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All Journal Jurnal Masyarakat Informatika 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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Implementasi Algoritma Convolutional Neural Network (CNN) Untuk Klasifikasi Jenis Tanah Berbasis Android Astuti, Yani Parti; Subhiyakto, Egia Rosi; Wardatunizza, Indah; Kartikadarma, Etika
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.v8i3.5026

Abstract

Bawen District is one of the sub-districts in Semarang Regency, Central Java. This region has an area of land used for agriculture around 63.29%. In this area the population still uses soil as a planting medium. Soil is one of the planting media which plays an important role for the survival of plants. With so many types of soil that have different properties and characteristics, the treatment of these soils is also different. So it is necessary to have a soil classification to know how to manage the soil properly. To facilitate the classification of soil types, Deep Learning technology can be utilized with images as input which are then processed using the Convolutional Neural Network (CNN) algorithm. In order to get a model that has a high level of accuracy, an experiment was carried out on several influential parameters and an evaluation of the model was carried out using a confusion matrix. The confusion matrix has several values such as accuracy, precision, recall, and f1-score. Tests have been carried out and the results of this study are models that have a training accuracy of 97% with a loss value of 0.0880 and a testing accuracy of 95% with a loss value of 0.1513.
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.
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 Miyanthi, Sheryn Aulia 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 Yuniarsi Rahayu