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All Journal Jurnal Algoritma JUITA : Jurnal Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Journal of Community Development Jurnal Diksatrasia : Jurnal Ilmiah Pendidikan Bahasa dan Sastra Indonesia Jurnal Algoritma
Leni Fitriani
Institut Teknologi Garut
Computer Science & IT Education Languange, Linguistic, Communication & Media Nursing Public Health Social Sciences

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Transformer-Based Detection Model for Number Recognition on Electric kWh Meters Leni Fitriani; Ahmad Sanusi; Rita Rismala; Dewi Tresnawati
JUITA: Jurnal Informatika JUITA Vol. 13 Issue 2, July 2025
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v13i2.26161

Abstract

Manual recording of analog kWh meters frequently results in user complaints due to discrepancies between recorded and actual electricity usage. These issues stem from the continued reliance on manual data collection. This study proposes a model that automatically detects and extracts numerical values from kWh electricity meters using the Detection Transformer (DETR) for object detection and EasyOCR for optical character recognition (OCR). The model was developed using the Machine Learning Life Cycle (MLLC) methodology, comprising data acquisition, preprocessing, modeling, evaluation, and deployment. Evaluation using the Mean Average Precision (mAP) metric yielded a score of 96.83%, demonstrating high object detection accuracy. The trained model was integrated into a simple web application built with the Flask framework. While the model performed well on high-quality images, its effectiveness declined on low-quality images, such as blurry or distant captures. This study highlights the potential of DETR for object detection and OCR-based text extraction in analog meter reading, while also identifying challenges in handling suboptimal image conditions for future improvements
Pendampingan Kelompok UMKM di Garut Dalam Penggunaan Dompet Digital Untuk Mendukung Ekonomi Digital Rina Kurniawati; Leni Fitriani; Muhammad Rikza Nashrulloh
Journal of Community Development Vol. 5 No. 3 (2025): April
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i3.1292

Abstract

The community service program aimed to enhance the competitiveness of Micro, Small, and Medium Enterprises (MSMEs) in Garut Regency through the implementation of digital wallet technology. Addressing the challenges of market access and financial management faced by MSMEs, the program developed an application called MitraREID. This application was designed to assist MSMEs in managing transactions, recording expenses, generating financial reports, and optimizing product management. The implementation process included socialization, intensive training, application deployment, and technical assistance involving the UMKM community, Mikromega, in Garut. The results indicated a significant improvement in the MSMEs' ability to utilize digital technology for daily operations. The MitraREID application facilitated business management, enhanced transaction efficiency, and allowed MSMEs to structure their financial management more effectively. The program's impact was quantitatively measured by pre- and post-test scores, which showed an increase from an average of 79/100 before training to 99/100 after training. This significant improvement demonstrated the enhanced digital skills and understanding of participants in utilizing digital wallet technology to support their business operations, enabling them to compete more effectively in local and national markets.
Melatih Cara Berfikir Komputasi Pada Siswa Sekolah Dasar dan Menengah di Kabupaten Garut Dewi Tresnawati; Detila Rostilawati; Ayu Latifah; Eri Satria; Asri Mulyani; Sri Rahayu; Leni Fitriani; Rinda Cahyana; Shopi Nurhidayanti; Cha Cha Nisya Asyah
Journal of Community Development Vol. 5 No. 2 (2024): December
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/comdev.v5i2.1373

Abstract

Technological advances in the 21st century demand Computational Thinking skills that are increasingly important in the digital era. However, the application of Computational Thinking (CT) at the primary and secondary education levels in Garut Regency is still limited. This study aims to improve the understanding of CT among students in elementary schools, junior high schools, and vocational high schools through a training program that includes pre-test, interactive training, and post-test. The training method was designed according to the educational level of the participants, using Bebras questions to train CT skills. The pre-test results showed an average initial score of 47.19, while the post-test results increased by an average of 25.75 points to 72.94. The findings show that the training successfully improved participants' understanding of CT through a structured approach, including introduction of concepts through sample Bebras problems, practice problems, as well as a comprehensive question and answer session.
Rancang Bangun Sistem Informasi Pengelolaan Barang Untuk Usaha Kecil Menengah Berbasis Web Raden Erwin Gunadhi Rahayu; Leni Fitriani; Gilang Adi Pratama
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.1129

