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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 117 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.
A Web-Based Teacher Performance Behavior Evaluation Using BARS Method Pratama, Farhan; Sholva, Yus; Asrin, Fauzan
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2132

Abstract

The assessment of teachers’ professional performance behavior plays a crucial role in improving the quality if educatuin. However, at Sekolah Dasar Negeri 01 Sungai Raya Kepulauan, the assessment system still relies on MS Excel and conventional document filing, leading to limitations in transparency, efficiency, and accuracy. Teachers and school principals face difficulties in managing assessment data, making the process suboptimal. To address this issue, a web-based assessment system was developed using the Behaviorally Anchored Rating Scale (BARS) method, which aligns with ASN BerAKHLAK values. This system allows for flexible teacher performance behavior assessments via electronic devices such as smartphones and computers. The system development method employed is the Systems Development Life Cycle (SDLC) with the Rapid Application Development (RAD) model. The system is implemented using PHP as the programming language, MySQL as the database, and designed with Unified Modeling Language (UML). Black Box tesing result indicated that the system successfully meets user needs and enhaces the efficiency and transparency of teacher performance behavior assessments in accordance with ASN BerAKHLAK values.
Performance Comparison of Ion Lithium Batteries and Lead Acid Batteries in Electrical Energy Storage Systems M.Si, musrawati ST; Faridah, Faridah; Sriwati, Sriwati
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2266

Abstract

Enrekang Regency, which is mostly mountainous and has an increasing need for a reliable electrical energy storage system along with the development of renewable energy technologies such as solar panels and wind turbines. Two types of batteries commonly used are ion lithium batteries and lead acid batteries. Research Objectives To determine the effectiveness of the performance of ion lithium batteries and lead acid batteries and to determine the relative energy storage capacity of these two types of batteries and to determine whether one of them has an advantage in greater storage capacity. The method used in this study is This study uses a comparative descriptive approach with a literature study method (library research) and quantitative data analysis. The aim is to compare the technical performance of two types of batteries based on relevant and valid secondary data. The results of this study indicate that ion lithium batteries have the advantage of lasting a long time when discharged with a load, ion lithium batteries last for 8 hours 43 minutes, compared to lead acid which only lasts for 7 hours 48 minutes, this data collection is carried out by charging and discharging tests, life cycle tests, self-tests, and safety tests
Prediction of Protein Content of Shredded Goldfish Based on Physical Characteristics and Processing Process Using Random Forest Regression Method Damayanti, Irene Devi; Adha, Muhammad Sofwan; Pairunan, Lisna Junita
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2270

Abstract

Shredded goldfish is a processed fishery product that has high nutritional value, especially in its protein content. This study aims to predict the protein content in shredded goldfish based on the physical characteristics of the ingredients (moisture, ash, fat, and crude fiber content) and processing parameters (temperature and frying time) using the Random Forest method. The data used consisted of 10 samples of proximate analysis results and were divided into training data (67%) and test data (33%). The model was evaluated using MAE, MSE, RMSE, and R-squared metrics. The evaluation results showed that the model produced an MAE of 0.5649, MSE of 0.5409, RMSE of 0.7354, and R² of 0.0898. The low R² value indicates that the model is still not optimal in explaining variations in the target data. The prediction of protein levels for new data with certain characteristics resulted in a value of 20.16%, which is still within the range of actual values. This research shows the potential of using machine learning methods in predicting the nutritional value of food products, although increased accuracy is still needed through additional data and exploration of other models. It is recommended that the frying temperature is 155°C to 160°C and the frying time is 11 minutes to 13 minutes to maintain optimal protein levels.
Design of IoT-Based Energy Meter for Efficiency and Disturbance Detection Bayu, Bayu Adrian Ashad; Ramdaniah, Ramdaniah
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2363

Abstract

The increasing need for energy consumption monitoring has driven the development of systems capable of providing accurate electrical information and detecting disturbances at an early stage. This study aims to design an IoT-Based Energy Meter capable of monitoring electrical parameters in real time and detecting load anomalies as a basis for energy efficiency analysis. The system uses a PZEM-004T sensor and an ESP32 microcontroller to measure voltage, current, power, energy, and power factor (cos φ). The data is transmitted to an IoT platform via a wireless connection so it can be monitored remotely. A Long Short-Term Memory (LSTM) model is applied to identify normal power consumption patterns and detect deviations, while a rule-based method is used to detect critical conditions such as overcurrent. Test results show that the device is capable of performing measurements with high accuracy, with error percentages for voltage, current, power, and cos φ parameters ranging between 0%–5% for three types of loads: iron, electric fan, and refrigerator. The LSTM model also successfully detects anomalies such as power spikes, sudden current changes, and disconnected loads with a confidence level of 0.99–1.00. The integration of IoT, artificial intelligence, and basic protection systems results in a reliable and responsive monitoring device. In the future, this system has the potential to be developed for automatic efficiency analysis and intelligent load control.
Enhancing Polyp Segmentation Using Attention U-Net with CLAHE Ramdaniah, Ramdaniah; Ashad, Bayu Adrian
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2370

Abstract

Colorectal cancer remains one of the leading causes of death worldwide, where early detection of polyps through colonoscopy plays a vital role in prevention. This study aims to enhance polyp segmentation performance by integrating Attention U-Net with Contrast Limited Adaptive Histogram Equalization (CLAHE) as a preprocessing technique. The proposed method was evaluated using two benchmark datasets, CVC-ClinicDB as the primary dataset and Kvasir-SEG for cross-domain testing. The model was trained using a combination of Binary Cross-Entropy and Dice losses, with a 70–15–15 split for training, validation, and testing. Experimental results show that applying CLAHE improves segmentation accuracy, achieving Dice and IoU scores of 0.84 and 0.76 on CVC-ClinicDB, and 0.62 and 0.50 on Kvasir-SEG, respectively. Statistical analysis using the Wilcoxon signed-rank test confirmed a significant difference between the baseline and enhanced models. These findings demonstrate that the integration of CLAHE with Attention U-Net effectively improves boundary detection and robustness against illumination variations across datasets, contributing to more accurate and reliable computer-aided diagnosis in colorectal cancer screening.

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