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Jurnal Dinamika Informatika (JDI)
Published by Universitas PGRI Yogyakarta
ISSN : 19781660     EISSN : 25498517     DOI : 10.31316
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Computer Science & IT
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Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 124 Documents
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Evaluasi Performa Algoritma FP-Growth Berdasarkan Variasi Parameter Minimum Support dan Confidence pada Dataset Groceries Arumeilia; Hasbi Firmansyah; Wahyu Arsiyani
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.410

Abstract

This research investigates the relationship patterns among products in the Groceries dataset by applying the FP-Growth algorithm as an approach to uncover association rules. The analysis was conducted by varying the values of minimum support and minimum confidence to observe how these parameters influence the number and quality of generated rules. The experimental findings reveal that the combination of a support value of 0.01 and a confidence value of 0.4 generated the largest number of rules, totaling 71, with the highest lift value reaching 2.344. These results indicate a strong association between several products that frequently appear together within a single transaction, where whole milk emerges as the most dominant item, both as an antecedent and as a consequent. A high lift value suggests that customers who purchase whole milk are more likely to buy related items such as yogurt, curd, or cream cheese. The insights from this study can serve as a valuable reference for retailers in designing more effective product placement, improving promotional strategies, and supporting data-driven business decisions, particularly in cross-selling and inventory optimization.
IMPLEMENTASI BASIS DATA UNTUK PENGOLAHAN DATA PENJUALAN DI TOKO CINSPEED STORE Inggried Rillya Sondakh; Rahellea Joiby Onibala; Kristofel Santa
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.413

Abstract

This study analyzes the implementation of a database in processing sales data at Cinspeed Store, a business selling motorcycle parts and equipment. Traditional methods often cause problems such as data loss risk, inconsistency, and data duplication, which impede service speed and financial report accuracy. The database serves as a fundamental solution due to its ability to store data centrally, structured, and organized, allowing for more systematic sales data processing. The methodology used is qualitative, with data collected through observation, interviews, and document analysis. The findings indicate that the database is designed with four main interconnected entities: Goods, Sales, Customer, and Employee, to maintain consistency and prevent information duplication. This implementation successfully overcomes the limitations of manual methods. The computerized system enables the store owner to monitor daily sales trends and track stock availability in real-time. This accurate information becomes the strategic foundation for decision-making, such as inventory control and sales evaluation. In conclusion, the database is an essential component that enhances the accuracy and efficiency of daily sales data management.
Riset Data Risiko Kehamilan Menggunakan Decision Tree Dari Dataset Risk Maternal Health Purwanti , Widya Rahmatika
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.421

Abstract

Pregnancy is a crucial stage in a woman's life that requires special attention to ensure the safety and health of both mother and fetus. This study aims to analyze potential health risks in pregnant women by applying the Decision Tree method to the Maternal Health Risk dataset. Poor health in pregnant women can have fatal consequences, including maternal and child mortality. Therefore, a systematic approach is needed to predict and manage these risks. The Decision Tree method was chosen because of its ability to produce clear and easy-to-understand models for health data classification. In this study, maternal health data was analyzed using the Decision Tree algorithm, which achieved 100% accuracy. These findings indicate that the resulting decision tree model can be used as a tool in the decision-making process related to health risk classification in pregnant women. Thus, this study makes a significant contribution to efforts to prevent undesirable health conditions during pregnancy.
Evaluasi Klasifikasi Akurasi dan Weighted Mean Precision pada Gradient Boosted Trees untuk Risiko Diabetes Awal Ihya Bahrul Alam; Hasbi Firmansyah; Wahyu Asriyani
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.423

Abstract

Diabetes mellitus is a chronic disease with a high prevalence that requires early‑stage risk detection to enable effective prevention efforts. This study aims to analyze the capability of the Gradient Boosted Trees algorithm to classify early‑stage diabetes risk based on clinical symptoms using the Early Stage Diabetes Risk Prediction dataset. The research methodology includes data preprocessing, splitting the data into training and test sets, and training a Gradient Boosted Trees classification model in RapidMiner with the class attribute set as the labeled target. Model performance is evaluated using accuracy, weighted mean precision, and weighted mean recall metrics to assess the balanced classification ability for each class. Experimental results show that the Gradient Boosted Trees model achieves good classification performance with an accuracy of 91.76%, a weighted mean precision of 92.04%, and a weighted mean recall of 90.49% on the test data, supported by a confusion matrix pattern dominated by correct predictions for both classes. These findings indicate that the Gradient Boosted Trees approach has strong potential to be used as a decision‑support component in early diabetes risk detection systems and is worth further development for broader clinical data scenarios.
Penerapan Algoritma Naive Bayes untuk Memprediksi Keputusan Berlangganan Deposito Berjangka pada Kampanye Pemasaran Langsung Faizal Izma; Hasbi Firmansyah
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.427

