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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 179 Documents
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Penerapan Algoritma Frequent Pattern Growth pada Aplikasi Pembelian Obat Berbasis Web pada Apotik Sumber Sehat : Studi Kasus: Apotik Sumber Sehat Elfrida Susanti Tanggu; Gergorius Kopong Pati; Alexander Adis
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.v3i4.1119

Abstract

The implementation of the Frequent Pattern Growth (FPG) algorithm in a web-based drug purchasing application at Sumber Sehat Pharmacy aims to improve efficiency and accuracy in analyzing customer drug purchasing patterns. The FPG algorithm is a method used to identify frequent purchase patterns or frequent itemsets in purchase transactions, which can then be used to make relevant drug recommendations for customers. This study uses a case study at Sumber Sehat Pharmacy to explore drug purchasing patterns and provide a data-driven solution that can help pharmacies improve service and adjust drug stocks according to customer needs. The results show that the application of the FPG algorithm can identify significant purchasing patterns and assist pharmacies in determining more appropriate promotional strategies and inventory management. By using a web-based application that implements this algorithm, Sumber Sehat Pharmacy can provide drug recommendations that are more in line with customer preferences, thereby increasing customer satisfaction and pharmacy operational efficiency.
Sistem Informasi Geografis Penyebaran Mahasiswa Magang Universitas Stella Maris Sumba Frengkianus Aprianto Kamuri; Gergorius Kopong Pati; Mitra Permata Ayu
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.v3i4.1120

Abstract

Geographic Information Systems (GIS) play a vital role in managing and analyzing data related to geographical locations. This research aims to develop a Geographic Information System to map the distribution of internship students at Stella Maris University, Sumba, based on their geographical locations. This system is expected to provide clearer and more detailed information regarding the distribution of internship students, making it easier for the university to plan and manage internship activities more efficiently. This system is built using web-based GIS software, with location data obtained from the university's internship management system. By utilizing GIS technology, users can easily access digital maps that show the locations of students' internships and analyze their distribution. The implementation of this system is expected to benefit students, the university, and partner companies in monitoring the progress of the internship program and ensuring its successful implementation. The advantage of this system is its ease of access and use, as well as its ability to provide more accurate and visual data. It is hoped that this system will serve as a reference for the development of similar systems in the future and contribute positively to higher education in Sumba
Implementasi Klasifikasi Datamining dengan Algoritma C4.5 untuk Rekomendasi Pemilihan Fakultas Perguruan Tinggi Berdasarkan Minat dan Bakat Siswa SMK Senna Hendrian; V.H Valentino; Wisdariah, Wisdariah; Riezca Talita Trista; Dudi Parulian
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.v3i4.1159

Abstract

Selecting a faculty that aligns with students’ interests and talents is a strategic step in determining the success of higher education and future career paths. However, most vocational high school (SMK) students still face difficulties in identifying the most suitable faculty due to the lack of data-driven analysis. This study implements the C4.5 classification algorithm within data mining techniques to build an automatic and measurable faculty recommendation system. The dataset consists of attributes such as SMK major, interest level, aptitude test results, academic grade average, and gender, with the output being the recommended faculty. The C4.5 algorithm was chosen for its ability to generate a transparent and interpretable decision tree, which helps both guidance counselors and students understand the rationale behind the recommendations. The experimental results show that the constructed classification model achieved an accuracy rate of 88%, based on cross-validation testing using data from 12th-grade students. The implementation of this system is expected to serve as an objective tool in the faculty selection process and to promote a data-driven decision-making approach in secondary education environments.
Perancangan Kerangka Kerja Pengambilan Keputusan untuk Penilaian Kinerja Trainer menggunakan Metode Simple Additive Weighting (SAW) pada LKP Bina Karya Mandiri Hartono, Rudi; Mualim, Imam
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 2 (2025): Mei : 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.v3i2.1201

Abstract

The evaluation of trainer performance plays a critical role in maintaining the quality and reputation of LKP Bina Karya Mandiri as a non-formal training institution. This study aims to assess trainer performance objectively using the Simple Additive Weighting (SAW) method as part of a Decision Support Sistem (DSS). A descriptive-quantitative approach was employed through a case study involving five trainers: Wasito, Diah Rita Sari, Ahmad Jayadi, Bayu Adi Guna, and Komarudin. Data were collected through observation and surveys based on four primary criteria: Material Mastery, Teaching Methods, Participant Satisfaction, and Discipline, each assigned a predetermined weight. The findings indicate that the SAW method effectively transforms performance data into a structured and interpretable ranking. Based on preference value calculations, Bayu Adi Guna achieved the highest score of 0.935, followed by Wasito, Komarudin, Ahmad Jayadi, and Diah Rita Sari. These results demonstrate that SAW is a reliable tool for supporting decision-making in trainer performance evaluation. The implications of this study suggest that training institutions can adopt SAW to enhance objectivity, transparency, and efficiency in performance assessments, thereby strengthening human resource management practices.
Rancang Bangun Media Tes Berbasis Komputer menggunakan Wondershare Quizcreator untuk Mendukung Pembelajaran Daring Imam Mualim; Nuari Anisa Sivi; Najla Asyila; Oriza Panduwinata
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 1 No. 1 (2023): Februari : 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.v1i1.1215

Abstract

The development of information technology provides opportunities for educators to create more efficient and interactive learning evaluation media. This study aims to design and develop a computer-based test media using Wondershare Quiz Creator as a tool to support online learning. The research method used is Research and Development (R&D) with a waterfall model, which includes the stages of needs analysis, design, development, testing, and implementation. The results show that the developed test media is capable of providing various types of questions, such as multiple choice, short answer, true-false, and matching, and is equipped with features such as automatic feedback, time settings, and exportable test results. User testing indicates that the media is easy to use, responsive, and effective in supporting the learning evaluation process in online learning. In addition, educators can quickly create, manage, and distribute tests through digital platforms without requiring advanced technical skills. Thus, the use of Wondershare Quiz Creator has proven to be an alternative solution for providing practical and interactive computer-based test media that supports improving the quality of online learning. This research is expected to serve as a reference for educators in utilizing technology to enhance the effectiveness of learning evaluation
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 : 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

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