Article Per Year (5 Year)
p-Index From 2021 - 2026
9.948
P-Index
This Author published in this journals
All Journal Jurnal Dedikasi SMATIKA Journal of Information Technology and Computer Science (JOINTECS) Jurnal Sains dan Informatika Jurnal Teknoinfo Multitek Indonesia : Jurnal Ilmiah JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JSAI (Journal Scientific and Applied Informatics) Progresif: Jurnal Ilmiah Komputer Jurnal ABDINUS : Jurnal Pengabdian Nusantara JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Abdi Insani Indonesian Journal of Cultural and Community Development Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Pengabdian Masyarakat Abdimas Madani International Journal of Engineering, Science and Information Technology Abdimas Singkerru JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer Jurnal Abdimas Indonesia : Jurnal Abdimas Indonesia Bulletin of Computer Science Research Decode: Jurnal Pendidikan Teknologi Informasi Bulletin of Information Technology (BIT) Proceedings Series on Physical & Formal Sciences Indonesian Journal of Innovation Studies Aptekmas : Jurnal Pengabdian Kepada Masyarakat PELS (Procedia of Engineering and Life Science) Procedia of Social Sciences and Humanities Prosiding University Research Colloquium JOINCS (Journal of Informatics, Network, and Computer Science) Jurnal Sarjana Ilmu Komunikasi (J-SIKOM) Jurnal Informatika Polinema (JIP) Physical Sciences, Life Science and Engineering Indonesian Journal of Applied Technology Journal of Technology and System Information Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Journal of Electrical Engineering Journal for Technology and Science Tepak Sirih : Jurnal Pengabdian Kepada Masyarakat Madani semanTIK Journal of Blockchain, Nfts and Metaverse Technology IJHCS Journal of Information Technology Smatika Jurnal : STIKI Informatika Jurnal Academia Open Jurnal Komunikasi Bisnis dan Teknologi Digital
Hamzah Setiawan
Program Studi Informatika, Universitas Muhammadiyah Sidoarjo, Indonesia
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Transportation Veterinary Other

Published : 65 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 65 Documents
Search

 3   4   5   6   7 
Implementasi Payment Gateway pada Platform Freelance Digital Menggunakan Rest API Muhammad Agung Laksono; Irwan Alnarus Kautsar; Hamzah Setiawan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v14i01.1227

Abstract

In the continuously evolving digital era, the use of payment gateways has become a crucial element in online business transactions. A payment gateway is a technology or service that enables companies or institutions and applications to accept electronic payments or other digital payment methods. This study employs an Agile Development approach to implement a payment gateway on a digital freelance platform. Agile Development was chosen for its flexibility and iterative approach, allowing for quick adjustments to changing needs. The results indicate that the payment gateway on this freelance platform operates quite effectively. Testing on the platform showed successful outcomes with a success rate of 87%. This research demonstrates that integrating a payment gateway via REST API not only enhances the operational efficiency of the freelance platform but also strengthens user trust in the digital payment system, thereby supporting the growth and sustainability of the freelance platform's business.
Application of Data Mining Using the Support Vector Machine (SVM) Method to Analyze Fashion Retail Products to Determine Trends: Penerapan Data Mining Dengan Menggunakan Metode Support Vector Machine (SVM) Untuk Menganalisa Produk Fashion Retail Untuk Menentukan Tren Hamzah Setiawan
Academia Open Vol. 9 No. 1 (2024): June
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/acopen.9.2024.8581

