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All Journal TEKNIK INFORMATIKA Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JIKO (Jurnal Informatika dan Komputer) JPM (Jurnal Pemberdayaan Masyarakat) SMARTICS Journal Jurnal SOLMA JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURIKOM (Jurnal Riset Komputer) Journal on Education Jurnal Telematika JOURNAL OF SCIENCE AND SOCIAL RESEARCH Jurnal Informatika Global Jurnal Review Pendidikan dan Pengajaran (JRPP) INTI Nusa Mandiri Jurnal Sistem Informasi dan Informatika (SIMIKA) Vocatech : Vocational Education and Technology Journal JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Darmabakti : Junal Pengabdian dan Pemberdayaan Masyarakat Jurnal Pendidikan dan Teknologi Indonesia Decode: Jurnal Pendidikan Teknologi Informasi Indonesian Community Journal Jurnal Algoritma Jurnal Bisnis Kolega Jurnal Informatika Polinema (JIP) Scientific Journal of Informatics INOVTEK Polbeng - Seri Informatika Karsa Bhakti : Journal of Community Service
Mula Agung Barata
Universitas Nahdlatul Ulama Sunan Giri
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Journal : journal of applied informatics and computing

 1 
Optimization of Random Forest Algorithm with Backward Elimination Method in Classification of Academic Stress Levels Amalia, Salsabila Dani; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9280

Abstract

Stress is a phenomenon experienced by all individuals as a natural response to pressure, which can impact mental and physical health. In an academic setting, the stress experienced by students is known as academic stress, which can affect their performance and mental well-being. Therefore, there is a need for effective prediction methods to aid in the management and prevention of academic stress. Therefore, there is a need to predict the level of academic stress to aid more effective management and prevention. This study uses a public dataset categorized based on the Student-life Stress Inventory (SSI), which includes psychological, physiological, social, environmental, and academic factors. Data mining is often used to detect diseases, one of which is the Random Forest algorithm. The Random Forest algorithm is applied as a classification technique for academic stress levels, with optimization using the Backward Elimination method for feature selection to improve model accuracy. The results showed that the accuracy of the Random Forest algorithm without feature selection obtained an accuracy of 86%, compared to the random forest algorithm with feature selection using the Backward Elimination method obtained a higher accuracy of 88%. This increase shows that the feature selection method can optimize model performance by selecting more relevant features. Thus, this research is expected to contribute to the management of student academic stress against the risk of academic stress.
A Improving House Price Clustering Results with K-means through the Implementation of One-hot Encoding Pre-processing Technique Maulani, Vicka Rizqi; Barata, Mula Agung; Yuwita, Pelangi Eka
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9481

Abstract

Basic human needs include a house that serves as a place to live and a shelter from everything. In Indonesia, owning a house is still a challenging aspect due to its high price. Information on house prices is needed for prospective buyers or consumers, so that buyers can adjust their needs and finances, and for producers or sellers it is used as a way to determine the segmentation of targeted market groups. House prices are influenced by several factors including, building area, number of bedrooms, number of bathrooms, location, condition and the presence of a garage. This research aims to improve the quality of house price clustering with K-means and the application of one-hot encoding in the data pre-processing process in representing categorical data. The dataset used has two types of data, namely numeric and categorical. The cluster evaluation is based on the silhouette score matrix and the determination of k is based on the elbow graph. The results showed an increase in the silhouette score value after applying one-hot encoding 0.15 which was previously 0.09, with the number of k = 3. The 0.15 matrix result is relatively low, which is caused by the overlap of house price values in the dataset, but it has been shown that one-hot encoding can represent categorical data well in the data pre-processing process so that the data can be processed with the k-means algorithm.
Indonesian Gold Price Forecasting Using Simple and Stacked LSTM with Expanding Window Lambang, Rahmat Tegar Patriot Hari; Prastya, Ifnu Wisma Dwi; Barata, Mula Agung Barata
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.12148

Abstract

This study investigates the performance of two deep learning architectures, namely Simple LSTM and Stacked LSTM, for Indonesian gold price forecasting, with a particular focus on evaluating the effect of optimizer selection and learning rate configurations. An experimental framework is implemented using daily Indonesian gold price data from 2021 to 2024. Model performance is assessed using five-fold expanding window time series cross-validation to ensure robustness and avoid data leakage. Four adaptive training optimizers (Adam, Nadam, Adamax, and RMSprop) are evaluated across three learning-rate settings as part of a systematic sensitivity analysis of training hyperparameters. The results indicate that the Simple LSTM consistently outperforms the Stacked LSTM. The best performance is achieved by the Simple LSTM using the Adam optimizer with a learning rate of 0.01, yielding an RMSE of 9.235, MAE of 7.060, and MAPE of 0.71%. These findings demonstrate that simpler architectures combined with appropriate training configurations can provide superior forecasting accuracy for volatile financial time series.
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.
Comparison of the Performance of K-Nearest Neighbor and Naive Bayes Algorithms for Sentiment Analysis of PinjamYuk Application User Reviews Using SMOTE and TF-IDF Lindya Rossita Handoko; Amelia Faza; Mula Agung Barata; 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.v10i4.13398

