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All Journal METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Jurnal Manajemen Informatika Jayakarta Indonesian Journal of Educational Development (IJED) Jurnal Informatika Dan Tekonologi Komputer
Dewi Anggraini
STIKOM Uyelindo
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Materials Science & Nanotechnology Other

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SISTEM MARKETPLACE UMKM UNTUK PENJUALAN PRODUK POHON LONTAR DARI DESA INGGUINAK KECAMATAN ROTE BARAT LAUT Raudatul Jannah Lamludin; Dewi Anggraini
Jurnal Manajamen Informatika Jayakarta Vol 5 No 2 (2025): Jurnal Manajemen Informatika Jayakarta (JMI Jayakarta)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jmijayakarta.v5i2.1897

Abstract

Penelitian ini bertujuan untuk membangun sistem marketplace UMKM untuk penjualan produk pohon lontar dari Desa Ingguinak, Kecamatan Rote Barat Laut. Meskipun pohon lontar memiliki manfaat penting dalam kehidupan ekonomi masyarakat, distribusi produknya masih dilakukan secara manual. Keterbatasan infrastruktur penjualan, akses teknologi, dan keahlian pemasaran daring menghambat pertumbuhan industri ini, sementara permintaan untuk produk lokal terus meningkat. Untuk mengatasi tantangan ini, penelitian ini akan merumuskan masalah, tujuan, dan manfaat dari sistem yang diusulkan, serta ruang lingkup yang mencakup pengelolaan oleh admin dan penjual, serta metode pembayaran yang efisien. Dengan pendekatan kualitatif melalui wawancara, observasi, dan studi literatur, serta menggunakan metode pengembangan perangkat lunak waterfall, diharapkan penelitian ini dapat meningkatkan akses pasar dan mendukung pertumbuhan industri lokal. Tujuan penelitian adalah mengembangkan sistem marketplace UMKM yang interaktif dengan fitur sesuai standar pengalaman pengguna, sehingga memberikan kenyamanan dan kemudahan bagi penjual, pelanggan, dan pengunjung website dalam berbelanja. Sistem ini diharapkan dapat meningkatkan pemasaran produk dan menjangkau pasar yang lebih luas.
EduVa: Prototyping and testing AI-powered interactive LMS with adaptive modules and assessments Sumarlin Sumarlin; Skolastika Siba Igon; Remerta Noni Naatonis; Dewi Anggraini; Heni Heni
Indonesian Journal of Educational Development (IJED) Vol. 7 No. 2 (2026): August 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/ijed.v7i2.6253

Abstract

The rapid integration of artificial intelligence (AI) in higher education has increased the demand for adaptive Learning Management Systems (LMS) capable of delivering personalized learning at scale. However, empirical studies covering the full lifecycle of designing, prototyping, and testing AI-powered LMS remain limited, particularly in developing regions. This study aimed to design, prototype, and test EduVa LMS (Education Virtual Assistant), an AI-powered interactive LMS with adaptive modules and AI-driven assessments, implemented across ten private universities in East Nusa Tenggara, Indonesia. A Design Science Research (DSR) approach integrated with the ADDIE model guided the development process. A total of 360 participants were involved, including experts, students, and lecturers. Validation results showed a Content Validity Index (CVI) of 0.80, confirming content validity. Usability testing using the System Usability Scale (SUS) yielded mean scores of 82.4 (students) and 85.8 (lecturers). Adaptive performance analysis indicated 82.1% assessment accuracy, 3.7 content adjustments per session, and an 87.9% completion rate. A strong correlation (r = 0.81, p < .01) was found between AI assessments and learning objectives. The findings demonstrate that the EduVa LMS is valid, usable, and effective for implementing adaptive learning.
Web-based knowledge sharing in Indonesian higher education to enhance students’ and lecturers’ knowledge capacity Dewi Anggraini; Sumarlin
Indonesian Journal of Educational Development (IJED) Vol. 7 No. 2 (2026): August 2026
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) Universitas PGRI Mahadewa Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59672/ijed.v7i2.6671