Abstract

This study addresses the escalating volume of research by proposing an efficient research storage system through data mining-based categorization. Employing the Support Vector Machine (SVM) method on a dataset comprising 541,910 retail product purchases, the research achieves a significant 96.2% accuracy in categorization using the cross-entropy loss function. The SVM method proves instrumental in systematically organizing research based on fields, methods, and outcomes, showcasing its efficacy in large-scale research storage and organization. This study highlights the SVM's potential as a vital tool for governments and private organizations to enhance access and utilization of research information. The results underscore the positive impact of SVM in overcoming the complexity of research storage on a broader scale, contributing to the advancement of efficient research management systems. Highlights: Efficient SVM Data Management: Proposes SVM-based data mining for effective research information storage. 96.2% Accuracy in Categorization: SVM with cross entropy achieves high accuracy in classifying research data. Organized Access for Better Utilization: SVM organizes research systematically, enhancing accessibility and utilization for government and private sectors. Keywords: Support Vector Machine, Data Mining, Dataset, Retail.
Penerapan Metode Support Vector Machine (SVM) untuk Memprediksi Pemilihan Karir bagi Alumni UMSIDA Qur'ani, Meisyilia Difanada; Setiawan, Hamzah; Kautsar, Irwan Alnarus
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 4 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i4.6630

Abstract

The success of a university is not only determined by its educational process but also by the ability of its graduates to get a job. The aim of this research is to develop and evaluate a predictive model using the Support Vector Machine (SVM) method to predict career choices for alumni of the Muhammadiyah University of Sidoarjo (UMSIDA) . This research uses a quantitative approach, in the topic of predicting sample data obtained from tracer data of Umsida students which is compiled into the title "Application of the Support Vector Machine (SVM) Method to Predict Career Choices for UMSIDA Alumni". The model evaluation results show that SVM has very good performance, with high precision, recall and f1-score for the dominant class. Feature importance analysis shows key features that have a significant influence on model decisions, providing valuable insight into the factors that influence alumni career choices. With an overall accuracy of 97%, this model is able to provide appropriate career recommendations for the majority of alumni.
Prediksi Kelulusan Mahasiswa Prodi Informatika dengan Algoritma Decision Tree (C4.5) dan Naïve Bayes Steven Gerrard; Ade Eviyanti; Hamzah Setiawan; Ika Ratna
Bulletin of Computer Science Research Vol. 6 No. 3 (2026): April 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i3.1035

Abstract

The primary parameter for measuring higher education quality, which also has a crucial impact on the accreditation process, is the percentage of students graduating on time. However, the reality on the ground shows that many students face obstacles in completing their studies within the ideal timeframe. Therefore, a data-driven strategy is needed to project students' chances of graduation early. This research aims to compare the performance of the Decision Tree (C4.5) and Naïve Bayes algorithms in classifying the potential for on-time graduation. The data utilized included 161 entries from the Informatics Study Program, class of 2022, at the University of Muhammadiyah Sidoarjo. The attributes analyzed were divided into academic and non-academic factors, including gender, first-semester social studies grades (IPS), GPA, PKMU (Community Service Program) graduation score and status, BQ and Ibadah scores, and accumulated SKEK points. The research process went through several phases: preprocessing, class labeling, model development, and performance evaluation through a confusion matrix and 5-fold cross-validation. The test was validated by separating the training and test data into ratios of 70:30, 80:20, and 90:10. Based on the test results, the C4.5 algorithm achieved a peak accuracy of 100% across all ratio scenarios, with an average cross-validation accuracy of 96.88%. Meanwhile, Naïve Bayes achieved a maximum accuracy of 94.13% with an average cross-validation of 93.00%. These findings indicate that the C4.5 algorithm has superior performance on this specific dataset. The output of this predictive model is expected to serve as an objective basis for institutions in establishing proactive academic policies.
Comparison of Naive Bayes and KNN for Honey-Mumford Learning Style Classification in Interpersonal Skill: Komparasi Naive Bayes dan KNN untuk Klasifikasi Gaya Belajar Honey-Mumford pada Interpersonal Skill Hari Moerti; Hamzah Setiawan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Developing soft skills competence, particularly interpersonal abilities, often presents a challenge for Informatics students accustomed to technical and structured thinking patterns. The mismatch between teaching methods and student learning preferences can hinder the absorption of non-technical material. This study aims to classify student learning style profiles in the Interpersonal Skill course using a Machine Learning approach based on the Honey-Mumford model (Activist, Reflector, Theorist, Pragmatist). The research methodology employs Educational Data Mining techniques by comparing the performance of Naive Bayes and K-Nearest Neighbor (KNN) algorithms in predicting learning styles based on academic history data and behavioral questionnaires. Experimental results indicate that the Naive Bayes algorithm outperforms KNN in recognizing student characteristic patterns, achieving an accuracy rate of 93.33%. These findings suggest that engineering students possess heterogeneous learning styles; therefore, adaptive and varied teaching strategies are essential to optimize the comprehension of soft skills materia.
Sistem Prediksi Kelulusan Mahasiswa Fakultas Saintek Universitas Muhammadiyah Sidoarjo Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation Moch Ridwan Alwi; Hindarto; Hamzah Setiawan
Jurnal Komunikasi Bisnis dan Teknologi Digital Vol. 1 No. 1 (2025): October
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/jkbtd.v1i1.264

