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All Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Router : Jurnal Teknik Informatika dan Terapan
Ali Akbar Rismayadi
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Computer Science & IT Electrical & Electronics Engineering

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IMPLEMENTASI RANCANG BANGUN APLIKASI MOBILE NOTEBOOK MENGGUNAKAN WATERFALL: IMPLEMENTATION OF MOBILE NOTEBOOK APPLICATION DESIGN USING WATERFALL Ahvan Muharam; Enda Suhadi; Tiki Ramdhani; Imam Saepul Azmi; Ali Akbar Rismayadi
Jurnal Ilmiah Teknik Informatika dan Komunikasi Vol. 2 No. 2 (2022): Juli : Jurnal Ilmiah Teknik Informatika dan Komunikasi
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1196.165 KB) | DOI: 10.55606/juitik.v2i2.157

Abstract

Aplikasi Mobile Notebook merupakan gabungan melalui teknologi penyimpan dan juga akivitas manusia mengacu pada interaksi antar manusia dan teknologi pada proses database sqlite dan mysql di servenya, serta dukungan pada aplikasi android sehingga menghasilkan data yang disimpan akan lebih aman dan catatan tidak mudah hilang. Pada saat ini buku catatan yang terdapat di mobile/handphone masih ada kekurangan diantaranya fitur yang ibisa dibilang belum lengkap, seperi tidak adanya pencarian dan tidak support nya pada system operasi IOS. Dengan dibuatnya aplikasi mobile notebook menggunakan metode waterfall sebuah metode klasik yang bersifat sistematis secara berurutan dalam membangun sebuah aplikasi mobile mulai dari tahap desain menggunakan unified modeling language (UML) seperti use icase diagram, activity diagram, JAVA sebagai Bahasa Pemograman, Pengujian aplikasi menggunakan Smartphone Android. Aplikasi mobile notebook ini dapat menyimpan dan mensingkron kan data catatan ke server serta bisa di share link kan apabila pengguna tidak membawa Smartphone .
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.
Optimasi Naïve Bayes dengan Feature Selection untuk Analisis Sentimen GTA V Roleplay Robi, Mohammad Robi; Ali Akbar Rismayadi
Router : Jurnal Teknik Informatika dan Terapan Vol. 4 No. 2 (2026): Juni : Router : Jurnal Teknik Informatika dan Terapan
Publisher : Asosiasi Profesi Telekomunikasi dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/router.v4i2.936

Abstract

Sentiment analysis is used to identify and classify opinions expressed in text. In GTA V Roleplay player reviews, high-dimensional textual features and noisy data can reduce classification Accuracy and increase computational complexity. This study addresses these challenges by optimizing the Multinomial Naïve Bayes classifier using Chi-Square and Information Gain Feature Selection techniques. The novelty of this research lies in evaluating these methods on Indonesian-language GTA V Roleplay reviews, which contain informal language and gaming-specific vocabulary, a domain that has received limited attention in previous Indonesian studies. The dataset consists of 604 preprocessed reviews represented using TF-IDF and evaluated through 10-fold stratified cross-validation. The results show that Chi-Square achieved the best performance, with 83% Accuracy, 81% Precision, 96% recall, and an F1-score of 88%, outperforming Information Gain in recognizing negative sentiment. These findings provide useful insights for GTA V Roleplay server developers and contribute to the development of Indonesian-language sentiment analysis in the online gaming domain.
Co-Authors Ahvan Muharam Enda Suhadi Imam Saepul Azmi Robi, Mohammad Robi Serly Agustin Tiki Ramdhani