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All Journal JSiI (Jurnal Sistem Informasi) Media Jurnal Informatika SEMINAR TEKNOLOGI MAJALENGKA (STIMA)
Pateman, Dimar
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Industrial & Manufacturing Engineering Mechanical Engineering Transportation

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Analisis Bibliometrik pada Efektivitas UI/UX pada Penerapan Web Development Pateman, Dimar; Pramudia, Galih
Media Jurnal Informatika Vol 16, No 1 (2024): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v16i1.3879

Abstract

Penelitian ini menguraikan pengertian UI (User Interface) dan UX (User Experience) dalam konteks pengembangan web. UI mencakup interaksi langsung antara pengguna dan sistem melalui media visual seperti komputer, smartphone, dan tablet, sedangkan UX mencerminkan persepsi dan respon pengguna terhadap produk, sistem, atau jasa yang mereka gunakan. Pentingnya UI/UX dalam web development ditekankan sebagai faktor krusial dalam menentukan keberhasilan suatu situs web atau aplikasi, karena desain UI yang intuitif dan menarik dapat meningkatkan keterlibatan dan kepuasan pengguna. Penelitian ini bertujuan untuk menggunakan analisis bibliometrik guna mengidentifikasi dan menganalisis tren dalam studi UI/UX dari awal hingga saat ini. Melalui penggunaan basis data yang terpercaya, penelitian ini akan mengumpulkan publikasi terkait, kemudian menganalisisnya dengan alat visualisasi bibliometrik seperti VOSviewer. Tujuannya adalah untuk memetakan pola hubungan penulis, kutipan, dan kata kunci, guna memahami kontribusi UI/UX dalam kesuksesan pengembangan web. Analisis ini diharapkan dapat mengungkap celah penelitian yang ada serta menjelajahi peluang-peluang penelitian masa depan yang dapat meningkatkan praktek dan teori dalam desain web berorientasi pengguna. Hasil penelitian menunjukkan bahwa efektivitas UI/UX pada Penerapan Web Development dapat diklasifikasikan berdasarkan sejumlah jurnal yang diambil dari database CSV yang diunduh dari Scopus, di mana penelitian ini menggunakan VosViewer untuk mengklasifikasikan artikel berdasarkan kata kunci yang dihasilkan dari database tersebut.
IMPLEMENTASI CHATBOT KONSELING ANAK BERBASIS DIALOGFLOW UNTUK PENCEGAHAN BULLYING Pateman, Dimar; Zaliluddin, Dadan
SEMINAR TEKNOLOGI MAJALENGKA (STIMA) Vol 9 (2025): Seminar Teknologi Majalengka (STIMA) 9.0 Tahun 2025
Publisher : Universitas Majalengka

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

Abstract

This study aims to design and implement a chatbot-based counseling service for children to prevent bullying, utilizing Dialogflow integrated with Telegram. The research addresses the limited access children have to report bullying cases and receive initial counseling, particularly in regions with constrained resources. The development applied the waterfall method, covering requirements analysis, system design, implementation, Testing, and maintenance. The chatbot provides three main features: counseling services, bullying reporting, and educational content. Black Box Testing and User Acceptance Testing (UAT) showed that all features function according to specifications, with positive user feedback on accessibility and usability. The integration of Dialogflow's Natural Language Processing (NLP) capabilities enables the chatbot to understand user intent and provide contextually relevant responses. The solution is expected to support the Dinas Pemberdayaan Perempuan, Perlindungan Anak, dan Keluarga Berencana (DP3AKB) in Majalengka in monitoring and handling bullying cases, while offering children a safe and interactive platform for reporting and education.
Prediksi Perubahan Luas Perkebunan Aren di Jawa Barat Berbasis Geospasial dengan Algoritma ARIMA dan Machine Learning Zaliluddin, Dadan; Heryadiana, Asep Dian; Pateman, Dimar
Jurnal Sistem Informasi Vol. 13 No. 1 (2026)
Publisher : Universitas Serang Raya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30656/djx30932

Abstract

Aren palm (Arenga pinnata) plays a significant role as an economic commodity and a renewable energy source in West Java, Indonesia. However, fluctuations in plantation areas caused by land use change, climate variability, and socio-economic factors have created challenges for sustainable management. Accurate prediction of aren plantation area dynamics is required to support decision-making and policy design for renewable energy development and environmental sustainability.This study aims to predict changes in aren plantation areas in West Java using a combination of Autoregressive Integrated Moving Average (ARIMA) for time-series forecasting and Machine Learning algorithms for enhanced prediction accuracy. Historical data of aren plantation areas from 2013 to 2023 were collected from official government databases. ARIMA was applied to model temporal trends, while Machine Learning approaches such as Random Forest and Long Short-Term Memory (LSTM) were employed to capture non-linear relationships and integrate external factors such as rainfall, soil characteristics, and urbanization patterns. In addition, a geospatial approach using Geographic Information System (GIS) was adopted to visualize spatial changes in plantation areas.Preliminary results indicate that ARIMA successfully models short-term trends with relatively low forecasting errors (RMSE < 15%). Machine Learning models demonstrate the potential to improve robustness and predictive accuracy by incorporating multidimensional variables. The integration of spatial visualization enables stakeholders to identify high-risk regions for land conversion and areas with strong potential for sustainable aren cultivation. The findings of this research provide a foundation for developing a decision support system to enhance sustainable plantation management and bioethanol policy planning in West Java. The proposed predictive framework contributes not only to the field of computational forecasting but also to the strategic alignment of renewable energy development with local socio-economic priorities. Keywords: ARIMA, Machine Learning, Geospatial, Aren Plantation, Forecasting  
Co-Authors Dadan Zaliluddin HERYADIANA, ASEP DIAN Pramudia, Galih