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Jeffry
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Jl. Andi Mangerangi No.73, Mamajang Dalam, Mamajang, Kota Makassar, Sulawesi Selatan 90132
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INDONESIA
Journal of System and Computer Engineering
Published by Universitas Pancasakti Makassar
ISSN : -     EISSN : 27231240     DOI : -
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
Programming Languages Algorithms and Theory Computer Architecture and Systems Artificial Intelligence Computer Vision Machine Learning Systems Analysis Data Communications Cloud Computing Object Oriented Systems Analysis and Design Computer and Network Security Data Mining
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 105 Documents
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Enhancing Human Activity Recognition with Attention-Based Stacked Sparse Autoencoders Batau, Radus; Sari, Sri Kurniyan; Aziz, Firman; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2148

Abstract

This study presents the development of an intelligent system for the classification of respiratory diseases using lung sound visualizations and deep learning. A hybrid Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN–BiLSTM) model was designed to classify four conditions: asthma, bronchitis, tuberculosis, and normal (healthy). Lung sound recordings were converted into time-frequency representations (e.g., mel-spectrograms), enabling spatial-temporal feature extraction. The system achieved an overall classification accuracy of 99.5%, with F1-scores above 0.93 for all classes. The confusion matrix revealed minimal misclassifications, primarily between asthma and bronchitis. These results suggest that the proposed model can effectively support real-time, non-invasive respiratory screening, particularly in telemedicine environments. Future work includes clinical validation, integration of patient metadata, and adoption of transformer-based models to further enhance diagnostic performance.
Analisis Perbandingan Algoritma Naive Bayes dan K-Nearest Neighbor dalam Klasifikasi Gaya Bahasa pada Teks Berbahasa Indonesia. Tinanda, Fika Tsalsabila; Sujaini, Herry; Nasution, Helfi
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2158

Abstract

In the digital era, Indonesian-language texts have rapidly proliferated across social media, online news, blogs, and digital documents, often containing various figurative language styles such as personification, metaphor, hyperbole, euphemism, and irony. Manual identification of these language styles is inefficient on a large scale, especially when class distribution is imbalanced. This study aims to compare the performance of the Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in classifying figurative language styles in Indonesian texts, and to evaluate the impact of applying the Synthetic Minority Over-sampling Technique (SMOTE) and hyperparameter tuning on model accuracy. The dataset consists of 5,155 original samples and 6,240 samples after SMOTE application, with an 80:20 train-test split. Evaluation was conducted under four scenarios: without SMOTE and without tuning, with SMOTE without tuning, without SMOTE with tuning, and with both SMOTE and tuning. The results show that Naïve Bayes demonstrated stable performance with an accuracy of up to 93.19%, while KNN achieved its highest accuracy of 93.43% after applying SMOTE and tuning. The implementation of SMOTE and hyperparameter tuning proved effective in improving accuracy, particularly for KNN. This study highlights the significant contribution of data balancing and parameter optimization in enhancing the automatic classification of figurative language styles in Indonesian texts.
Book Recommendation System Based on Collaborative Filtering: User-Based, Item-Based, and Singular Value Decomposition Analysis Ishak, Ishak; Yahya, Ahmad; Yusri, Yusri
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2201

Abstract

Recommender systems have become essential in the digital era to help users navigate overwhelming content. This study develops a book recommendation system using three collaborative filtering methods: user-based, item-based, and matrix factorization using singular value decomposition. We evaluate the system on a real-world dataset of 1,149,780 book ratings from 278,858 users across 271,360 books. A subset of 500 active users is used for experimental evaluation. The models are assessed using root mean square error and mean absolute error to measure rating prediction accuracy. The results show that the item-based collaborative filtering method achieves the best accuracy (root mean square error 7.362; mean absolute error 6.761), slightly outperforming the user-based approach (7.365; 6.809) and the matrix factorization method (7.643; 7.413). We analyze the results to understand the performance differences, noting the stability of item similarity as a key factor and the need for optimal tuning in the matrix factorization model. In conclusion, item-based collaborative filtering proved most effective for this context. This work provides insights into the comparative performance of foundational recommendation techniques and highlights practical considerations for improving book recommender systems.
Development of an Internet of Things (IoT) System for Real-Time Monitoring and Control of Moringa Powder Processing. AMRAN, ROZALINA
Journal of System and Computer Engineering Vol 6 No 4 (2025): JSCE: October 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i4.2262

Abstract

Moringa is a widely recognized food plant in Indonesia due to its numerous health benefits and availability across various regions. One of its processed forms is moringa leaf powder. However, the production process is relatively challenging, primarily due to limited human resources and the time-consuming nature of manual processing. With advancements in technology, these challenges can be addressed through the application of Internet of Things (IoT) systems in the production process. This study aims to design and implement an IoT-based monitoring and control information system using the waterfall development method, which includes the stages of requirements analysis, system design, implementation, testing, and evaluation. The resulting system integrates various sensors, devices, and a NodeMCU microcontroller to automate the production process. The system is connected to the Firebase platform and an Android application, enabling efficient monitoring and control. The primary components used include a DHT-11 temperature and humidity sensor, ultrasonic sensor, Loadcell sensor, MG996 servo motor, adapter, blender, and heating box. The results demonstrate that this system can serve as a modern, technology-based model for efficient moringa plant processing.
Pemanfaatan Data Pengguna untuk Sistem Rekomendasi dalam Aplikasi Pemesanan Tiket Event Berbasis Android Yunendar, Wakhid; Jeffry, Jeffry
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.2369

Abstract

Penelitian ini bertujuan untuk memanfaatkan data pengguna pada aplikasi pemesanan tiket event berbasis Android sebagai dasar dalam pengembangan sistem rekomendasi event. Sistem ini dirancang agar dapat memberikan saran event yang relevan berdasarkan preferensi pengguna sebelumnya. Metode penelitian yang digunakan adalah metode deskriptif kuantitatif dengan pendekatan prototyping dalam pengembangan perangkat lunak. Data diperoleh melalui observasi, wawancara, dan kuesioner terhadap pengguna aplikasi di Kota Makassar. Hasil penelitian menunjukkan bahwa sistem rekomendasi berbasis content-based filtering mampu menyesuaikan daftar event dengan minat pengguna, meningkatkan kenyamanan serta efisiensi dalam proses pencarian dan pemesanan tiket. Berdasarkan uji persepsi terhadap 21 responden, sebanyak 90% menyatakan fitur rekomendasi memudahkan mereka menemukan event yang relevan.

Page 11 of 11 | Total Record : 105

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