Abstract

This research aims to develop and evaluate a web-based knowledge sharing model designed to increase the knowledge capacity of students and lecturers. Effective knowledge sharing is essential to support collaborative learning and improve access to information in academic environments. The study employed the Research and Development (R&D) method with the ADDIE model to design and evaluate a platform integrating a content management system (CMS), discussion forums, a question and answer feature, and project collaboration spaces, while analyzing user interaction data to identify usage patterns and content preferences. Results show that the platform successfully increased user engagement, expanded access to information, and strengthened connections between students and lecturers. Functionality testing achieved an 86.7% success rate, system uptime reached 98%, and average user satisfaction was 4.2 out of 5. Expert validation confirmed a "very valid" rating across all dimensions. Paired sample t-test analysis revealed a significant difference between pre-test and post-test scores (Sig. 0.000 < 0.05), with a gain score of 0.56 (medium category). These findings confirm that the model effectively enhances users' knowledge capacity and academic collaboration, and is recommended as a reference for higher education institutions in developing more effective, technology-based collaborative learning strategies.
ANALISIS ROBUSTNESS CONVOLUTIONAL NEURAL NETWORK TERHADAP VARIASI PENCAHAYAAN PADA SISTEM PENGENALAN WAJAH Ezra Ananta Pandie; Franki Yusuf Bisilisin; Dewi Anggraini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5833

Abstract

This study aims to evaluate the robustness of Convolutional Neural Networks (CNN) in face recognition systems under varying illumination conditions. The evaluation was conducted using a dataset comprising 36 subjects, with facial images captured under three distinct lighting scenarios: dim, normal, and bright. The research methodology involved training the CNN model using K-Fold Cross-Validation and assessing its stability against visual disturbances using artificial adversarial attacks based on the Fast Gradient Sign Method (FGSM). The novelty and main contribution of this study lie in the dual-evaluation approach, which simultaneously tests the model's resilience against natural illumination variations and artificial adversarial perturbations. Experimental results demonstrated that the CNN model achieved optimal face recognition performance at 50 epochs, maintaining an average accuracy rate of 81.48%. In conclusion, the evaluated CNN architecture is reliable and stable for face recognition in uncontrolled lighting environments, providing a solid foundation for developing more secure biometric systems against visual disturbances.
ANALISIS PENJUALAN PADA HAPPYMART MENGGUNAKAN ALGORITMA FP-GROWTH Hendrikus Lambertho Laba Kumanireng; Franki Yusuf Bisilisin; Dewi Anggraini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5953

Abstract

Sales analysis is a crucial process for evaluating transaction data to understand consumption patterns and maximize business performance through a data-driven approach. HappyMart faces the challenge of significant transaction data growth, collecting a total of 1,500 transaction records during the period of August to October 2025. Inefficient manual analysis potentially triggers overstocking due to a lack of understanding of consumer purchasing patterns. This research aims to analyze purchasing patterns using the FP-Growth algorithm to formulate operational recommendations. The analysis stages include data collection, preprocessing, data transformation, and the extraction of association rules. System evaluation was conducted by comparing manual calculations in Excel, Python output, and RapidMiner. This experiment utilized a minimum support parameter of 0.2% and a minimum confidence of 60%. The research results identified product association patterns, where one of the strongest rules indicates: if consumers buy Terigu Kompas 1Kg and Aqua 1500ml, they will also buy Terigu Kompas 500G (support 0.2%, confidence 60%, and lift ratio 21.95). Practically, this highly correlated figure provides a direct contribution to the store in the form of recommendations for placing these products adjacent to each other in the same aisle, as well as implementing bundling promotion strategies to minimize stock accumulation.
ANALISIS AKTOR PENENTU DAN PREDIKSI JENIS KONTRASEPSI PADA AKSEPTOR KB MENGGUNAKAN ALGORITMA RANDOM FOREST Mario Edmon Gasa; Tri Ana Setyarini; Dewi Anggraini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.5960

