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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer JPM (Jurnal Pemberdayaan Masyarakat) Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal of Information Systems and Informatics JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jifosi Eduvest - Journal of Universal Studies Jurnal Informatika: Jurnal Pengembangan IT The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika NAWASENA BHAKTI Jurnal Pengabdian Masyarakat (J-AbMas)
Ifnu Wisma Dwi Prastya
Universitas Nahdlatul Ulama Sunan Giri
Religion Aerospace Engineering Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Neuroscience Public Health Social Sciences Other

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Rice Quality Identification Built on Indonesian Food Standards Based on Electronic Nose using Naïve Bayes Algorithm Muhammad Jauhar Vikri; Ifnu Wisma Dwi Prastya; Ucta Pradema Sanjaya; Mula Agung Barata
Journal of Innovation and Technology Polbeng Series on Informatics (INOVTEK Polbeng - Seri Informatika) Vol. 10 No. 1 (2025): March
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/0y0xct32

Abstract

Rice is a staple food in Indonesia, where its quality is regulated by the National Food Standards outlined in National Food Agency Regulation No. 2 of 2023 on Rice Quality and Labeling Requirements. Rice is classified into four grades: premium, medium 1, medium 2, and medium 3. The widespread practice of mislabeling lower-quality rice as a premium through repackaging highlights the critical need for quality control measures. An electronic nose (e-nose) is a reliable device for food quality control. Previous studies have demonstrated its ability to classify rice into two quality grades with 80% accuracy. This study uses exponential data transformation and the Naive Bayes algorithm to enhance the classification accuracy for four rice quality grades according to national standards. The methodology includes signal acquisition, feature extraction using statistical parameters, exponential data transformation, classification, and performance evaluation. The results show that exponential data transformation improves classification accuracy to 97%. This technology can be implemented for automated quality control in milling facilities, storage warehouses, and distribution centres, ensuring consistent rice quality while enhancing supply chain efficiency. The e-nose-based model offers a fast and reliable solution, minimising reliance on human operators.
Pelatihan Pembuatan Lilin Abadi sebagai Upaya Peningkatan Keterampilan dan Kemandirian Ekonomi Masyarakat Ifnu Wisma Dwi Prastya
Nawasena Bhakti Vol. 2 No. 1 (2026): Nawasena Bhakti: Jurnal Pengabdian Masyarakat
Publisher : Badan Usaha Milik Desa Berkaho Pungpungan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64084/nawasenabhakti.v2i1.179

Abstract

The economic problems of rural communities and the low utilization of household waste into economically valuable products remain challenges in efforts to improve community welfare. Used cooking oil waste, which is generally discarded directly, has the potential to cause environmental pollution if not managed properly. This community service activity aims to enhance the skills and economic independence of the community through training in making eternal candles in Gunungrejo Village, Kedungpring District, Lamongan Regency. The method of implementing the activities was carried out through several stages, namely preparation, socialization, practical training, evaluation, and follow-up community assistance. The training was conducted on December 24, 2024, using a hands-on method for processing used cooking oil into eternal candles that have utility and market value. The results of the activities showed an increase in the knowledge and skills of the community in utilizing household waste into creative products, as well as the emergence of motivation to develop home-based businesses centered on eternal candles. This activity has a positive impact on increasing environmental awareness, productive skills, and opportunities for sustainable economic independence of the community.
Comparison of Feature Selection Methods in Classifying Poverty Levels in Indonesia Using Comparative Machine Learning Methods Nuniska Dwi Kamayanti; Ifnu Wisma Dwi Prastya; Sahri Sahri
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12604

Abstract

Poverty classification requires models capable of handling multidimensional data and imbalanced class distributions. This study aims to develop and compare several machine learning algorithms for classifying poverty levels in Indonesia, as well as to analyze the impact of feature selection and reduction methods on model performance. The study employs a comparative approach using a secondary dataset consisting of 514 districts/cities with socio-economic indicators and a binary target variable. The methodology includes data preprocessing, the application of Chi-Square, Pearson Correlation, and Principal Component Analysis (PCA), and the handling of imbalanced data using the Synthetic Minority Oversampling Technique (SMOTE). Modelling is conducted using Random Forest, Support Vector Machine (SVM), Logistic Regression, and Artificial Neural Network (ANN), with evaluation performed using Stratified K-Fold Cross Validation and metrics including accuracy, precision, recall, and F1-score. The results indicate that Chi-Square and Pearson Correlation outperform PCA, with Random Forest achieving the best performance, attaining an accuracy of 0.9854 and an F1-score of 0.9507, while effectively detecting the minority class. Therefore, the combination of Chi-Square and Random Forest is identified as the most effective approach in this study, as it produces a model that is accurate, stable, and capable of handling imbalanced data.
Attention-Enhanced Multivariate Forecasting for Intelligent Microservice Autoscaling Nur Saifuddin; Mula Agung Barata; Ifnu Wisma Dwi Prastya
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12662

