalanced nature of the dataset, model evaluation emphasizes not only accuracy but also Recall, F1-Score, and AUC-ROC to better assess infected class detection. Prior to SMOTE implementation, all models achieved high accuracy but relatively low recall for the positive class; after resampling, CatBoost demonstrated the most significant improvement, with recall increasing from 63% to 77% and F1-Score from 72% to 79%, achieving an overall accuracy of 90%. In comparison, XGBoost reached an accuracy of 88.63% with a more moderate recall improvement, while LightGBM and Random Forest showed consistent yet smaller gains, indicating that the combination of SMOTE and CatBoost is more effective in minimizing False Negatives in AIDS infection cases. The main contribution of this study lies in the integration of robust outlier handling, feature normalization, and class balancing within a structured experimental framework, with a specific emphasis on sensitivity optimization to enhance early detection reliability in clinical screening contexts.
Co-Authors Abu Salam Achmad Wahid Kurniawan Adhitya Nugraha Agus Winarno, Agus Ahmed, Foez Akbar, Ananda Ikhwana Khairur Anam, Muhammad Khoirul Andreas Wilson Setiawan Ardianto, Luluk Ardytha Luthfiarta Bagaswara, Dwiky Ihza Cahyani, Okta Nur Catur Supriyanto Christy Atika Sari D, Ishak Bintang Darmawan, Aditya Aqil Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Fauzi Adi Rafrastara Firmansyah, Muhammad Raihan Guruh Fajar Shidik Hafidhoh, Nisa'ul Harisa, Ardiawan Bagus Heribertus Himawan Ifan Rizqa Isinkaye, Folasade Olubusola Iskandar, Marcelino Liya Umaroh, Liya Mubarok, Ahmad Hasan Mukharyahya, Zulfa Alviandri Ningrum, Novita Kurnia Nova Rijati Nugroho, Setiawan Octaviani, Dhita Aulia Oktaridha, Harwinanda Paramita, Cinantya Pradana, Rendy Risqi Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Zahwa Bunga Putri Purwanto Purwanto Rahadian, Arief Rohman, Muhammad Syaifur Sausan Claudia, Salwa Rona Septiani, Karlina Dwi Sindhu Rakasiwi Sudibyo, Usman Sugiyanto - Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Winarsih, Nurul Anisa Sri Yulianto, Pramudya Ridwan Yuniarsi Rahayu