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Utilization of Generative Pre-trained Transformer Model for Automatic Evaluation and Feedback on Scientific Manuscripts Wicaksono, Aditya Eka Putra; Mahenra, Ridwan
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8466

This study aims to explore the use of the Generative Pre-trained Transformer (GPT) model in the automatic evaluation of scientific papers, with a focus on the conformity of the feedback provided with the applicable academic writing guidelines. In this study, GPT was used to analyze manuscripts published in accredited scientific journals and to provide feedback on errors in grammar, spelling, citation format, use of academic terms, content organization, and quality of argument. The results showed that the GPT was highly effective in detecting and correcting technical errors in the manuscripts, with high correction rates for spelling and grammar errors (95% and 93%, respectively). In addition, the GPT also provided relevant suggestions for correcting formatting errors, such as citation and bibliography formats, with an improvement rate of 90%. The model also successfully provided suggestions to improve content organization and argument strengthening in scientific papers. Although GPT is effective in correcting technical errors and providing structural feedback, human editing is still required to improve substantial and in-depth aspects of scientific papers. This study concludes that GPT can be used as an effective tool in the process of automatic evaluation of scientific papers, but the role of human editors is still needed for optimal results. This study also suggests further development in fine-tuning GPT to improve substantial analysis and strengthening of argument quality in scientific writing.

Analisis Kinerja Convolutional Neural Networks Baseline untuk Identifikasi Jenis Jenis Penyakit Kentang: Performance Analysis of Baseline Convolutional Neural Networks for Identifying Potato Disease Types Prasetyo, Khoir; Mahenra, Ridwan
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1722

Penelitian ini bertujuan untuk mengembangkan dan mengevaluasi model Convolutional Neural Network (CNN) baseline dalam mengidentifikasi jenis-jenis penyakit pada daun kentang. Dataset yang digunakan terdiri dari citra daun kentang yang terinfeksi dan sehat, yang diklasifikasikan ke dalam beberapa kategori penyakit seperti late blight, early blight, dan penyakit bakteri. Model CNN dirancang dengan arsitektur dasar yang meliputi beberapa lapisan konvolusi, pooling, dan fully connected, serta dilatih menggunakan Optimizer Adam dengan fungsi loss categorical cross-entropy. Hasil evaluasi menunjukkan bahwa model mencapai akurasi 82% pada validation set dan rata-rata 95% pada data acak. Meskipun model menunjukkan performa yang baik dalam mengklasifikasikan citra, indikasi overfitting terlihat dari perbedaan antara akurasi training dan validation. Analisis lebih lanjut mengidentifikasi kesalahan prediksi yang terjadi, terutama pada kelas dengan gejala visual yang mirip. Penelitian ini merekomendasikan penerapan teknik regulasi, augmentasi data, dan penggunaan arsitektur lebih kompleks untuk meningkatkan akurasi model. Hasil penelitian ini diharapkan dapat memberikan kontribusi bagi pengembangan sistem deteksi penyakit tanaman berbasis kecerdasan buatan yang lebih efisien.

Optimasi Hyperparameter Gaussian Naive Bayes Untuk Prediksi Risiko Stroke Pada Data Tidak Seimbang Nida, Khoirun; Mahenra, Ridwan; Susanto, Erliyan Redi
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8497

Stroke is a serious disease with global impact that requires high-accuracy early detection. Significant difficulties in designing machine learning-based predictive models arise due to disproportionate data conditions (imbalanced datasets). This occurs because the number of stroke cases (minority class) is very small compared to non-stroke cases. This imbalanced data situation often causes models to become biased and potentially produce high false negative rates, which is very risky in a clinical setting. This study focuses on improving the sensitivity of the Gaussian Naive Bayes (GNB) model through hyperparameter optimization and classification threshold adjustment. The research process included data preprocessing, stratified dataset division (70% training and 30% testing), feature scaling, var_smoothing parameter optimization using GridSearchCV, and threshold adjustment to maximize the Recall value. The results showed that the standard GNB model only achieved a Recall value of 0.4400. However, after var_smoothing optimization (1.00×10⁻¹⁰) and threshold adjustment to 0.0100, the Recall value increased significantly to 0.8000. This increase was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). This improvement was accompanied by a decrease in Accuracy (0.5988) and Precision (0.0909). The high Recall (0.8000) indicates that the model is better for mass screening (early detection phase), although it must be balanced with further diagnostic processes due to low precision. This high Recall value confirms the model's success in minimizing False Negatives, which is a top priority in stroke risk prediction cases.

Analisis Respon Publik Terhadap Tren Penggabungan Foto Gemini AI Menggunakan Naive Bayes Afiani, Nanda; Mahenra, Ridwan
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8504

The rapid advancement of Artificial Intelligence (AI) technology has brought numerous innovations to the digital world, one of which is Gemini AI — an application capable of automatically merging photos based on user instructions. This phenomenon has gone viral on the TikTok platform and has sparked diverse public reactions, ranging from admiration for its visual results to concerns about ethical issues and the potential misuse of deepfake technology. This study aims to analyze public sentiment toward the trend of Gemini AI photo merging on TikTok using a sentiment analysis method based on the Naïve Bayes algorithm. Data were collected through a web scraping technique using the Apify platform, resulting in 5,061 user comments. The data processing stages included text preprocessing, TF-IDF transformation, and sentiment classification into three categories: positive, negative, and neutral. The results indicate that neutral sentiment dominates (4,059 comments), followed by positive (745 comments) and negative (257 comments). The dominance of neutral sentiment occurs because most user comments are informative or descriptive, expressing ordinary responses without strong emotional tones, rather than showing indifference to ethical concerns. The Naïve Bayes model demonstrated good performance with an accuracy of 85.72%, precision of 87.84%, recall of 85.72%, and F1-score of 81.95% through 5-fold cross-validation. These findings confirm that the Naïve Bayes algorithm is effective for classifying public opinion toward generative AI technologies. Overall, this study contributes to a deeper understanding of public perception of AI innovations in the creative digital domain and their social implications on social media platforms.

