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All Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Bulletin of Computer Science Research
Deborah Kurniawati
Universitas Teknologi Digital Indonesia, Yogyakarta
Computer Science & IT Decision Sciences, Operations Research & Management

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Pengembangan Sistem Transformasi dan Konversi Data Berbasis Web Menggunakan Arsitektur RESTful API Deborah Kurniawati; Adi Kusjani; Robby Cokro Buwono; Muhammad Aldo Ridhoni
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.818

Abstract

Heterogeneous data management often faces challenges such as format inconsistency, high latency, and lack of automation, leading to inefficiencies and errors in data transformation. This research aims to develop a web-based system to automate data transformation and conversion across formats using a Representational State Transfer (RESTful) Application Programming Interface (API) architecture, with a Mithril.js frontend and Go backend. An experimental and system development approach was employed, comprising three stages: client-server architecture design, implementation, and testing. The system provides 11 primary API endpoints, such as /api/tasks and /api/transformations, to manage tasks and data transformations. The Single Page Application frontend offers intuitive navigation with menus for task management, data sources, and activity logs. Functional testing on Comma-Separated Values, JavaScript Object Notation, and SQLite formats yielded accurate transformations, including text prepending, data type conversion, and lowercase normalization. Performance evaluation using Google Lighthouse recorded a median score of 85, indicating high performance. The system enhances efficiency and accuracy compared to manual methods, supporting cross-platform interoperability. However, limitations include support for only simple tabular formats and lack of security features. This research offers a lightweight solution for data transformation, with potential applications in organizational data integration and business analytics.
Analisis Sentimen Keluhan Pelanggan ISP menggunakan Support Vector Machine (SVM) dan TF-IDF Dini Fakta Sari; Deborah Kurniawati; Endang Wahyuningsih; Tediyan Rahmat Wibowo
Bulletin of Computer Science Research Vol. 5 No. 6 (2025): October 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i6.822

Abstract

This study aims to analyze the sentiment of customer complaints regarding Internet Service Provider (ISP) services in Indonesia, where the primary issues frequently reported include connection disruptions, slow internet speeds, weak signals, and unresponsive or uninformative complaint handling, as reflected in various consumer reports on social media. These issues contribute to customer dissatisfaction and necessitate data analysis solutions to deeply understand public opinions. Data was collected via API from a social media platform using keywords related to internet services, such as "internet disruption" and "internet complaints." The data underwent text preprocessing stages, including cleaning, case folding, tokenization, stopword removal, and stemming to produce consistent text. Text features were extracted using Term Frequency–Inverse Document Frequency (TF-IDF), which were then classified using the Support Vector Machine (SVM) algorithm. Model evaluation using 10-Fold Cross Validation yielded an average accuracy of 91.47%, precision of 94.27%, recall of 99.20%, and F1-score of 96.67%. Word frequency analysis revealed dominant words such as “slow,” “disruption,” and “signal” as the main issues in customer complaints. The combination of SVM and TF-IDF proved effective for sentiment analysis in Indonesian, providing academic and practical contributions for ISPs to monitor customer opinions and improve service quality. Future research is recommended to employ deep learning models like BERT and more diverse data.
Sistem Pendukung Keputusan Pemilihan Bibit Padi Unggul Menggunakan Metode Simple Additive Weighting (SAW) Fina Febriyani; Asyahri Hadi Nasyuha; Deborah Kurniawati
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7627

Abstract

The selection of superior rice seeds is a crucial stage in improving agricultural productivity in Indonesia. However, farmers often select seeds subjectively without systematically considering important factors. To address this issue, this study designs and develops a Decision Support System (DSS) based on the Simple Additive Weighting (SAW) method to assist farmers in selecting the best rice seeds using six criteria: pest resistance, harvest age, amylose content, yield, irrigation water efficiency, and rice texture. Data were collected through interviews with five farmers in Mangir Lor. The results showed that the rice variety Inpari 32 achieved the highest score of 0.87, thus recommended as the best alternative. The SAW method proved effective in managing multicriteria data and producing objective and accurate results. This DSS is expected to serve as a practical decision-making tool for farmers in selecting high-quality rice seeds and contribute to the achievement of sustainable national food security.
Co-Authors Adi Kusjani Asyahri Hadi Nasyuha Dini Fakta Sari Endang Wahyuningsih Fina Febriyani Muhammad Aldo Ridhoni Robby Cokro Buwono Tediyan Rahmat Wibowo