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INDONESIA
Jurnal Software Engineering and Computational Intelligence
Published by Universitas Indo Global Mandiri
ISSN : -     EISSN : 29882028     DOI : https://doi.org/10.36982/jseci.v1i1
Core Subject : Science,
Computer Science & IT
Journal of Software Engineering and Computational Intelligence (JSECI) is a scientific journal in software engineering and computational intelligence containing the scientific literature on studies of pure and applied research in informatics and computer sciences, public review of the development of theory, method, and applied sciences related to the subject. The topics covered include but are not limited to: Artificial Intelligence, Computer Vision, Cryptography, Genetic Algorithm, Human-Computer Interaction, Image Processing, Intelligent Home Environments, Machine Learning, Natural Language Processing, Neural Network, Pattern Recognition, Software Engineering (Implementation of Computational Intelligent), Steganography
Arjuna Subject : Ilmu Komputer (semua kategori) - Kecerdasan Buatan
Articles 5 Documents
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Search results for , issue "Vol 3 No 01 (2025)" : 5 Documents clear
Identification of Determinants of Inclusive Economic Growth Using the Metaheuristic Whale Optimization Algorithm Approach Septian, Firza; Putriani, Nina Dwi
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5396

Abstract

Inclusive economic growth demands the identification of key factors that drive equitable improvements in regional welfare. However, the complex interrelationships among social, economic, and demographic variables make traditional approaches insufficient for handling high-dimensional data. This study introduces an innovative approach by combining the Whale Optimization Algorithm (WOA) for feature selection with a Random Forest Regressor model to predict Gross Regional Domestic Product (GRDP) per capita as the main indicator of regional prosperity. The dataset consists of 210 regional observations and 18 independent variables. Feature selection using WOA was guided by minimizing the mean squared error (MSE), resulting in the identification of the 8 most relevant features. The retrained Random Forest model on the selected features achieved a high prediction performance, with an R² value of 0.9938 and a low RMSE. Furthermore, GRDP values were categorized into three regional welfare classes (Low, Medium, High), and the classification yielded 97.92% accuracy with high precision, recall, and F1-scores across all classes. These findings demonstrate that combining metaheuristic optimization and machine learning enables efficient and accurate identification of the key determinants of inclusive economic growth. The results provide valuable insights for formulating more targeted regional development policies.
Implementasi Preprocessing dan Synonym Expansion untuk Sistem Temu Kembali Berita Bahasa Indonesia Adrian Suparto; Michael Joy Clement; Abdul Rahman; Hafiz Irsyad
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5405

Abstract

In Indonesian information retrieval systems, vocabulary differences between user queries and target documents are often a major obstacle in obtaining relevant search results. This research examines the effectiveness of applying synonym-based query expansion techniques to improve search relevance in IR systems. The system is designed using TF-IDF weighting and Cosine Similarity technique to calculate the closeness between query and document. A total of 10 queries were tested against a collection of news documents, with a manual approach in expanding keywords based on synonyms referred from KBBI. The evaluation was conducted using Precision@20 as the main metric. The results showed that the precision increased significantly from an average of 0.51 without query expansion to 0.725 after synonyms were added to the query. This shows that query meaning expansion can improve search accuracy in the context of a rich natural language such as Indonesian. This research indicates that the integration of semantic-based expansion techniques has great potential in optimizing the performance of IR systems. In the future, automated approaches such as semantic embedding or digital synonym mapping can be an alternative for more extensive and efficient development.  
Data Hiding menggunakan Play Fair Kriptografi dan Steganografi pada Domain DCT dengan Operasi Logika XOR Zulfikar, Dian Hafidh
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5410