Abstract

The Family Planning (KB) program aims to control population growth, yet the high discontinuation rate due to mismatched contraceptive choices remains a major challenge in the field. Therefore, this study aims to develop an objective contraceptive prediction model using the Random Forest algorithm to minimize the risk of program failure. The methodology involved processing 4,500 acceptor records balanced into 9 contraceptive classes with 12 demographic variables, optimized via GridSearchCV, and evaluated using 5-Fold Cross Validation. The results indicate that the model operates stably with an average accuracy of 78.87%, achieving the best performance in the Fold-1 test at 81.67%. The model also demonstrated optimal recognition for the MOP and MAL classes (F1-Score 0.98), proving the algorithm's reliability in identifying classes with highly distinctive characteristics despite data overlap challenges within the Injectable and Pill classes. Feature Importance analysis reveals that Age (22.60%), Gender (14.39%), and Age at Marriage (12.44%) are the most dominant determining factors. This prediction model is implemented in a Flask application, serving as a practical decision-support tool for healthcare workers to provide instant, transparent, and targeted contraceptive recommendations.
KLASIFIKASI SENTIMEN MASYARAKAT TERHADAP AKSI 17+8 DI MEDIA SOSIAL MENGGUNAKAN LSTM DAN BERT Sonia Roselina Correia; Sumarlin; Dewi Anggraini
METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi Vol. 12 No. 2 (2026): Volume 12 Nomor 2 Tahun 2026
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/mtk.v12i2.6096

Abstract

This study aims to compare the performance of Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT) models in classifying public sentiment on platform X (Twitter) regarding the "17+8 Tuntutan Rakyat" movement. The dataset consists of 1,000 Indonesian tweets collected between August and September 2025, with a subset of 200 data evaluated using a 5-Fold Cross Validation scheme. The average evaluation results show that the LSTM model achieved an accuracy of 0.6900, whereas BERT achieved 0.5400. However, per-class metric analysis reveals that LSTM suffered from severe majority-class bias by predicting all instances as neutral (F1-score of 0.0000 for both positive and negative classes), whereas BERT demonstrated discrimination capability on minority classes (negative recall of 26.83% and positive recall of 14.29%). This research is limited by a small evaluation subset size and the absence of class imbalance handling techniques, which implies the crucial need for GPU acceleration and resampling methods in future social media text sentiment analysis studies.
Implementasi Teknologi Augmented Reality dengan Visual Tracking Untuk Pengenalan dan Promosi UMKM di Kota Kupang Adriana Z. Laurha Sadi; Dewi Anggraini; Yohanis Malelak
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 6 No. 2 (2026): Juli : Jurnal Informatika dan Tekonologi Komputer
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v6i2.11606

Abstract

The development of digital technology encourages Micro, Small, and Medium Enterprises (MSME) to adapt to more innovative and interactive promotional methods. In Kupang City, most UMKM still rely on traditional promotions, resulting in limited marketing reach and less ability to attract maximum consumer attention. This study aims to design and implement a visual tracking-based Augmented Reality (AR) application as a medium for introducing and promoting UMKM  products. The method used is Research and Development (R&D) with a Waterfall software development model, which includes the stages of needs analysis, design, implementation, testing, and maintenance. The application was developed using ZapWorks Studio for the visual tracking feature and Blender for creating 3D objects. Test results using a user evaluation approach show that the application is able to detect markers well and display product information interactively through 3D objects. Respondents from UMKM and general users stated that this application is easy to use, the AR display is attractive, and it is effective in increasing the attractiveness of product promotions. Thus, Augmented Reality technology is proven to be feasible and has the potential to become an innovative digital promotional medium for UMKM in Kupang City.
Co-Authors Adriana Z. Laurha Sadi Ezra Ananta Pandie Franki Yusuf Bisilisin Hendrikus Lambertho Laba Kumanireng Heni Heni Mario Edmon Gasa Raudatul Jannah Lamludin Remerta Noni Naatonis Skolastika Siba Igon Sonia Roselina Correia Sumarlin Sumarlin Sumarlin Sumarlin Tri Ana Setyarini Yohanis Malelak