Abstract

Proactive autoscaling in cloud-native microservices requires anticipatory decisions because reactive controllers often lag under abrupt workload shifts. This study aims to improve autoscaling decision quality through a two-stage machine learning pipeline. The research adopts an experimental design using production-grade microservice traces, with strict time-respecting train-validation-test splits and training-only fitting for preprocessing and oracle-threshold estimation to prevent leakage. In the first stage, multivariate forecasting models predict future CPU and memory utilization from engineered temporal features. In the second stage, the predicted signals are combined with observed features to classify three autoscaling actions: scale down, hold, and scale up. Benchmarking shows recurrent neural models are strong baselines, while an attention-enhanced encoder-decoder performs best. The best Bahdanau-attention model with residual connection reduces test CPU RMSE from 0.030977 to 0.028924 and memory RMSE from 0.010322 to 0.005452 relative to the strongest BiLSTM baseline. For decision learning, the optimized Extreme Gradient Boosting model using prediction-augmented features achieves an accuracy of 0.950602 and an F1 score of 0.951026. Supporting downstream validation also yields lower SLO violation rates than horizontal and vertical baselines while maintaining zero downtime in the evaluated scenarios. These findings indicate that improving forecasting quality and explicitly transferring predictive signals to the decision stage strengthens proactive autoscaling performance.
TikTok Sentiment Analysis on Koperasi Merah Putih Using SVM and ANN Meliysa Pasa Bagna Aprilia Said; Kholifatus Sholihah; Ifnu Wisma Dwi Prastya; Afril Efan Pajri
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13394

Abstract

TikTok has become a relevant social media source for observing public responses to public issues, including the Koperasi Merah Putih program. This study compares Support Vector Machine (SVM) and Artificial Neural Network (ANN) for classifying sentiment in TikTok comments. The dataset was obtained through TikTok comment scraping and consisted of 25,669 raw comments. After removing empty comments and applying preprocessing stages consisting of cleaning, case folding, normalization, tokenization, stopword removal, and stemming, 21,026 comments were used for sentiment analysis. Sentiment labels were generated automatically using a lexicon-based sentiment labeling approach and grouped into three classes: positive, negative, and neutral. TF-IDF was used for feature extraction with a maximum of 5,000 features and unigram-bigram representation. The dataset was split into training and testing sets with an 80:20 ratio, while Stratified K-Fold Cross Validation and SMOTE were applied to strengthen evaluation and address class imbalance. The results show that SVM achieved the best overall performance before SMOTE with an accuracy of 86.66% and an F1-score of 86.76%. ANN achieved an accuracy of 85.31% before SMOTE and improved slightly after SMOTE to 85.47%. These findings indicate that SVM is more stable for TF-IDF-based TikTok comment classification, while SMOTE can improve ANN performance slightly but does not always increase all models equally.
Co-Authors Afril Efan Pajri Ahmad Abil Rizky Ramadhan Basuki Rahmat Devi Nova Kharisma Diah Fitriani Diana Purwitasari Dimas Saputra Dwi Issadari Hastuti Elok Salma Nabila Salma Falach Asy'ari, Misbachul Februyani, Nawafilla Ferdita Inayah Hestu Saputri Guruh Purbo Dirgontoro Hakim, Fernanda Amri Ita Aristia Sa'ida Jauhar Vikri, Muhammad Jauhara Rana Budiani, Jauhara Rana Kholifatus Sholihah Lailatul Qodriyah Lailatul Qodriyah Lambang, Rahmat Tegar Patriot Hari M. Ahsanul Fikri M. Khoirul Risqi M. Khoirul Risqi M. Ridlwan Hambali Meliysa Pasa Bagna Aprilia Said Mohammad Alif Awwalin Muh. Mashdarul Hilmi Aufa Muhammad Bisri Mustofa Muhammad Jauhar Vikri Mula Agung Barata Nuniska Dwi Kamayanti Nur Azizah, Anisa Nur Saifuddin Pradema Sanjaya, Ucta Pribadi , Teguh Rahmat Tegar Patriot Hari Lambang Sahri Sahri Sahri Sahri Sanjaya, Ucta Pradema Shofiatuz Zulfia Shofiatuz Zulfia Siti Azizah Aini suudin aziz Teguh Pribadi Ucta Pradema Sanjaya Yuniar, Intan