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All Journal Jurnal Masyarakat Informatika Jurnal Teknologi Informasi dan Ilmu Komputer JTERA (Jurnal Teknologi Rekayasa) Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Infomedia
Utama, Nugraha Priya
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Eligibility Study of Targeted Electricity Subsidies Using DBSCAN on 450 VA and 900 VA Households at PLN UP3 Bandung Suchardy, Randy Zakya; Firmansyah, Adi; Utama, Nugraha Priya; Kesuma, Rahman Indra
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.26818

Abstract

PT PLN (Persero), a State-Owned Enterprise (SOE), is mandated by Law No. 30/2007 on Energy and Law No. 30/2009 on Electricity to provide subsidy funds for the poor. The objective of this study is to analyze eligibility criteria for electricity subsidy recipients for customers using 450 VA and 900 VA power groups, to target the electricity subsidy program better. The data used is postpaid customer data from UP3 Bandung in September 2023. The variables used are the amount of electricity consumption, the number of bills, late fees, installment fees, and 50 other variables. The method used in this research is DBScan Clustering which is applied to each power group. Within each group, we analyzed two normalized versions of the dataset standard version and the minmax version. Furthermore, to assess the optimal clustering results, we integrated various metrics, including the Davies-Bouldin Index and Silhouette Score with visual assessment. After that, the best factor suggestions were sought through decision trees, by performing different decision tree classifiers for each power group, using normalized versions of cluster labels. The results showed that among the 50 features available in the raw dataset, it was successful in identifying key features, such as late fees, installment fees, electricity consumption, and bill charges to be important criteria
Crowd Density Level Classification for Service Waiting Room Based on Head Detection to Enhance Visitor Experience Istiqomah, Atika; Seida, Fatih; Daradjat, Nadhira Virliany; Kesuma, Rahman Indra; Utama, Nugraha Priya
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.29965

Abstract

The crowd within a confined space can potentially lead to air stagnation in waiting areas. Constantly running air conditioning throughout the day to balance air circulation may result in excessive energy consumption by the building. To address this issue, Heating, Ventilating, and Air-Conditioning (HVAC) systems are employed to manage and regulate indoor energy usage. However, sensor-based detection often fails to capture human variables promptly, resulting in less accurate density readings. Camera footage proves to be more reliable than sensors in accurately detecting crowds. This research utilizes You Only Look Once version 8 (YOLOv8), a robust algorithm for object detection, particularly effective in crowd detection for images, along with Convolutional Vision Transformer (CvT) for crowd density level classification into "Normal" and "Crowded" levels. CvT enhances classification accuracy by incorporating function from Convolutional Neural Network (CNN) in model training, including receptive field, shared weights, etc. By integrating YOLOv8 and CvT, this method focuses on accurately classifying crowd density levels after identifying human presence in the waiting area (indoor). Evaluation metrics include mean Average Precision (mAP) for YOLOv8, and accuracy, precision, recall, and f1-score for CvT. This approach directly influences the management of HVAC systems.
Predictive Maintenance for Electrical Substation Components Using K-Means Clustering: A Case Study Roosadi, Hizkia Raditya Pratama; Emiliano, Hughie Alghaniyyu; Astari, Satria Dina; Utama, Nugraha Priya; Kesuma, Rahman Indra
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 2 (2024): September 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i2.26815

Abstract

PT PLN (Persero) UP2D Kalselteng aims to provide reliable electricity supply, necessitating effective substation maintenance. This study proposes a predictive maintenance approach using K-means clustering on electrical current performance data from eight components in the Amuntai main electrical substation. The data undergoes preprocessing, including mapping to absolute z-scores to address electricity fluctuations. The K-means algorithm clusters performances, and models are evaluated using Silhouette scores. Results indicate the potential for predicting maintenance needs, as clusters align with real power outage data. The proposed method provides a proactive strategy for substation maintenance, enhancing system reliability. Feature combination experiments reveal that individual models for transformers and feeders are optimal. Hyperparameter tuning refines models, showcasing silhouette scores above 0.5, indicative of high-quality clusters. Comparisons with real-world power outage data validate the model's capability to identify anomalies, reinforcing the feasibility of the predictive maintenance approach. While the study demonstrates promise, on-field implementation and additional experiments are crucial for comprehensive validation and refinement of the predictive maintenance models.
Analisis Komparatif Algoritma LSTM, GRU, BiGRU, dan BiLSTM Untuk Prediksi Degradasi Bearing Turbin PLTU Raymond, Rifky; Saputra, Neva; Tupamahu, Meldrin; Herawati, Neng Ayu; Purwarianti, Ayu; Utama, Nugraha Priya
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 1 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i1.7127

