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All Journal Jurnal Transformatika Jurnal Informatika Upgris Jurnal Sains dan Informatika Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Sinus Building of Informatics, Technology and Science Joined Journal (Journal of Informatics Education JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Information System Journal (INFOS) Bulletin of Computer Science Research Arcitech: Journal of Computer Science and Artificial Intelligence Adopsi Teknologi dan Sistem Informasi Journal of Data Insights Tematik The Indonesian Journal of Computer Science
Setiaji, Galet Guntoro
Universitas Semarang
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Analisa Perbandingan Quality of Service Protokol VPN antara Protokol SSTP Dan Open VPN Berbasis Router Mikrotik Surono; Guntoro Setiaji, Galet
The Indonesian Journal of Computer Science Vol. 11 No. 1 (2022): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v11i1.3034

Abstract

The internet itself is a public network that can connect anyone into a very large network, and of course there are threats of data theft in it, VPN is a communication technology that allows an employee in the office to connect to a public network and use it to join the local network. . The advantage of a VPN is that the data sent via a VPN is encrypted so it is quite safe and the secret is maintained even though it is through the internet network, a comparison analysis of VPN performance between the SSTP (Secure Socket Tunneling Protocol) protocol and the Open VPN protocol will be carried out to determine the Quality Of Service (Qos) performance of both For this protocol, a study is needed to determine the use of the method so as to produce an optimal QOS (Quality of Service)
Implementasi Sistem Informasi Penjualan Mebel Berbasis Web Menggunakan Metode SCRUM Bernandika Reyhan Groovytala; Galet Guntoro Setiaji; Ahmad Rifa'i
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9947

Abstract

This study focuses on the design and implementation of a web-based furniture sales application at UD. Andhireyma, Boyolali Regency, Central Java Province. The rationale for developing this software stems from the urgent need to optimize efficiency in daily transaction management, product inventory updates, and sales reporting. Involving the active participation of 14 respondents, including business owners, operational staff, and customers, this study adopted the Scrum framework. The methodology’s phases—product backlog, sprint planning, sprint review, and sprint retrospective—were implemented to ensure that system development iterations could dynamically adapt to user expectations. From a technical perspective, the application architecture was built using the PHP programming language and MySQL database management, integrated with HTML, CSS, and JavaScript elements. Comprehensive data collection was conducted through a series of field observations, continuous documentation, and in-depth interviews. Furthermore, to ensure the overall functionality of all features, the Black Box Testing method was strictly applied. Evaluation findings indicate that the implementation of this digital system successfully accelerated and structured the management of commodity data as well as transaction history reports. The risk of manual recording errors was significantly reduced, which directly corresponds to an improvement in the quality and effectiveness of customer service. Based on the successful functional test results, which recorded a success rate of 80%, this e-commerce platform is recommended and deemed highly suitable for immediate implementation in UD. Andhireyma business operations.
Implementasi Sistem Informasi Kluster Penjualan Beras Menggunakan Algoritma K-Means Syahrul Adi Saputra; Galet Guntoro Setiaji; Ahmad Rifa’i
Arcitech: Journal of Computer Science and Artificial Intelligence Vol. 6 No. 1 (2026): June 2026
Publisher : Institut Agama Islam Negeri (IAIN) Curup

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29240/arcitech.v6i1.17056

Abstract

Manual processing of rice sales transaction data results in data being archived and cannot be used optimally in decision-making. Therefore, business owners find it difficult to manage stock in sales planning. The K-Means Clustering algorithm was implemented in a web-based information system to create a recommendation feature for the best-selling rice. UD Maju Mapan, located in Demak Regency, was the location for the sales transaction data collection process for the sales period from October 2025 to March 2026. Data was processed using the Min-Max Normalization method and the K-Means algorithm with 3 clusters: premium, standard, and economy. The grouping will automatically appear in the dashboard display of the web-based information system, providing information for decision-making. The results show that the standard cluster has the largest amount of data compared to the premium and economy clusters. A value of 0.5361 is the result of the evaluation process using the Davies Bouldin Index method, which can be interpreted as quite good cluster quality. The rice sales information system is capable of managing and determining sales strategies and stock procurement based on the results of real transaction data analysis.
Analisis Perbandingan Performa Backend REST API Node.js, .NET, dan Laravel Octane Menggunakan Load Testing Djoko Handoko; Galet Guntoro Setiaji; Ahmad Rifa'i
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.9838