Abstract

Direct marketing campaigns via telephone calls are a key strategy for banks to offer term deposit products. However, the effectiveness of this strategy is often hindered by the uncertainty of customer responses. This study aims to predict customer decisions in subscribing to term deposits by utilizing data mining techniques. The data used is sourced from the UCI Machine Learning Repository which is multivariate, covering demographic attributes, financial history, and campaign interactions. Through data pre-processing stages to handle missing values and class imbalance, this study applies classification models to map potential customer patterns. Experimental results show that the classification model is able to predict non-subscribing customers very well (92.67% precision), but still faces challenges in detecting subscribing customers (35.00% precision). These findings indicate that while the model can help filter marketing targets, further optimization is needed to address data imbalance to improve prediction accuracy in the minority class.
Komparasi Algoritma Naive Bayes dan Support Vector Machine pada Analisis Sentimen Komentar Instagram Laga El Clásico Barcelona vs Real Madrid Maulana, Muhammad Irvan; Savana Putra Aditama; Harun Al Rosyid
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.432

Abstract

The rapid development of information and communication technology has driven social media to become a primary platform for users to express opinions on various events, including prestigious football matches such as El Clásico between Barcelona and Real Madrid. The high level of interaction among Instagram users generates a large volume of comments with unstructured text characteristics and diverse sentiments, making automatic sentiment analysis necessary to understand public opinion trends. This study aims to analyze the sentiment of Instagram user comments related to the El Clásico match by comparing the Naive Bayes and Support Vector Machine (SVM) algorithms. The dataset consists of 1,526 comments with an imbalanced sentiment class distribution. The research stages include text preprocessing, term weighting using Term Frequency–Inverse Document Frequency (TF-IDF), and sentiment classification. The experimental results show that the SVM algorithm outperforms Naive Bayes, achieving an accuracy of 62.88% and a weighted F1-score of 0.62, while Naive Bayes achieves an accuracy of 59.53% and a weighted F1-score of 0.52. These results indicate that SVM is more effective in handling high-dimensional data and imbalanced class distributions in social media sentiment analysis.
Sistem Cerdas Berbasis Website untuk Deteksi dan Analisis Acne Vulgaris Menggunakan CNN Aditya, Chandra; Wahyu Sugianto
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.433

Abstract

Perkembangan teknologi kecerdasan buatan, khususnya dalam bidang computer vision, membuka peluang besar dalam mendukung pemeriksaan dermatologi. Acne vulgaris merupakan salah satu kondisi kulit yang memerlukan identifikasi awal secara akurat untuk menentukan perawatan yang sesuai. Penelitian ini bertujuan mengembangkan website pendeteksi acne vulgaris berbasis Convolutional Neural Network (CNN). Dataset yang digunakan terdiri dari beberapa jenis lesi acne vulgaris, antara lain blackheads, whiteheads, pustules, Papules, dan kategori non-acne. Data citra diolah melalui tahap preprocessing seperti normalisasi, resizing, dan augmentation untuk meningkatkan kualitas pelatihan model. Model CNN yang dibangun kemudian diintegrasikan ke dalam aplikasi berbasis web/mobile agar dapat digunakan secara praktis. Hasil pengujian menunjukkan bahwa model mampu melakukan klasifikasi jenis lesi dengan tingkat akurasi yang cukup baik dengan accuracy: 0.8450, loss: 0.4269, val_accuracy: 0.8050, val_loss: 0.6159. Aplikasi ini diharapkan dapat membantu pengguna dalam mengenali jenis lesi acne vulgaris secara mandiri sebelum melakukan konsultasi lebih lanjut dengan tenaga medis.
Perancangan Perangkat Lunak UMKM Batik Sendang Lamongan Faren Tresandra Nafasya; Kirana Isna Dewi; Ima Muhimmah Falasifa
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.448