Abstract

Pembangkit Listrik Tenaga Uap (PLTU) merupakan salah satu sumber utama pasokan listrik nasional, di mana keandalan komponen kritis seperti bearing turbin sangat menentukan kontinuitas operasional. Kegagalan pada bearing dapat menyebabkan downtime tidak terduga dan kerugian biaya yang signifikan. Oleh karena itu, pendekatan predictive maintenance menjadi strategi penting dalam memitigasi potensi kegagalan tersebut. Penelitian ini bertujuan untuk membandingkan performa empat algoritma deep learning yaitu LSTM, GRU, BiGRU, dan BiLSTM dalam memprediksi Remaining Useful Life (RUL) dari bearing turbin. Data yang digunakan merupakan data sensor aktual dari pembangkit, yang telah direduksi dimensinya menggunakan Principal Component Analysis (PCA) untuk membentuk Health Index sebagai representasi degradasi peralatan. Evaluasi dilakukan menggunakan metrik MAE (Mean Absolute Error) dan RMSE (Root Mean Squared Error). Hasil eksperimen menunjukkan bahwa model BiLSTM memiliki performa terbaik dengan nilai MAE sebesar 0.27 dan RMSE sebesar 0.37. Penelitian ini berkontribusi dalam menyediakan panduan pemilihan model prediksi RUL berbasis data sensor riil pada peralatan PLTU, yang mendukung penerapan pemeliharaan prediktif secara lebih akurat dan efisien
Systematic Literature Review on Medical Image Captioning Using CNN-LSTM and Transformer-Based Models Fadhilah, Husni; Utama, Nugraha Priya
Jurnal Masyarakat Informatika Vol 16, No 1 (2025): May 2025
Publisher : Department of Informatics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jmasif.16.1.73127

Abstract

Creating descriptive text from medical images to aid in diagnosis and treatment planning is known as medical image captioning, and it is a crucial duty in the healthcare industry. In this study, medical image captioning techniques are systematically reviewed in the literature with an emphasis on Transformer-based models and Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Aspects like as model designs, datasets, evaluation measures, and difficulties encountered in practical implementations are all examined in this paper. According to the results, Transformer-based models, specifically Swin Transformer and Vision Transformer (ViT), perform better than CNN-LSTM-based models in terms of BLEU, ROUGE, CIDEr, and METEOR scores, resulting in more accurate clinically relevant caption generation. However, there are still a number of issues, including interpretability, computing requirements, and data restrictions. Potential future routes to increase the efficacy and practical application of medical image captioning systems are covered in this paper, including hybrid model approaches, data augmentation techniques, and enhanced explainability methodologies.
Causal Discovery of ICU Stay Length: PC Algorithm Approach with ICD-Lab Data Halim, Ismail Syababun; Martalia, Anastasia Mia; Hibatullah, Muhammad Helmi; Utama, Nugraha Priya; Purwarianti, Ayu
JTERA (Jurnal Teknologi Rekayasa) Vol 10, No 1: Juni 2025
Publisher : Politeknik Sukabumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31544/jtera.v10.i1.2025.173-182