Abstract

The increasing demand for web-based applications capable of handling a large number of concurrent users requires the selection of backend technologies that provide optimal performance and resource efficiency. However, previous studies generally employed different configurations and testing environments, making performance comparisons among backend technologies less objective and difficult to generalize. This study aims to analyze and compare the performance of backend REST APIs developed using Node.js, .NET, and Laravel Octane within a standardized testing environment. The research adopted a quantitative experimental approach using load testing with Grafana K6 by simulating workloads of up to 150 virtual users through HTTP GET requests. The evaluated performance metrics included throughput or requests per second (RPS), response time (latency), error rate, as well as CPU and memory utilization. The results showed that Node.js achieved the best performance with a throughput of 493.93 requests per second and an average latency of 77.43 ms, followed by .NET with a throughput of 324.94 requests per second and a latency of 163.06 ms, while Laravel Octane achieved a throughput of 192.29 requests per second with a latency of 358.62 ms. All backend technologies maintained a 0% error rate throughout the testing process. In terms of resource efficiency, Node.js also demonstrated the lowest CPU and memory utilization compared to .NET and Laravel Octane. These findings indicate that differences in execution models significantly influence throughput, response time, and resource utilization efficiency, providing valuable insights for selecting appropriate backend technologies to develop high-performance and highly scalable web applications.
Rancang Bangun Sistem Informasi Manajemen Service Kendaraan Berbasis Web Bengkel Menggunakan Metode Prototype Muhammad Dedi Setiawan; Galet Guntoro Setiaji; Ahmad Rifa'i
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10059

Abstract

Vehicle service administration at Syasa Workshop in Semarang is currently suboptimal because the recording of customer profiles, vehicle history, transactions, and spare parts inventory still relies on manual systems. This leads to inefficiencies in data processing, a high risk of document loss, and delays in the preparation of operational reports. This study aims to design and build a web-based vehicle service management information system to accelerate the efficiency and accuracy of workshop data management. The software development adopts the Prototype method, which was chosen because it allows for rapid and adaptive development iterations based on direct feedback from users. The stages of this method include requirements analysis, initial prototype design, periodic user evaluation, iterative improvements, and final implementation. Data collection involved 12 respondents—comprising owners, employees, and customers, through observation, interviews, and document review. System testing using Black Box Testing confirmed that all core functionalities operate according to specifications without technical issues. Meanwhile, the User Acceptance Test (UAT) yielded an acceptability score of 80%, confirming that the system is ready and suitable for implementation. This integrated system successfully digitized the management of customer data, transactions, inventory, booking services, and revenue reporting. Furthermore, the implementation Role-Based Access Control (RPC) not only secures data but also facilitates customers to make reservations independently, while employees can focus on technical management. The main contribution of this research is the provision of a digitalization model for MSME-scale workshop administration that integrates operational management efficiency with ease of customer reservations, thus serving as a practical reference for similar businesses. As a result, workshop administration processes are transformed into faster, more structured, and more efficient.
Rancang Bangun Sistem Informasi Pemesanan Air Galon Berbasis Web Menggunakan Metode RAD Muhammad Rizal; Galet Guntoro Setiaji; Badroe Zaman
TIN: Terapan Informatika Nusantara Vol 7 No 1 (2026): June 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v7i1.10069

