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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) International Journal of Engineering, Science and Information Technology Abdimas Singkerru JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer 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 MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA 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 Information Technology Smatika Jurnal : STIKI Informatika Jurnal Academia Open Jurnal Komunikasi Bisnis dan Teknologi Digital
Hamzah Setiawan
Informatika, Universitas Muhammadiyah Sidoarjo
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Revolutionizing Inventory Management: Web-Based System for Accurate and Efficient Reporting: Merevolusi Manajemen Inventaris: Sistem Berbasis Web untuk Pelaporan yang Akurat dan Efisien Triwahono, Handi; Rosid, Mochamad Alfan; Setiawan, Hamzah; Hindarto, Hindarto
Indonesian Journal of Innovation Studies Vol. 22 (2023): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v22i.869

Abstract

The aim of this research is to develop a systematic information system for logistics of consumable goods by applying the FIFO method. It also aims to synchronize expenditure reports with inventory reports to minimize discrepancies between financial and inventory reports. The research approach consists of two methods, qualitative case study and FIFO inventory management method. The Web-based Consumable Goods Logistics Information System is built using PHP version 8.1 programming language with Laravel version 9 framework on the server-side, Vue.js on the client-side, and MySql as the database. It can be operated on browsers such as Chrome, Mozilla Firefox, or Microsoft Edge. The system is expected to help local governments generate the required reports for the BPKAD, which mandates monthly inventory reports on incoming and outgoing goods. The application is user-friendly and capable of producing accurate and fast reports. Highlights: 1. This research aims to create a systematic information system for consumable goods logistics using the FIFO method to minimize discrepancies between financial and inventory reports. 2. The Web-based system is user-friendly, capable of producing accurate and fast reports, and fulfills the inventory reporting needs of each department/unit while providing valid data to the Health Department. 3. The system is built using PHP 8.1 and Laravel 9 framework on the server-side, Vue.js on the client-side, and MySql as the database, and can be operated on Chrome, Mozilla Firefox, or Microsoft Edge browsers.
Comment Sentiment Analysis of JNE Using K-Nearest Neighbor (KNN) Method on Twitter: Analisis Sentimen Komentar terhadap JNE Menggunakan Metode K-Nearest Neighbor (KNN) pada Twitter Arisandi, Ricky Renaldo; Sumarno, Sumarno; Setiawan, Hamzah
Indonesian Journal of Innovation Studies Vol. 23 (2023): July
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/ijins.v22i.883

Abstract

Social media has evolved into a prominent public space for virtual criticism, particularly on platforms like Twitter, facilitated by widespread smartphone usage. Netizens utilize Twitter as an effective communication channel due to its accessibility and vast reach. This study focuses on sentiment analysis of comments from the public on Twitter, aiming to expedite the acquisition of accurate information about the general sentiment towards JNE (a logistics company). The K-Nearest Neighbor (KNN) classifier is employed, employing the TF-IDF weighting method to classify Indonesian language comments and assess the achieved accuracy. Highlights: Study focused on sentiment analysis of Twitter comments concerning JNE services using the K-Nearest Neighbor (KNN) method with Indonesian language text. Employed the TF-IDF weighting to classify comments and achieved an impressive 90% accuracy in sentiment analysis. The obtained classification proves valuable in evaluating public perception of JNE's services based on feedback from the social media community on Twitter.
language Inggris Moch Bagus Tri Cahyo; Hamzah Setiawan; Ika Ratna Indra Astutik
J-INTECH ( Journal of Information and Technology) Vol 13 No 02 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i02.2083

