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INDONESIA
Jurnal Buana Informatika
Published by Universitas Atma Jaya Yogyakarta
ISSN : 20872534     EISSN : 20897642     DOI : -
Core Subject : Science,
Computer Science & IT
Arjuna Subject : -
Articles 594 Documents
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Sentiment Analysis of DKI Jakarta 2024 Election (Case Study: Anies Baswedan and Ridwan Kamil) Safrudin, Muhammad Safrul; Aini, Syarifah
Jurnal Buana Informatika Vol. 15 No. 2 (2024): Jurnal Buana Informatika, Volume 15, Nomor 02, Oktober 2024
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

This study analyzes public sentiment toward two potential candidates for the 2024 Jakarta gubernatorial election, Anies Baswedan and Ridwan Kamil, using Twitter data. Applying the TextBlob model for text extraction and Naive Bayes for sentiment classification found that sentiment toward Anies Baswedan is mostly positive, 52.2%, while neutral sentiment dominates for Ridwan Kamil. The accuracy of the Naive Bayes model reached 80% for Anies Baswedan and 72% for Ridwan Kamil, with higher precision, recall, and F1-score for Anies' data. These results indicate that the model is more accurate in classifying sentiment toward Anies compared to Ridwan Kamil. The implications of these findings are important for political campaign strategies, where Anies can leverage the existing positive support, while Ridwan Kamil has an opportunity to strengthen public engagement through a more strategic approach, in line with the sentiment emerging on social media.
Malicious JavaScript Detection using Obfuscation Analysis and String Reconstruction Techniques Alamsyah, Alfin Gusti; Hermawan, Latius
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Detecting malicious JavaScript remains a persistent challenge in cybersecurity, particularly as obfuscation techniques become more sophisticated. This study presents a dual-model detection framework that separates the analysis of obfuscation from malicious behavior to enhance precision. The first model detects obfuscated scripts using 20 features, including entropy, string ratios, and syntax. The second model classifies malicious code based on 92 features, incorporating outputs from the first model and semantically meaningful strings reconstructed using a novel technique called atomic search. Both models utilize the random forest algorithm and are trained on balanced datasets of labeled JavaScript samples. Experimental results demonstrate high performance, with the obfuscation model achieving 99.1% accuracy and the malicious detection model reaching 99.52%. The proposed approach provides a scalable and effective solution for detecting hidden threats in modern web environments by clearly addressing obfuscation and incorporating semantic reconstruction.
Pengaruh Jenis Stopwords terhadap Akurasi Model Multinomial Naïve Bayes dalam Proses Sentimen Analisis Tjen, Jimmy
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Implementing machine learning in business has enabled producers and sellers to assess product quality by analyzing customer reviews through Sentiment Analysis (SA). This study investigates the impact of different stopword categories on the accuracy of the Multinomial Naïve Bayes (MNB) model for SA. This research considered ten stopword categories: general, conjunctions, slang, temporal terms, nouns, pronouns, interjections, adverbs, and single-letter words. A Friedman test conducted on commentary from three shoe products revealed that removing conjunction stopwords (MNB-conjunction) could potentially improve the predictive accuracy of the MNB model for SA by approximately 1%. A T-test further validated this result, showing that two out of three datasets provided evidence that MNB-conjunction outperformed the MNB model without removing stopwords.
Pengaruh Faktor Adaptasi Model UTAUT terhadap Intensi Adopsi Sistem Hijau pada Bank Indonesia Kaeksi, Racana Ayu; Maghfiroh, Intan Sartika Eris; Akbar, Muhammad Aminul
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Bank Indonesia (BI) plays a strategic role in promoting a green financial system, yet faces internal challenges in adopting environmentally sustainable technologies, as reflected in its low “leading by example” score in the Green Central Banking Scorecard. This study applies an adapted UTAUT model, incorporating stakeholder engagement, to examine green Information Systems (IS) adoption at BI. PLS-SEM results show stakeholder engagement significantly influences adoption (β = 0.792, p < 0.001) and performance expectancy positively affects behavioral intention (β = 0.420, p = 0.014). In contrast, facilitating conditions negatively impact adoption (β = –0.374, p = 0.027), indicating limited resource support. Effort expectancy and social influence are not statistically significant. Stakeholder feedback suggests BI remains at the initial stage of green IT maturity (level 1: incipient), highlighting the need for stronger institutional and government support and clearer implementation strategies to advance its green digital transformation.
Peningkatan Akurasi Rekomendasi Dokter pada Kondisi Data Sparsity Menggunakan Algoritma Content-Based Filtering Prasetya, Alwan; Khudori, Ahsanun Naseh; Pradini, Risqy Siwi
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

The growth of healthcare applications such as Halodoc, Alodokter, and Klikdokter has enabled easier access to doctor recommendations. However, generating relevant recommendations remains challenging. One key issue is data sparsity, where limited doctor attributes reduce the system’s accuracy. This study develops a doctor recommendation system using a Content-Based Filtering (CBF) approach based on five main attributes: specialization, rating, consultation fee, years of practice, and gender. Data imputation and attribute weighting techniques are applied to enhance accuracy. Results show that the proposed method reduces the Mean Absolute Error (MAE) from 0.142 to 0.102 and the Root Mean Squared Error (RMSE) from 0.205 to 0.150. These findings indicate that the implemented techniques improve the recommendation system under sparse data conditions.
Sistem Pakan Cerdas Berbasis IoT Untuk Optimalisasi Peternakan Kambing Umbaran di Era Digital Farm Zahrowani, Rizal; Kuswanto, Jeki; Pramono, Eko
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

