eJournal PoliTekniK TEGAL (Politeknik Harapan Bersama Tegal)
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Pengembangan Aplikasi Presensi QR Code Berbasis Website Dengan Metode Agile
No full textAttendance recording for students at Pondok Pesantren Mahasiswa (PPM) Al-Hikmah Semarang is still conducted manually, making it prone to recording errors and time-consuming when compiling attendance data. This study aims to develop a QR Code-based attendance system to improve the efficiency and accuracy of attendance recording. The method used involves designing and developing a web-based system using the Laravel framework and the Agile methodology. The system is designed to be used by both students and class supervisors during learning activities at the dormitory. The research results show that the system can automate student attendance through QR Code scanning, store data in a structured manner, and provide accurate attendance reports that are easily accessible to the dormitory administrators. Additionally, features such as schedule management, student data management, and attendance reporting based on specific criteria have been implemented to support more effective administration. The system is also equipped with a feature to print attendance recap reports. With the implementation of this system, student attendance management becomes more efficient, transparent, and less prone to errors compared to the previously used manual method
Ricetection: Implementasi Arsitektur Vision Transformer untuk Klasifikasi Varietas Beras dengan Presisi Tinggi
No full textThis research presents Ricetection, a rice variety classification system developed using the Vision Transformer (ViT) architecture to achieve high-precision rice grain recognition. Productivity and quality control in rice post-harvest handling remain crucial challenges, particularly in cases where rice varieties are visually similar. Rice quality assessment that is still performed manually often leads to inconsistency and misclassification. To address this, this study proposes an image-based classification approach utilizing a publicly available rice grain dataset consisting of four Indonesian varieties: IR64, Inpari 32, Mekongga, and Ciherang. The image preprocessing stage includes normalization, resizing, and augmentation to enhance data variation and improve model learning. TheViT architecture is applied as the core classifier due to its ability to capture global image features more effectively compared to traditional CNN-based models. Model performance evaluation shows that ViT achieves superior accuracy, reaching above 98% in the testing phase. Additionally, a web-based application prototype is implemented to provide real-time prediction through an intuitive user interface, enabling users to upload rice grain images and obtain classification results immediately. The proposed system is expected to assist rice farmers, quality control institutions, and supply chain stakeholders in improving decision-making related to rice standardization and variety detection. Future research may explore broader datasets, multi-class classification expansion, and integration into industrial-scale quality inspection systems
Perbandingan Logistic Regression, SVM, dan Random Forest untuk Analisis Sentimen Ulasan Aplikasi Gopay
No full textThe expansion of Indonesia's digital financial landscape has triggered a surge in the adoption of e-wallets, most notably GoPay. Within this context, feedback available on application platforms such as the Google Play Store serves as a crucial metric for assessing user sentiment and service quality. Sentiment analysis based on machine learning algorithms allows for systematic and objective identification of public opinion. This study used 3,000 user reviews collected through web scraping from the Google Play Store, received up to April 21, 2025, with initial labeling based on a lexicon approach. Although many studies have compared sentiment classification algorithms, there has been no research specifically comparing the performance of Logistic Regression, Support Vector Machine (SVM), and Random Forest in the context of GoPay user reviews with lexicon based labeling. This paper aims to fill the existing void by evaluating the comparative performance of three algorithms based on sentiment classification metrics. Preprocessing procedures encompassed cleaning, case-folding, stemming, slang normalization, tokenizing, filtering, and labeling to ensure data quality. The models, built within the Scikit-learn environment, were tested for accuracy, precision, recall, and F1-score. Empirical results confirm that Logistic Regression outperformed the alternatives, securing 88.16% accuracy while maintaining stability across all sentiment categories. SVM recorded 87.5% accuracy but was weak in detecting negative sentiment. Random Forest showed the lowest performance with 79.33% accuracy and less consistent classification results. Thus, Logistic Regression is recommended as the most effective algorithm for GoPay user sentiment analysis. Future research can explore deep learning-based approaches to handle higher sentiment complexity
Comparison of IndoBERT and Bi-LSTM Models for Indonesian Law Violation Text Classification