Abstract

The utilization of digital technology has become an essential aspect of improving business management effectiveness, including in the water refill depot sector. At Air Tirta Kencana Depot in Demak Regency, the ordering process and transaction recording are still carried out manually, resulting in slower data processing, an increased risk of recording errors, and customer service that has not been performed optimally. This study aimed to develop a website-based water gallon ordering system by applying the Rapid Application Development (RAD) approach. Research data were collected through observation, interviews, and literature studies. System testing was conducted involving 13 respondents to evaluate system functionality and usability using Black Box Testing and User Acceptance Testing. The developed system was equipped with integrated features for customer data management, transaction ordering, and sales information within a single platform. The integration of these features facilitated ordering, recording, and transaction monitoring processes in a more organized manner according to the operational needs of the depot. In addition, the system enabled users to access order information more quickly and systematically. Based on the testing results, the web-based information system was able to support transaction recording processes to become faster, more organized, and easier to manage. Furthermore, the system achieved a testing success rate of 80% and assisted in monitoring orders and transactions more accurately. The contribution of this research lies in the development of an integrated ordering and transaction management system within a single platform, enabling depot operational processes to be monitored in a more structured manner. Based on these findings, the system also supports more effective business decision-making and helps improve the overall operational quality of the water depot.
Kuantifikasi Risiko Introspection pada Tiga Kategori Otorisasi OWASP: Studi Komparatif REST API dan GraphQL Naufal Hanif Athallah; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Information System Research (JOSH) Vol 7 No 4 (2026): July 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v7i4.9874

Abstract

The advancement of Application Programming Interfaces (APIs) demands measurable architectural-level security evaluation. This study quantifies the security risks of REST API and GraphQL based on three authorization categories from the OWASP API Security Top 10 2023 (API1, API3, and API5). The exclusive limitation to these three categories was established to focus purely on access control logic flaws rather than infrastructure-level vulnerabilities. The experiment utilizes TixVuln, a parallel-architecture testbed instrument explicitly designed to eliminate external database bias a comparative advantage not present in standard single-architecture vulnerable applications. Authorization evaluation was executed contextually to avoid the high false-negative rates typically produced by automated security scanning tools (SAST/DAST) in business logic testing. Quantification results using the OWASP Risk Rating Methodology reveal a novelty that GraphQL experiences a risk category escalation from Medium to Critical levels in API3 and API5 compared to REST API. This significant leap in the Ease of Discovery metric is absolutely triggered by the operational schema exposure through the introspection feature. Mitigation testing validates that implementing field whitelisting and resolver-level Role-Based Access Control is imperative to suppress inherent risks in single-endpoint architectures. The main contribution of this research is the provision of an isolated empirical evaluation framework that quantitatively proves the flexibility of GraphQL architecture is directly proportional to the increased fatality of authorization risks if the schema discovery feature is not strictly configured.
Comparison of Decision Tree and Random Forest Performance for Sentiment Analysis of Public Service App Reviews: Perbandingan Kinerja Decision Tree dan Forest Performance untuk Analisis Sentimen Ulasan Aplikasi Layanan Publik Aditya Rizky Purnama; Galet Guntoro Setiaji; Ahmad Rifa'i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1112

Abstract

The Info BMKG application is a government-developed digital public service platform designed to provide real-time weather, seismic, and climate information to Indonesian citizens. The substantial volume of user reviews accumulated on the Google Play Store holds significant potential as a service evaluation resource; however, the limitations of manual review processes necessitate an efficient computational approach. This study proposes a machine learning-based sentiment analysis framework to classify user reviews of the Info BMKG application, while systematically comparing the performance of two algorithms Decision Tree and Random Forest using a dataset of 10,000 reviews collected via web scraping. The data underwent text preprocessing, rating-based sentiment labeling, and TF-IDF feature extraction, followed by evaluation using accuracy, precision, recall, F1-score, cross-validation, and computational time metrics. Experimental results demonstrate that Random Forest achieved 81% accuracy with a 77% F1-score, outperforming Decision Tree which attained 78% accuracy and 75% F1-score. In terms of efficiency, Decision Tree exhibited faster testing time (0.114 seconds) compared to Random Forest (0.201 seconds), while Random Forest proved more efficient in training time (7.347 seconds versus 12.421 seconds). These findings confirm that Random Forest represents the more optimal algorithm for sentiment classification tasks involving public service application user reviews.
Sentiment Analysis of FlyGaruda Review Using Support Vector Machine and Naive Bayes Algorithm: Analisis Sentimen Ulasan FlyGaruda Menggunakan Algoritma Support Vector Machine dan Naive Bayes Gibran Masta Pangestu Baskoro; Galet Guntoro Setiaji; Ahmad Rifa’i
Journal of Data Insights Vol 4 No 1 (2026): Journal of Data Insights
Publisher : Department of Sains Data UNIMUS Universitas Muhammadiyah Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26714/jodi.v4i1.1117

