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Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Published by Asosiasi Riset Teknik Elektro Dan Informatika Indonesia
ISSN : 30318998     EISSN : 3031898X     DOI : 10.61132
Core Subject : Science,
Computer Science & IT
hasil-hasil penelitian di bidang Ilmu Komputer Dan Teknologi Informasi. Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi berkomitmen untuk memuat artikel berbahasa Indonesia yang berkualitas dan dapat menjadi rujukan utama para peneliti dalam bidang Ilmu Komputer Dan Teknologi Informasi.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 183 Documents
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Penerapan Algoritma Naïve Bayes untuk Analisis Sentimen Ulasan Produk E-Commerce Nuari Anisa Sivi; Imam Mualim; Muhammad Taufik Kussofyan
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 1 No. 1 (2023): Februari: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v1i1.1216

Abstract

The rapid growth of e-commerce in Indonesia has generated a massive and continuous volume of product reviews. This user-generated content is vital for business intelligence, yet its sheer scale makes manual analysis inefficient, subjective, and practically impossible. Automated sentiment analysis is therefore crucial for businesses to efficiently understand customer feedback and market perception. This research addresses this gap by implementing the Naïve Bayes Classifier (NBC) algorithm to automatically classify the sentiment of Indonesian-language e-commerce product reviews. This study utilized a dataset of 2,000 reviews collected from a major e-commerce platform's "Electronics" category. The data underwent critical text preprocessing stages (case folding, tokenizing, stopword removal, and stemming using the Sastrawi library) to handle the complexities of informal Indonesian text. The dataset was split using an 80/20 ratio, resulting in 1,600 training reviews and 400 testing reviews. Model performance was then evaluated using a Confusion Matrix, focusing on the key metrics of Accuracy, Precision, and Recall. The test results showed excellent performance, achieving an Accuracy of 90.00%, Precision of 91.93%, and Recall of 95.00%. These results demonstrate that the Naïve Bayes algorithm, when supported by robust preprocessing, is a highly effective, reliable, and computationally efficient method for this task, providing a valuable tool for e-commerce stakeholders.
Deteksi Sampah Plastik di Lantai Menggunakan Thresholding dan Countour Detection Saprina Putri Utama Ritonga; Asro Hayati Berutu; Anggi Jelita Sitepu; Supiyandi, Supiyandi
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1236

Abstract

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.
Strategi Organisasi untuk Memastikan Nilai Tambah Investasi Teknologi Informasi: Mitigasi Ketergantungan Merugikan melalui Alignment Strategis dan Manajemen Risiko Silvi Andini; Muhammad Irwan Padli Nasution
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1249

Abstract

Investments in information technology (IT) often fail to deliver the expected added value due to excessive dependence on external suppliers, inflexible technological systems, or infrastructures that are highly vulnerable to various operational and security risks. This article analyzes organizational strategies to ensure the realization of added value from IT investments through the integration of strategic alignment and comprehensive risk management practices. By implementing strategic alignment, organizations are able to synchronize IT initiatives with core business objectives, organizational processes, and long-term strategic goals. At the same time, effective risk management plays a crucial role in reducing detrimental dependencies, including risks related to data breaches, system failures, cyber threats, and operational disruptions. This approach is supported by an extensive review of literature from credible and relevant academic sources, which demonstrates that systematic risk mitigation can significantly enhance organizational resilience, reliability, and overall value creation from IT investments. As a result, organizations are better positioned to optimize performance, improve decision-making capabilities, and ultimately achieve a sustainable competitive advantage in an increasingly digital business environment.
Optimasi Kurva Daya Turbin Angin Menggunakan Model Logistic Berbasis Particle Swarm Optimization (PSO) Henrydunan, John Bush; Purba, Jogi; Amanah, Fadilla; Perdana, Adidtya
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1252

