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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 JUITA : Jurnal Informatika Seminar Nasional Informatika (SEMNASIF) MIX : Jurnal Ilmiah Manajemen Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Matrix : Jurnal Manajemen Teknologi dan Informatika JITK (Jurnal Ilmu Pengetahuan dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Journal of Information Systems and Informatics Jambura Journal of Electrical and Electronics Engineering 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 BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Algoritma Jurnal Pengabdian Mitra Masyarakat (JPMM) CSRID Journal of Educational and Learning Studies
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 Energy Engineering Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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Sentiment Analysis of Reviews on Lazada Apps using Naïve Bayes Algorithm Nurdiyansa, Zhafran Afif; Berlilana, Berlilana
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7255

Abstract

Lazada app reviews on the Google Play Store become useful information if processed properly. Existing or new users can analyze app reviews to get information that can be used to evaluate the service. The activity of analyzing app reviews is not enough just to look at the number of stars, it is necessary to look at the entire content of the review comments to be able to know the purpose of the review. A sentiment analysis system is a system used to automatically analyze reviews to obtain information including sentiment information that is part of online reviews. This time the data will be classified using the Naive Bayes method. A total of 1,000 user reviews of the Lazada app were collected to form a dataset. The purpose of this study was to conduct sentiment analysis of Lazada app reviews on Google Play Store using Naive Bayes algorithm. This stage of research involves data collection, labeling, pre-processing, sentiment classification, and evaluation. In the pre-processing stage, there are 6 stages, namely Cleaning, Case Factoring, Word Normalization, Tokenization, Hyphen Removal, and Base Word Formation. The TF-IDF (Term Frequency - Inverse Document Frequency) method is used for word weighing. The data will be grouped into two categories, namely negative and positive. Next, the data will be evaluated using accuracy parameters. The test results showed an accuracy value of 84%, then for the grouping of negative and positive reviews, it was found that Lazada application reviews tended to be negative.
Exploring the Impact of Discount Strategies on Consumer Ratings: An Analytical Study of Amazon Product Reviews Berlilana, Berlilana; Wahid, Arif Mu’amar; Fortuna, Dewi; Saputra, Alfin Nur Aziz; Bagaskoro, Galih
Journal of Applied Data Sciences Vol 5, No 1: JANUARY 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i1.163

Abstract

This research delves into the influence of discount strategies on consumer ratings within the e-commerce landscape, particularly on Amazon. A logistic regression model assessed how discount percentages and product categories affect consumer ratings. The study followed a rigorous methodology, beginning with comprehensive data collection across diverse product categories on Amazon. This was succeeded by a detailed exploratory data analysis (EDA), data preprocessing, and subsequent model building. The model was then subjected to an extensive evaluation process, encompassing accuracy, precision, recall, F1-score, and ROC-AUC metrics. The evaluation revealed that the model achieved an accuracy of 74.94%, a precision of 72.69%, and a recall of 74.94%. The F1 score was calculated at 69.26%, and the ROC-AUC score was notably 78.24%. These metrics underscore the model’s capability to accurately predict consumer ratings influenced by discount strategies. Key findings highlighted the significant predictive power of discount percentages and specific product categories, particularly 'Home Kitchen', suggesting a complex relationship between discounts, product types, and consumer responses. Theoretically, the study enriches the understanding of consumer behavior in e-commerce, highlighting the nuanced impact of discount strategies on consumer satisfaction, especially in online retail contexts. For e-commerce businesses and marketers, the findings underscore the importance of strategically employing discount strategies and tailoring marketing approaches to specific product categories. This study emphasizes managing customer expectations and maintaining product quality alongside discounts. This research provides valuable insights for optimizing e-commerce strategies and paves the way for future investigations. It opens up avenues for further exploration into factors like product quality, brand reputation, shipping times, and the potential of consumer segmentation and sentiment analysis in enhancing marketing effectiveness. The study marks a significant contribution to the field by linking discount strategies with consumer ratings, using advanced data analytics to inform e-commerce practices in the digital age.
Machine Learning and Deep Learning Approaches for Energy Prediction: A Systematic Literature Review Nanjar, Agi; Saputro, Rujianto Eko; Berlilana, Berlilana
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 4 (2024): Article Research Volume 8 Issue 4, October 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.14208

