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All Journal Jurnal Informatika SALAM: Jurnal Sosial dan Budaya Syar-i RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Primary : Jurnal Keilmuan dan Kependidikan Dasar ACCRUALS (Accounting Research Journal of Sutaatmadja) Jurnal Akuntansi dan Bisnis JURNAL TEKNOLOGI INFORMASI Kumawula: Jurnal Pengabdian Kepada Masyarakat Journal of Information Systems and Informatics Techno Xplore : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Sistem Informasi dan Sains Teknologi Journal of Electrical Engineering and Computer (JEECOM) Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Daengku: Journal of Humanities and Social Sciences Innovation Sisfo: Jurnal Ilmiah Sistem Informasi Bulletin of Network Engineer and Informatics (BUFNETS) International Journal of Artificial Intelligence in Medical Issues
Setiawan, Rudi
Information Systems Study Program, Faculty Of Science, Technology And Design, Universitas Trilogi
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Public Health Social Sciences Other

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Tingkat Kesadaran dan Kesiapan Pelaku Umkm Dalam Menyusun Laporan Keuangan dan Pajak Rachmawati, Nurul Aisyah; Ramayanti, Rizka; Setiawan, Rudi
Jurnal Akuntansi dan Bisnis Vol 21, No 2 (2021)
Publisher : Accounting Study Program, Faculty Economics and Business, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.734 KB) | DOI: 10.20961/jab.v21i2.646

Abstract

This study aims to analyze the level of awareness and readiness of Micro, Small, and Medium Enterprises (MSMEs) in preparing financial and tax reports. Data used in this study is primary data, which was obtained from the survey in April-May 2021. The data obtained were analyzed using descriptive qualitative research methods. The results show that the level of awareness and readiness of MSMEs in preparing financial and tax reports is still relatively low. MSMEs with the support of regulators need to make extra efforts to minimize the obstacles faced when compiling financial and tax reports.
SEGMENTATION OF SUPERBANK APPLICATION USER CHARACTERISTICS USING K-MEANS CLUSTERING Romi Pandu Wynalda; Rudi Setiawan
Bulletin of Network Engineer and Informatics Vol. 4 No. 1 (2026): BUFNETS (Bulletin of Network Engineer and Informatics) April 2026
Publisher : PT. GWEX NET PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59688/722834

Abstract

This study aims to identify the segmentation of Superbank user behavior based on application usage patterns and their relationship with onboarding channels. The research was conducted using a data analytics approach using the K-Means clustering method on user behavior data, which includes logins month, transactions month, feature usage and onboarding source. The results showed that the grouping of users resulted in three main clusters that were aligned with the onboarding channel, namely OVO, Grab, and Superbank App. The user clusters of the Superbank App showed the highest level of activity, characterized by a greater frequency of logins, higher transaction intensity, and wider exploration of features. Grab's user cluster is at moderate usage and shows potential to be improved through a phased engagement strategy. Meanwhile, the user cluster of OVO tends to have low login frequency, more transactional usage patterns, and limited feature exploration. These findings suggest that onboarding channels not only serve as user acquisition pathways, but also relate to post-acquisition behavior in app usage. Thus, the results of clustering can be used as the basis for developing a more targeted business strategy, especially in efforts to retain users, activate features, and increase engagement according to the characteristics of each channel.
Designing a Pocket Finance App to Empower Indonesian MSMEs: An Innovative Approach Nurul Aisyah Rachmawati; Rizka Ramayanti; Rudi Setiawan
Daengku: Journal of Humanities and Social Sciences Innovation Vol. 6 No. 3 (2026)
Publisher : PT Mattawang Mediatama Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/454RI.daengku5002

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a vital role in economic development; however, many face difficulties in preparing accurate financial and tax reports due to limited human resources, inadequate accounting expertise, and fragmented reporting systems. Although accounting information systems have been widely adopted to support financial management, applications that integrate both financial and tax reporting functions remain limited. This study aims to develop AccounTax, a cloud-based application that integrates financial and tax reporting to support MSME compliance and operational efficiency. The research employed the Scrum methodology, an agile software development framework that facilitates iterative design, development, and continuous stakeholder feedback. The study was conducted through three main phases: planning, system development, and testing and evaluation. During the planning phase, user requirements were identified through consultations with MSME actors and domain experts. The development phase focused on designing and implementing key features, including automated financial reporting, tax calculation, and cloud-based data management. The testing and evaluation phase assessed system functionality, usability, and user acceptance. The findings indicate that both experts and MSME users perceive AccounTax as an innovative and practical financial management solution. Features such as the integrated tax calculator and automated reporting tools effectively address common reporting challenges faced by MSMEs. The implementation of AccounTax is expected to simplify financial and tax administration processes, improve reporting accuracy, and enhance compliance with financial and taxation regulations, thereby supporting the sustainable growth of MSMEs.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN LAYANAN PENGIRIMAN BARANG MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING Rudi Setiawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 8 No 2 (2023): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v8i2.3375