Abstract

Dalam sistem pelayanan kesehatan, Unit Perawatan Intensif (ICU) merupakan komponen penting untuk menangani pasien dalam kondisi kritis yang membutuhkan pemantauan intensif. Namun, durasi rawat inap atau Length of Stay (LoS) seorang pasien di ICU biasanya sangat bervariasi dan perpanjangan LoS berdampak signifikan pada beban biaya, penggunaan sumber daya, dan efisiensi pelayanan di rumah sakit. Penelitian ini bertujuan untuk mengidentifikasi faktor kausal yang memengaruhi LoS ICU menggunakan Algoritma Peter-Clark (PC) untuk penemuan kausal atau Causal Discovery. Data yang digunakan berasal dari MIMIC-IV, sebuah basis data klinis komprehensif dari Beth Israel Deaconess Medical Center tahun 2008–2019, yang mencakup demografi pasien, kode diagnosis ICD, dan hasil pemeriksaan laboratorium. Metode yang digunakan meliputi penerapan Algoritma PC, yang dipilih karena kemampuannya pada data berdimensi tinggi dengan Fisher's Z-test untuk pengujian independensi, yang diimplementasikan pada berbagai tingkat signifikansi (ɑ = 0.01, 0.05, 0.1). Validasi dilakukan melalui 500 iterasi bootstrap untuk mengetahui stabilitas dari struktur graf kausal. Hasil analisis menunjukkan enam variabel yang secara konsisten menjadi penyebab langsung LoS ICU diantaranya admission type, APR-DRG severity, high mortality risk, category, flag, dan anchor age. Struktur kausal yang dihasilkan memberikan gambaran hubungan sebab-akibat yang stabil dan signifikan antar variabel klinis, yang dapat digunakan untuk mendukung pengambilan keputusan berbasis data dalam manajemen pasien dan alokasi sumber daya ICU. Studi ini juga menegaskan potensi pendekatan Causal Discovery dalam analitik layanan kesehatan, khususnya dalam memahami faktor determinan LoS ICU secara mendalam.
Segmentasi Awan pada Citra Satelit Multispektral Menggunakan Convolutional Neural Networks Wijaya, Bagus Setyawan; Munir, Rinaldi; Utama, Nugraha Priya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 12 No 6: Desember 2025
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2025125

Abstract

Citra satelit multispektral adalah jenis citra yang diambil oleh satelit penginderaan jauh yang menangkap informasi dari berbagai rentang spektrum elektromagnetik. Citra satelit multispektral memiliki peran yang sangat penting karena kemampuannya untuk memberikan informasi untuk area yang luas secara berkala. Akan tetapi, salah satu permasalahan utama dari citra satelit multispektral adalah kontaminasi awan. Tutupan awan pada area yang luas menyebabkan informasi yang ada pada citra satelit menjadi bias. Oleh karena itu, segmentasi awan yang akurat pada citra satelit multispektral menjadi sangat penting. Penelitian ini berfokus untuk mengembangkan model segmentasi awan berbasis Convolutional Neural Networks (CNN) dengan kinerja yang baik. Penelitian diawali dengan proses pembuatan dataset citra satelit multispektral Sentinel-2 Level-2A. Algoritma s2cloudless digunakan untuk membentuk label dengan 4 kelas, yaitu: shadow, clear, cirrus, dan cloud. Selanjutnya, model segmentasi awan berbasis CNN dikembangkan berdasarkan beberapa model segmentasi semantik yang ada. Model tersebut dilatih dan dievaluasi pada 11.240 citra yang telah dibuat sebelumnya. Melalui ablation study, diperoleh model segmentasi awan terbaik yaitu Deeplabv3+ dengan backbone ResNet18. Arsitektur tersebut memberikan kinerja yang sangat menjanjikan dengan nilai F1-score, pixel accuracy, dan mIoU sebesar 0.922, 0.923, dan 0.733 secara berurutan. Dengan demikian penelitian terkait citra satelit diharapkan menjadi lebih akurat dalam melakukan klasifikasi atau prediksi objek yang ada di permukaan bumi.   Abstract Multispectral satellite imagery is a type of imagery captured by remote sensing satellites that record data from various ranges of the electromagnetic spectrum. Its importance lies in its ability to provide information over large areas periodically. However, one of the main challenges with multispectral satellite imagery is cloud contamination. Cloud cover over large regions can bias the information captured in the imagery. Therefore, accurate cloud segmentation in multispectral satellite imagery is crucial. This study focuses on developing a high-performance cloud segmentation model based on Convolutional Neural Networks (CNN). The research began with the creation of a multispectral satellite imagery dataset from Sentinel-2 Level-2A. Labels with four classes—shadow, clear, cirrus, and cloud—were generated using the s2cloudless algorithm. Subsequently, a CNN-based cloud segmentation model was developed using several existing semantic segmentation models. The model was trained and evaluated on 11,240 images from the dataset. Through an ablation study, the best cloud segmentation model was identified as Deeplabv3+ with a ResNet18 backbone. This architecture demonstrated a highly promising performance, achieving F1-score, pixel accuracy, and mIoU values of 0.922, 0.923, and 0.733, respectively. As a result, this research is expected to improve the accuracy of satellite imagery classification and object prediction on the Earth's surface.
Retrieval-Augmented Generation (RAG) Chatbot for Handling Customer Complaints in the Energy Sector Haryono Putro, Ibnu Prastowo; Antoni, Jefry; Adhitya, Maulana Krisna; Herawati, Neng Ayu; Purwarianti, Ayu; Utama, Nugraha Priya
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 2 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i2.7169

