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Pembuatan Aplikasi Penjadwalan Mata Kuliah Menggunakan Algoritma Genetika Ryan Eka Wiratna; Afina Lina Nurlaili; Agung Mustika Rizki
Jurnal Teknologi dan Manajemen Vol 4, No 1 (2023): January
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat ITATS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.jtm.2023.v4i1.3990

Penjadwalan matakuliah yang dilakukan perguruan tinggi merupakan kegiatan yang selalu dilakukan pada setiap semesternya. Pembuatan jadual seringkali dilakukanoleh instansi terkait masih dilaksanakan secara manual sehingga hal tersebut menjadi kurang efisien sehingga membutuhkan waktu lama dan akurasi jadual yang dibuat tidak bisa dipastikan. Berdasarkan dari kondisi tersebut, maka dibuatkan system berbasis website yang digunakan untuk mempermudahkan dalam proses pembuatan jadual matakuliah sehingga pembuatan jadual dapat dilakukan secara otomatis sehingga menjadi efisien. Proses pembuatan jadual menerapkan algoritma genetika sebagai metode mencapai hasil jadwal yang tepat. Algoritma genetika bekerja melalui beberapa tahapan yaitu inisialisasi, seleksi, crossover dan mutasi untuk mendapatkan hasil akhir berupa jadual mata kuliah dengan nilai akhir fitness = 1. Pada penelitian ini terdapat beberapa ujicoba yang dilakukan yaitu melakukan variasi pada indikator algoritma dengan nilai yang berbeda. Hasil yang didapatkan dari keseluruhan ujicoba, jadual yang dihasilkan dari system yang dibuat menghasilkan nilai fitness = 1, sehingga jadwal yang terbentuk tidak bertabrakan dengan jadwal lainnya serta rata â€“ rata jadwal yang dihasilkan pada keseluruhan penelitan membutuhkan waktu 2,8 menit untuk jadwal terbentuk.Â Â

Design and Implementation of Digital Medical Record System Using the Boyer–Moore Algorithm Fidela Carissa Aramintha; Afina Lina Nurlaili; Firza Prima Aditiawan
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.2959

Praktik Mandiri Bidan (PMB) Siti Nur Aini, A.Md.Keb., a primary healthcare facility specializing in maternal and child health, continues to rely on manual, paper-based medical records. This practice causes slow data retrieval, high risk of data loss, and storage limitations. To address these issues, this study developed BIDIG (Bidan Medis Digital), a web-based medical record system integrating the Boyer–Moore string matching algorithm to accelerate and enhance the accuracy of patient record searches. The Boyer–Moore algorithm was selected for its proven efficiency in pattern matching, enabling larger shift steps during mismatches and outperforming other methods such as Rabin–Karp, Brute Force, and Knuth–Morris–Pratt in prior research. BIDIG was implemented using the Laravel framework and MySQL database, with features for patient registration, medical record management, and reporting. Usability testing with the System Usability Scale (SUS) involved 10 respondents and achieved an average score of 87.25 (category: Acceptable, grade B, adjective rating: Excellent). Comparative performance evaluation using a Confusion Matrix on 160 records yielded accuracy of 0.9977, precision of 0.9318, and recall of 1.00, confirming near-perfect retrieval capability with minimal false positives. These results demonstrate that integrating an efficient search algorithm with an intuitive interface can substantially improve service quality and operational efficiency in small-scale healthcare facilities. The findings underscore the potential of algorithm-driven digitalization to overcome resource constraints, reduce errors, and accelerate access to critical health information offering a scalable model for broader adoption in similar clinical settings.

Implementation of GRU with Attention Mechanism for Classifying Lung Diseases from Respiratory Sounds Kartika Sari; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3210

Early and accurate detection of lung diseases plays a crucial role in improving treatment outcomes and reducing mortality rates, particularly in low-resource healthcare settings. Conventional auscultation using a stethoscope is a fundamental, fast, and affordable method for initial lung examination. However, its effectiveness is limited by subjectivity, as it depends on the examiner’s expertise and can be influenced by environmental noise. To overcome these limitations, this study proposes a deep learning approach for lung diseases classification using a combination of Gated Recurrent Unit (GRU) and Attention Mechanism with log Mel spectrogram as an input based on respiratory sound. Unlike previous works that employed standalone methods such as GRU or CNN, the integration of Attention mechanism in this study allows the model to focus on prominent temporal patterns within respiratory sounds, thereby enhancing classification accuracy. Experiments were conducted on the ICBHI 2017 dataset, which underwent preprocessing stages consisting of minor class removal, recording location restriction, data augmentation, and log Mel spectrogram feature extraction. The test results show that the model produces high performances with an accuracy of 90.85%, precision of 93%, recall of 90.85%, and an F1-score of 91.14%, outperforming several works that reported in prior studies. These results demonstrate the effectiveness of combining GRU and Attention mechanism in capturing the temporal features of respiratory signals. Future research could focus on enhancing model robustness through improved data quality, other model architecture, and multimodal integration for broader clinical applicability.

