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All Journal JSM (Jurnal SIFO Mikroskil) Transformasi: Jurnal Pengabdian Masyarakat Journal La Multiapp Engineering Science Letter
Nadya Sikana
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Religion Aerospace Engineering Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology

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Movie Success Prediction Based on Feature and Trailer Comments Using Ensemble+LSTM Model Nadya Sikana; Purba, Ronsen
Journal La Multiapp Vol. 5 No. 5 (2024): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v5i5.1417

Abstract

Predicting the success of a movie is a very important aspect due to the high risks involved in movie production. The challenge lies in the uncertainty within the movie industry and selecting the appropriate machine learning model. We can combine movie features and sentiment analysis from social media using machine learning techniques to achieve movie success prediction. The methods used for predicting based on movie features are Ensemble models (Random Forest + Gradient Boosting). Meanwhile, the methods used for sentiment analysis of trailer comments is LSTM. The evaluation of the models used is based on RMSE and accuracy calculation. The final prediction of success obtains an RMSE of 0,8807 and an accuracy of 91,19%. This represents an improvement from previous research. Further research is recommended to implement the model in the movie industry
BERT Model Implementation for Dynamic Sentiment Analysis of Pertamina on Social Media X Purba, Ronsen; Lubis, Rivaldi; Sikana, Nadya; Situmorang, Gilbert Fernando
Engineering Science Letter Vol. 4 No. 02 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001139

Abstract

This study aims to investigate the dynamics of public sentiment on platform X in response to the Pertamina corruption scandal, exploring how trust and perception shifted before and after the incident. Utilizing BERT-based sentiment classification model trained on real-world social media posts, the model achieved a validation loss of 0.5078 and an F1-score of 82.12%, demonstrating strong predictive performance for large-scale sentiment analysis. Results revealed a significant rise in negative sentiment and a decline in positive sentiment following the public disclosure of the scandal on February 25, 2025, reflecting a deep erosion of public trust in Pertamina. Qualitative thematic analysis further identified a shift from neutral or positive discussions focused on service quality and innovation to emotionally charged critiques emphasizing betrayal, distrust and institutional failure. These findings highlight the value of integrating deep learning classification with qualitative insights to monitor real-time public opinion and institutional reputation. The study underscores the critical need for transparency and effective communication strategies during reputational crises to rebuild public confidence. Limitations include the focus on a single social media platform, suggesting future research should incorporate cross-platform and multilingual analyses. Practically, this research offers actionable insights for corporate crisis management and contributes to understanding social media’s role in shaping public trust and accountability in the digital age.
Analisis Sentimen untuk Ulasan Produk E-Commerce Shopee Menggunakan BERT Sikana, Nadya; Winardi, Sunaryo; -, Gunawan; Situmorang, Gilbert Fernando; Lubis, Rivaldi
Jurnal Sifo Mikroskil Vol. 26 No. 2 (2025): JSM VOLUME 26 NOMOR 2 TAHUN 2025
Publisher : Fakultas Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55601/jsm.v26i2.1796