This research aims to create a smart feeding system based on the Internet of Things (IoT) to enhance the efficiency of feed delivery in goat farming. The system automatically regulates the feed dispenser according to a predetermined schedule, making it easier for farmers to manage feed. System testing demonstrates its effectiveness in reducing feed delivery time and minimizing waste. The system features an LCD screen that displays the dispenser status, providing real-time information to farmers. This technology also allows for remote monitoring, enabling farmers to manage feed more effectively. The implementation of this system is expected to improve productivity and animal welfare while promoting modernization in farming practices in Indonesia. This innovation is anticipated to offer a sustainable solution to challenges in feed management, providing long-term benefits for farmers and the livestock industry.
Implementasi Algoritma Apriori sebagai Association Rule Learning untuk Mengidentifikasi Pola Item Dataset Penjualan Supriana, I Wayan; Rahning Putri, Luh Arida Ayu
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Retail store competition is becoming more intense, so marketing and product arrangement are crucial for shopping efficiency, maintaining comfort, and increasing profits. This study analyzes consumer shopping habits for goods in each transaction through market basket analysis. The Apriori algorithm is a common technique for finding frequent item search techniques in building association rules, namely the relationships between item combinations in a dataset. The aim is to implement the Apriori algorithm as an association rule learning method to identify patterns within sales data. The Apriori association rule is compared to the frequent pattern growth algorithm, which finds the most frequently occurring patterns in a dataset. Based on the tests, the average lift ratio for the Apriori algorithm is 1.58, while for the frequent pattern growth algorithm, it is 1.28. This indicates that the Apriori algorithm performs better than the frequent pattern growth algorithm.
Ekstraksi Pengetahuan dari Ulasan Aplikasi CapCut Menggunakan Metode Aspect-Based Sentiment Analysis dan Klasifikasi Ariyani, Ishlah Putri; Tania, Ken Ditha; Wedhasmara, Ari; Meiriza, Allsela
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Indonesia is experiencing rapid technological development, especially in the use of the internet and editing platforms like CapCut. These platforms enable video editing on various devices; however, user satisfaction is not always guaranteed due to individual differences in experience. This research aims to identify user sentiment towards the CapCut application based on aspects, using an Aspect-Based Sentiment Analysis (ABSA) approach supported by Machine Learning algorithms for the aspect-based sentiment classification task. The algorithm used in the classification process is Support Vector Machine. The data used are reviews of the CapCut application from the Google Play Store, with a total of 22,668 data points. The results show that the Support Vector Machine (SVM) algorithm performs well in each aspect, with accuracy values of 0.88 for the feature aspect and 0.87 for the user experience aspect. The results of knowledge extraction are obtained in the form of XML, which contains user sentiment information on two main aspects: features and user experience.
Malicious JavaScript Detection using Obfuscation Analysis and String Reconstruction Techniques Alamsyah, Alfin Gusti; Hermawan, Latius
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Detecting malicious JavaScript remains a persistent challenge in cybersecurity, particularly as obfuscation techniques become more sophisticated. This study presents a dual-model detection framework that separates the analysis of obfuscation from malicious behavior to enhance precision. The first model detects obfuscated scripts using 20 features, including entropy, string ratios, and syntax. The second model classifies malicious code based on 92 features, incorporating outputs from the first model and semantically meaningful strings reconstructed using a novel technique called atomic search. Both models utilize the random forest algorithm and are trained on balanced datasets of labeled JavaScript samples. Experimental results demonstrate high performance, with the obfuscation model achieving 99.1% accuracy and the malicious detection model reaching 99.52%. The proposed approach provides a scalable and effective solution for detecting hidden threats in modern web environments by clearly addressing obfuscation and incorporating semantic reconstruction.
Pengaruh Jenis Stopwords terhadap Akurasi Model Multinomial Naïve Bayes dalam Proses Sentimen Analisis Tjen, Jimmy
Jurnal Buana Informatika Vol. 16 No. 01 (2025): Jurnal Buana Informatika, Volume 16, Nomor 01, April 2025
Publisher : Universitas Atma Jaya Yogyakarta

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Abstract

Penerapan dari machine learning dalam bisnis telah memungkinkan produsen atau penjual untuk mengetahui kualitas produk dagangan mereka berdasarkan pada analisis ulasan pelanggan menggunakan Sentiment Analysis (SA). Penelitian ini bertujuan untuk mengetahui pengaruh dari jenis stopword terhadap akurasi dari metode Multinomial Naïve Bayes (MNB) dalam proses SA. Terdapat 10 jenis stopword yang digunakan dalam penelitian ini: umum, konjungsi, bahasa gaul, keterangan waktu, kata benda, kata ganti orang, kata seruan, kata kerja, dan kata dengan satu huruf. Berdasarkan pada uji Friedman pada tiga ulasan dari tiga produk sepatu, diketahui bahwa menghilangkan stopword konjungsi (MNB-konjungsi) dapat meningkatkan akurasi model MNB dalam proses SA sebesar 1%. Hasil uji T pada dua dari tiga himpunan data menunjukkan bahwa MNB-konjungsi memiliki akurasi yang lebih baik ketimbang MNB tanpa menghilangkan stopword. 

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