Abstract

Small and medium enterprises (SMEs) Karya Bhakti face challenges in inventory management, such as stock calculation errors, manual recording, and limited access for owners to reports and inventory in two separate stores. To overcome these problems, this study aims to design and develop a web-based inventory management information system that can be accessed online by owners and employees. This study uses the Rational Unified Process (RUP) method, which consists of four stages: Inception, Elaboration, Construction, and Transition. The system design was carried out using Unified Modeling Language (UML) with four main diagrams: use case, activity, sequence, and class diagrams. The system was implemented using the PHP programming language and the Laravel framework. The results of this study indicate that this system can facilitate the process of managing incoming and outgoing goods, migrating stock between stores, and compiling monthly reports. The main contribution of this study is the development of a web-based goods management system that supports multi-users and remote access, which can significantly improve the operational efficiency of SMEs.
Pemanfaatan Arsitektur Mobilenetv3 Untuk Klasifikasi Hama Tanaman Padi Leni Fitriani; Garnis Kirani
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2761

Abstract

Rice is a key commodity in national food security, yet its productivity often declines due to pest attacks. Manual pest identification is considered inefficient, necessitating an accurate automated system. This study proposes an image classification model for rice crop pests using a Convolutional Neural Network with the MobileNetV3-Small architecture through a transfer learning approach. The model development process follows the SEMMA methodology (Sample, Explore, Modify, Model, Assess). The dataset consists of 5,395 images across seven major pest classes—brown planthopper, green planthopper, rice stem borer, larvae, thrips, false white pest, and rice water weevil—sourced from Kaggle, along with additional test data obtained through web scraping to simulate real-world conditions.Image quality analysis revealed variations in blur levels and lighting across classes, which were addressed using augmentation and class weighting. The model was trained with fine-tuning on upper layers, using the Adam optimizer and early stopping over 30 epochs. The results show a training accuracy of 94.18%, validation accuracy of 97.58%, and test accuracy of 98%, with average precision, recall, and F1-score values of 0.98. A 5-fold cross-validation yielded an average accuracy of 97.13% with a deviation of ±0.81, indicating stable performance. Compared with MobileNetV2, the MobileNetV3-Small model performed significantly better in both accuracy and computational efficiency (p-value = 0.0269). These findings demonstrate that lightweight architectures such as MobileNetV3-Small are effective for rice pest classification and hold potential for implementation in automated detection systems for smart agriculture applications.
Penerapan Arsitektur VGG-16 dalam Pengenalan Wajah Bermasker untuk Sistem Presensi Leni Fitriani; Rinda Cahyana; Fauzan Abdurrahman
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.2845

Abstract

Covid-19 membatasi interaksi fisik dan mendorong penggunaan masker, sementara sejumlah sistem pengenalan wajah konvensional mengharuskan masker dilepas sehingga risiko paparan meningkat oleh karena itu penting adanya sistem pengenalan wajah bermasker dalam pencegahan penularan Covid-19. Tujuan dari penelitian ini membuat model Convolutional Neural Network (CNN) untuk pengenalan wajah bermasker yang dapat diterapkan pada sistem presensi. Metode yang digunakan Machine Learning Life Cycle dan model dibuat menggunakan arsitektur VGG-16. Hasil penelitian ini berupa model yang diterapkan pada prototype sistem presensi yang dapat mengindentifikasi pengguna bermasker. Model dilatih 40 epoch dengan hasil nilai training accuracy 0.9900 serta nilai training loss 0.2694 sedangkan nilai validation accuracy 0.9500 serta nilai validation loss 0.4065. Evaluasi model oleh confusion matrix dengan hasil rata-rata akurasi sebesar 0.95 atau 95%. Pada tahap akhir pengujian, model digunakan pada prototype sistem presensi dengan hasil deteksi tercepat yaitu 6 detik dan terlama 42 detik yang mana hal tersebut menjadi kontribusi utama penelitian ini dengan sistem realtime dan pipeline augmentasi yang relevan untuk skenario wajah bermasker. Keterbatasan terletak pada skala data dan lingkungan uji yang terbatas. Penelitian selanjutnya diharapkan mencakup evaluasi menggunakan Real World Masked Face Recognition Dataset (RMFRD) atau Simulated Masked Face Recognition Dataset (SMFRD), eksplorasi arsitektur yang lebih bervariasi, serta percepatan inferensi.
A Bilingual Academic Chatbot Based on Semantic Retrieval Using mBERT Leni Fitriani; Sahrudin Fiqri Muzahidar; Ade Sutedi; Fitri Nuraeni
JUITA: Jurnal Informatika JUITA Vol. 14 Issue 2, July 2026
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v14i2.29667

