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Lontar Komputer: Jurnal Ilmiah Teknologi Informasi
Published by Universitas Udayana
ISSN : 20881541     EISSN : 25415832     DOI : 10.24843/LKJITI
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering
Lontar Komputer: Jurnal Ilmiah Teknologi Informasi focuses on the theory, practice, and methodology of all aspects of technology in the field of computer science and engineering. It provides an international publication platform to boost the scientific and academic publication of research in the field. Submissions are invited concerning any theoretical or practical implementation of algorithm design, methods, and development. The subject of articles contributed may cover, but is not limited to: Data Analysis Natural Language Processing Artificial Intelligence Neural Networks Pattern Recognition Internet of Things (IoT) Remote Sensing Image Processing Fuzzy Logic Genetic Algorithm Bioinformatics/Biomedical Applications Biometrical Application Computer Network and Architecture Network Security Content-Based Multimedia Retrievals Information System
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 36 Documents
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Data Governance Design for Optimization of Hospital Management Information System (SIM-RS) at ABC Regional Hospital Muhammad Furqan Nazuli; Irfan Walhidayah; Neng Ayu Herawati; Lenny Putri Yulianti; Kridanto Surendro
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p03

Abstract

The increasing complexity of hospital data management requires a robust Data Governance (DG) framework to ensure data quality, security, and compliance. This study focuses on developing a DG framework tailored to the Hospital Management Information System (SIM-RS) at RSUD ABC to enhance data integration, accessibility, and regulatory adherence. A qualitative approach with a case study method was employed, involving interviews and document analysis to identify key challenges in data management. The proposed DG framework aligns with ICD-10 and regulatory requirements, ensuring interoperability and efficient data processing. Implementing the Master Patient Index (MPI) reduces duplicate records, while Two-Factor Authentication (2FA) and AES-256 encryption strengthen data security. FHIR standards facilitate seamless data exchange across healthcare systems, optimizingoperational efficiency. AI-driven data analytics further enhances clinical decision-making and administrative workflows. Evaluation of the framework demonstrates significant improvements in data quality, regulatory compliance, and risk management, leading to improved patient care and reduced medical errors. The High-Level Roadmap outlines a phased implementation strategy for sustainable DG adoption. Future research may explore performance metrics, Blockchain integration, and organizational change management to refine DG practices in healthcare institutions further.
Annotation Error Detection and Correction for Indonesian POS Tagging Corpus Muhammad Alfian; Umi Laili Yuhana; Daniel Siahaan; Harum Munazharoh
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p04

Abstract

Linguistic Corpus is the primary material for training and evaluating machine learning models, especially for POS Tagging. However, the human-annotated corpus is not free from annotation errors. Annotation errors have a negative impact on model performance. Therefore, we propose annotation error detection and correction. We detect annotation errors in the Indonesian POS Tagging corpus using the n-gram variation method. Then, we correct the corpus using an expert-voting approach. Annotation error detection successfully collected 6,536 annotation error candidates. Each candidate has two possibilities: (i) an ambiguous word or (ii) an incorrect annotation. Annotation error correction validated and corrected the candidates using the majority-voting method in an expert group. Annotation error correction successfully identified and corrected 503 words from 1918 sentences. Then, we compared the performance of the POS Tagging model with the corpus before and after correction. The results showed a significant improvement in the F1-score value (+9.69%) compared to the uncorrected corpus.
Maintenance Scheduling for Buildings Using Fuzzy Logic Application I Nyoman Dita Pahang Putra; I Gede Susrama Mas Diyasa; Anak Agung Diah Parami Dewi; Bambang Trigunarsyah
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p05

Abstract

This research proposes an innovative approach to building maintenance scheduling using fuzzy logic. Fuzzy logic addresses uncertainty and complexity in decision-making processes concerning prioritizing and scheduling maintenance tasks. This study aims to enhance the efficiency of maintenance scheduling, reduce maintenance costs, and consider the variability in building conditions. Traditional methods, such as PERT (Program Evaluation and Review Technique) and CPM (Critical Path Method), have limitations in accurately predicting scheduling times. At the same time, fuzzy logic offers a more precise approach to overcoming uncertainty. Implementing a maintenance scheduling model based on fuzzy logic is expected to yield a more adaptive and responsive maintenance plan in response to changes in building conditions. The results of this research are expected to contribute positively to building maintenance management by leveraging the advantages of fuzzy logic in addressing the challenges of complexity and uncertainty in building maintenance management. By applying fuzzy logic-based maintenance scheduling, it is hoped that precise and efficient building maintenance scheduling can be achieved, thereby minimizing project completion time and assisting project managers. The fuzzy logic method can be employed for construction project scheduling according to the schedule determined by the contractor. This allows the contractor to use it as a consideration for the total duration, along with detailed timing in the project proposal. For the owner, it provides insights into the potential project completion time.
Development of Secure API to Support ICD-10 Based Electronic Medical Records Interoperability I G N Lanang Wijayakusuma; Made Sudarma; I Ketut Gede Darma Putra; Oka Sudana; Minho Jo; I Putu Winada Gautama
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 16 No. 01 (2025): Vol.16, No. 01 April 2025
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2025.v16.i01.p06

