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INDONESIA
Jurnal Buana Informatika
Published by Universitas Atma Jaya Yogyakarta
ISSN : 20872534     EISSN : 20897642     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 602 Documents
 57   58   59   60   61 
Design and Implementation of Load Balancing for Quality of Service Improvement Indrastanti Ratna Widiasari; Rissal Efendi
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9530

Abstract

At the Information Technology Faculty, Satya Wacana Christian University, load balancing systems are implemented where the web server serves 500 users. This is to prevent server overload or downtime during simultaneous access to the web server. Test results indicate significant differences in CPU usage, request time, and bandwidth between load balancing and single servers. The use of load balancing is more effective than relying on a single server, as evidenced by test results. The CPU usage with load balancing is significantly lower, with a difference of up to 45% compared to a single server. The request time with load balancing is also slightly better, with only 21.5ms compared to 42ms for a single server. However, the difference in bandwidth between load balancing and a single server is not very significant. The highest bandwidth recorded on a single server is 182kb/s, while with load balancing it reaches 165kb/s.
Prediksi Penyakit Batu Ginjal dengan Menerapkan Convolutional Neural Network Bagus Satrio Waluyo Poetro; Sri Mulyono; Vani Aulia Pramesti
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9838

Abstract

Kidney stones are a health problem that requires intensive treatment. If the disease is not treated quickly, it can lead to impaired kidney function and complications to other organs. Computerized Tomography Scan (CT Scan) with high resolution is used to scan the human body for disease diagnosis. The doctor will explain the diagnosis within a few days or one week. This research aims to create a prediction model for the classification of kidney stone disease through CT Scan images by applying the Convolutional Neural Network (CNN) method of DenseNet-121 architecture and deployment using Streamlit. The results of the model in this study with the application of CNN DenseNet-121 architecture are accuracy 98.18%, precision 96.36%, recall 100%, and F1-score 98.14%.
Sistem Penjadwalan Karyawan dengan Algoritma Genetika Maria Karmelia Fajarlestari; Mawar Hardiyanti
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9850

Abstract

Employee scheduling is a complex problem in Human Resource Management (HRM) that significantly impacts operational efficiency. This study develops an employee scheduling system using a genetic algorithm. The employee schedules are constructed by considering scheduling rules and various components such as the number of days, shifts, employee quality, and scheduling requests. The genetic algorithm, proven effective in solving various optimization problems, is employed to generate optimal schedules through the processes of selection, crossover, and mutation. The results indicate that the genetic algorithm can effectively produce employee schedules, with fitness values indicating improved schedule quality as iterations increase. The findings of this study are anticipated to be useful in HRM, aiming to improve both employee efficiency and satisfaction.
Sentiment Analysis of DKI Jakarta 2024 Election (Case Study: Anies Baswedan and Ridwan Kamil) Muhammad Safrul Safrudin; Syarifah Aini
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v15i2.9864

Abstract

This study analyzes public sentiment toward two potential candidates for the 2024 Jakarta gubernatorial election, Anies Baswedan and Ridwan Kamil, using Twitter data. Applying the TextBlob model for text extraction and Naive Bayes for sentiment classification found that sentiment toward Anies Baswedan is mostly positive, 52.2%, while neutral sentiment dominates for Ridwan Kamil. The accuracy of the Naive Bayes model reached 80% for Anies Baswedan and 72% for Ridwan Kamil, with higher precision, recall, and F1-score for Anies' data. These results indicate that the model is more accurate in classifying sentiment toward Anies compared to Ridwan Kamil. The implications of these findings are important for political campaign strategies, where Anies can leverage the existing positive support, while Ridwan Kamil has an opportunity to strengthen public engagement through a more strategic approach, in line with the sentiment emerging on social media.
Bayesian Tuning terhadap Model Pre-Trained PEGASUS untuk Peringkas Teks Informatif Berbahasa Indonesia Kadek Artha Darma Pradnyana; I Nyoman Prayana Trisna Trisna; Wayan Oger Vihikan
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.12915