Abstract

This study proposes a bilingual academic chatbot based on a semantic retrieval approach using the Multilingual BERT (mBERT) transformer architecture to support academic information services in higher education. The dataset was constructed from official academic information at Garut Institute of Technology, including new student admissions, academic calendars, institutional profiles, and lecturer and staff data. The data were organized in a bilingual question–and–answer format in Indonesian and English. The mBERT model was fine-tuned using a Sentence-BERT framework to generate sentence embeddings for semantic retrieval tasks, with MultipleNegativesRankingLoss applied during training. Model performance was evaluated using BERTScore to measure semantic similarity between chatbot responses and human reference answers. Experimental results show that the fine-tuned model outperformed the base model, achieving an average F1-score improvement from 0.7638 to 0.8152 for Indonesian and from 0.7556 to 0.8005 for English. The results also demonstrate more stable score distributions, indicating consistent semantic performance. The optimized model was subsequently integrated into a web-based prototype to enable real-time bilingual academic question answering. These findings confirm that combining mBERT with semantic retrieval effectively enhances the relevance and contextual accuracy of chatbot responses, thereby supporting digital transformation and improving the efficiency of academic services in higher education.
TRANSFORMER-BASED GENERATIVE CHATBOT FOR HIGHER EDUCATION INFORMATION SERVICES WITH HYPERPARAMETER OPTIMIZATION AND MODEL EVALUATION Leni Fitriani; Endang Prayoga Hidayatulloh; Dede Kurniadi; Muhammad Rikza Nashrulloh
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8295

Abstract

Generative AI has revolutionized the creation of realistic multimedia content, including chatbots that generate human-like responses. This technology significantly improves higher education by enabling universities to provide fast, accurate, and efficient information services. Generative chatbots can handle multiple users simultaneously and operate 24/7, increasing productivity and accessibility. The model was developed using Machine Learning Lifecycle (MLLC) with deep learning algorithm and Transformer architecture. The dataset used consists of 5,403 question-answer pairs from Institut Teknologi Garut (ITG), which are divided into 5,089 pairs for training and 314 pairs for testing. From 12 hyperparameter configurations, the best combination (maxlen 80, num_layers 2, batch_size 128, embedding_dim 256, fully_connected_dim 256, num_heads 2, positional_encoding_length 512, learning_rate 0.0002, and epoch 100) achieved a BLEU score of 71.03% on the ITG dataset. Evaluation using ROUGE and METEOR also shows consistent performance, indicating good content coverage and semantic similarity. Retraining with another dataset using the same approach resulted in a slightly higher BLEU score of 72.05%, with a different optimal learning rate of 0.00025. The results of this study indicate that Transformer-based generative chatbots can support higher education services and highlight the importance of adjusting hyperparameters based on dataset characteristics. This research also provides opportunities to develop similar models in other universities by adapting datasets and exploring more advanced methods to improve performance in broader educational contexts.
Co-Authors A.A. Ketut Agung Cahyawan W Abdurohman Iqbal Abi Abdurahim Ade Sutedi Adi Haddaryadi Agas Moch Fauzan Ahmad Ilham Maulana Ahmad Sanusi Aji Pangestu Aldy Rialdy Atmadja Annisa Ridha Ramadhani Asri Mulyani Ayu Latifah Ayu Latifah Azmi Inda Roby Bunyamin . Cecep Iqbal Hambali Cha Cha Nisya Asyah Dede Kurniadi Detila Rostilawati Dewi Tresnawati Dini Destiani Siti Fatimah Endang Prayoga Hidayatulloh Eri Satria Fakhrul Hawariyan Fassa Fauzan Abdurrahman Febriansyah Adli Ramadhan Fikri Zakaria Rahman Fitri Nuraeni Garnis Kirani Gema Semesta Sholihat Gilang Adi Pratama Husni Mubarok Ikbal Lukmanul Hakim Ilpad Mutaqin Intania Sintiani Isan Priana Ivan Muhammad Hanif M. Iqbal Ismail Safei Pamungkas Mohamad Fikri Haekal Mohamad Ramdan Nurul Falah Muhammad Bagja Sukriyansah Muhammad Dzulfi Muwaffaq Muhammad Ghopur Muhammad Rikza Nashrulloh Muhammad Rois Dien Nenden Zakiatinnufus Popi Maspupah Raden Erwin Gunadhi Rahayu Raden Erwin Gunadhi Rahayu Raden Nazar Labani Rendi Nurdian Ridwan Setiawan Rifan Miptah Purnama Rina Kurniawati Rina Kurniawati Rinda Cahyana Rinda Cahyana Risma Liyana Ulfa Rita Rismala Rival Firmansyah Sahrudin Fiqri Muzahidar Sendra Ilham Mandala Sherine Valentin Margaretha Shopi Nurhidayanti Siti Hapsah Azizah Sri Rahayu ST Dini Nur Ismiyanti Taofik Faturochman Titin Rostini Tova Nanda Pratama Yoga Handoko Agustin Yosep Septiana