Abstract

This study aims to analyze the differences in scalability and performance between a traditional monolithic system hosted on a Virtual Private Server (VPS) and a cloud-native serverless architecture using AWS services for an automotive workshop information system. An experimental method was employed using a post-test only control group design. Performance testing was conducted with K6 as the stress testing tool under a ramp-up load pattern of up to 60 Virtual Users (VU) to simulate peak traffic conditions, while Grafana was used for real-time monitoring and visualization of system metrics.The results indicate that under peak load scenarios, the cloud-native architecture reduced the average response time by 89.1% (from 6.05 seconds to 657.10 milliseconds) and eliminated the error rate completely (from 0.154% to 0%), compared to the monolithic system. Additionally, the throughput improved by 38.2%, demonstrating better responsiveness and stability. These findings confirm that serverless cloud-native systems offer superior scalability and reliability in handling dynamic and high-demand workloads, making them well-suited for public service platforms such as automotive workshop information systems.
Pelatihan Akupresur Kader Lansia Guna Meningkatkan Imunitas di Desa Penatarsewu Tanggulangin Sidoarjo Amelia, Paramitha; Setiawan, Hamzah; Wicaksono, Arief; Cholifah, Siti; Jakaria, Ribangun Bamban
Jurnal ABDINUS : Jurnal Pengabdian Nusantara Vol 5 No 2 (2021): Volume 5 Nomor 2 Tahun 2021
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/ja.v5i2.16569

Abstract

This Covid-19 pandemic has a multidimensional impact on life, especially elderly who face a significant risk of contracting Covid-19. Several ways to increase immunity, especially elderly, include nutritious food, increase activity, and acupressure. The purpose of research is to provide acupressure training for elderly cadres to increase immunity of elderly during Covid-19 pandemic. The stages of implementing acupressure training were the survey service team to partner locations, collaborate with village government, midwives and elderly cadres then plan concept of solutions and stages. The team made media in form of posters of acupressure points and leaflets of acupressure points for hands and body. The result of community service, namely attitude of participants about acupressure has increased from attitude of the good category 11 people (73%) to very good as many as 14 participants (93%). The skills of participants, namely that before training did not have acupressure skills and after training all participants had acupressure skills. Number of participants who have skills to determine acupressure points correctly is 13 people (86%) and participants who can do acupressure correctly are 12 people (80%).
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.
Co-Authors Abidin, Husnul Ade Eviyanti Ade Eviyanti Adi Putra, Lutfi Adiffanani Ramdansyah Alshaf Pebrianggara Amelia, Paramitha Angga Wibawa Saputra Angga Arief Wicaksono, Arief Arif Senja Fitrani Arif Senja Fitriani Arisandi, Ricky Renaldo Asiddiq, Afnizar Maulana Aulia Aliffiandi, Rizca Aziziyah, Ismi Anisa Azmuri Wahyu Azinar Duwi Rahayu Enggi Sabrilla Assara Fuad Azis Muslim Gilang Pralaya Grahita Albarika, Ayu Hari Moerti Hasan, Jamal Hindarto Hindarto Hindarto Ika Ratna Ika Ratna Indra Astutik Ika Ratna Indra Astutik Imanda, Almyra Gitta Intan Nuraini Irwan A. Kautsar Irwan Alnarus Kautsar Jefry Fernando 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, Fajar Muhammad, Khithoh Sabda Novia Ariyanti Nur Maslikhatun Nisak Nuril Lutvi Azizah Paramitha Amelia Kusumawardani Pratiwi, Rosa Machmuda Qur'ani, Meisyilia Difanada Rachmat Firdaus Rayhanantha Akbar Putra Prasetyo Ribangun Bamban Jakaria Rina Safitri Riswanto Rizky Budi Aprianto Rizky Rahmahdian Sandy Rohman Dijaya sandy, Mochamad septa Saputra Budianto Putra Senja Fitrani, Arief Sinta Nuriyah, Rizky Siti Cholifah SITI CHOLIFAH Siti Cholifah Steven Gerrard Sumarno . Sumarno Sumarno Suprianto Suprianto Suprianto1, Suprianto Taurusta, Cindy Triwahono, Handi Uce Indahyanti Usqi Salsabila, Firdausi Wildan Arif Hidayatulloh Wirabumi Putra, Cakra Wiwik Sumarmi Yansah, Muhammad Kahfi Yunianita Rahmawati Zulham Efendi, Muhammad