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All Journal International Journal of Electrical and Computer Engineering TEKNOLOGI: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Pro Bisnis Telematika Seminar Nasional Informatika (SEMNASIF) MIX : Jurnal Ilmiah Manajemen Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Matrix : Jurnal Manajemen Teknologi dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) Building of Informatics, Technology and Science Jurnal Audience Journal of Innovation Information Technology and Application (JINITA) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Jurnal Pendidikan dan Teknologi Indonesia DECODE: Jurnal Pendidikan Teknologi Informasi BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Pengabdian Mitra Masyarakat (JPMM) CSRID
Berlilana Berlilana
Universitas Amikom Purwokerto
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Journal : Building of Informatics, Technology and Science

 1 
Implementation of K-Means Clustering Algorithm to Determine the Best-Selling Snack In Purwokerto MSMES Ayuni, Ratih; Berlilana, Berlilana
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5524

Abstract

This research was conducted to provide information related to the sale of existing MSME snacks, which products have many buyers and which are not, besides that this research can provide a view for the sale of various snacks whether they are still sold or selling the same snacks but with new innovations so that there are many enthusiasts. To do this, a grouping is needed, therefore the researcher chooses a k-means algorithm which will later be used for the clustering process. The grouping is divided into two, namely best-selling and under-selling products, for research data collected from January to March 2024. Then the data used includes the name of the snack, the number of stocks and the number of sales. After calculating the results of this study, the k-means algorithm performs a calculation of as many as two rounds so as to form two clusters where in cluster one the names of snacks that sell well are grouped such as cimol, noodle skewers, dimsum, egg rolls and white bread. Then in cluster two, the less selling product falls to seblak snacks so that seblak snacks can make new innovations to sell better and can compete with other products. This research succeeded in grouping and providing an overview of products that sell well or not, for future research can be reproduced related to the data used.
Penerapan Algoritma Naïve Bayes pada Analisis Sentimen Aplikasi Traveloka pada Platform Playstore Putri, Eka Ardiya; Berlilana, Berlilana
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6130

Abstract

The number of internet users in Indonesia is increasing every year, making it the fastest-growing country in the world, next only to China, India and the United States. In 2017, in Indonesia, the digital economy sector had a high impact on GDP, showing a figure of 7.3%, while the total economic development only reached 5.1%. Traveloka appeared in 2012 and has grown rapidly to be classified as the most superior travel application in Southeast Asia. As applied by the Traveloka application, it applies data scraping to collect 5000 review data from the intended platform. With the increase of Traveloka app user reviews on Playstore, the main challenge is to classify the sentiment of the reviews automatically and accurately. The purpose of this research is to find out the extent of user assessment of the Traveloka application. The results show that the model has an Accuracy of 0.91, indicating that 91% of the total data was predicted correctly. The model'sF1 Score of 0.90 reflects the optimal balance between Precision and Recall, indicating that the model is not only correct in predicting positive results, but also able to capture almost all positive examples. Precision of 0.92 indicates a high level of accuracy in positive predictions, while Recall of 0.88 indicates that the model's ability to detect all positive data is very good. In this analysis, out of the 940 data used, 250 True Positive (TP), 18 False Positive (FP), 608 True Negative (TN) and 64 False Negative (FN) were found, with an 80:20 data split. The findings show that the model can predict most of the data accurately, despite some errors in positive and negative classification. These results indicate that the model has high effectiveness in the identification and prediction of positive data, providing a strong basis for further applications in data analysis.
Analisis Sentimen dan Pemodelan Topik pada Ulasan Pengguna Aplikasi myIM3 Menggunakan Support Vector Machine dan Latent Dirichlet Allocation Prastyo, Priyo Agung; Berlilana, Berlilana; Tahyudin, Imam
Building of Informatics, Technology and Science (BITS) Vol 6 No 3 (2024): December 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i3.6268

