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Analisis Yuridis Terhadap Tindak Pidana Perampasan Kendaraan Bermotor Oleh Debt Collector Dalam Praktek Leasing di Indonesia (Studi Putusan No. 176/Pid.B/2018/PN. Lbo)
Jurnal Penelitian Serambi Hukum
Vol 19
, No 01
(2026)
This study aims to determine 1) the legal analysis of the criminal act of unilateral confiscation of motor vehicles by debt collectors in leasing practices in Indonesia (Study of Decision No. 176/Pid.B/2018/PN. Lbo). 2) the legal responsibility of leasing companies for the criminal act of unilateral confiscation of motor vehicles carried out by debt collectors in leasing practices in Indonesia (Study of Decision No. 176/Pid.B/2018/PN. Lbo). This research method uses a normative legal approach. T...
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Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework
Journal of Computing Theories and Applications
Vol 3
, No 3
(2026)
Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near...
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The Future of Learning: Integrating Robotic Efficiency for Enhanced Educational Efficacy
Journal of Global Hospitality and Tourism Technology
Vol 1
, No 1
(2026)
The Covid-19 pandemic has posed learning barriers for communities in Indonesia and around the world. It has disrupted normal teaching and learning activities, necessitating the adoption of distance learning. Students face challenges during distance learning, including low motivation to succeed and complete online classes, as well as difficulty in handling encountered problems or difficulties. Robotics was chosen as a learning medium because it has been proven to stimulate self-efficacy. The deve...
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Implementasi Penggunaan Teknik Keamanan Data Hashing Dan Limit Login Pada Login Sistem Pengelolaan Pencatatan Barang Dppesdm Berbasis Web
Syarhanazmi
; M. Miko Sahputra Sembiring
; Vima Zikra Adha Lubis
; Ilhamuddin Ilhamuddin
; Mhd. Furqan
Vol 5
, No 3
(2026)
Penelitian ini berfokus pada penguatan arsitektur keamanan modul autentikasi sistem SIGUDANG di DPPESDM Provinsi Sumatera Utara, yang sebelumnya memiliki kerentanan terhadap serangan brute force otomatis. Masalah utama yang diidentifikasi adalah risiko pengambilalihan akun secara ilegal akibat ketiadaan pembatasan laju permintaan (rate limiting) dan perlindungan kredensial yang kuat pada lapisan basis data. Tujuan penelitian ini adalah menciptakan sistem autentikasi yang tangguh melalui implemen...
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Pengaruh Jenis Katalis Heterogen Terhadap Nilai Kalor Dari Minyak Jelantah Melalui Proses Transesterifikasi
Vol 5
, No 3
(2026)
Thel delpleltion of fossil fuell relsourcels and increlasing elnvironmelntal issuels havel intelnsifield thel neleld for sustainablel relnelwablel elnelrgy altelrnativels. Biodielsell has elmelrgeld as a promising substitutel fuell duel to its relnelwablel naturel, elnvironmelntal compatibility, and its production from velgeltablel oils and wastel cooking oil. This study invelstigatels thel influelncel of biomass-baseld heltelrogelnelous catalysts on thel calorific valuel of biodielsell delrivel...
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Analisis Kinerja Metode Long Short-Term Memory (LSTM) dalam Klasifikasi Sentimen Ulasan Pengguna Shopee
Jurnal Elektronika dan Komputer
Vol 18
, No 2
(2026)
User reviews on the Shopee e-commerce platform represent an important source of information for understanding consumer perceptions of products and services. Sentiment analysis is commonly applied to classify user opinions into positive, neutral, and negative sentiment categories based on textual data. This study aims to analyze the performance of the Long Short-Term Memory (LSTM) method in sentiment classification of Shopee user reviews. The dataset used in this study consists of Indonesian-lang...
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Implementasi Pemasaran Digital dan Online Branding sebagai Strategi Pengembangan UMKM
Wawasan : Jurnal Ilmu Manajemen, Ekonomi dan Kewirausahan
Vol 4
, No 1
(2026)
The development of digital technology has triggered significant changes in marketing and brand management, particularly in the Micro, Small, and Medium Enterprises (MSMEs) sector. This study aims to conduct a comprehensive analysis of the implementation of digital marketing and online branding as strategies for developing MSMEs to increase competitiveness and business sustainability in the digital economy era. This study focuses on the use of social media, marketplaces, and various other digital...
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Klasifikasi Jenis Bunga Menggunakan Algoritma Convolutional Neural Network (CNN)
Ade Irgi Firdaus
; Ade Irgi Firdaus
; Dwi Okta Djoas
; Riefaldi Diofano Saputra
; Indry Anggraeny
; Hilda Apriliya Ningsih
Jurnal Elektronika dan Komputer
Vol 18
, No 2
(2026)
This research aims to develop a multiclass flower image classification system using the Convolutional Neural Network (CNN) algorithm with the EfficientNet architecture. The main problem addressed is the difficulty of manual identification of flower species that share high visual similarity. The research stages include collecting 17,299 flower images across 19 classes, performing data preprocessing such as image resizing, pixel normalization, and augmentation, followed by model training using the...
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Integrating Lean Warehousing and Systematic Analysis to Minimize Waste in Finished Product Storage: A Case Study and Generalizable Improvement Framework
Advance Sustainable Science, Engineering and Technology (ASSET)
Vol 8
, No 1
(2026)
Efficient warehouse management is critical for make-to-order production systems. This study investigates waste reduction in a metal casting company’s finished-product warehouse by applying Lean Warehousing principles. Using time-motion studies, Value Stream Mapping (VSM), Pareto diagrams, and fishbone diagrams, the research analyzed 13 workstations and identified five primary areas of waste. Simulation results demonstrated a significant cycle time reduction from 2,559 to 2,128 seconds, alongside...
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Android Malware Detection Using Machine Learning with SMOTE-Tomek Data Balancing
Masari, Maryam Sufiyanu
; Danladi, Maiauduga Abdullahi
; Onyinye, Ilori Loretta
; Tohomdet, Loreta Katok
Journal of Computing Theories and Applications
Vol 3
, No 3
(2026)
This study presents a comprehensive comparative analysis of four traditional machine learning algorithms Decision Tree, Random Forest, K-Nearest Neighbors, and Support Vector Machine for Android malware detection using the preprocessed TUANDROMD dataset comprising 4,465 instances and 241 features representing both static and dynamic application characteristics. Motivated by the limitations of conventional signature-based and hybrid detection methods, especially in managing imbalanced datasets an...
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