Abstract

Fast and accurate customer service is critical in the energy sector, especially for large-scale utilities like PLN. This study introduces a novel Retrieval-Augmented Generation (RAG)-based chatbot tailored for PLN’s internal operational context to automate customer complaint resolution in Bahasa Indonesia. In contrast to previous approaches that utilize only fine-tuned LLMs or retrieval-based question answering, our system uniquely integrates internal complaint records stored in internal database with a local Indonesian-optimized LLM through LangChain orchestration. The proposed architecture features temporal and linguistic preprocessing, vector embedding using FAISS, and a dynamic clarification-fallback mechanism, ensuring context-aware and grounded responses. This work contributes a scalable framework for deploying generative AI in high-stakes public utility settings, emphasizing data privacy, language fidelity, and real-time applicability. Evaluation results both simulated and human-reviewed demonstrate the chatbot’s effectiveness, achieving BLEU-4 of 46.5 and ROUGE-L of 0.63, with 92% of answers rated helpful. These findings underscore the model's potential to enhance customer experience and operational efficiency in Indonesia’s energy sector.
Sistem Rekomendasi Lokasi Optimal dan Potensi Penghematan Energi Pemasangan PLTS Atap Berbasis AI di Pulau Jawa Aminuddin, Amir; Supanto, Supanto; Saputra, Hadi; Herawati, Neng Ayu; Purwarianti, Ayu; Utama, Nugraha Priya
Jurnal Infomedia: Teknik Informatika, Multimedia, dan Jaringan Vol 10, No 2 (2025): Jurnal Infomedia
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jim.v10i2.7219

Abstract

Transisi menuju energi terbarukan di Indonesia menuntut pendekatan berbasis data dalam menentukan lokasi optimal pemasangan Pembangkit Listrik Tenaga Surya (PLTS) atap dan dalam memperkirakan dampak ekonomisnya. Penelitian ini mengembangkan sistem rekomendasi berbasis Artificial Intelligence (AI) yang mengintegrasikan data penyinaran matahari dari BMKG dan data konsumsi listrik dari PLN untuk mendukung perencanaan PLTS atap di Pulau Jawa. Pendekatan dilakukan melalui tiga metode pembelajaran mesin utama: klasifikasi untuk mengevaluasi kelayakan pelanggan, klasterisasi wilayah menggunakan algoritma clustering, dan regresi untuk memprediksi potensi penghematan energi. Lima algoritma klasifikasi dibandingkan, dengan LightGBM menunjukkan performa tertinggi (akurasi 87%). Segmentasi wilayah optimal diperoleh melalui KMeans (silhouette score 0,5566). Estimasi penghematan energi paling akurat dihasilkan oleh XGBoost Regressor dengan koefisien determinasi (R²) sebesar 0,9999. Hasil ini menunjukkan bahwa pendekatan integratif berbasis AI dapat menyediakan informasi prediktif yang akurat dan aplikatif bagi penyusunan strategi promosi dan investasi PLTS atap, sekaligus memberikan estimasi manfaat kuantitatif bagi pelanggan. Penelitian ini memberikan kontribusi signifikan dalam pengembangan sistem pendukung keputusan untuk energi terbarukan berbasis analitik spasial dan perilaku konsumsi.
Co-Authors Adhitya, Maulana Krisna Adi Firmansyah Aminuddin, Amir Antoni, Jefry Astari, Satria Dina Ayu Purwarianti Daradjat, Nadhira Virliany Emiliano, Hughie Alghaniyyu Fadhilah, Husni Hadi Saputra Halim, Ismail Syababun Haryono Putro, Ibnu Prastowo Hibatullah, Muhammad Helmi Istiqomah, Atika Martalia, Anastasia Mia Neng Ayu Herawati Rahman Indra Kesuma Rifky Raymond, Rifky Rinaldi Munir Roosadi, Hizkia Raditya Pratama Saputra, Neva Seida, Fatih Suchardy, Randy Zakya Supanto Supanto Tupamahu, Meldrin Wijaya, Bagus Setyawan