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All Journal CESS (Journal of Computer Engineering, System and Science) Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal UNITEK KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Jurnal Algoritma J-COSCIS : Journal of Computer Science Community Service ABDINE Jurnal Pengabdian Masyarakat Jurnal Teknologi Komputer dan Informasi Literasi: Jurnal Pengabdian Masyarakat dan Inovasi Journal Of Artificial Intelligence And Software Engineering Jurnal Komputer dan Teknologi (JUKOMTEK) Smart Dedication: Jurnal Pengabdian Masyarakat
Gellysa Urva
Sekolah Tinggi Teknologi Dumai
Arts Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Public Health Social Sciences Other

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Digitalisasi Laporan Perizinan Tempat Wisata pada Dinas Pariwisata Kota Dumai Urva, Gellysa; Desriyati, Welly; Azmi, Khairul; Julanos; Halimahtussadiyah
JDISTIRA - Jurnal Pengabdian Inovasi dan Teknologi Kepada Masyarakat Vol. 6 No. 1 (2026)
Publisher : Yayasan Rahmatan Fidunya Wal Akhirah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58794/jdt.v6i1.1663

Abstract

Kegiatan pengabdian masyarakat ini bertujuan untuk membantu Dinas Pariwisata Kota Dumai dalam melakukan digitalisasi laporan perizinan tempat wisata melalui pengembangan dan penerapan sistem informasi. Metode yang digunakan adalah Participatory Action Research (PAR) yang dipadukan dengan model pengembangan sistem waterfall, meliputi identifikasi masalah, perancangan sistem, pelatihan, implementasi, dan evaluasi. Peserta kegiatan terdiri dari 10 aparatur Dinas Pariwisata. Hasil menunjukkan bahwa penerapan sistem digital berhasil mempercepat proses perizinan dari 7–14 hari menjadi hanya 2–3 hari, meningkatkan akurasi melalui validasi otomatis, serta memperbaiki transparansi melalui akses data terintegrasi. Selain itu, aparatur dapat mengoperasikan aplikasi secara mandiri setelah mengikuti pelatihan. Hal ini membuktikan bahwa digitalisasi laporan perizinan tidak hanya meningkatkan efisiensi, tetapi juga memperkuat inovasi layanan publik pada tingkat pemerintah daerah.
A Classification Model of Children’s Digital Device Dependency Based on the Learning Vector Quantization (LVQ) Algorithm Urva, Gellysa; Nazir, Refdinal
Journal of Artificial Intelligence and Software Engineering Vol 5, No 4 (2025): Desember
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i4.8244

Abstract

Digital device dependency among children has become a critical issue in the modern era, influencing cognitive, social, and health aspects. Excessive use of digital devices may lead to decreased concentration, academic performance, and social interaction. The identification of children's digital dependency levels has often relied on manual observation by parents or teachers, which tends to be subjective. Therefore, this study aims to develop a classification model for children's digital device dependency using the Learning Vector Quantization (LVQ) algorithm. The data were collected through a questionnaire distributed to 110 respondents, consisting of parents of elementary school students in Dumai City. The questionnaire contained 34 items measured using a five-point Likert scale (1–5). The data were processed using Python with supporting libraries such as NumPy, Pandas, Matplotlib, Scikit-learn, and Neupy. The experimental results showed that the LVQ algorithm successfully classified children's dependency levels into three categories low, moderate, and high with an accuracy of 87.5%, an average precision of 85.4%, and an average recall of 86.2%. The findings revealed that most children belong to the moderate dependency category, with an average score of 3.03. The main factors influencing digital dependency include usage duration, habits of using devices while eating or before sleeping, and decreased social interaction. The application of the LVQ algorithm proved effective in identifying children’s digital usage patterns and can serve as a foundation for developing early detection systems and promoting digital literacy policies within elementary education environments
Evaluasi Komparatif Neural Network dan Random Forest untuk Prediksi Produktivitas Tandan Buah Segar Kelapa Sawit Berbasis Fitur Musiman Gellysa Urva; Welly Desriyati
Jurnal Algoritma Vol 23 No 1 (2026): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.23-1.3352

Abstract

Fresh Fruit Bunch (FFB) productivity in oil palm exhibits seasonal patterns that pose challenges for predictive modeling, particularly given the limited amount of data. This study aims to compare the performance of Neural Networks and Random Forests in predicting FFB productivity based on temporal features, including lag, rolling mean, and cyclical encoding. Evaluation was conducted using time-series validation with MAE, RMSE, and R² metrics. The results indicate that Neural Networks face generalization limitations with limited data, reflected in poor performance on the test data. Conversely, Random Forest delivers more stable and accurate performance with an MAE of 0.2581, an RMSE of 0.3325, and an R² of 0.9675. These findings confirm the superiority of tree-based ensemble approaches in handling seasonal data with small sample sizes. The contribution of this research is to provide empirical evidence and recommendations for more reliable models for TBS productivity prediction as a basis for developing decision support systems in the plantation sector.
Co-Authors Amallia Amiroel Oemara Syarief Anisa Rahmawati Aulia Sari Burelia, Ulfhi Desyanti - Desyanti Halimahtussadiyah Indradewa, Rhian Julanos Khairul Azmi Marpaung, Roki Melingga Milyani Merina Pratiwi Novia Ulfa Ravie, Muhammad Refdinal Nazir Savitry, Eka Sellyana, Ari Siti Aminah Suhaidi, Mustahzzihim Syahrul Ramadhani Syamsiar, Syamsiar Tina Wati Manalu Tri Handayani Tri Handayani Tri Yuliati Welly Desriyati Welly Desriyati Welly Desriyati Yuliati, Tri Yuliati, Tri