Abstract

In the current digital era, mobile applications play a crucial role in enhancing user experience. This study analyzes user sentiment towards the myIM3 application and identifies key topics discussed in user reviews using Support Vector Machine (SVM) and Latent Dirichlet Allocation (LDA). The dataset comprises 1,000 user reviews from the Google Play Store, including review text, star ratings, review dates, and application versions. Data preprocessing involved cleaning, normalization, stop word removal, and lemmatization. Text data was transformed using Term Frequency-Inverse Document Frequency (TF-IDF). The dataset was split into training and testing sets (80:20 ratio). The SVM model, optimized with a linear kernel, achieved an accuracy of 84.65%, with a precision of 85% for negative sentiment, 84% for positive sentiment, and challenges in classifying neutral sentiment. Cross-validation ensured model robustness. LDA identified five primary topics: general user experience, application usability and purchase experience, positive feedback and functionality, general application evaluation, and network issues and pricing concerns. Techniques like oversampling, undersampling, and hybrid methods addressed imbalanced datasets to enhance model performance. The results revealed that 43% of reviews were positive, 42% were negative, and 15% were neutral. The key topics indicated that network issues and pricing were significant user concerns. These findings provide valuable insights for developers and stakeholders to improve user experience and refine application features based on user feedback.
Enhancing Student Sentiment Classification on AI in Education using SMOTE and Naive Bayes Saekhu, Ahmad; Berlilana, Berlilana; Saputra, Dhanar Intan Surya
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6469

Abstract

This study investigates student sentiment regarding the use of artificial intelligence (AI) in education, employing the Naive Bayes model enhanced with the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalance issues. Class imbalance, a common challenge in sentiment classification, often skews model performance toward majority classes, reducing its effectiveness in recognizing minority classes. To mitigate this, SMOTE was applied to generate synthetic samples for minority classes, achieving a more balanced class distribution. The results demonstrate that incorporating SMOTE improved the Naive Bayes model's accuracy from 65% to 78.87% and significantly increased sensitivity to minority classes. Evaluation metrics, including precision, recall, and F1-score, showed satisfactory performance for certain classes, notably classes 2 and 4. However, challenges remained with class 1, where classification accuracy was lower, indicating inherent complexities in its data patterns. While SMOTE successfully enhanced model performance, it also introduced a potential risk of overfitting, particularly with limited original datasets, highlighting the importance of data quality and size. This research offers actionable insights for educators, developers, and policymakers, emphasizing the need for AI systems in education that are adaptive and responsive to student perceptions. The study concludes that Naive Bayes combined with SMOTE is an effective approach for sentiment analysis in imbalanced datasets. Future research should explore more sophisticated models and larger datasets to achieve more comprehensive and representative outcomes.
Co-Authors Agnis Nur Afa Zumaroh Alif Yahya Syafaat Andi Dwi Riyanto Anggi Zahriyatun Anisa Antonius Agung Hartono Ayuni, Ratih Azhari Shouni Barkah Bagaskoro, Galih Bagus Adhi Kusuma Dewi Fortuna Dhanar Intan Surya Saputra Dhanar Surya Saputra, Dhanar Surya Didit Suhartono Eko Priyanto El Emary, Ibrahiem M. M. Emha Taufiq Luthfi Erliyana Nurrahma Fandy Setyo Utomo Fiby Nur Afiana Fikri Ramadhan Giat Karyono Harimanto, Bambang Harimato, Bambang Hasri Akbar Awal Rozaq Hendra Marcos Imam Tahyudin Imam Tahyudin Ipo Novianto istiqomah istiqomah Khoirul Abbi Rokhman M. Suyanto Melia Dianingrum Min Tsai Lai Muhamad, Nurita Murtiyoso Murtiyoso Nanjar, Agi Nur Aini Nurdiyansa, Zhafran Afif Pambudi, Rahmat Prastyo, Priyo Agung Prayoga, Fandhi Dhuga Primandani Arsi Putri, Eka Ardiya Rahman Rosyidi Rinda Utami Rizki Wahyudi Rona Sepri Ananda Rujianto Eko Saputro Saekhu, Ahmad Santi Dwi Astuti Saputra, Alfin Nur Aziz Sarmini - Sarmini Setiyawan, Dillyana Tugas Seto Wahyu Widayat, Seto Wahyu Sumartono Sumartono Taqwa Hariguna Taqwa Hariguna Taqwa Hariguna Tri Astuti Tri Wahyuningsih Trisna Maulida Udianti, Asih Wahid, Arif Mu’amar Wenti Risma Damayanti Wiga Maulana Baihaqi Yuli Purwati Zanuar Rifai