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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING Teknika Jurnal Informatika: Jurnal Pengembangan IT
Putri, Desy Purnami Singgih
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Design and Construction of a Web-Based Fixed Asset Management System with a Combination of Straight Line Method, MAUT, and Telegram Bot Integration: Case Study of North Lombok District Hospital Arthana, I Made Teguh; Wirdiani, Ni Kadek Ayu; Putri, Desy Purnami Singgih
Teknika Vol. 13 No. 3 (2024): November 2024
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v13i3.1061

Abstract

North Lombok District Hospital is a health service institution in North Lombok District, West Nusa Tenggara Province, that provides health facilities and services to the community. Health service facilities provided to the community come from fixed assets owned by the North Lombok District Hospital. Management of fixed assets used for health service facilities at the North Lombok District Hospital is still done manually in planning, receiving, repairing, maintaining, and releasing assets. So, hospital employees have difficulty managing the assets they own. This study was conducted to help design and build a fixed asset management information system at the North Lombok Hospital using the SDLC Method with the Waterfall Model approach and system development using PHP, HTML, CSS, and JS languages with the Laravel Framework and MYSQL Database. This study uses the Straight Line Method to calculate asset depreciation, the MAUT Method to assist in decision-making for the elimination of damaged assets, and the Telegram Bot to send notifications from the website to each unit group in the hospital. The final result of this study is a web-based fixed asset management information system with developed features, namely asset planning features, asset planning change features, asset handover minutes features, asset inventory features, asset maintenance features, asset repair features, asset write-off features, asset whitening features, asset reporting features, master data features, and user access rights management features. The testing method used in this study is the Blackbox testing method, which tests the functionality of the system using 150 test scenarios on eight employees of the North Lombok Regional Hospital, with the test results showing that the system is running well and in accordance with the SOP that has been given, PSSUQ testing was carried out to evaluate user satisfaction with the system. The test results showed a SysUse subscale value of 1.93, IntQual 1.6, InfoQual 1.92, and Overall 1.93. Based on the results of the PSSUQ test, it can be concluded that the fixed asset management system has run very well and meets user expectations.
Fine-Tuned Transformer Models for Keyword Extraction in Skincare Recommendation Systems Ni Putu Adnya Puspita Dewi; Putri, Desy Purnami Singgih; Trisna, I Nyoman Prayana
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9687

Abstract

The skincare industry in Indonesia is experiencing rapid growth, with projected revenues reaching nearly 40 billion rupiah by 2024 and expected to continue to increase. The large number of products in circulation makes it difficult for consumers to find products that suit their needs. In this context, a text-based recommendation system that utilizes advances in Natural Language Processing (NLP) technology is a promising solution. This research aims to develop a skincare product recommendation system based on user needs by applying the DistilBERT model, which is specifically fine-tuned with text in the skincare recommendation domain to perform keyword extraction. The resulting keywords are then used as parameters to provide recommendations by using co-occurrence as well as using a modification of Jaccard Similarity to assess the suitability between the content and benefits of the product and user preferences. The trained extraction model achieved the best performance with a micro F1-score of 0.96 at the token level and an exact match rate of 74.25% at the entity level. The evaluation of the recommendation system showed excellent results, with an nDCG value of 0.96 and a user satisfaction rate (CSAT) of 91.9%.
Comparison of IndoBERT and Bi-LSTM Models for Indonesian Law Violation Text Classification Pramana, Made Wahyu Adwitya; Putri, Desy Purnami Singgih; Purnawan, I Ketut Adi
Jurnal Informatika: Jurnal Pengembangan IT Vol 10, No 4 (2025)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v10i4.8795

Abstract

Legal 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.
Co-Authors Arthana, I Made Teguh Ayu Wirdiani Ni Putu Adnya Puspita Dewi Pramana, Made Wahyu Adwitya Purnawan, I Ketut Adi Trisna, I Nyoman Prayana