Abstract

Perguruan tinggi swasta maupun negeri mewajibkan mahasiswanya untuk lulus. Begitu pula dengan perguruan tinggi swasta di Sidoarjo, seperti Universitas Muhammadiyah Sidoarjo (UMSIDA), tingkat kelulusan mahasiswanya dapat berdampak pada akreditasi program studi. Pentingnya menggunakan berbagai metode untuk menentukan jumlah mahasiswa yang akan mendaftar dan lulus, mengingat pentingnya nilai akreditasi dalam kelulusan mahasiswa. Memprediksi kelulusan siswa memungkinkan persiapan dan dukungan yang memadai bagi siswa untuk berhasil menyelesaikan studinya. Memiliki sistem yang dapat meramalkan seberapa cepat atau lambat seorang mahasiswa akan lulus akan memperlancar pengembangan sistem kampus bagi mahasiswa. Penelitian ini memanfaatkan Artificial Neural Network (JST) dengan pendekatan backpropagation untuk meramalkan kelulusan siswa. Data masukan untuk pelatihan JST ini bersumber dari Fakultas Sains dan Teknologi Universitas Muhammadiyah Sidoarjo (UMSIDA) tentang tingkat kelulusan mahasiswa tahun 2015 sampai dengan tahun 2019. Hasil pengujian menunjukkan bahwa Mean Square Error (MSE) pada keluaran JST sebesar 0,000141295, pada pengujian akurasi didapatkan nilai akurasi 93.428901%. Hal ini menunjukkan bahwa metode backpropagation dengan ANN dapat dimanfaatkan secara efektif untuk memprediksi kelulusan mahasiswa.
Analisis Sentimen Layanan Perwalian Mahasiswa UMSIDA Menggunakan Metode Support Vector Machine (SVM) Angga Wibawa Saputra Angga; Hamzah Setiawan Hamzah; Rohman Dijaya Rohman
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 1 (2025): Januari
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i1.7387

Abstract

The myUMSIDA application supports academic activities at Universitas Muhammadiyah Sidoarjo, but student reviews reveal complaints about its services and facilities. Sentiment analysis is necessary to classify these reviews into positive or negative categories, providing insights to improve service quality.This study uses the Support Vector Machine (SVM) algorithm with a linear kernel, known for its high accuracy, combined with the TF-IDF feature extraction method to enhance text classification. A total of 1,300 reviews from 2023 were processed through labeling, preprocessing, transformation, and classification. Data were split into three scenarios: 70:30, 60:40, and 50:50 for training and testing. Performance was evaluated using accuracy, precision, recall, and F1-Score.The best results were achieved in the 70:30 scenario, with an accuracy of 86.92%, precision of 86.60%, recall of 84.72%, and F1-Score of 85.7%. This study highlights the effectiveness of SVM with a linear kernel and TF-IDF in analyzing sentiment, offering a basis for enhancing the myUMSIDA application's services.
Optimalisasi Pengelolahan Jaringan Dengan Pembatasan Bandwidth dan Blokir Akses Tertentu Pada PT Laxo Global Akses Dengan Menerapkan Metode NDLC Adiffanani Ramdansyah; Hamzah Setiawan; Uce Indahyanti; Ade Eviyanti
JSAI (Journal Scientific and Applied Informatics) Vol 8 No 2 (2025): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v8i2.8165