No full textLegal violations in Indonesia, particularly those under the Criminal Code (KUHP) and the Information and Electronic Transactions Law (UU ITE), are often difficult for the general public to interpret due to the complexity of legal language and article structures. This research aims to build a multilabel classification model that can automatically identify relevant legal articles from user-provided case descriptions. Two models were developed and compared: Bidirectional Long Short-Term Memory (Bi-LSTM) and IndoBERT. Using a manually labeled dataset, both models were evaluated through accuracy, F1-score, and Hamming Loss metrics, as well as 5-fold cross-validation. The results showed that IndoBERT outperformed Bi-LSTM with an average accuracy of 97% and a Hamming Loss of 0.027. However, t-test analysis revealed no statistically significant difference in F1-scores, indicating that both models have comparable effectiveness in capturing multiple labels. A confusion matrix analysis further identified patterns of misclassification in semantically similar articles. This study demonstrates the potential of NLP and deep learning to support legal awareness and provide the public with easier access to legal information
Integrasi Akuntansi dan Teknologi : Pengembangan Website untuk Meningkatkan Pengelolaan Keuangan Pada Bank Sampah
No full textKeberadaan bank sampah KKBS yang berlokasi di kawasan komplek pertokoan dan pasar tradisional di kota Tanjungpinang dapat menjadi nilai tambah strategis dalam pengembangan dan keberlanjutan usahanya. Hal ini menjadi peluang besar untuk menjaring lebih banyak nasabah, terutama dari kalangan pedagang dan pengunjung pasar. Meskipun memiliki potensi yang besar, Bank Sampah KKBS masih menghadapi beberapa tantangan, terutama dalam aspek manajemen dan pemasaran. Dari sisi manajemen, sistem pencatatan keuangan masih dilakukan secara sederhana dan belum mengikuti siklus akuntansi yang terstruktur. Sementara itu, dari segi pemasaran, bank sampah ini belum memiliki platform digital yang dapat memperluas jangkauan. Kegiatan ini bertujuan untuk meningkatkan kapasitas Bank Sampah KKBS melalui perbaikan sistem manajemen dan strategi pemasaran yang lebih efektif. Metode PkM yang digunakan yaitu metode pelatihan dan pendampingan kepada mitra Bank Sampah KKBS. Setelah adanya pelatihan dan pendampingan, Bank Sampah dapat melakukan sistem pencatatan dengan baik dan terstruktur, dan Bank Sampah dapat mengoperasikan platform digital berupa web yang dapat mmperluas jangakaun. Hal ini menunjukan peningkatan sekitar 30-40%. Peningkatan Kapasitas Bank Sampah ini penting untuk keberlanjtan BANK Sampah kedepannya sehingga akan terus beroperasi sampai kapanpun
Transformasi Ekonomi Digital Desa Melalui Pendampingan UMKM dan Affiliate Marketing
No full textUsaha Mikro, Kecil dan Menengah (UMKM) memiliki peranan penting dalam mendukung perekonomian desa, namun masih menghadapi hambatan, seperti kurangnya pemahaman tentang legalitas usaha, keterbatasan akses ke pasar, dan rendahnya penerapan teknologi digital. Di Desa Tegal Ombo, Kecamatan Way Bungur, Kabupaten Lampung Timur, banyak pelaku UMKM sudah memiliki Nomor Induk Berusaha (NIB), tetapi belum memahami fungsi dan manfaatnya secara menyeluruh. Selain itu, sistem pembayaran masih didominasi secara tunai, sementara kalangan muda lebih sering menggunakan media digital hanya untuk hiburan. Kegiatan pengabdian ini ditujukan untuk memperkuat UMKM melalui peningkatan legalitas usaha dan literasi keuangan digital, sekaligus melibatkan remaja desa dalam pemasaran produk dengan sistem affiliate marketing. Metode yang digunakan mencakup observasi, sosialisasi, pelatihan teknis, workshop, pendampingan, serta evaluasi kegiatan. Mitra sasaran terdiri dari 5 pelaku UMKM dan 10 remaja desa. Hasil menunjukkan adanya peningkatan pemahaman UMKM mengenai manfaat NIB dan pentingnya digitalisasi usaha meskipun perilaku masyarakat yang terbiasa dengan pembayaran tunai masih menjadi tantangan. Secara umum, program ini telah berkontribusi dalam peningkatan literasi digital, pemahaman legalitas usaha, serta keterampilan pemasaran digital sebagai langkah awal transformasi ekonomi desa menuju arah yang lebih inklusif dan berkelanjutan
Classroom Language Simulation Video bagi Guru Non Bahasa Inggris SMAS Islam TH Bumiayu Brebes
No full textPermasalahan prioritas mitra, yaitu: kurang menguasai keterampilan berbahasa secara menyeluruh khususnya keterampilan berbicara dalam classroom language sebagai bahasa kelas; minat dan motivasi mitra untuk belajar bahasa Inggris sebagai bahasa Internasional masih rendah; kurang menguasai kosakata, tata bahasa, dan budaya bahasa Inggris; belum adanya pembiasaan interaksi bilingual dengan penggunaan bahasa pendamping pengajaran (classroom language); dan belum adanya pelatihan classroom language bagi para guru non bahasa Inggris. Tujuan pengabdian adalah meningkatkan minat, pengetahuan, motivasi, dan keterampilan para guru non bahasa Inggris SMA Islam Ta’Allumul Huda Bumiayu dalam proses kegiatan belajar mengajar di kelas menggunakan bahasa Inggris. Metode pengabdian terdiri dari 4 tahapan yaitu: persiapan, pelaksanaan (dengan memberikan pelatihan dengan menggunakan drilling dan simulasi peer-teaching), monitoring dan evaluasi, dan keberkelanjutan. Kegiatan ini diikuti oleh 27 guru non bahasa Inggris SMA Islam TH Bumiayu, untuk mengukur peningkatan keterampilan dilakukan dengan melakukan wawancara setelah kegiatan. Hasil pelaksanaan menunjukkan peningkatan minat, pengetahuan, motivasi, dan keterampilan para guru non bahasa Inggris SMA Islam Ta’Allumul Huda Bumiayu dalam proses kegiatan belajar mengajar di kelas menggunakan bahasa Inggris. Dan adanya peningkatan keterampilan berbahasa Inggris mitra pengabdian (para guru non bahasa Inggris SMA Islam Ta’Allumul Huda Bumiayu) dalam menggunakan bahasa bilingual sebagai bahasa pendamping pengajaran (classroom language)
