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All Journal Jurnal Transformatika CESS (Journal of Computer Engineering, System and Science) Jurnal Pengembangan Rekayasa dan Teknologi Jurnal Ilmiah Sinus Journal of Information Systems and Informatics JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) 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 Jurnal DIMASTIK
Ahmad Rifa’i
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Aerospace Engineering Arts Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Social Sciences Transportation Veterinary Other

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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.
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.
Peningkatan Transparansi Tata Kelola Keuangan Desa melalui Pengembangan Sistem Berbasis Web dengan Menerapkan Model Agile Septiano Cepeda Da Costa; Galet Guntoro Setiaji; Ahmad Rifa'i
Bulletin of Computer Science Research Vol. 6 No. 1 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i1.821

Abstract

This research discusses village financial management as a primary aspect in supporting village progress and community welfare. Transparent and effective budget management is crucial for ensuring that village funds are utilized optimally for development programs. However, several obstacles persist, including delays in financial reporting and limited access to information for residents. The purpose of this research is to design and develop a web-based village financial information system using the Laravel framework and an Agile methodology to improve transparency, data accessibility, and the effectiveness of real-time financial reporting. The Agile method was selected for its iterative and collaborative development process, which is responsive to user needs. This approach allows both residents and village officials to participate actively in the development process, ensuring the resulting system serves as a viable solution to existing problems. Functional testing results using the Black Box method indicate that the system facilitates easier financial reporting, provides accurate data presentation, and supports accountability in village fund management. This system is expected to increase community participation in budget oversight and serve as a technological solution for the digitalization of village financial management. Ultimately, this system aims to make village budget management transparent, fast, and accessible to all residents at any time, thereby fostering community trust and engagement in village development.
IMPLEMENTASI METODE SCRUM PADA APLIKASI WEB MANAJEMEN PEMBAYARAN KOS BERBASIS LARAVEL DAN FILAMENT Rio Eko Saputro; Galet Guntoro Setiaji; Ahmad Rifa'i
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2283

Abstract

Penelitian ini mengajukan solusi atas kendala operasional dalam manajemen kos konvensional melalui perancangan sebuah aplikasi web terintegrasi yang memusatkan fungsi pembayaran dan penanganan keluhan. Proses pengembangan mengadopsi metodologi Scrum dengan tumpukan teknologi modern: framework Laravel sebagai fondasi, toolkit Filament untuk akselerasi pembangunan antarmuka, dan library Filament Shield untuk implementasi kontrol akses berbasis peran. Hasil akhir penelitian adalah platform fungsional dengan arsitektur dua peran: peran Admin difasilitasi dengan dasbor analitik dan kontrol manajerial penuh, sementara peran User Kos memperoleh antarmuka yang terfokus untuk menyederhanakan interaksi pembayaran dan keluhan melalui widget informatif. Secara konklusif, sistem yang dikembangkan ini mampu meningkatkan transparansi manajerial dan kenyamanan penghuni, sekaligus memvalidasi kombinasi metodologi dan teknologi yang dipilih sebagai model implementasi yang praktis untuk mendukung digitalisasi sektor properti skala kecil.
Pemberdayaan Pokdarwis Kandri dalam Manajemen Website Demografi untuk Mendukung Pariwisata Berbasis Data Ahmad Rifa'i; Eka Putri Rachmawati; Victor Gayuh Utomo; Titis Handayani
Jurnal DIMASTIK Vol. 3 No. 2 (2025): Juli
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/dimastik.v3i2.12680

Abstract

Program Pengabdian kepada Masyarakat (PkM) ini bertujuan untuk meningkatkan kapasitas digital Kelompok Sadar Wisata (Pokdarwis) Desa Wisata Kandri dalam mengelola website demografi kependudukan. Desa Kandri, sebagai desa wisata berbasis alam dan budaya di Kota Semarang, menghadapi tantangan minimnya pengelolaan data kependudukan secara digital, yang berdampak pada perencanaan pembangunan, transparansi administrasi, serta penyajian informasi bagi wisatawan dan calon investor. Kegiatan dilaksanakan melalui serangkaian tahapan yang meliputi survei kebutuhan, pengembangan website demografi, serta pelatihan teknis berbasis praktik. Peserta dibekali keterampilan dalam manajemen data, visualisasi informasi kependudukan, integrasi informasi pariwisata, serta penguatan aspek keamanan digital. Evaluasi menggunakan pre-test dan post-test menunjukkan peningkatan signifikan dalam aspek pengetahuan, keterampilan teknis, dan kesiapan peserta dalam mengelola website.
GA-SVM Wrapper Feature Selection untuk Penanganan Data Berdimensi Tinggi Ahmad Rifa'i; Joko Suntoro; Galet Guntoro Setiaji
Jurnal Transformatika Vol. 21 No. 2 (2024): Januari 2024
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v21i2.8886

