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An Adaptive Computational Model for Detecting Concept Drift in Long Term Data Streams Using Incremental Learning Approaches
Indonesian Journal of Infomatics
Vol 1
, No 1
(2026)
Concept drift, the phenomenon where the statistical properties of data streams change over time, poses a significant challenge in machine learning, particularly for long term data streams. Traditional machine learning models, including batch learning and non-adaptive approaches, struggle to detect and adapt to these changes, leading to degraded performance and inaccurate predictions. This study proposes an adaptive computational model designed to detect and respond to concept drift using increme...
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Implementasi Sistem Informasi Eksekutif untuk Evaluasi Kinerja Penjualan Tiket Kapal Menggunakan Metode Visualisasi Data dan Drill-Down (Studi Kasus: Seapass)
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Vol 3
, No 6
(2026)
The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific d...
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Implementasi Sistem Informasi Eksekutif untuk Evaluasi Kinerja Penjualan Tiket Kapal Menggunakan Metode Visualisasi Data dan Drill-Down (Studi Kasus: Seapass)
Mars: Jurnal Teknik Mesin, Industri, Elektro Dan Ilmu Komputer
Vol 3
, No 6
(2025)
The rapid development of the sea transportation industry produces a massive and complex volume of transaction data, requiring strategic management to support managerial decision-making. This research aims to implement the Executive Information System on SeaPass in order to evaluate the performance of ship ticket sales. The research method uses data visualization with a two-level drill-down mechanism, which allows the presentation of information hierarchically from general summaries to specific d...
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DOI
Machine Learning-Based Spatiotemporal Modeling for Detecting Disease Hotspots in Primary Care Data
Journal of Information Technology and Computer Science
Vol 1
, No 4
(2025)
Diseases in primary health services exhibit complex spatial-temporal dynamics due to urbanization and population mobility. Conventional surveillance approaches are difficult to capture these patterns adaptively. Machine learning (ML) based on spatio-temporal modeling offers a solution with the ability to detect disease clusters automatically and with high precision. Research Objectives: This research aims to develop a machine learning model to detect disease hotspots from primary service data in...
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Gelora: Sosialisasi dan Pelatihan Pembuatan Pakan Lele Alternatif Berbasis Limbah Pertanian Fermentasi di Desa Sebanen
Nasihah, Nila Durrotun
; Moch Alvin Najich Robbany
; Ningrum, Ria Putri
; Salma Affro
; Nur Intan Mutiara
; Shinta Dhevi Ayu Kusuma Kedhaton
; Bella Safira Rachmatika
; Muh. Kholid Assaid
; Adelia Maharani Putri Suhartono
; Betaria Yekti Mumpuni
; Rio Agustian Siregar
; Muhammad Farhan
Manfaat : Jurnal Pengabdian Pada Masyarakat Indonesia
Vol 2
, No 4
(2025)
This community service article presents the implementation of the GELORA outreach and training program conducted in Sebanen Village, Jember, Indonesia, focusing on the promotion of low-cost and eco-friendly fermented feeds formulated from locally available agricultural residues such as papaya leaves, rice bran, molasses, and EM4. The program aimed to strengthen the capacity of smallholder catfish farmers and the village enterprise (BUMDes) through a series of integrated activities including a ra...
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