Abstract

Penelitian ini mengeksplorasi peringkasan teks abstraktif untuk berita berbahasa Indonesia dengan melakukan fine-tuning pada model PEGASUS menggunakan Bayesian Optimization dan input kontekstual yang diperkaya. Dataset berisi 286.277 pasangan dokumen–ringkasan yang diambil dari JPNN.com, lengkap dengan judul dan kata kunci yang digunakan untuk membentuk input informatif. Evaluasi menggunakan ROUGE dan BERTScore menunjukkan peningkatan substansial dari informative input: +16.75% (ROUGE-1), +27.25% (ROUGE-2), +18.58% (ROUGE-L & ROUGE-LSUM), dan +2.7% (BERTScore-F1) dibandingkan dengan input reguler. Analisis saliency menunjukkan bobot kalimat kontekstual yang konsisten tinggi. Penerapan hyperparameter tuning Bayesian melalui Optuna memberikan kenaikan marginal (+1.21% ROUGE-1, +2.1% ROUGE-2, +1.38% ROUGE-L & ROUGE-LSUM, +0.23% BERTScore) yang dipengaruhi oleh jumlah trial terbatas (12) dan ruang pencarian yang sempit. Temuan ini menegaskan efektivitas desain input kontekstual dan potensi hyperparameter tuning untuk peringkasan berbasis Transformer pada bahasa dengan sumber daya terbatas.   This research explores abstractive text summarization of Indonesian news by fine-tuning the PEGASUS model using Bayesian optimization and enriched contextual inputs. The dataset contains 286,277 document-summary pairs scraped from JPNN.com, including titles and keyphrases used to construct informative input. Each section is marked with special tokens such as <TITLE>, <KEYPHRASES>, and <ARTICLE>. Evaluation using ROUGE and BERTScore shows that informative input substantially improves performance: +16.75% (ROUGE-1), +27.25% (ROUGE-2), +18.58% (ROUGE-L and ROUGE-Lsum), and +2.7% (BERTScore-F1) compared with regular input. Saliency analysis also shows consistently high sentence weights for contextual input components. Additionally, Bayesian hyperparameter tuning via Optuna yields marginal gains (+1.21% ROUGE-1, +2.1% ROUGE-2, +1.38% ROUGE-L & ROUGE-Lsum, +0.23% BERTScore) due to a limited number of trials (12) and a constrained hyperparameter search space. These findings demonstrate the effectiveness of contextual input design and the potential of Bayesian tuning to improve Transformer-based summarization for low-resource languages.
LexIndoLLM: Large Language Model untuk Konsultasi Regulasi Daerah di Indonesia Novianto Rahmadi; Arief Setyanto
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.14326

Abstract

Large Language Model (LLM) berpotensi meningkatkan akses terhadap layanan konsultasi regulasi daerah, tetapi model generik masih sering menghasilkan jawaban yang kurang akurat pada dokumen hukum Indonesia yang panjang, formal, dan kontekstual. Penelitian ini mengembangkan LexIndoLLM, model ringan berbasis Llama 3.2-1B, melalui fine-tuning bertahap pada 393 dokumen regulasi Kabupaten Kutai Kartanegara dan integrasi Retrieval-Augmented Generation (RAG) berbasis FAISS. Evaluasi dilakukan menggunakan RAGAS, perplexity, ROUGE-L, dan metrik efisiensi inferensi. Hasil menunjukkan bahwa pendekatan yang diusulkan meningkatkan kualitas jawaban, ditandai dengan penurunan perplexity dari 9,13 menjadi 1,74, peningkatan ROUGE-L dari 0,2058 menjadi 0,4429, serta nilai faithfulness 0,77 dan answer correctness 0,66. Waktu respons rata-rata di bawah 3,4 detik sehingga cocok untuk deployment lokal. Temuan ini menunjukkan bahwa model ringan yang dipadukan dengan retrieval layak digunakan untuk konsultasi regulasi daerah pada lingkungan komputasi terbatas.   Large Language Models (LLMs) have the potential to improve access to local regulatory consultation services, yet general-purpose models often produce inaccurate responses when handling Indonesian legal documents that are lengthy, formal, and highly contextual. This study develops LexIndoLLM, a lightweight model based on Llama 3.2-1B, through staged fine-tuning on 393 local regulatory documents from Kutai Kartanegara Regency and the integration of FAISS-based Retrieval-Augmented Generation (RAG). The model was evaluated using RAGAS, perplexity, ROUGE-L, and inference efficiency metrics. The results show that the proposed approach improves answer quality, as indicated by a reduction in perplexity from 9.13 to 1.74, an increase in ROUGE-L from 0.2058 to 0.4429, and faithfulness and answer correctness scores of 0.77 and 0.66, respectively. The system maintains an average response time under 3.4 seconds, suitable for local deployment. These findings indicate that a lightweight model combined with retrieval is feasible for local regulatory consultation in resource-constrained environments.
Implementasi LightGBM dengan KNN Imputation untuk Deteksi Dini Risiko Kehamilan Syahnur Alawiyah; Dian Yuliati; Nurissaida Ulinnuha
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.14454