Abstract

Analisis sentimen sangat penting untuk memahami opini konsumen dan menyempurnakan strategi e-commerce. Analisis ini menghadapi tantangan seperti bahasa informal, ambiguitas semantik, dan inkonsistensi antara sentimen tekstual dan peringkat bintang, yang memengaruhi akurasi klasifikasi. Penelitian ini menerapkan model BERT (Bidirectional Encoder Representations from Transformersi) untuk mengklasifikasikan sentimen dalam ulasan pengguna Shopee. Data dikumpulkan dari penelitian sebelumnya dan menjalani praproses, termasuk tokenisasi, penghapusan stopword, dan normalisasi teks. Pendekatan analisis sentimen berbasis leksikon digunakan sebagai dasar perbandingan. Model BERT disempurnakan menggunakan optimasi hiperparameter, mencapai akurasi 83,08%, presisi 82,91%, recall 83,08%, dan F1-score 82,87%. Dibandingkan dengan studi sebelumnya yang menggunakan Naïve Bayes dengan N-Gram dan Information Gain, yang mencapai akurasi 92% tetapi presisi lebih rendah (56%), recall (65%), dan F1-score (60%), BERT mengungguli dengan metrik evaluasi yang lebih seimbang dan keandalan prediktif yang lebih besar. Hasil ini menunjukkan kemampuan BERT untuk menangkap konteks semantik dua arah, melampaui metode tradisional dalam menangani tugas analisis sentimen yang kompleks.
Hybrid Machine Learning for Crime Prediction in Indonesia toward Society 5.0 Sikana, Nadya; Lubis, Rivaldi; Situmorang, Gilbert Fernando; Prisella, Naomi
Engineering Science Letter Vol. 4 No. 03 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001359

Abstract

Crime remains a major social challenge in Indonesia, requiring innovative approaches to enhance prevention and law enforcement. This study proposes a hybrid machine learning framework that integrates the Temporal Fusion Transformer (TFT) for time-series forecasting and Extreme Gradient Boosting (XGBoost) for classification and feature analysis. Using socio-economic and demographic data from the Indonesian Central Bureau of Statistics (2010-2023) across 38 provinces, the framework aims to predict crime incidence and classify crime resolution effectiveness. The results show that TFT effectively captures temporal dependencies, achieving robust forecasting accuracy (R2 = 0.9893), while XGBoost delivers high classification performance (Accuracy = 98.87%). Feature importance analysis highlights the dominant role of case resolution rate, government consumption expenditure, school participation rates and life expectancy in shaping crime patterns. Compared to baseline models such as LSTM and Random Forest, the hybrid TFT + XGBoost approach demonstrates superior balance between accuracy, robustness and interpretability. These findings provide actionable insights for policymakers to design data-driven crime prevention strategies, align with Indonesia’s digital transformation agenda, and support the vision of Society 5.0.
Forecasting Rice Price Volatility Utilizing BiLSTM-SHAP to Ensure National Food Stability Manurung, Juliana Damayanti; Sikana, Nadya; Simamora, Fandi Presly; Manurung, Zoni Zikro
Engineering Science Letter Vol. 4 No. 03 (2025): Engineering Science Letter
Publisher : The Indonesian Institute of Science and Technology Research

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56741/IISTR.esl.001360

Abstract

Rice price volatility in Indonesia remains a persistent economic issue, partly driven by climate variability and fluctuations in national rice production, prompting the government to resort to substantial annual imports. However, the extent to which domestic production factors and weather conditions influence future rice prices has not been quantitatively evaluated. This study aims to forecast short-term rice prices in Indonesia by integrating multiple time-series features, including rice prices, harvested area, paddy production, and weather features, using a Bidirectional Long Short-Term Memory (BiLSTM) network. Daily data from 2013 to 2024 were collected from the National Statistics Agency, Food Price Panel, and the Meteorology and Climatology Agency. Chronological split was applied for training, validation, and testing to preserve temporal dependency. The optimal model predicts rice prices seven days ahead using 256 hidden units, achieving MAE of 128.84 IDR, RMSE of 157.98 IDR, and R² of 0.694. SHAP analysis shows that historical rice prices have the strongest contribution with a SHAP value of 0.969652, significantly higher compared to other features. The results demonstrate that integrating agricultural and climatic inputs improves predictive performance while providing interpretable insights into price-forming factors.
Enhancing students’ digital competencies through basic web training at SMKS Indonesia Membangun 1 Situmorang, Gilbert Fernando; Winardi, Sunaryo; Lubis, Rivaldi; Sikana, Nadya; Purba, Ronsen
Transformasi: Jurnal Pengabdian Masyarakat Vol. 21 No. 2 (2025): Transformasi Desember
Publisher : LP2M Universitas Islam Negeri Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20414/transformasi.v21i2.14414