Indonesian Sign Language (SIBI) Recognition from Audio Mel-Spectrograms Using LSTM Architecture Enryco Hidayat; Mohammad Idhom; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3229

Persistent communication barriers continue to challenge Deaf and Hard of Hearing (DHH) individuals in accessing spoken language, underscoring the need for effective and inclusive translation technologies. Existing audio-to-sign language systems typically employ multi-stage pipelines involving speech-to-text transcription, which may propagate recognition errors and fail to preserve acoustic nuances. Addressing these limitations, this study developed and evaluated a deep learning framework for translating spoken Indonesian audio directly into classifications of the Indonesian Sign Language System (SIBI), eliminating explicit text conversion. The dataset comprised 495 eight-second WAV recordings (22,050 Hz) representing five SIBI phrase classes, augmented through time stretching, pitch shifting, and noise addition to improve generalization. Mel-Spectrogram features were extracted and input to a stacked Long Short-Term Memory (LSTM) network implemented in TensorFlow/Keras, trained to learn temporal–spectral mappings between audio patterns and SIBI categories. Evaluation on a held-out test set demonstrated robust performance, achieving 98 % accuracy with consistently high precision, recall, and F1-scores. The trained model was further integrated into a prototype web application built with Flask and React, confirming its feasibility for real-time assistive communication. While results highlight the viability of direct Mel-Spectrogram-to-LSTM translation for SIBI recognition, current findings are constrained by the limited dataset size and restricted speaker diversity. Future research should therefore expand the dataset to include more speakers, varied acoustic environments, and continuous-speech inputs to ensure broader applicability and real-world robustness.

Optimization of Ride Routes in a Tourist Attraction Using Dijkstra’s and Genetic Algorithm Firyal Wishal Nabili; Eva Yulia Puspaningrum; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3264

This research presents a hybrid optimization framework that integrates Dijkstra’s Algorithm, the Genetic Algorithm (GA), and a 2-Opt local search procedure to generate optimal and demographically tailored tourist routes at Wisata Bahari Lamongan (WBL). The methodological novelty lies in the layered design of the hybrid pipeline: Dijkstra is used as a pre-processing stage to reconstruct a complete shortest-path distance matrix from partially measured field data, ensuring that GA operates on accurate inter-attraction distances and avoids unrealistic transitions. The GA then performs route evolution using PMX crossover, swap mutation, and elitism, while 2-Opt refines local segments to prevent suboptimal edge structures. Experiments involved 12 parameter-testing scenarios (CR = 0.7–0.9, MR = 0.05–0.1, population sizes of 50 and 100) across three visitor categories children, adults, and seniors. Benchmark validation on ATSP datasets from TSPLIB (BR17, P43, RY48, FT53) resulted in a mean error rate of 6.189%, confirming the robustness and generalizability of the method. The optimal configuration (CR = 0.7, MR = 0.05, PopSize = 100) produced route distances of 184,750 cm (children), 197,340 cm (adults), and 180,190 cm (seniors), yielding efficiency improvements of 30–50% compared to a pure GA and 3–7% compared to the initial measured paths. These findings demonstrate that the proposed hybrid Dijkstra–GA–2Opt framework offers a conceptually distinct, scalable, and empirically validated approach for real-world tourism route optimization.

Feature Augmentation with XGBoost to Improve 1D CNN Performance in Anemia Recognition Raissa Atha Febrianti; Anggraini Puspita Sari; Afina Lina Nurlaili
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3282

Anemia is one of the most prevalent nutritional and hematological disorders worldwide, characterized by low hemoglobin levels caused by iron deficiency, genetic factors, or chronic diseases. Diagnosis commonly relies on Complete Blood Count (CBC) interpretation, a manual process that is time-consuming and susceptible to human error. This study proposes a novel hybrid framework that integrates Extreme Gradient Boosting (XGBoost) and a One-Dimensional Convolutional Neural Network (1D-CNN) to enhance anemia classification. The methodological novelty lies in employing XGBoost as a feature-augmentation mechanism, where its class-probability outputs are fused with the original CBC features before being processed by the 1D-CNN, enabling richer representation learning compared to conventional single-model approaches. The model was trained and evaluated using a CBC dataset consisting of 364 samples covering four anemia classes (normocytic, microcytic, macrocytic, and normal), with performance assessed through an 80:20 stratified train–test split. Experimental results demonstrate that the proposed XGB–1DCNN model achieves a testing accuracy of 97.26%, precision of 98.68%, recall of 96.46%, and F1-score of 97.48%, outperforming the baseline 1D-CNN model (83.56%). These findings demonstrate that combining ensemble learning and deep learning significantly improves the model’s ability to capture complex nonlinear patterns in CBC data, offering a more reliable solution for AI-based early anemia diagnosis and clinical decision support.

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