Abstract

Risiko kehamilan merupakan isu penting dalam kesehatan maternal yang berkontribusi pada tingginya angka kesakitan dan kematian ibu serta bayi, sehingga diperlukan metode analisis yang akurat untuk deteksi dini. Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model klasifikasi tingkat risiko kehamilan dengan menggunakan K-Nearest Neighbor Imputation (KNNI) untuk menangani missing value dan LightGBM sebagai metode utama. Model dioptimalkan melalui uji parameter dan dievaluasi menggunakan Stratified K-Fold Cross-Validation (SKCV). Hasil penelitian menunjukkan bahwa model yang diusulkan mampu mencapai akurasi sebesar 97,64%, sehingga menunjukkan kinerja yang sangat baik dalam mengklasifikasikan tingkat risiko kehamilan. Dengan demikian, pendekatan yang digunakan memiliki potensi untuk dikembangkan sebagai sistem pendukung keputusan dalam bidang kesehatan maternal.   Pregnancy risks are a critical issue in maternal health that contributes to high rates of maternal and infant morbidity and mortality; therefore, accurate analytical methods are needed for early detection. This study aims to develop and evaluate a pregnancy risk classification model using K-Nearest Neighbor Imputation (KNNI) to handle missing values and LightGBM as the primary method. The model was optimized through parameter tuning and evaluated using Stratified K-Fold Cross-Validation (SKCV). The results show that the proposed model achieved an accuracy of 97.64%, demonstrating excellent performance in classifying pregnancy risk levels. Thus, the approach used has the potential to be developed as a decision support system in the field of maternal health.
Employee Performance Evaluation Using RECA-based Weighting and RAWEC: Evidence from Textile Manufacturing Setiawansyah Setiawansyah; Junhai Wang; Sufiatul Maryana; Pritasari Palupiningsih
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.13709

Abstract

Employee performance evaluation in the textile industry production division still faces issues of subjectivity, limited indicators, and inconsistency in ranking that do not yet reflect the real contribution of employees. This study aims to assess employee performance using a multi-criteria decision-making approach by integrating the RECA method for determining objective criterion weights and the RAWEC method for generating performance rankings. Performance data is collected based on several key criteria, namely work productivity, production quality, timeliness, work discipline, and production error rates, which reflect the operational conditions in the textile manufacturing environment. The analysis results indicate that the applied approach clearly distinguishes employee performance and produces a stable ranking, with Gina taking first place with a final score of 0.483 and Citra with a score of 0.2933. These findings indicate that RECA and RAWEC support more reliable and data-driven managerial decisions in the textile industry.   Evaluasi kinerja karyawan di divisi produksi industri tekstil masih menghadapi masalah subjektivitas, keterbatasan indikator, dan ketidakkonsistenan pemeringkatan yang belum mencerminkan kontribusi nyata karyawan. Penelitian ini bertujuan untuk menilai kinerja karyawan menggunakan pendekatan pengambilan keputusan multi-kriteria dengan mengintegrasikan metode RECA untuk menentukan bobot kriteria objektif dan metode RAWEC untuk menghasilkan peringkat kinerja. Data kinerja dikumpulkan berdasarkan beberapa kriteria utama, yaitu produktivitas kerja, kualitas produksi, ketepatan waktu, disiplin kerja, dan tingkat kesalahan produksi, yang mencerminkan kondisi operasional pada lingkungan manufaktur tekstil. Hasil analisis menunjukkan bahwa pendekatan yang diterapkan mampu membedakan kinerja karyawan secara jelas dan menghasilkan pemeringkatan yang stabil, di mana Gina menempati peringkat pertama dengan nilai akhir 0.483 Citra dengan nilai 0,2933. Temuan ini menunjukkan RECA dan RAWEC mendukung keputusan manajerial yang lebih andal dan berbasis data di industri tekstil.
Detecting the Impact of Social Media on Users' Mental Health Using Machine Learning and XAI Ara Bela Zulfa Laila
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.13409