Abstract

Accurate wind turbine power curve modeling plays a crucial role in performance evaluation, energy yield estimation, and data-driven control strategies. However, actual power curves often exhibit non-linear behavior influenced by atmospheric variability, measurement noise, and SCADA anomalies, making conventional modeling approaches less effective. This study proposes an optimized logistic power curve model whose parameters are tuned using Particle Swarm Optimization (PSO) to improve predictive accuracy. The analysis uses the Wind Turbine SCADA Dataset from Kaggle, which undergoes extensive preprocessing including physical rule filtering, outlier detection with the Interquartile Range (IQR) method, anomaly removal, and smoothing of the power signal. A three-parameter logistic model is selected due to its ability to capture the typical S-shaped relationship between wind speed and power output. PSO is applied to identify optimal model parameters by minimizing the Mean Squared Error (MSE), utilizing 40 particles over 200 iterations. The optimized model achieves strong predictive performance with RMSE of 404.09, MAE of 179.96, and R² of 0.904 on the test set, indicating that more than 90% of the variability in actual power can be explained by wind speed. Residual analysis reveals heteroscedastic patterns and slight overestimation in mid-range wind speeds, yet overall model consistency remains high. Comparative evaluation against Linear Regression, Random Forest, and logistic modeling using curve_fit shows that the Logistic–PSO approach provides the most accurate and stable predictions. These findings demonstrate that combining logistic modeling with PSO offers an effective and robust method for data-driven wind turbine power curve optimization.
Optimasi Parameter Model LightGBM Menggunakan Algoritma Grey Wolf Optimizer untuk Prediksi Penyakit Ginjal Kronis Muhammad Alfin; Alvin Hafiz; Muhammad Budi Akbar; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1263

Abstract

Chronic kidney disease is an increasingly prevalent health issue that requires more precise clinical data-based early detection methods to enable timely and appropriate treatment. This study focuses on developing a predictive model for chronic kidney disease using the Light Gradient Boosting Machine (LightGBM) algorithm and enhancing its performance through hyperparameter optimization with the Grey Wolf Optimizer (GWO). The dataset used originates from public sources and undergoes several preprocessing steps, including missing value imputation, categorical feature encoding, outlier handling, initial feature selection, and stratified data splitting to maintain model quality. Three modeling approaches were evaluated: LightGBM with default parameters, LightGBM enhanced using Random Search, and LightGBM optimized with GWO. The experimental results indicate that the baseline model already performs well, Random Search improves accuracy and F1-score, and GWO achieves the highest AUC-ROC value despite requiring longer computation time. Significance testing through cross-validation shows that the performance differences among the three models are not statistically significant, suggesting that the observed improvements are not strong enough to determine a definitively superior optimization method. The feature importance analysis highlights that clinical indicators such as creatinine levels, glomerular filtration rate, blood pressure, and urine protein contribute most prominently to the prediction. Overall, the study demonstrates that LightGBM is a reliable model for early detection of chronic kidney disease, and hyperparameter optimization still offers added value that can support the development of AI-based clinical decision-support systems
Feature Selection pada Dataset NSL-KDD Menggunakan Algoritma Genetic Algorithm untuk Deteksi Serangan Jaringan Freyro Dobry Sianipar; Ruth Amelia Vega S Meliala; Yoseph Christian Sitanggang; Adidtya Perdana
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1275

Abstract

Information system security faces serious challenges due to increasingly complex cyber attacks. Intrusion Detection Systems (IDS) require efficient approaches to handle high-dimensional data such as the NSL-KDD dataset with 41 features. This study aims to implement the Genetic Algorithm (GA) for feature selection on the NSL-KDD dataset to improve the efficiency and accuracy of network attack detection. The method used is computational experimental research, involving data preprocessing, GA implementation for feature selection, building a classification model using Random Forest, and performance evaluation based on accuracy, precision, recall, F1-score, and computation time. The results show that GA successfully reduced features from 41 to 12 features (70.7% reduction), significantly improving computational efficiency. However, model accuracy slightly decreased from 0.4973 to 0.4951, indicating that while GA is effective for feature selection, the elimination of certain features may reduce classification capability. The implication of this study is that GA can be used as a tool to simplify intrusion detection models, but it should be combined with parameter optimization and data imbalance handling to achieve more optimal performance.  
Perancangan Sistem Informasi Arsip Surat pada Kantor Hukum Kofipindo Milawati, Milawati; Alisya Alfina Rizki Ritonga; Aidil Halim Lubis
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1291