Abstract

UMKM Batik Sendang Lamongan memiliki peran penting dalam sektor ekonomi kreatif, namun masih menghadapi permasalahan dalam pengelolaan operasional, khususnya pada pencatatan penjualan dan pengelolaan stok yang dilakukan secara manual. Kondisi tersebut berpotensi menimbulkan ketidakakuratan data, keterlambatan informasi, serta kesulitan dalam penyusunan laporan penjualan. Penelitian ini bertujuan untuk merancang perangkat lunak penjualan yang sesuai dengan kebutuhan proses bisnis UMKM Batik Sendang Lamongan. Metode penelitian yang digunakan meliputi analisis kebutuhan sistem, perancangan sistem menggunakan Unified Modeling Language (UML), perancangan antarmuka berbasis wireframe, serta perencanaan pengujian menggunakan Requirement Traceability Matrix (RTM). Hasil penelitian menunjukkan bahwa rancangan sistem yang dihasilkan mampu mengakomodasi kebutuhan fungsional dan non-fungsional, serta mendukung proses penjualan, pengelolaan produk, pemesanan, pembayaran, dan pembuatan laporan secara terstruktur dan terintegrasi
Analisis Sentimen Opini Warga X terhadap Banjir di Sumatera Menggunakan Naive Bayes Frans, Paulina Gorat; Frans Steven Pakpahan; Sardo Pardingotan Sipayung
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.461

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen opini masyarakat terhadap peristiwa banjir di wilayah Sumatera berdasarkan data dari media sosial X. Banjir merupakan bencana alam yang sering terjadi di Sumatera dan menimbulkan berbagai respons masyarakat yang banyak disampaikan melalui media sosial. Media sosial X dipilih sebagai sumber data karena bersifat terbuka dan real-time sehingga dapat merepresentasikan opini publik secara luas. Data penelitian terdiri dari 1.030 tweet berbahasa Indonesia yang dikumpulkan melalui proses crawling menggunakan API resmi X dengan kata kunci terkait banjir di Sumatera. Setelah dilakukan pembersihan data, diperoleh 873 tweet yang kemudian diproses melalui tahapan text mining, meliputi preprocessing teks, pelabelan sentimen secara manual, serta pembagian data menjadi data latih dan data uji. Data latih berjumlah 650 tweet, sedangkan data uji sebanyak 223 tweet. Klasifikasi sentimen dilakukan menggunakan algoritma Naive Bayes dengan bantuan perangkat lunak RapidMiner. Evaluasi model dilakukan menggunakan confusion matrix dengan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa algoritma Naive Bayes mampu memberikan kinerja yang cukup baik dalam mengklasifikasikan sentimen. Selain itu, hasil analisis menunjukkan bahwa opini masyarakat terhadap peristiwa banjir di wilayah Sumatera didominasi oleh sentimen negatif. Penelitian ini diharapkan dapat memberikan gambaran mengenai persepsi masyarakat dan menjadi bahan pertimbangan dalam pengambilan kebijakan penanggulangan bencana.
Analisis Sentimen Opini Warga X terhadap Banjir di Sumatera Menggunakan Naive Bayes Ummah, Rahmatul; Al Rosyid, Harun; Rahmawati, Novi Eka
Jurnal Dinamika Informatika Vol. 15 No. 1 (2026): Vol. 15 No. 1 (2026)
Publisher : Program Studi Informatika Universitas PGRI Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31316/jdi.v15i1.462

Abstract

This study aims to analyze public sentiment regarding flooding in Sumatra based on data from social media platform X. Flooding is a frequent natural disaster in Sumatra and elicits a variety of public responses, many of which are expressed through social media. Social media platform X was chosen as the data source because it is open and real-time, allowing it to broadly represent public opinion. The research data consists of 1,030 Indonesian-language tweets collected through a crawling process using the official API for X, using keywords related to flooding in Sumatra. After data cleaning, 873 tweets were obtained, which were then processed through text mining stages, including text preprocessing, manual sentiment labeling, and dividing the data into training and test data. The training data consisted of 650 tweets, while the test data consisted of 223 tweets. Sentiment classification was performed using the Naive Bayes algorithm with the assistance of RapidMiner software. Model evaluation was performed using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The results showed that the Naive Bayes algorithm performed quite well in sentiment classification. Furthermore, the analysis shows that public opinion regarding the flooding in Sumatra is dominated by negative sentiment. This research is expected to provide insight into public perceptions and inform disaster management policymaking.

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