Abstract

Delivery service is the distribution of goods from the manufacturer to the customer, but the service has a number of risks that may occur at the time of the delivery process, such as delayed delivery and damaged goods. For this, it is necessary to make the selection of the best delivery services by business operators so that customer satisfaction remains awake until the ordered product reaches the customer’s hands. To support this decision-making process, the Simple Additive Weighting (SAW) method is used by determining the weight value on each attribute and is continued with the best alternative weighing process that aims to help the public determine the choice of delivery services well and according to the desired criteria, the study uses 5 criteria namely tariffs, delivery speed, goods security, customer service and branch office distribution. There are 3 alternative goods delivery services assessed using the SAW method by 30 respondents who come from UMKM Tajurhalang with the highest ranking results available on JNE services.
Systematic Literature Review: Faktor-Faktor Penentu Keberhasilan Proyek Perangkat Lunak Virginia Widianingrum; Rudi Setiawan
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 1 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Mei 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v9i1.26916

Abstract

Penelitian ini mengidentifikasi dan menganalisis faktor penentu keberhasilan dalam proyek perangkat lunak menggunakan metode Systematic Literature Review pada publikasi 2014-2024. Hasil utama menunjukkan bahwa keberhasilan proyek perangkat lunak dipengaruhi oleh faktor global seperti manajemen proyek, komunikasi, evaluasi, dukungan manajemen puncak, dan kompetensi staf mempengaruhi keberhasilan proyek perangkat lunak. Di Indonesia, faktor utama mencakup manajemen proyek, evaluasi, dukungan manajemen puncak, pengelolaan perubahan, dan teknologi. Proyek perangkat lunak yang paling banyak dibahas adalah ERP, E-Learning, dan sistem berbasis web. Penelitian ini menekankan pentingnya pemahaman faktor-faktor yang mempengaruhi keberhasilan proyek untuk mencegah kegagalan dan meningkatkan kinerja proyek perangkat lunak di tingkat global dan Indonesia.
Evaluasi Model Machine Learning untuk Prediksi Diagnosis Cancer Payudara Berdasarkan Data Wisconsin Diagnostic Rudi Setiawan; Mira Febriana Sesunan
Sisfo: Jurnal Ilmiah Sistem Informasi Vol. 9 No. 2 (2025): Sisfo: Jurnal Ilmiah Sistem Informasi, Oktober 2025
Publisher : Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/sisfo.v9i2.28886

Abstract

Cancer payudara merupakan salah satu penyakit dengan tingkat kejadian dan kematian yang tinggi pada perempuan sehingga membutuhkan metode diagnosis yang cepat dan akurat. Penelitian ini bertujuan mengevaluasi dan membandingkan performa lima algoritma machine learning dalam mengklasifikasikan cancer payudara sebagai jinak dan ganas menggunakan dataset Breast Cancer Wisconsin Diagnostic yang terdiri atas 569 observasi dan 30 fitur numerik karakteristik inti sel. Algoritma yang dibandingkan meliputi Logistic Regression, Decision Tree, Random Forest Classifier, Support Vector Machine, dan K-Nearest Neighbors. Evaluasi dilakukan pada 171 data uji menggunakan accuracy, precision, recall, F1-score, confusion matrix, dan ROC-AUC. Hasil penelitian menunjukkan bahwa Logistic Regression memberikan performa terbaik dengan accuracy sebesar 98,83%, precision 0,98, recall 0,98, F1-score 0,98, dan ROC-AUC 1,00. Model ini hanya menghasilkan satu false negative dan satu false positive. Support Vector Machine menempati urutan kedua dengan accuracy 97,66% dan ROC-AUC 1,00, diikuti K-Nearest Neighbors sebesar 95,91%, Random Forest sebesar 93,57%, dan Decision Tree sebesar 91,81%. Hasil tersebut menunjukkan bahwa Logistic Regression memiliki keseimbangan terbaik antara kemampuan mendeteksi cancer ganas dan mengenali cancer jinak. Model ini dapat digunakan sebagai model acuan dalam klasifikasi cancer payudara pada dataset WDBC. Namun, penerapan klinis masih memerlukan validasi eksternal menggunakan data yang lebih besar dan beragam.
Co-Authors Agam Paluthfi Agustin, Heny Anisa Faradhila Annisa kartika puspitasari Apriansyah Apriansyah Arifin Maksum Aty Herawati Effan Najwaini Indirana Soraya Iskandar, Rossi Jasmin Nisphia Kabul Wahyu Utomo M Daniel Adriansyah Mira Febriana Sesunan Munajah, Robiatul Nurul Isha Aprilia Gusti Putri Qassandra Chaidir Rachmawati, Nurul Aisyah Rafi Antareza Putra Ramayanti, Rizka Rasmiati Rasyid Rezania Agramanisti Azdy Rifandra Zumaryan Rizki Dito Subekti Romi Pandu Wynalda Umar Al Faruq Virginia Widianingrum Wahyu Nugroho Wahyu Ramadhan Winda Amelia