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All Journal ProBank: Jurnal Ekonomi dan Perbankan Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JSiI (Jurnal Sistem Informasi) JURNAL MAKRO MANAJEMEN semanTIK
Moh. Aminollah Hamzah
Universitas Islam Madura
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Social Sciences

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Makna Kepercayaan dan Risiko dalam Adopsi Fintech oleh UMKM: Pendekatan Fenomenologi Moh. Aminollah Hamzah; Leily Nur Indah Fitriana; Mohammad Faris; Astri Furqani
Makro: Jurnal Manajemen dan Kewirausahaan Vol. 11 No. 1 (2026): Makro: Jurnal Manajemen dan Kewirausahaan
Publisher : Universitas Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53712/jmm.v11i1.2966

Abstract

Penelitian ini bertujuan untuk mengeksplorasi makna kepercayaan (trust) dan persepsi risiko (perceived risk) dalam adopsi financial technology (fintech) oleh Usaha Mikro, Kecil, dan Menengah (UMKM) melalui pendekatan fenomenologi. Penelitian menggunakan metode kualitatif dengan pendekatan Interpretative Phenomenological Analysis (IPA) melalui wawancara mendalam terhadap pelaku UMKM. Hasil penelitian menunjukkan bahwa kepercayaan berkembang melalui pengalaman penggunaan yang berulang serta pengaruh sosial, sementara persepsi risiko terbentuk dari pengalaman negatif dan ketidakpastian yang dirasakan. Keputusan adopsi fintech merupakan hasil interaksi dinamis antara kepercayaan dan risiko yang dimediasi oleh pengalaman individu. Penelitian ini menawarkan kontribusi teoretis berupa model adopsi fintech berbasis pengalaman (experience-based fintech adoption model), yang memperluas perspektif adopsi teknologi dari pendekatan rasional menuju pendekatan berbasis makna dan pengalaman. Temuan ini memberikan implikasi penting bagi pengembang fintech dan pembuat kebijakan dalam meningkatkan kepercayaan pengguna dan mendorong inklusi keuangan digital yang lebih efektif.
Analisis Sentimen Publik Debat Pilkada Pamekasan menggunakan BERT Imamah Mailah; Moh. Aminollah Hamzah; Hozairi
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.v11i2.152

Abstract

Pemilihan kepala daerah merupakan momen penting dalam demokrasi yang memunculkan beragam opini publik di media sosial. Debat calon bupati dan wakil bupati Pamekasan tahun 2024 menjadi perhatian masyarakat dan menghasilkan banyak komentar daring. Penelitian ini bertujuan untuk menganalisis sentimen publik terhadap debat tersebut menggunakan pendekatan deep learning berbasis transformer. Data penelitian berupa 818 komentar dari YouTube dan TikTok yang diperoleh melalui web scraping. Tahapan penelitian meliputi pembersihan data, case folding, tokenisasi, serta terjemahan. Proses pelabelan sentimen dilakukan dengan TextBlob, sedangkan klasifikasi menggunakan model DistilBERT yang telah di-fine-tune. Hasil penelitian menunjukkan model mampu mengklasifikasikan komentar menjadi tiga kategori, yaitu positif, netral, dan negatif, dengan akurasi 80% serta F1-score tertinggi 0,91 pada kelas positif. Sebagian besar komentar tergolong netral (44,03%), diikuti positif (37,03%) dan negatif (18,96%). Temuan ini menunjukkan bahwa respon publik cenderung biasa tanpa ekspresi emosional yang kuat. Penelitian ini menyimpulkan bahwa model berbasis transformer efektif untuk menganalisis opini publik dalam konteks politik lokal, sehingga dapat membantu pengambil kebijakan, pengamat politik, maupun tim kampanye memahami persepsi masyarakat secara lebih cepat dan akurat. Regional elections are a crucial moment in democracy that generate diverse public opinions on social media. The 2024 Pamekasan regent and deputy regent candidate debate attracted public attention and sparked many online comments. This study aims to analyze public sentiment toward the debate using a transformer-based deep learning approach. The dataset consists of 818 comments collected from YouTube and TikTok through web scraping. The research process included data cleaning, case folding, tokenization, and translation. Sentiment labeling was carried out using TextBlob, while classification employed a fine-tuned DistilBERT model. The results show that the model successfully categorized comments into three sentiment classes—positive, neutral, and negative—with an accuracy of 80% and the highest F1-score of 0.91 in the positive class. Most comments were classified as neutral (44.03%), followed by positive (37.03%) and negative (18.96%). These findings indicate that the majority of the public responded in a neutral manner without strong emotional bias. This study concludes that transformer-based models are effective in analyzing public opinion in local political contexts, providing valuable insights for policymakers, political observers, and campaign teams to better understand community perceptions quickly and accurately.
RANCANG BANGUN ROLE BASED ACCESS CONTROL (RBAC) PADA SISTEM DONASI BERBASIS WEB UNTUK PANTI ASUHAN dzaki diana; Dzaki Mutammadien Illiyin; Moh. Aminollah Hamzah; Anwari; Fathorrozi Ariyanto
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 2 (2026): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i2.7975

