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All Journal Jurnal Penelitian Rumpun Ilmu Teknik Jurnal Ilmiah Teknik Informatika dan Komunikasi Jurnal Informatika Dan Tekonologi Komputer
Serly Agustin
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Other

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Optimasi Feature Selection Menggunakan Algoritma Neural Network Untuk Klasifikasi Brain Stroke Serly Agustin; Rizkia Meinita; Fiqri Khalid Aziz Al-rasyid; Amelia Anjani; Rehan Alif Albani; Ricky Firmansyah
Jurnal Penelitian Rumpun Ilmu Teknik Vol. 2 No. 3 (2023): Agustus : Jurnal Penelitian Rumpun Ilmu Teknik
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juprit.v2i3.2009

Abstract

One of the deadliest strokes is a brain stroke. According to the results of many cases of brain stroke patients, there is a possibility that bad lifestyles such as smoking and drinking alcohol can cause high blood pressure. The goal is to classify triggers for brain structure symptoms by comparing several algorithms. From the results of this comparison, it is possible to obtain triggers with the highest number of triggers so that later brain structures can be diagnosed more quickly. In several algorithms namely nn , feature selection and GA. To group triggers for several brain stroke symptoms, to maximize feature weight and feature selection, data processing using rapidminer was continued with four algorithms: X-Fold validation and split validation with ratios of 0.5, 0.6, 0.7, 0.8 and 0.9. After this test, the most popular AUC values and methods, together with the Neural Net algorithm, the Optimize Selection (Evolutionary) feature, and using a Split Validation ratio of 0.9, produce numbers with very high accuracy. AUC of 0.549 and an accuracy value of 95.88%..
Analisis Sentimen terhadap Ulasan Pembeli Smartphone IOS di Platform Shopee Mengunakan Metode Naïve Bayes Ali Akbar Rismayadi; Serly Agustin
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 5 No. 3 (2025): November: Jurnal Ilmiah Teknik Informatika dan Komunikasi 
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/juitik.v5i3.1712

Abstract

The development of digital technology has significantly shifted consumer behavior, especially in online shopping through e-commerce platforms such as Shopee. One of the most sought-after products is smartphones, including the iPhone, which is well-known for its quality and durability. This study aims to analyze user sentiment toward iOS smartphone products from the iBox store on Shopee using the Naïve Bayes method. This method is chosen for its simplicity in handling text classification and relatively high accuracy. The dataset used consists of customer reviews that have been preprocessed and manually labeled. The evaluation results show that the Naïve Bayes algorithm achieves an accuracy of 82.6%, with the best performance on the positive sentiment class (precision 0.95, recall 0.85, f1-score 0.90). However, the model performs poorly on the negative sentiment class, with a precision of only 0.33 and an f1-score of 0.44. These findings indicate that while the model is highly effective at identifying positive sentiment, further improvement is needed to enhance its ability to detect negative sentiment. This research is expected to serve as a reference for businesses such as iBox in understanding customer opinions automatically and making more informed strategic decisions.
IMPLEMENTASI STRUKTUR DATA TREE PADA WEB BLOG SEBAGAI MEDIA PEMBELAJARAN Serly Agustin; Arifin Yusuf Permana; Hari Noer Fazri; M. Rahssyal Daffa H; Mohammad Robi; Ricky Firmansyah
Jurnal Informatika Dan Tekonologi Komputer (JITEK) Vol. 2 No. 2 (2022): Juli : Jurnal Informatika dan Teknologi Komputer
Publisher : Pusat Riset dan Inovasi Nasional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jitek.v2i2.316

Abstract

In writing this study aims to review the implementation of tree data structures on web blogs as learning media, starting from a discussion of the implementation of data structures. After that, it was continued with the use of blogs as an applied learning medium. The use of tree data structures is one of the most common data structures used in the development of a web. One of the efforts to streamline and provide a variation in the realm of learning media is to create a web blog, especially in the era of globalization, everyone who lives in that era should at least know about one of the results of technological and communication advances. This study will see how the description or description of the application of tree data structures on blogs as learning media. The making of this research uses a qualitative descriptive method in which the data is obtained from observation and documentation of matters relating to the topic under study. The core discussion of this research is the implementation of data structures on blogs as learning media.
Co-Authors Ali Akbar Rismayadi Amelia Anjani Arifin Yusuf Permana Fiqri Khalid Aziz Al-rasyid Hari Noer Fazri M. Rahssyal Daffa H Mohammad Robi Rehan Alif Albani Ricky Firmansyah Rizkia Meinita