Abstract

This research focuses on optimizing network management at PT Laxo Global Akses by implementing bandwidth restrictions and restricting access to non-work related websites and applications. Internet usage in the company was previously unrestricted, resulting in decreased productivity and unstable network performance, especially during peak hours. To address these issues, this research applies the Network Development Life Cycle (NDLC) methodology and utilizes MikroTik devices for configuration. Key features such as Queue Tree for bandwidth distribution, Firewall Filtering for access control, and Hotspot User Management for user authentication were implemented. The redesigned network topology allows for more structured traffic management and real-time monitoring. The test results show that all test scenarios have succeeded as expected, so it can be concluded that the network configuration and policies implemented in the NDLC process were 100% successful at the implementation and testing stages.
Perbandingan Algoritma Machine Learning dalam Memprediksi Kelulusan Mahasiswa M Cholis Afandi; Uce Indahyanti; Hamzah Setiawan; Irwan A. Kautsar
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.154

Abstract

Penelitian ini bertujuan untuk memprediksi kelulusan mahasiswa Program Studi Informatika Universitas Muhammadiyah Sidoarjo menggunakan algoritma klasifikasi Machine Learning, yaitu Naïve Bayes, Decision Tree, dan Random Forest. Data yang digunakan merupakan data akademik mahasiswa angkatan 2020–2021, mencakup nilai IPS dan jumlah SKS dari semester 1 hingga 6. Proses analisis mengikuti tahapan CRISP-DM, mulai dari pemahaman bisnis hingga evaluasi model. Evaluasi dilakukan menggunakan confusion matrix serta pengukuran akurasi, presisi, recall, dan F1-score untuk membandingkan performa tiap algoritma. Hasil menunjukkan bahwa Random Forest memiliki akurasi tertinggi yaitu 97.50% pada skenario 80:20, disusul Decision Tree dengan 96.25%, dan Naïve Bayes sebesar 86.25%. Selain itu, Random Forest juga mencatatkan nilai presisi dan recall yang tinggi serta F1-score sebesar 97%, menunjukkan kestabilan dan keunggulan model dalam menangani data akademik. Berdasarkan temuan ini, Random Forest dinilai paling optimal dan direkomendasikan untuk digunakan sebagai sistem pendukung keputusan dalam memantau kelulusan mahasiswa secara prediktif dan akurat. This study aims to predict student graduation in the Informatics Study Program at Universitas Muhammadiyah Sidoarjo using Machine Learning classification algorithms, namely Naïve Bayes, Decision Tree, and Random Forest. The dataset consists of academic records from the 2020–2021 cohort, including GPA scores and the number of credits (SKS) taken from semesters 1 to 6. The data analysis process follows the CRISP-DM methodology, covering business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Model evaluation is carried out using confusion matrices along with accuracy, precision, recall, and F1-score to compare the performance of each algorithm. The results show that Random Forest achieved the highest accuracy of 97.50% in the 80:20 scenario, followed by Decision Tree at 96.25%, and Naïve Bayes at 86.25%. In addition, Random Forest demonstrated high precision and recall values with an F1-score of 97%, confirming its stability and effectiveness in academic data classification. Based on these findings, Random Forest is considered the most optimal algorithm and is recommended as a decision support tool for accurately monitoring and predicting student graduation in higher education institutions.
Implementation of Real-Time DoS Attack Detection and Automatic Mitigation on C-Based VPS Server : Implementasi Deteksi Serangan DoS Real-Time dan Mitigasi Otomatis pada Server VPS Berbasis C Saputra Budianto Putra; Hamzah Setiawan; M. Alfan Rosyid
SemanTIK : Teknik Informasi Vol. 11 No. 2 (2025): SemanTIK : Teknik Informasi
Publisher : Informatics Engineering Department of Halu Oleo University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55679/semantik.v11i2.170