Comparasi Model DeepSeek dan OpenAI dalam Meningkatkan Efisiensi Pencarian Informasi pada Sistem Pencarian Algoritma Mahenra, Ridwan; Setiawan, Dandi
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10287

This study evaluates the efficiency of two artificial intelligence models, DeepSeek and OpenAI, in generating code for algorithmic systems. Efficiency is assessed through execution speed, code accuracy, and the number of code characters produced. Data were collected from 100 tests covering search, sorting, graph, dynamic programming, optimization, data processing, text, and machine learning algorithms. The objective is to compare the performance of both models to support the development of efficient information retrieval systems. The method involves algorithm testing with statistical analysis of execution time, accuracy, and code length. Results indicate that DeepSeek has an average execution time of 28.74 seconds, slightly slower than OpenAI’s 28.49 seconds. However, DeepSeek’s accuracy (85.88%) surpasses OpenAI’s (85.03%). The average number of code characters is identical at 96.35 characters. The study concludes that DeepSeek excels in accuracy, while OpenAI is faster in certain cases, providing valuable insights for developers in selecting AI models for information retrieval applications.

Analisis Efektivitas Pembelajaran Bahasa Jepang Melalui Anime dan Buku Teks dengan Algoritma AI K-Means dan Decision Tree Al Farhan, M Haidar Amir; Mahenra, Ridwan
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10288

The growing interest in learning the Japanese language in Indonesia, driven by popular culture such as anime, creates a need to understand the effectiveness of different learning media. The non-uniform effectiveness of media for each individual poses a major challenge. Therefore, this study aims to analyze the effectiveness of both anime and textbooks by segmenting learner profiles and identifying key determinants of success using an artificial intelligence approach. This research employed a quantitative method through a questionnaire survey of 120 respondents. The data were analyzed in two stages: the K-Means Clustering algorithm was used to group respondents into learner profiles, and the Decision Tree algorithm was used to identify the most significant factors that differentiate these profiles. The analysis successfully identified three distinct learner profiles: "Intensive & Adaptive Learner," "Flexible Learner," and "Passive Learner." The decision tree revealed that the perception of textbook effectiveness and the frequency of anime use are the strongest predictors in determining a learner's profile, more so than theoretical learning style preferences. It is concluded that media effectiveness is highly dependent on the learner's behavioral and perceptual profile, which underscores the importance of a personalized approach in language education technology.

Perbandingan Efisiensi Algoritma Sorting Hybrid untuk Data Skala Menengah Yuanggara, Virnu; Mahenra, Ridwan
Dinamik Vol 31 No 1 (2026)
Publisher : Universitas Stikubank

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35315/dinamik.v31i1.10337

Penelitian ini mengevaluasi efisiensi tiga algoritma sorting hybrid, yaitu TimSort, IntroSort, dan Merge-Insertion Sort, pada dataset skala menengah yang memiliki jumlah elemen antara 10.000 hingga 1.000.000. Tujuan utama penelitian adalah untuk menganalisis performa algoritma berdasarkan waktu eksekusi, konsumsi memori, dan stabilitas, dengan pengujian dilakukan pada berbagai jenis dataset, termasuk data acak, terurut, hampir terurut, dan data dengan banyak elemen duplikat. Pengujian dilakukan melalui simulasi komputasi menggunakan bahasa pemrograman Python dalam lingkungan terkontrol untuk memastikan hasil yang konsisten. Dataset sintetis dibuat untuk mencerminkan kasus dunia nyata, seperti pengolahan log sistem, pengurutan data pelanggan dalam aplikasi e-commerce, atau pengolahan data sensor dalam sistem Internet of Things (IoT). Hasil pengujian menunjukkan bahwa TimSort memiliki performa unggul pada dataset hampir terurut dengan waktu eksekusi rata-rata 0,12 detik untuk 1.000.000 elemen, sedangkan IntroSort lebih cepat pada dataset acak dengan waktu 0,09 detik dan konsumsi memori rendah sekitar 120 MB. Merge-Insertion Sort menonjol dalam hal stabilitas, tetapi memerlukan memori lebih besar, yaitu sekitar 180 MB untuk dataset yang sama. Analisis mendalam menunjukkan bahwa pemilihan algoritma yang optimal sangat bergantung pada karakteristik dataset dan kebutuhan aplikasi, seperti kecepatan untuk data acak atau stabilitas untuk pengurutan data berurutan. Penelitian ini merekomendasikan TimSort untuk aplikasi yang memerlukan stabilitas tinggi, seperti pengolahan data transaksi keuangan, dan IntroSort untuk aplikasi yang mengutamakan kecepatan pada data acak, seperti analitik data real-time. Untuk pengembangan lebih lanjut, penelitian ini menyarankan eksplorasi optimasi paralel atau implementasi algoritma pada perangkat dengan sumber daya terbatas guna meningkatkan skalabilitas dan efisiensi.

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