Abstract

The growth of online lending services has driven the increasing adoption of digital financial applications, providing users with convenient access to financial services. This growing number of users has generated a large volume of reviews on the Google Play Store, which can serve as a valuable source for understanding users’ perspectives on the benefits and performance of these applications. This study aims to compare the performance of the K-Nearest Neighbor (KNN) and Naive Bayes algorithms in classifying the sentiment of user reviews of the PinjamYuk application. The study uses a secondary dataset obtained from Kaggle, consisting of 500 user reviews of the PinjamYuk application on the Google Play Store during the 2023–2024 period. The reviews were categorized into three sentiment classes: positive, neutral, and negative, based on their rating scores. Because the class distribution in the dataset was imbalanced, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the classes before the classification process. The research procedure included data preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), data balancing using SMOTE, and KNN parameter optimization using GridSearchCV. The models were evaluated using accuracy, precision, recall, F1-score, confusion matrix, stratified K-fold cross-validation, and the McNemar test. The results show that the Naive Bayes algorithm outperformed KNN. The stratified K-fold cross-validation results yielded an average accuracy of 81.0% for Naive Bayes and 80.8% for KNN. Furthermore, the McNemar test produced a p-value of 0.014 (p < 0.05), indicating that the performance difference between the two algorithms was statistically significant. These findings demonstrate that the Naive Bayes algorithm is more effective for analyzing user sentiment in reviews of the PinjamYuk application.
Co-Authors Abdul Aziz Affan Agung Prabowo Afril Efan Pajri Alfianto Faidatul Aldi Yumardiansyah Alvinatul Hidayah Amalia Nur Laily Amalia, Salsabila Dani Amelia Faza Andiyani, Putri Aprillia Dwi Ardianti Buyung Panigoro Deni Reskianto Deni Denny Nurdiansyah Diah nawang wulan Dina Selvi Rahmadani Dina, Intan Rachma Distira, Riski Putra Ayu Dwi Irnawati Dwi Issadari Hastuti Dwi Syafi'i, Ahmad Dwi Tiyas Novitasari Dwi Tiyas Novitasari Edi Noersasongko Eka Wahyu Andriyani Elok Fathiyatul Laili Ervina Putri Efendi Fannisa Salsabila Pratiwi Fina Indri Silfana Guruh Putro Dirgantoro Hidayah, Alvinatul Ifnu Wisma Dwi Prastya Ilmiyah, Miftakhul Indra Dharma Wijaya Indra Dharma Wijaya, Indra Dharma Ita Aristia Sa&#039;ida Ita Aristia Sa&#039;ida Ita Aristia Sa'ida Ita Aristia Sa'ida Jauhar Vikri, Muhammad Lambang, Rahmat Tegar Patriot Hari Levia, Zachdyna Aurelya Lindya Rossita Handoko Lisa Aminatul Mukarromah M. Khoirul Risqi M. Ridlwan Hambali Maulani, Vicka Rizqi Moch Arief Soeleman Moh. Miftahul Choiri Moh. Muhajir Moh. Yusuf Efendi Munir, Ach Sirojul Muzakka, Moch. Arifuddin Naili Nafa Khatirokimmah Nasirudin, M. Nirma Ceisa Santi Nisa, Siti Khoirun Novitasari, Dwi Tiyas Nur Mahmudah Nur Mahmudah Nur Mahmudah Nur Saifuddin Pelangi Eka Yuwita Pradema Sanjaya, Ucta Purwanto Purwanto Putri Amelia Reza Anggapratama Rheyna Anggri Setyani Rochmatin, Novia Nur Roihatur Rohmah Roihatur Rohmah Sahri Sahri Sahri Sahri Saputra, Agus Bima Shafa Kirana Aralia Shofiatuz Zulfia Shofiatuz Zulfia Silfana, Fina Indri Sinta Ningrum Taufik Hidayat Teguh Pribadi Ucta Pradema Sanjaya Usman Nurhasan Viki Mei Adi Saputra Vita Dwi Rahmawati Wulan, Diah Nawang Yaqin, Ahmad Ainul Zainul Abidin Zakki Alawi Zulfiana Nur’aini