Abstract

This research aims to design and build a letter archive information system at the KOFIPINDO Law Office to improve the effectiveness and efficiency of document management. The manual filing system that has been used so far poses various obstacles, such as search delays, the risk of losing documents, and low storage accuracy. To overcome these problems, this study applies the Waterfall System Development Life Cycle (SDLC) model in the process of designing and building the system. Web-based technologies used include PHP, HTML, CSS, Bootstrap, and MySQL. The results of the study show that the developed letter archive information system is able to simplify the process of storing, searching, and managing incoming and outgoing letters in a faster, structured, and safer manner. The implementation of this system not only improves administrative performance, but also strengthens accountability and supports the need for professional legal documentation within the KOFIPINDO Law Office. Thus, this web-based mail archive information system can be a strategic solution in modernizing legal document management.
Penerapan Perangkat Lunak Inovatif Guna Meningkatkan Efisiensi Sistem Manajemen Informasi pada Bank Syariah Indonesia Zahir Muhammad Fadhilah Harahap; Haya Aghnia Azzahra; Nabila Nasywa; Nurbaiti Nurbaiti
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1309

Abstract

This study discusses the implementation of innovative software to improve the efficiency of information management systems at Bank Syariah Indonesia (BSI). The backround of this research begin with growing need for effective and efficient information systems in the digital era, particularly in Islamic banking which requires compliance with sharia principles. The purpose of this study is to identify how software innovation can support better service, data security, and operational effectiveness at BSI. By employing a qualitative descriptive methodology along with a literature review, this research analyzes various technological innovations such as artificial intelligence (AI), big data, and Customer Relationship Management (CRM) applications in Islamic banking systems. The findings show that the adoption of modern information technology significantly enhances operational efficiency, service quality, and competitiveness. The conclusion highlights that software innovation integrated with sharia principles strengthens the management information system and supports BSI’s vision to become a global center of Islamic finance. Future development of software should focus on improving data security and adapting to advanced technologies to further enhance customer service quality.
Implementasi Jaringan Syaraf Tiruan Backpropagation pada Klasifikasi Jenis Kopi Berdasarkan Cita Rasa dan Aroma Mimi Sartika Ritonga; Lailan Sofinah; Saiba Siregar
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1186

Abstract

Coffe is one of Indonesia’s leading commodities, known for its diverse flavors and aromas. Traditionally, coffee quality assessment is conducted manually through cupping tests performed by expert panelists. However, this method is subjective and requires considerable time and cost. This study aims to implement an Artificial Neural Network (ANN) using the backpropagation algorithm to classify coffee types based on sensory parameters such as flavor, aroma, acidity level, and body. Simulated data were generated from five common Indonesian coffee varieties: Arabica Gayo, Robusta Lampung, Arabica Toraja, Liberica Jambi, and Excelsa. The results show that the ANN-based classification system with a 4-8-1 architecture achieved an accuracy rate of 93% after 500 training epochs, with a final error value of 0.07. The implementation of this method provides an efficient and objective technological alternative to assist the coffee industry in maintaining product quality and automatically identifying coffee types.
Peran Keamanan dalam Meningkatkan Kepercayaan Pengguna pada E-Commerce: Studi Literatur Yulita Sirinti Pongtambing; Alif Rezky Maulana; Eliyah Acantha Manapa Sampetoding
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 4 (2025): November: Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i4.1200

Abstract

Security in e-commerce applications is a crucial factor that significantly affects user trust. Many users often feel anxious about the confidentiality of personal data, transaction security, and the potential for misuse of information. This study is a systematic literature review (SLR) using the PRISMA model, aiming to analyze in depth the influence of security on user trust in the context of e-commerce applications. Through this review, relevant previous studies on user security and trust were identified and evaluated to provide a more comprehensive understanding. The results of the analysis show that the improvement and implementation of superior security features, including strict data protection, multi-layered authentication, and transparent and robust privacy policies, have an essential role in growing and strengthening user trust. Guaranteed security not only creates a sense of convenience during the transaction process, but it is also very effective in increasing and maintaining user loyalty to the e-commerce platform in question. Improving security can be interpreted as a strategic investment for the sustainability of digital businesses.

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