Abstract

This paper offers a literature review on the application of Machine Learning (ML) and Deep Learning (DL) techniques in energy prediction. Contemporary energy systems' challenges, such as load fluctuations and uncertainties linked to renewable energy sources, render traditional methods like ARIMA and linear regression insufficient. The objective of this paper is to identify the most widely used ML and DL approaches, compare their performance against conventional methods, and explore the implementation challenges along with potential solutions. The methodology for this literature review involves analyzing publications from Scopus, IEEE Xplore, and ScienceDirect covering the period from 2019 to 2024. The findings indicate that DL methods, particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, are effective in handling sequential data, while hybrid models like CNN-GRU enhance prediction accuracy in innovative grid applications. Challenges identified include overfitting and data complexity, which can be addressed through regularization techniques and computational optimization using GPUs. In conclusion, this paper asserts that ML and DL play a significant role in improving prediction accuracy and facilitating the transition towards sustainable energy and smart grids. To further enhance performance in the future, the paper recommends the development of ensemble models and the integration of attention mechanisms.
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.
Analisis Relevansi Kompetensi Alumni dengan Pekerjaan di Pendidikan Tinggi Menggunakan Pendekatan PCA dan Clustering Priyanto, Eko; Berlilana, Berlilana; Tahyudin, Imam
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 12 (2024): JPTI - Desember 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.515

Abstract

Penelitian ini bertujuan untuk menganalisis relevansi kompetensi alumni dengan kebutuhan dunia kerja di sektor pendidikan tinggi melalui penerapan Principal Component Analysis (PCA) dan K-Means Clustering. PCA digunakan untuk mereduksi kompleksitas data kompetensi alumni sehingga pola keterampilan interpersonal (soft skills) dan teknis dapat divisualisasikan secara lebih sederhana. Hasil clustering menunjukkan adanya tiga kelompok utama alumni berdasarkan profil kompetensi, yaitu kelompok dengan dominasi soft skills, kelompok dengan dominasi keterampilan teknis, dan kelompok dengan keseimbangan kedua keterampilan. Temuan ini menegaskan bahwa keterampilan interpersonal memiliki segmentasi yang lebih jelas dibandingkan keterampilan teknis, yang masih menunjukkan tumpang tindih antar cluster. Penelitian ini memberikan implikasi penting bagi institusi pendidikan tinggi untuk menyesuaikan kurikulum dengan kebutuhan pasar kerja, memperkuat pengembangan soft skills dan keterampilan teknis guna meningkatkan daya saing lulusan. Dengan pendekatan ini, institusi dapat lebih responsif terhadap tuntutan dunia kerja yang dinamis serta mendukung perumusan kebijakan pendidikan yang lebih efektif.
Pengembangan Produksi dan Pemasaran Produk Pangan Non Terigu Untuk Meningkatkan Kualitas SDM dan Mengentaskan Kemiskinan Ekstrim di Desa Pangebatan Kecamatan Karanglewas Kabupaten Banyumas Dianingrum, Melia; Zanuar Rifai; Berlilana, Berlilana; Astuti, Santi Dwi; Aini, Nur; Istiqomah, Istiqomah
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 5 No. 4 (2024): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN) Edisi September - Desembe
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55338/jpkmn.v5i4.4707

Abstract

Tepung Mocaf merupakan tepung dari singkong yang dimodifikasi dengan proses fermentasi menggunakan bakteri asam laktat (BAL).Jawa Tengah menempati posisi ketiga sebagai sentra komoditas singkong terbesar di Indonesia. Di Jawa Tengah selain di Kabupaten Banjarnegara, Kabupaten Banyumas termasuk penyumbang penghasil singkong terbanyak, salah satunya Desa Pangebatan Kecamatan Karanglewas Penggunaan tepung mocaf memiliki banyak keunggulan dibanding tepung terigu. Akan tetapi masyarakat masih kurang paham apa itu tepung mocaf Metode Pengabdian Metode yang dilakukan dengan memberi penyuluhan secara teori atau materi tentang cara mengolah singkong menjadi tepung mocaf dan mengolah tepung mocaf menjadi beberapa aneka makanan seperti mie, brownies, cookies, cupcake, muffin, egg rolls. Indicator Keberhasilan. Kegiatan pelatihan dan pendampingan serta penerapan teknologi inovasi telah berhasil dilaksanakan, karena mitra telah berhasil reorganisasi dan membuat olahan tepung mocaf. Kegiatan pengabdian masyarakat berfokus kepada penanganan masalah nilai harga jual singkong yang cenderung murah khususnya Kabupaten Banyumas merupakan termasuk penyumbang penghasil singkong terbanyak, salah satunya Desa Pangebatan Kecamatan Karanglewas, dan sekaligus juga untuk mengembangkan kelompok kerja UP2K (Usaha Peningkatan Pendapatan Keluarga) bagian dari tim PKK desa pangebatan Berdasarkan kegiatan pengabdian kosabangsa yang dilakukan di Desa Pangebatan dapat disimpulkan Program ini meningkatkan keterampilan dan pemahaman masyarakat Desa Pangebatan dalam memproduksi dan memasarkan produk pangan alternatif, seperti produk berbasis tepung mocaf, yang menjadi pengganti tepung terigu
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.
Sentiment Analysis on Slang Enriched Texts Using Machine Learning Approaches Prastyo, Priyo Agung; Berlilana, Berlilana; Tahyudin, Imam
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.626