Sistem Presensi Otomatis Menggunakan Pengenalan Wajah Berbasis Deep Learning dan Real-Time Database
No full textThe attendance system is a crucial component in the operations of any organization. However, most existing attendance systems still require significant time or manual intervention from users. This study aims to develop a deep learning-based face recognition application with a real-time database to record attendance automatically. This approach is expected to make the attendance process more accurate, faster, and more convenient compared to traditional attendance methods. The study employs a quantitative method through primary data analysis from laboratory testing using dummy data. This testing aims to measure the accuracy of the face recognition system in automatically recording attendance. A face recognition application prototype has been successfully developed with real-time database integration using the Python programming language. The test results show that the application can recognize all faces in the database with a very high accuracy level. The system performance metrics indicate an accuracy of 99.1%, precision of 98.7%, recall of 98.7%, and F1-score of 98.7%. Additionally, the model has been implemented on an NVIDIA Jetson Nano mini-processor, demonstrating efficient operation on low-power hardware and real-time face recognition with optimal processing speed
Pengembangan Prototipe untuk Prediksi Tingkat Penyeduhan Kopi Menggunakan Data Spektroskopi dan Deep Learning
No full textConsistency in coffee flavor is a crucial factor for coffee enthusiasts, thus requiring a method capable of objectively measuring the coffee brewing level in accordance with the standard brewing chart. This study utilizes the AS7265X spectroscopy sensor to capture the characteristics of coffee based on the resulting light spectrum. The spectral data is then used in a deep learning model using the Convolutional Neural Network (CNN) algorithm to classify the coffee brewing level into five distinct classes. A total of 150 data samples were used in the training and testing process. Initial results show that the model achieved a very high average accuracy of approximately 97%. After hyperparameter tuning using the Random Search method, the model's accuracy further improved, reaching a very high accuracy. However, this performance improvement resulted in a trade-off in computational time, with execution time increasing from 15 seconds to 1 minute and 43 seconds. This research is expected to contribute to ensuring consistent coffee brew quality and to open opportunities for further studies that combine sensor technology and artificial intelligence in the food and beverage sector
Analisis Berbasis Convolutional Neural Network untuk Pendeteksian Kanker Prostat dengan Citra Magnetic Resonance Imaging (MRI)
No full textKanker prostat adalah tumor ganas yang berada dari kelenjar prostat, yang merupakan bagian penting dari sistem reproduksi pria. Adanya peningkatan prevalensi kanker prostat maka diperlukan deteksi dini yang akurat. Penelitian ini memfokuskan pada pemanfaatan deep learning, khususnya metode Convolutional Neural Network (CNN) untuk mendiagnosis kanker prostat melalui citra MRI. Diperlukan penelitian untuk mengkaji tiga arsitektur CNN: U-Net, nnU-Net, dan nnDetection agar didapatkan arsitektur yang terbaik. Data penelitian ini menggunakan data sekunder sejumlah 1294 citra MRI dari The PI-CAI Challenge “Artificial Intelligence Radiologists Prostate Cancer Detection in MRI” tahun 2022. Data tersebut menjalani proses pre-processing, termasuk normalisasi intensitas piksel, augmentasi data seperti rotasi dan scaling, serta pemotongan gambar untuk menghilangkan area yang tidak relevan. Proses selanjutnya data tersebut akan masuk ke tahap pelatihan model dengan menggunakan ketiga arsitektur. Hasil dari pelatihan tersebut akan dievaluasi kinerja modelnya dengan menggunakan metrik Area Under the Receiver Operating Characteristic Curve (AUROC) dan Average Precision (AP). Hasil evaluasi menunjukkan bahwa arsitektur U-Net mencapai AUROC 89,94% dan AP 51,22%, arsitektur nnU-Net mencapai AUROC 97,75% dan AP 86,67%. dan arsitektur nnDetection mencapai AUROC 83,66% serta AP 49,91%. Dari perbandingan hasil ketiga arsitektur maka didapatkan hasil terbaik adalah arsitektur nnU-Net dengan capaian AUROC 97,75% dan AP 86,67%. Penelitian ini menunjukkan potensi penggunaan CNN dalam diagnosis kanker prostat melalui citra MRI. Temuan penelitian menegaskan pentingnya pemilihan arsitektur yang tepat dalam aplikasi deep learning untuk citra medis