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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatik : Jurnal Ilmu Komputer Journal of Science and Technology DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY
Rahmi Putri Kurnia
Politeknik Negeri Padang
Computer Science & IT Engineering Mathematics

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ANALISIS REKOMENDASI FILM DARI DATA IMDB MENGGUNAKAN PYTHON Rahmi Putri Kurnia; Yori Adi Atma
DEVICE : JOURNAL OF INFORMATION SYSTEM, COMPUTER SCIENCE AND INFORMATION TECHNOLOGY Vol 3, No 2 (2022)
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/device.v3i2.2698

Abstract

ABSTRAK Penelitian ini memanfaatkan penggunaan beberapa library yang ada pada bahasa pemrograman Python dalam menentukan rekomendasi film dari situs IMDb sebagai sumber data dengan berdasarkan pada kategori atau genre film yang diinginkan / dicari oleh pengguna situs IMDb. Penentuan rekomendasi film ini sering kali menjadi kebingungan bagi pecinta film dalam menentukan pilihannya pada film apa yang akan dinikmati. Penelitian ini menggunakan bahasa pemrograman Python dengan librarynya digunakan untuk menaganalisis dan menyelesaikan masalah, dimana ada terlalu banyak film yang tersedia di IMDb.sehingga akan menghasilkan output berupa rekomendasi film yang dapat menjadi acuan bagi pecinta film pengguna situs IMDb. Kata Kunci : Rekomendasi, IMDB, Pyhton, Library, Analisis.
Pengembangan Sistem Informasi Kredit Rumah Pegawai Pada Balai Pengelolaan Das Agam Kuantan Padang Yulia Jihan sy; Rahmi Putri Kurnia; Novi Novi
JOSTECH Journal of Science and Technology Vol 3, No 1: Maret 2023
Publisher : UIN Imam Bonjol Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15548/jostech.v3i1.5598

Abstract

After conducting directly research into the Balai Pengelolaan DAS Kuantan Agam to find out the problems being faced by observations and interviews (interviews) with interested parties, it was found that data in the processing of home loan system was still manually. Processing data uses Microsoft Office Excel by input data to the tables and using formulas in Excel. This is find the problems about processing data. The problem that find double data in the management data. The information have a long time for processing data. The solve for this problem is development of housing credit data management. Development is make a program that uses a database to data storage and data processing to quickly and precisely. So the report will be faster, precise and accurate. For running this system have a very adequate device. The methodology in this research is field research, library research and laboratory research.
Customer Churn Prediction in Waste Banks Using XGBoost and SMOTE Indri Rahmayuni; Rahmi Putri Kurnia; Yance Sonatha; Yulherniwati Yulherniwati; Afcha Arel Pratama
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 4 (2026): August 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i4.7811

Abstract

Customer retention has become an important challenge in waste bank programs because declining member participation may reduce operational sustainability and weaken community-based waste management initiatives. However, churn prediction studies in non-commercial environmental programs such as waste banks remain limited. This study proposes a machine learning approach for customer churn prediction using operational transaction data from a waste bank managed by the Environmental Agency of Padang City, Indonesia. The dataset consisted of 34,188 transaction records representing 1,015 members collected between May 2024 and April 2026. Customer behavioral features were constructed from transaction history indicators, while class imbalance was handled using SMOTE and churn classification was performed using XGBoost under a leakage-aware customer-level train-test separation, ensuring a realistic evaluation on previously unseen members. Experimental results showed that the proposed model achieved an accuracy of 0.84, an F1-score of 0.73, and a ROC AUC value of 0.898. Feature analysis revealed that recency and transaction frequency were among the strongest predictors of churn behavior. The findings demonstrate the potential of machine learning to support participation monitoring and sustainability management in community-based waste bank systems.  
Explainable Deep Learning for Multi-Class Plant Disease Classification Using ResNet and EfficientNet with Grad-CAM Analysis Wahyuni Zalmi; Rahmi Putri Kurnia; Dyah Listianing Tyas
Informatik : Jurnal Ilmu Komputer Vol 22 No 2 (2026): August 2026
Publisher : Fakultas Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52958/iftk.v22i2.14396

Abstract

Plant diseases can reduce crop quality and productivity, making early detection an important aspect of modern agriculture. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have shown promising performance in image-based plant disease classification. This study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization. The experiments were conducted using the PlantVillage dataset consisting of 15 classes of healthy and diseased plant leaves.The research process included image preprocessing, data augmentation, transfer learning, model training, performance evaluation, and explainability analysis. The dataset was divided into training and validation sets with a ratio of 80:20. Model performance was evaluated using accuracy, loss, confusion matrix, precision, recall, and f1-score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 92% and a validation loss of 0.19, outperforming EfficientNetB0 which obtained 76% accuracy and 0.82 validation loss. The classification report demonstrated that ResNet50 provided more stable and consistent predictions across most disease classes. Furthermore, Grad-CAM visualization successfully highlighted disease-relevant regions such as lesions, discoloration, and damaged leaf areas, improving the interpretability of the CNN model. The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
Co-Authors - Yulherniwati Afcha Arel Pratama Dyah Listianing Tyas Indri Rahmayuni Novi Novi Wahyuni Zalmi Yance Sonatha Yori Adi Atma Yulia Jihan Sy