Abstract

This research develops a machine learning-based predictive system to detect potential depression due to social media use, and compares the performance of algorithms such as Random Forest, XGBoost, and Naïve Bayes. Survey data, including age, gender, relationship status, daily usage duration, and social media platform, were used to build the model, with accuracy, precision, recall, and F1-score evaluated. XGBoost showed the best performance with 90% accuracy and a high F1-score. The main features that affect depression prediction include duration of social media use, age, and platforms. Explainable AI (XAI) techniques with LIME increase the transparency of the model, provide relevant explanations for individuals, and strengthen confidence in the predictions. This research emphasizes the importance of transparency in model implementation in the mental health field and offers a flexible solution that can be adopted for digital applications such as chatbots or real-time mental health monitoring dashboards.   Penelitian ini mengembangkan sistem prediktif berbasis machine learning untuk mendeteksi potensi depresi akibat penggunaan media sosial, serta membandingkan kinerja algoritma seperti Random Forest, XGBoost, dan Naïve Bayes. Data survei yang meliputi usia, jenis kelamin, status hubungan, durasi penggunaan harian, dan platform media sosial digunakan untuk membangun model dengan evaluasi akurasi, precision, recall, dan F1-score. XGBoost menunjukkan kinerja terbaik dengan akurasi 90% dan F1-score tinggi. Fitur utama yang memengaruhi prediksi depresi meliputi durasi penggunaan media sosial, usia, dan platform. Teknik Explainable AI (XAI) dengan LIME meningkatkan transparansi model, memberikan penjelasan yang relevan untuk individu, dan memperkuat kepercayaan terhadap prediksi. Penelitian ini menekankan pentingnya transparansi dalam penerapan model di bidang kesehatan mental dan menawarkan solusi fleksibel yang dapat diadopsi untuk aplikasi digital seperti chatbot atau dashboard pemantauan kesehatan mental real-time.
Integrated Analog-Digital Robotics Learning System to Improve Elementary Students Computational Thinking Agung Kridoyono; Mochamad Sidqon; Anton Breva Yunanda; Istantyo Yuwono
Jurnal Buana Informatika Vol. 17 No. 1 (2026): Jurnal Buana Informatika, Volume 17, Nomor 1, April 2026
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v17i1.14470

Abstract

This study explores the implementation of an integrated analog–digital robotics learning model to improve computational thinking skills among elementary school students. The learning activities were arranged progressively, starting from analog sensor-based robotics experiments and continuing to programmable digital robot control. A total of 30 fifth-grade students participated in a four-week intervention program involving hands-on robotics activities. Students’ computational thinking abilities were evaluated through four dimensions: decomposition, pattern recognition, abstraction, and algorithmic thinking. The findings revealed improvements in all assessed indicators. The average score increased from 61.3 on the pretest to 82.7 on the posttest, with a medium normalized gain (N-gain) of 0.55. Statistical analysis using a paired-sample t-test also showed a significant difference between pretest and posttest scores (p < 0.05). These results indicate that integrated robotics learning can provide meaningful support for developing computational thinking skills in primary education.   Penelitian ini mengeksplorasi penerapan model pembelajaran robotika analog-digital terpadu untuk meningkatkan kemampuan berpikir komputasi pada siswa sekolah dasar. Kegiatan pembelajaran disusun secara progresif, dimulai dari eksperimen robotika berbasis sensor analog dan dilanjutkan dengan pengendalian robot digital yang dapat diprogram. Sebanyak 30 siswa kelas lima berpartisipasi dalam program intervensi empat minggu yang melibatkan aktivitas robotika langsung. Kemampuan berpikir komputasi siswa dievaluasi melalui empat dimensi: dekomposisi, pengenalan pola, abstraksi, dan berpikir algoritmik. Temuan ini menunjukkan adanya perbaikan pada seluruh indikator yang dinilai. Nilai rata-rata meningkat dari 61,3 pada pretest menjadi 82,7 pada posttest, dengan gain ternormalisasi sedang (N-gain) sebesar 0,55. Analisis statistik menggunakan uji-t berpasangan juga menunjukkan perbedaan yang signifikan antara skor pretest dan posttest (p < 0,05). Hasil tersebut menunjukkan bahwa pembelajaran robotika terpadu dapat memberikan dukungan yang berarti bagi pengembangan keterampilan berpikir komputasi pada pendidikan dasar.

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