Abstract

FlyGaruda is an official digital application owned by Garuda Indonesia that provides ticket booking and online check-in services for users. This study analyzed the sentiment of reviews on the Google Play Store by comparing the performance of Support Vector Machine and Multinomial Naive Bayes. The methods used include scraping, text preprocessing, extraction of the Term Frequency-Inverse Document Frequency (TF-IDF) feature, and evaluation using the Confusion Matrix. The dataset used totaled 4,790 reviews with positive, negative, and neutral categories. The results showed that both models obtained an accuracy of 82.25%. However, the Support Vector Machine produces a weighted precision of 77.66% and an F1-Score of 78.91%, better at handling data imbalances. Meanwhile, Multinomial Naive Bayes excels in computing efficiency with a training time of 0.08 seconds compared to 90.60 seconds on the Support Vector Machine. In conclusion, although it is slower, the Support Vector Machine provides more consistent and accurate classification performance. This research contributes to the development of a machine learning-based opinion analysis system to improve the quality of aviation digital services in a sustainable manner. These findings can serve as a reference in the selection of the best algorithms between accuracy and computational speed in large text data and support data-driven decision-making in the modern air transportation industry in the current era of global sustainable digital transformation
Analisis Komparasi Kinerja LSTM dan CNN dalam Deteksi Spam Email Berbasis Deep learning Maugy Al Kautsar; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.572

Abstract

Spam email remains a critical issue in digital communication due to its potential misuse in spreading false information and online fraud. This study aims to evaluate and compare the performance of two deep learning models Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for text-based spam email classification. The dataset used in this study was obtained from Kaggle and contains 5,572 labeled email entries categorized as spam and non-spam. The preprocessing stage included labeling, cleaning, lowercasing (casefolding), tokenization, stopword removal, and stemming. The data was split into training and testing sets with a 70:30 ratio. Both models were trained using the same configuration and evaluated using accuracy, loss, confusion matrix, and F1-score metrics. The results indicate that the LSTM model achieved the highest accuracy of 98.72% with a loss value of 0.0377, outperforming the CNN model, which achieved 87.78% accuracy and a loss of 0.3659. Based on these findings, LSTM demonstrated superior performance in detecting spam emails using text-based input. This research is expected to serve as a reference for developing more accurate and effective spam detection systems in the future.
Co-Authors Aditya Rizky Purnama Ahmad Rifa'i Ahmad Rifai Ahmad Rifa’i April Firman Daru Astrid Novita Putri Badroe Zaman Basworo Ardi Pramono Bernandika Reyhan Groovytala Cholil, Saifur Rohman Djoko Handoko Galih Setiarso Gibran Masta Pangestu Baskoro Hafiq Ibnu Wardana Hakim, Adya Abdu Azizul Handini Arga Damar Rani Hanif, Mohammad Burhan Joko Suntoro Khoirudin Khoirudin Krida Pandu Gunata Maugy Al Kautsar Mohammad Burhan Hanif Mohammad Burhan Hanif Muhammad Dedi Setiawan Muhammad Rizal Naufal Hanif Athallah Nurtriana Hidayati Pecaro, R Immanuel Giovanni Italiano Retno Utami, Tri Rifa'i, Ahmad Rio Eko Saputro Saputra, Satria Nugraha Septiano Cepeda Da Costa Soiful Hadi Surono Surono Syahrul Adi Saputra Tirta Jurista Kumkamdhani Tirta Kumkamdani Vensy Vydia Whisnumurti Adhiwibowo Wicaksana, Dinar Anggit Widiyanto, Max Teja Ajie Cipta