Abstract

This study explores sentiment analysis of slang-enriched user reviews using machine learning techniques, specifically Naive Bayes, Support Vector Machine (SVM), and Random Forest, to classify user sentiment into Positive, Negative, and Neutral categories while addressing challenges posed by informal and conversational language through slang normalization. A lexicon-based scoring method was employed to standardize slang terms such as “gak,” “aja,” and “banget,” ensuring consistency in sentiment analysis. The results indicate that Neutral sentiment dominates the dataset (51%), followed by Negative (28%) and Positive (21%), with lexicon-based scores confirming this distribution. Negative sentiment exhibits a broader intensity range, reflecting user dissatisfaction primarily related to network quality, service reliability, and pricing, as evident from recurring terms like “sinyal” (signal), “jaringan” (network), and “mahal” (expensive). Word cloud visualizations reinforce these findings, highlighting the prevalence of these concerns in user feedback. Performance evaluation of the machine learning models reveals that SVM and Random Forest achieved the highest accuracy (96%), significantly outperforming Naive Bayes (73%), demonstrating their effectiveness in handling high-dimensional text data and accurately classifying slang-rich content. These findings underscore the importance of slang normalization in preprocessing, as it significantly enhances sentiment classification accuracy. This study provides actionable insights for service providers, helping them identify and address key sources of user dissatisfaction. Future research can explore deep learning models such as BERT and LSTM to further enhance sentiment analysis by capturing contextual relationships within text data, while topic modeling techniques could uncover deeper thematic patterns in user feedback, enabling data-driven strategies to improve customer satisfaction.
Enhancing Digital Marketing Strategies with Machine Learning for Analyzing Key Drivers of Online Advertising Performance Berlilana, Berlilana; Hariguna, Taqwa; El Emary, Ibrahiem M. M.
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.658

Abstract

The rapid growth of digital advertising has underscored the need for data-driven strategies to optimize campaign performance. This study applies machine learning techniques to analyze online advertising data, aiming to identify key performance drivers and provide actionable insights for optimizing marketing strategies. The dataset includes metrics such as clicks, displays, costs, and revenue, which were preprocessed, analyzed, and modeled using ensemble methods, including Random Forest and Gradient Boosting. These ensemble methods were chosen for their ability to handle high-dimensional data, mitigate overfitting, and capture complex, nonlinear relationships between variables. Random Forest, with its bagging approach, enhances generalization by reducing variance, while Gradient Boosting incrementally corrects errors by focusing on hard-to-predict instances, improving overall predictive performance. Descriptive analysis revealed significant variability in campaign outcomes, with cost and user engagement emerging as primary predictors of revenue. Machine learning models demonstrated strong predictive accuracy, with Random Forest achieving 92% accuracy and an F1-score of 89%. Visualizations such as feature importance charts, correlation heatmaps, and learning curves validated the robustness of the models and highlighted key insights, including inefficiencies in cost allocation and the limited impact of certain categorical features like placement. The study emphasizes the potential of machine learning to optimize digital marketing strategies by identifying critical factors that influence campaign success. The findings provide a scalable framework for resource allocation, audience targeting, and strategic decision-making in online advertising. Future research could further enhance predictions by incorporating additional features, such as audience demographics and temporal trends, to provide deeper insights into campaign dynamics.
Co-Authors Agnis Nur Afa Zumaroh Alif Yahya Syafaat Andi Dwi Riyanto Anggi Zahriyatun Anisa Antonius Agung Hartono Ayuni, Ratih Azhari Shouni Barkah Bachtiar Mujaddidi Bagaskoro, Galih Bagus Adhi Kusuma Dhanar Intan Surya Saputra Dhanar Intan Surya Saputra Dhanar Surya Saputra, Dhanar Surya Didit Suhartono Dilliana Tugas Setiyawan Dwi Angesti Dinda Parameswara Eko Priyanto El Emary, Ibrahiem M. M. Emha Taufiq Luthfi Erina Setyawati 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 Junindra Yuga Pamungkas Khoirul Abbi Rokhman M. Suyanto Mardiyanto Mardiyanto Melia Dianingrum Min Tsai Lai Mukhlisin Mukhlisin Murtiyoso Murtiyoso Nanjar, Agi Nur Aini Nurdiyansa, Zhafran Afif Oktavia Mulyo Nurdiyanti Prastyo, Priyo Agung Prayoga, Fandhi Dhuga Primandani Arsi Purwadi Purwadi Putri, Eka Ardiya Rahman Rosyidi Rinda Utami Rizki Wahyudi Rona Sepri Ananda Rujianto Eko Saputro Rujiyanto Eko Saputro Saekhu, Ahmad Santi Dwi Astuti Saputra, Alfin Nur Aziz Sarmini - Sarmini Setiyawan, Dillyana Tugas Seto Wahyu Widayat, Seto Wahyu Slamet Endro Prianto Slamet Widodo 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 Yusuf Nur Alam Yuwono Wisudo Pramono Zanuar Rifai Zhafran Afif Nurdiyansah