Abstract

The rapid advancement of information technology has driven digital transformation across various sectors of life, including donation management in non-profit institutions such as orphanages. At Al-Muthi Orphanage, recording practices still rely on manual Microsoft Excel, leaving significant gaps in data security and accountability, primarily due to the absence of access restrictions between users. This research aims to develop and implement an online donation platform by adopting the Role Based Access Control (RBAC) framework, which regulates access rights into three user categories: Admin, Caregiver, and Donor. The Waterfall method was chosen as the development approach, covering system requirements analysis, design using UML and ERD, coding utilizing PHP Native and MySQL, as well as testing phases. The resulting platform presents various innovations including horizontal RBAC (access restrictions between users with equal authority), layered verification mechanisms for financial transactions, state-based lock system (data locking after verification with privileged access for Admin), account lifetime, proportional transparency, donation schemes without login requirements, public activity galleries, and password recovery facilities. Based on test results, the system successfully achieved all targets for vertical RBAC (100%), horizontal RBAC (100%), state-based lock system (100%), account lifetime (100%), and donor privacy protection (100%), while cross-check verification reached 80%. The usability assessment using the SUS method yielded a score of 81.00 classified as the Excellent category, and donor trust level reached 4.3 out of 5, or Very High. Overall, the integration of RBAC into the donation information system has proven effective in strengthening data security and donation management accountability at Al-Muthi Orphanage.
Implementation of Agile Methods in the Development of a Mobile-based Hybrid Learning Management System Application Wiliramayanti Wiliramayanti; Hoiriyah Hoiriyah; Moh. Aminollah Hamzah; Rofiuddin Rofiuddin
SISTEMASI Vol 15, No 7 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i7.6597

Abstract

The rapid advancement of digital technology in higher education has increased the demand for learning systems that not only support academic activities but are also easily accessible through smartphones, the devices most frequently used by university students. Although Learning Management Systems (LMSs) have been widely adopted, most existing implementations remain web-based, limiting the optimization of mobile learning experiences. This study aims to develop a mobile-based Hybrid Learning Management System (HyLMS) that integrates synchronous and asynchronous learning within a single platform. The system was developed using the Agile Development methodology to facilitate iterative development and continuous adaptation to user requirements. The development process consisted of requirements gathering, system design, implementation, testing, and evaluation. The resulting application provides essential features, including user registration, authentication, course management, learning material access, assignment submission, discussion forums, notifications, and assessment. Black Box Testing confirmed that all system functionalities operated as expected. Furthermore, user experience was evaluated using the User Experience Questionnaire (UEQ) involving 35 respondents, with positive results across all evaluation dimensions. The highest score was achieved in the Stimulation dimension (1.52), indicating a high level of user engagement and motivation. These findings demonstrate that the proposed mobile-based HyLMS effectively supports hybrid learning by providing a more flexible, integrated, and user-centered learning environment for higher education institutions.
Pengaruh Penggunaan Digital Marketing pada Penjualan Produk UMKM Menggunakan Model UTAUT (Unified Theory of Acceptance and Use of Technology) Leily Nur Indah Fitriana; Nur Syakherul Habibi; Moh. Aminollah Hamzah
ProBank Vol 10, No 2 (2025)
Publisher : STIE AUB Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36587/probank.v10i2.2011

Abstract

The MSME sector plays a crucial role in Indonesia's economic growth. MSMEs contribute significantly to job creation, income generation, and economic growth. This research, conducted in Pamekasan Regency, will provide valuable insights for the government and other stakeholders in designing appropriate policies and programs to support MSMEs in effectively adopting digital technology. To identify factors influencing the acceptance and use of digital marketing among Micro, Small, and Medium Enterprises (MSMEs) in Pamekasan Regency, the Unified Theory of Acceptance and Use of Technology (UTAUT) model can be used as a conceptual framework. Based on the UTAUT model, technology acceptance and use are influenced by four main factors: performance expectations, business expectations, social influence, and supporting conditions. This research will analyze the influence of these four factors on the acceptance and use of digital marketing among MSMEs in Pamekasan Regency, as well as their impact on product sales.
APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS (CNN) FOR HEPATITIS C VIRUS (HCV) DISEASE DETECTION Fathorrozi Ariyanto; Indra Maulana; Moh. Aminollah Hamzah; Aang Kisnu Darmawan
Jurnal Sistem Informasi Vol. 12 No. 2 (2025)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/jsii.v12i2.10542

Abstract

Hepatitis C is a disease that attacks the liver and can progress to more serious conditions, such as cirrhosis or liver cancer, if not diagnosed and treated properly. Conventional diagnostic methods for Hepatitis C often face challenges in terms of efficiency and accuracy, so an innovative AI-based approach is needed to improve early detection. In this study, we apply a 1D Convolutional Neural Network (CNN) to classify Hepatitis C patients, using a dataset from Kaggle consisting of 615 samples with various medical parameters. The dataset goes through a series of preprocessing stages, including data cleaning, normalization, and feature transformation, before being applied to a 1D CNN model. The model is trained using the Adam optimizer, with ReLU activation functions in the convolution layer and sigmoid in the output layer. Model performance is evaluated through accuracy, precision, recall, and F1-score metrics. The results show that the developed 1D CNN model achieves an accuracy of 75% in detecting Hepatitis C. Although these results show promising potential, there is still room for improvement through exploration of more complex architectures or the use of larger datasets. Thus, this research is expected to make artificial intelligence an effective tool in the diagnosis of Hepatitis C, increasing accuracy and efficiency in the process. Keywords: Hepatitis C, 1D CNN, Deep Learning, Disease Classification, Medical Diagnosis
Co-Authors Aang Kisnu Darmawan Anwari Astri Furqani dzaki diana Dzaki Mutammadien Illiyin Fathorrozi Ariyanto Hoiriyah, . Hozairi Imamah Mailah Indra Maulana Leily Nur Indah Fitriana Leily Nur Indah Fitriana Mohammad Faris, Mohammad Nur Syakherul Habibi Rofiuddin Wiliramayanti Wiliramayanti