Abstract

Artikel ini membahas strategi untuk menyembunyikan data dengan menggabungkan Teknik kriptografi dan steganografi. Kriptografi digunakan untuk mengubah teks biasa menjadi teks terenkripsi, sementara steganografi digunakan untuk menyembunyikan teks terenkripsi tersebut dalam sebuah gambar. Metode yang diusulkan menggabungkan Play Fair Cipher untuk enkripsi teks dan teknik Discrete Cosine Transform (DCT) serta operasi logika XOR untuk menyembunyikan pesan terenkripsi dalam gambar. Hasilnya menunjukkan tingkat keamanan yang tinggi dan analisis histogram yang mendukung efektivitas sistem ini.  
Similarity Identification Model of Thesis Titles with Mahalanobis Distance Approach Fajri Munawar, Muhammad; Heriansyah, Rudi; Irfani, Muhammad Hafiz; Jambak, Muhammad Ikhwan; Ferano, Dwi Asa
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5413

Abstract

This study aims to identify the similarity of thesis titles by applying the Mahalanobis Distance method which is known to be effective in measuring the distance between vectors by considering data distribution and correlation between variables. In its implementation, each thesis title is represented in vector form using the TF-IDF scheme before calculating the level of similarity using Mahalanobis Distance. The test results show that this method is able to produce similarity values between titles, but its performance has not shown optimal effectiveness in the context of similarity classification. The highest precision value obtained of 1.0 indicates that this method is quite reliable in identifying pairs of titles that are truly similar. However, the low recall value of only 0.5 indicates that there are many pairs of similar titles that fail to be detected, resulting in an F1-score value of only 0.638. This shows an imbalance between the system's ability to detect similarity and its classification accuracy. Although the accuracy value is relatively high, ranging from 0.958 to 0.988, these results do not necessarily reflect the overall effectiveness of the method in handling minor classification errors. Testing of the threshold parameters also shows that a value of 0.1 provides the best performance compared to other threshold values because it is able to maintain a balance between precision, recall, F1-score, and accuracy.
Implementasi Term Frequency - Inverse Document Frequency dan Cosine Similarity untuk Analisis Kemiripan Deskripsi Produk Halal Santoti, Jennifer Velensia; Jocelyn, Jennifer; Irsyad, Hafiz
Jurnal Software Engineering and Computational Intelligence Vol 3 No 01 (2025)
Publisher : Informatics Engineering, Faculty of Computer Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36982/jseci.v3i01.5421

Abstract

Di era digital saat ini, kejelasan informasi produk telah menjadi aspek penting untuk mendukung keputusan konsumen dalam proses pembelian. Penelitian ini difokuskan pada implementasi ekstraksi fitur dari deskripsi produk menggunakan metode TF-IDF (Term Frequency - Inverse Document Frequency) dan Cosine Similarity untuk memprediksi deskripsi produk yang membingungkan.  Metodologi penelitian ini meliputi beberapa tahap preprocessing, yang meliputi tokenizing, stopword removal, filtering, penghapusan data null dan data NaN, serta ekstraksi fitur teks menggunakan metode TF-IDF dan Cosine Similarity. Hasil evaluasi menunjukkan bahwa sistem berhasil mengenali produk halal dengan nilai precision sebesar 96%, recall sebesar 98%, dan F1-score sebesar 97%, yang mengindikasikan bahwa adanya keseimbangan yang baik antara precision dan recall. Untuk produk haram mencapai precision sebesar 98%, recall sebesar 95%, dan F1-score sebesar 97%. Secara keseluruhan, sistem berhasil mendapatkan nilai akurasi sebesar 97%. Hasil evaluasi menunjukkan bahwa model lebih baik dalam mengenali produk halal, dengan hasil recall sebesar 98%, sementara hasil recall produk haram sebesar 95%. Hal ini mengindikasikan bahwa metode yang digunakan sangat efektif dalam memprediksi kejelasan deskripsi produk. Kesimpulan dari penelitian ini menegaskan bahwa kombinasi TF-IDF dan Cosine Similarity efektif dalam mengidentifikasi ambiguitas deskripsi produk, sehingga dapat meningkatkan transparansi informasi bagi konsumen.

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