Abstract

Peningkatan data dalam beberapa tahun terakhir ini mengalami peningkatan yang sangat signifikan karena penggunaan sosial media dan peralihan menjadi era digital. Teknik untuk pengolahan data menjadi informasi yang berguna dinamakan dengan data mining. Namun masalah yang terjadi ketika menerapkan data mining, khususnya metode klasifikasi adalah data berdimensi tinggi karena data berdimensi tinggi mempengaruhi hasil evaluasi dalam klasifikasi menjadi rendah. Data berdimensi tinggi didefinisikan sebagai data dengan jumlah fitur yang banyak dan kompleks, kompleksitas fitur mengakibatkan sulitnya memilih subset fitur yang optimal karena terdapat fitur yang tidak relevan. Dalam penelitian ini akan digunakan teknik wrapper dengan menerapkan metode metaheuristik yaitu algoritma genetika (GA) untuk pemilihan subset fitur agar lebih optimal, dan algoritma pengklasifikasi yang digunakan adalah algoritma Support Vector Machine (SVM), metode ini disebut dengan GA-SVM WFS. Hasil akurasi metode GA-SVM WFS lebih tinggi dibandingkan dengan metode SVM, dengan rata-rata hasil akurasi masing-masing sebesar 0,902 dan 0,874. Dalam penelitian ini terdapat perbedaan secara signfikan antara metode GA-SVM WFS dan metode SVM setelah dilakukan uji paired t-test dengan nilai p-value sebesar 0,01 dengan nilai α sebesar 0,05.
PENGGUNAAN METODE K-MEANS PADA ANALISA DAN KLASIFIKASI CAPRES 2019 DI TWITTER Ahmad Rifa i; Galet Guntoro Setiaji; Vensy Vydia
Jurnal Pengembangan Rekayasa dan Teknologi Vol. 3 No. 1 (2019): Mei (2019)
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jprt.v15i1.1489

Abstract

Media sosial merupakan wadah untuk mencurahkan kata-kata melalui tulisan pada dunia maya (online), sehingga pada nantinya akan dapat dilihat oleh banyak orang baik yang dikenal maupun tidak dikenal. Twitter merupakan jejaring sosial atau media sosial yang memiliki teks terbatas, yaitu memiliki   Panjang teks sebanyak 280 karakter. Metode crawling merupakan metode yag digunakan untuk pengambilan data dari media sosial yang dapat digunakan sebagai developer application.Crawling data pada media sosial twitter ini menggunakan aplikasi spyder python 3.7 yang berada didalam aplikasi anaconda. Dengan aplikasi tersebut akan di dapatkan data-data berkenaan dengan capres 2019. data-data tersebut akan diolah menggunakan metode K-Means. Setelah dilakukan pengolahan data menggunakan K-Means di dapatkan untuk cluster 1 (Jokowi) muncul sebanyak 3 kali sedangkan untuk cluster 2 (Prabowo) muncul sebanyak 1 kali.
Co-Authors Aditya Rizky Purnama Asmiatun, Siti Astrid Novita Putri, Astrid Badroe Zaman Basworo Ardi Pramono Bernandika Reyhan Groovytala Cholil, Saifur Rohman Dinar Anggit Wicaksono Djoko Handoko Edi Widodo Eka Putri Rachmawati Elfonda Dandi Kurnia Putra Galet Guntoro Setiadji Galet Guntoro Setiaji Gibran Masta Pangestu Baskoro Hafiq Ibnu Wardana Hakim, Adya Abdu Azizul Hetty Catur Ellyawati, Hetty Catur Joko Suntoro Krida Pandu Gunata Kusumaningrum, Yulinda Maugy Al Kautsar Mohammad Burhan Hanif Muhammad Dedi Setiawan Naufal Hanif Athallah Retno Utami, Tri Rio Eko Saputro Septiano Cepeda Da Costa Setiaji, Galet Guntoro Soiful Hadi Syahrul Adi Saputra Tirta Jurista Kumkandani Tirta Yurista Kumkamdani Titis Handayani Vensy Vydia Victor Gayuh Utomo