Abstract

Previous research in 2021 and 2022 has yielded a revolutionary health examination system. This system seamlessly integrates the World Health Organization's International Classification of Diseases-10 (ICD-10) data, ensuring diagnoses align with global standards and thereby enhancing the quality of healthcare provision. A pivotal achievement is the creation of a sophisticated doctor's examination interface, designed for precision and efficiency. Complementing this interface, a search engine autonomously generates relevant keywords, successfully passing the rigorous black-box test, which attests to its robustness and reliability in retrieving critical medical information. A new challenge arises in enabling seamless access to the stored medical record data for various stakeholders, including the Ministry of Health, BPJS, insurance companies, and other relevant entities. To address this, the research team has devised the Application Programming Interface (API). Functioning as a crucial bridge, this API facilitates interoperability among diverse systems. Adherence to the stringent security standards set by the Open Web Application Security Project (OWASP) ensures that the exchange of medical data occurs within a secure environment. Consequently, sensitive patient information can be shared across platforms without compromising confidentiality or integrity.
Training VGG16, MobileNetV1 and Simple CNN Models from Scratch for Balinese Inscription Recognition Ida Ayu Putu Febri Imawati; Made Sudarma; I Ketut Gede Darma Putra; I Putu Agung Bayupati; Minho Jo
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p01

Abstract

Many inscriptions in Bali are damaged. Damage to these inscriptions can be caused by natural disasters, overgrown with moss, algae and bacteria. Damage can also be caused by warfare, or deliberately erased. This inscription contains the knowledge and civilization of the ancestors so it is very important to be able to read its contents. Based on these problems, this research conducted training from scratch on 3 CNN models namely VGG16, MobileNetV1 and Simple CNN. The purpose of this research is to choose one recognition model that has the best performance and produces the highest recognition rate to proceed to the inscription restoration stage. The dataset used is Balinese inscription: Isolated Character Recognition of Balinese Script in Palm Leaf Manuscript Images in Challenge-3-ForTrain.zip. The training process of three models with five different training files resulted in the finding that VGG16 has the highest accuracy in the training, testing, and validation process with the least number of epochs. This research contributes to specific datasets, such as the Isolated Character Recognition of Balinese Script using the training process from the beginning of VGG16, involving all stages of the process. It will produce the best model performance compared to the other four training models.
IoT-Based a Control System for Household Waste Management Machines at Waste Disposal Sites using Human Machine Interface method Pawenary; Hendri; Dwi Listiawati; Andi Dyah Harum Hardyanti; Yessy Asri
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p02

Abstract

To manage waste efficiently and sustainably , Integrating Algoritma Bellman-Ford in IoT-Based Control Systems for Household Waste Management Machines, the use of automation technologies based on the Internet of Things (IoT) is becoming increasingly relevant. The integration of HMI allows operators to manage and control the machine with precision and convenience. The synergy between IoT and HMI promises significant improvements in waste processing speed, accuracy, and safety. Developing a control system using the HMI method is not just a solution but an innovative approach that aims to find an effective solution in managing or destroying waste that requires less labor, so there is no need to increase assistance. The method that will be used in this research is the descriptive statistical method, namely, assessing the technical data needed to develop a control system that complies with the standard. This innovative approach is one of the solutions to the problem of labor shortages in landfills that simplify work and speed up the process of operating machines.
Predicting the Number of Passengers in Public Transportation Areas Using the Deep Learning Model LSTM Joko Siswanto; Sri Yulianto Joko Prasetyo; Sutarto Wijono; Evi Maria; Untung Rahardja
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p03