Abstract

[Bahasa]: Keterbatasan siswa SMKS Indonesia Membangun 1, khususnya program keahlian Teknik Komputer dan Jaringan (TKJ), dalam mengonversi pengetahuan pemrograman dasar menjadi artefak digital yang aplikatif menjadi latar belakang kegiatan ini. Pembelajaran yang berfokus pada sistem jaringan menyebabkan penerapan konsep pemrograman belum terintegrasi dengan kebutuhan pengembangan digital. Kegiatan pengabdian masyarakat ini bertujuan untuk menjembatani kesenjangan tersebut melalui pelatihan pengembangan antarmuka web berbasis HTML dan CSS. Program dirancang dengan pendekatan Participatory Action Research (PAR) yang mencakup analisis kebutuhan, perancangan pembelajaran, pelaksanaan pelatihan tatap muka, dan evaluasi. Sebanyak 55 siswa berpartisipasi dalam program. Evaluasi kuantitatif dilakukan melalui pre-test dan post-test menggunakan 10 soal objektif pada platform Kahoot!, dengan analisis data secara deskriptif berdasarkan skor total dan jumlah jawaban yang benar. Skor Kahoot! merepresentasikan akurasi dan kecepatan jawaban. Capaian praktik dievaluasi melalui produk antarmuka web sederhana yang dikembangkan oleh siswa dalam siklus PAR. Hasil menunjukkan peningkatan kompetensi yang signifikan. Rerata skor peserta meningkat dari 2.041,91 menjadi 4.828,78, dan rerata jawaban benar meningkat dari 2,91 menjadi 5,98 dari 10 soal. Meski efektif, evaluasi berbasis daring menghadapi kendala teknis seperti ketergantungan pada koneksi internet. Oleh karena itu, disarankan agar kegiatan serupa di masa depan mengembangkan instrumen penilaian yang kompatibel dengan pembelajaran luring serta mempertimbangkan pendekatan low-code/no-code sebagai strategi pedagogis inklusif untuk memfasilitasi beragam tingkat kesiapan siswa dalam menghasilkan artefak digital. Kata Kunci: desain web, keterampilan digital, kurikulum independen, teknologi [English]: The limitations of SMKS Indonesia Membangun 1 students, particularly those in the Computer and Network Engineering (TKJ) program, in converting basic programming knowledge into applicable digital artifacts served as the background for this activity. Learning focused on network systems has resulted in the application of programming concepts not being integrated with digital development needs. This community service activity aims to bridge this gap by providing training in developing HTML- and CSS-based web interfaces. The program was designed using a Participatory Action Research (PAR) approach that included needs analysis, learning design, face-to-face training implementation, and evaluation. A total of 55 students participated in the program. Quantitative evaluation was conducted through pre-tests and post-tests using 10 objective questions on the Kahoot! platform, with descriptive data analysis based on the total score and the number of correct answers. The Kahoot! score represents the accuracy and speed of answers. Practical achievements were evaluated through a simple web interface product developed by students in the PAR cycle. The results showed a significant increase in competency. The average score of participants increased from 2,041.91 to 4,828.78, and the average correct answer rate increased from 2.91 to 5.98 out of 10 questions. Although effective, online-based evaluations face technical challenges such as dependence on an internet connection. Therefore, it is recommended that similar activities in the future develop assessment instruments that are compatible with offline learning and consider low-code/no-code approaches as inclusive pedagogical strategies to facilitate diverse levels of student readiness in producing digital artifacts. Keywords: web design, digital skills, independent curriculum, technology
Co-Authors Fandi Presly Simamora Gunawan - Manurung, Juliana Damayanti Manurung, Zoni Zikro Prisella, Naomi Rivaldi Lubis Ronsen Purba Situmorang, Gilbert Fernando Winardi, Sunaryo