Abstract

Di era digital, keamanan menjadi aspek penting dalam menjaga sistem IT, terutama pada Virtual Private Server (VPS) yang paling sering terpapar ancaman siber. Denial-of-Service (DoS) merupakan risiko yang dapat dikurangi dengan mengganti server dengan protokol yang lebih canggih seperti TCP, UDP, dan ICMP, serta menerapkan sistem yang mendeteksi dan mengurangi DoS secara real time dan otomatis menggunakan bahasa pemrograman C. Proses pengembangan sistem dengan menggunakan metodologi Agile Scrum memungkinkan proses yang iteratif, fleksibel, dan fleksibel. Sprint meliputi analisis antrean server dengan libpcap, manajemen log dengan SQLite, pemblokiran IP otomatis dengan iptables, dan pembaruan log melalui log sistem. Studi ini menunjukkan bahwa sistem dapat mendeteksi dan mengurangi HTTP Flood, ICMP Flood, dan Slowloris dalam waktu 0,5 detik dengan CPU dan memori yang rendah. Meskipun tidak ada integrasi visual real-time atau notifikasi real-time untuk manajemen, sistem ini efisien dan efektif dalam memproses data dengan cepat. Studi ini menyimpulkan bahwa penggunaan bahasa pemrograman C dalam pengembangan keamanan VPS sangat penting untuk mitigasi dan pemulihan yang cepat. Fase pengembangan meliputi deteksi tingkat aplikasi (lapisan 7), visualisasi dasbor, dan pembelajaran mesin untuk mengidentifikasi kerentanan dan kompromi dengan cepat.
Co-Authors Abidin, Husnul Ade Eviyanti Adi Putra, Lutfi Adiffanani Ramdansyah Alshaf Pebrianggara Amelia, Paramitha Angga Wibawa Saputra Angga Arief Senja Fitrani Arief Wicaksono, Arief Arif Senja Fitrani Arif Senja Fitriani Arisandi, Ricky Renaldo Asiddiq, Afnizar Maulana Aulia Aliffiandi, Rizca Aziziyah, Ismi Anisa Azmuri Wahyu Azinar Azmuri Wahyu Azinar Cakra Wirabumi Putra Cindy Taurusta Denny Gunawan Duwi Rahayu Enggi Sabrilla Assara Evi Rinata Firdausi Usqi Salsabila Fuad Azis Muslim Gilang Pralaya Grahita Albarika, Ayu Hari Moerti Hindarto Hindarto Hindarto Hindarto Ika Ratna Ika Ratna Indra Astutik Imanda, Almyra Gitta Intan Nuraini Irwan A. Kautsar Irwan Alnarus Kautsar Jamal Hasan Jefry Fernando Kurnia Ningtiyas Luluk Asti Qomariah M Cholis Afandi M. Alfan Rosyid Moch Bagus Tri Cahyo Moch Ridwan Alwi Moch Ridwan Alwi Mochamad Alfan Rosid Mochamad Surohadi Mochammad Septa Sandy Mohamad Haris Muzadi Muhammad Agung Laksono Muhammad Fikri Muhammad Mursidil Arif Muhammad Saddam Heykal Bustomy Muhammad, Fajar Muhammad, Khithoh Sabda Nanda Fitriana Novia Ariyanti Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Pratiwi, Rosa Machmuda Qur'ani, Meisyilia Difanada Rachmat Firdaus Ratih Sri Yunarti Rayhanantha Akbar Putra Prasetyo Ribangun Bamban Jakaria Rina Safitri Riswanto Rizky Budi Aprianto Rizky Rahmahdian Sandy Rohman Dijaya sandy, Mochamad septa Saputra Budianto Putra Sinta Nuriyah, Rizky SITI CHOLIFAH Siti Cholifah Siti Cholifah Steven Gerrard Sumarno . Sumarno Sumarno Suprianto Suprianto Suprianto1, Suprianto Triwahono, Handi Uce Indahyanti Vidya Wati Dwi Ramadhani Wildan Arif Hidayatulloh Wiwik Sumarmi Yunianita Rahmawati Yunianita Rahmawati Zulham Efendi, Muhammad