Abstract

Accurate predictions of the number of public transport passengers on buses in each region are crucial for operations. They are required by the planning and management authority for bus public transport. A deep learning-based LSTM prediction model is proposed to predict the number of passengers in 4 bus public transportation areas (central, north, south, and west), evaluated by MSLE, MAPE, and SMAPE with dropout, neuron, and train-test variations. The CSV dataset obtained from Auckland Transport(AT) New Zealand metro patronage report on bus performance(1/01/2019-31/07/2023) is used for evaluation. The best prediction model was obtained from the lowest evaluation value and relatively fast time with a dropout of 0.2, 32 neurons, and train-test 80-20. The prediction model on training and testing data improves with the suitability of tuning for four predictions for the next 12 months with mutual fluctuations. The strong negative correlation is central-south, while the strong positive correlation is north-west. Predictions are less closely interconnected and dependent, namely central-south. With its potential to significantly impact policy-making, this prediction model can increase public transport mobility in each region, leading to a more efficient and accessible public transport system and ultimately enhancing the public's daily lives. This research has practical implications for public transport authorities, as it can guide them in making informed decisions about service planning and resource allocation.
Determining The Ripeness Level Of Crystal Guava Fruit Using Backpropagation Neural Network Shofia Nabila Azzahra; Ahmad Kamsyakawuni; Abduh Riski
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p04

Abstract

The ripeness of crystal guava fruit is currently sorted conventionally by analyzing the colour of the rind visually with the human eye. However, this method has several weaknesses that result in low accuracy and inconsistency. Therefore, automatic determination of ripeness level is necessary to increase accuracy and obtain precise information. This research uses the HSI colour space as an interpretation of fruit image characteristics and uses the Backpropagation algorithm to perform classification. This study utilizes image data of crystal guava fruit, categorizing them into four stages of ripeness: unripe, half-ripe, ripe, and very ripe. There are 140 fruit image data with 35 data for each ripeness category. Each image will be processed with median filter, cropping and segmentation. The HSI value will be taken from the image and processed at the classification stage using the Backpropagation algorithm. In classification using Backpropagation Neural Network, the best network model in this study was achieved in the 3 10 4 network architecture with a binary sigmoid activation function, learning rate = 0.3, and batch size = 64. This model produces a loss value of 0.5364 with an accuracy of 0.9 in testing process.
C2C Startup Model of Balinese Ceremony Ticketing System in Ubud Bali I Wayan Dharma Suryawan; Ni Wayan Sumartini Saraswati; Eddy Hartono
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p05

Abstract

Ubud is one of the tourist destinations in Bali. Ubud combines natural attractions, culture, and spiritual life harmoniously. One of the elements of cultural attraction in Ubud is the implementation of traditional and religious ceremonies that uphold ancestral customs. With this potential, Ubud can be a tourist destination supporting Balinese cultural tourism's progress. Currently, information regarding the implementation of traditional and religious ceremonies in Ubud is limited to tourists. Tourists can witness the traditional and religious ceremonies held through information from tour guides, who are ceremony organizers. The availability of a ceremony ticketing system that connects tourists with traditional village/banjar communities can directly address the problem of access to information and services for the implementation of traditional and religious ceremonies in Ubud. This study aims to develop a C2C e-commerce model that involves klian adat and tourists directly selling tickets for traditional and religious ceremonies in Singakerta Village, Ubud District. Given the limited time for system development, the RAD software development method was selected as the system development method. The user experience test's Likert scale results showed that the system's quality reached 84.13% for tourist satisfaction and 84.19% for klian adat satisfaction. This indicates that the system is at an excellent level based on the user.
QnA Chatbot with Mistral 7B and RAG method: Traffic Law Case Study Muhammad Roiful Anam; Agus Subhan Akbar; Heru Saputro
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol. 15 No. 03 (2024): Vol. 15, No. 03 December (2024)
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2024.v15.i03.p06

Abstract

Mistral 7B is a language model designed to achieve high efficiency and performance in handling Natural Language Processing (NLP). This research will evaluate the model's effectiveness in legal data processing using the Retrieval-Augmented Generation (RAG) method, focusing on road traffic and transportation law No 22/2009. The system was built using the LangChain framework, followed by fine-tuning the model and evaluated using BERTScore. Results showed that the fine-tuned Mistral 7B achieved an F1 score of 0.9151, higher than the version without fine-tuning (0.8804) and GPT-4 (0.8364). To improve accuracy, the model utilizes specific keywords that make it easier to find relevant data. Fine-tuning was shown to enhance precision, while the use of key elements in questions helped the model focus more on important information. The results are expected to support the development of artificial intelligence (AI) in Indonesia's legal system and provide practical guidance for applying AI technology in other areas of law.

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